Mapping the Mind: Validating the Neural Correlates of Design Thinking Versus Traditional Problem Solving

Abstract

This article synthesizes current neuroscience research to delineate the distinct brain networks and dynamic states underlying design thinking compared to traditional, analytical problem-solving. Targeting researchers and drug development professionals, it explores foundational theories, advanced neuroimaging methodologies like fMRI and EEG, and the challenges of translating these findings into practice. By providing a comparative framework of neural validation, the content aims to inform the development of targeted cognitive interventions and biomarkers for enhancing innovation and treating neuropsychiatric conditions characterized by cognitive rigidity.

The Creative Brain: Foundational Neuroscience of Design Thinking and Problem Solving

In both scientific research and drug development, the approach to problem-solving can significantly influence outcomes. Two dominant paradigms exist: Traditional Problem-Solving, a linear, analytical method rooted in established logic and efficiency, and Design Thinking, an iterative, human-centered process that emphasizes empathy and experimentation [1] [2]. While traditional methods excel in optimizing well-defined processes, Design Thinking shines when tackling ill-defined or "wicked" problems where the solution is not immediately obvious and user needs are paramount [1] [3]. This guide objectively compares these frameworks, supporting the broader thesis of validating their distinct neural correlates to provide a biological basis for selecting an appropriate problem-solving strategy.

The comparison is particularly relevant for research professionals. Emerging neuroscientific evidence suggests these different cognitive approaches recruit distinct brain networks [4] [5]. Understanding these underlying mechanisms can guide teams in structuring their innovation processes, whether for designing a novel clinical trial, improving laboratory workflows, or understanding patient adherence challenges.

Conceptual Framework Comparison

At their core, these two paradigms operate on different fundamental principles, mindsets, and processes. The table below summarizes their key conceptual distinctions.

Table 1: A Comparative Analysis of Conceptual Frameworks

Aspect	Traditional Problem Solving	Design Thinking
Problem Focus	Well-defined, often technical or operational issues [1].	Ill-defined, complex, human-centered "wicked" problems [1] [3].
Starting Point	An existing problem definition, data, or expert analysis [1].	A deep, empathetic understanding of user needs and experiences [1] [6].
Core Approach	Linear, analytical, logical, and deductive [1] [7].	Iterative, empathetic, creative, and exploratory [1] [6].
Primary Mindset	Efficiency, optimization, and risk aversion [1].	Curiosity, empathy, experimentation, and "learning through doing" [6] [2].
View of Failure	A setback to be avoided [1].	A valuable learning opportunity; "fail fast, learn faster" [1] [2].
Typical Outcome	Optimized processes and efficient, predictable results [1].	User-loved products/services and innovative breakthroughs [1] [7].
Role of the User	Often limited, primarily for validation at later stages [1] [8].	High and continuous involvement throughout the entire process [1] [2].

The Traditional Problem-Solving Process

Traditional problem-solving often follows a sequential, linear path: problem identification, root cause analysis, solution generation, implementation, and evaluation [1]. This method is highly effective for questions of "how" – for instance, how to optimize a known manufacturing process for a drug or debug a piece of laboratory equipment. It relies heavily on critical thinking, existing data, and proven best practices, with solutions frequently developed by subject matter experts [1].

The Design Thinking Process

In contrast, Design Thinking is a non-linear, iterative cycle. The most common framework, from Stanford's d.school, involves five phases: Empathize, Define, Ideate, Prototype, and Test [6]. These stages are not sequential; teams often run them in parallel and repeat them as needed [6]. The process begins not with a defined problem but with deep user empathy to uncover latent needs. It involves divergent thinking to explore possibilities and convergent thinking to narrow down solutions, ultimately aiming for outcomes that are desirable to users, feasible technologically, and viable for the business [1] [6].

Figure 1: The Non-Linear, Iterative Process of Design Thinking

Neuroscientific Evidence and Experimental Data

Emerging neuroimaging studies provide tangible, biological evidence for the distinct cognitive natures of these two frameworks. The research indicates they engage separate, large-scale brain networks.

Neural Correlates of Insight vs. Analytical Problem-Solving

A key distinction lies in the brain states associated with the sudden "Aha!" moments of creative insight (a hallmark of Design Thinking) versus methodical analysis (characteristic of traditional approaches).

A 2025 fMRI study investigating spatial insight problem-solving, using matchstick arithmetic tasks, revealed clear neural divergences [4] [5]. Participants reported their solutions as "Quick," "Analytical," or "Insight-based." The analysis showed:

• Insight-Based Solutions: Were associated with significantly greater activation in the Default Mode Network (DMN), including regions like the right angular gyrus, left superior temporal gyrus, and posterior cingulate cortex [4] [5]. The DMN is linked to self-referential thought, mental simulation, and mind-wandering.
• Analytical & Quick Solutions: Showed increased activation in the Executive Control Network (ECN), which includes areas like the middle frontal gyrus and insula [4]. The ECN is responsible for focused attention, logical reasoning, and working memory.

Table 2: Summary of Key Neuroscientific Findings from fMRI Studies

Study Focus	Experimental Methodology	Key Finding: Traditional/Analytical	Key Finding: Design Thinking/Insight
Spatial Insight [4] [5]	fMRI during matchstick arithmetic problems; solutions categorized by subjects; GLM and Hidden Markov Model (HMM) analysis.	Increased activation in the Executive Control Network (ECN); more stable, predictable brain state dynamics.	Increased activation in the Default Mode Network (DMN); high variability in brain state dynamics, indicating cognitive flexibility.
Group Problem Solving [9]	fMRI during individual vs. group solving of Raven-like matrix problems; communication device enabled discussion.	Individual problem-solving relied more on standard cognitive processing networks.	Group solving activated the "social brain" (e.g., medial PFC, temporal poles), indicating a re-configuration of network connectivity.

Experimental Protocol: Imaging Creative Insight

To objectively compare these cognitive frameworks in a research setting, the following methodology, adapted from current studies, can be employed.

1. Task Design:

• Stimuli: Employ matchstick arithmetic problems or similar spatial/verbal insight problems [4] [5]. In these tasks, an incorrect equation is presented (e.g., using Roman numerals), and the subject must correct it by moving a single stick.
• Procedure: Subjects are instructed to solve the problems and subsequently categorize their solution process as either "Insight" (a sudden Aha! moment), "Analytical" (a step-by-step logical process), or "Quick" (immediately obvious) [4].

2. Data Acquisition:

• Imaging Technique: Use functional Magnetic Resonance Imaging (fMRI) to measure blood-oxygen-level-dependent (BOLD) signals throughout the task.
• Parameters: Acquire whole-brain images with standard EPI sequences. Include a resting-state scan before or after the task for baseline network connectivity analysis.

3. Data Analysis:

• General Linear Model (GLM): Use GLM to identify brain regions with significantly different activation levels during insight solutions compared to analytical solutions (e.g., Insight > Analytical; Analytical > Insight) [4].
• Dynamic State Analysis: Apply a Hidden Markov Model (HMM) to the fMRI data to estimate discrete, transient brain states and their temporal evolution. This reveals the dynamic interplay between networks during prolonged problem-solving [4].
• Statistical Testing: Compare the fractional occupancy (FO) of specific brain states across different solution types using paired t-tests [4].

Figure 2: Experimental Workflow for Imaging Problem-Solving Strategies

The Scientist's Toolkit: Key Research Reagents & Materials

For researchers aiming to replicate or build upon these neuroscientific investigations, the following table details essential "research reagents" and their functions.

Table 3: Essential Materials for Neuroscientific Problem-Solving Research

Item / Solution	Function in Experimental Protocol
3-Tesla fMRI Scanner	High-field magnetic resonance scanner for acquiring high-resolution functional and structural brain images with adequate temporal resolution for BOLD signal detection.
Insight Problem Battery	A standardized set of problems (e.g., matchstick arithmetic, Remote Associates Test) designed to elicit both insight and analytical solving strategies, serving as the cognitive stimulus.
Response & Categorization Interface	A button-box or touchscreen system for subjects to indicate their solution and subsequently categorize their problem-solving experience (Insight, Analytical, Quick).
General Linear Model (GLM) Software	Statistical software (e.g., SPM, FSL, AFNI) to model the fMRI data, localize task-related brain activity, and create contrasts between different problem-solving conditions.
Hidden Markov Model (HMM) Toolkit	Advanced computational tools (e.g., HMM-MAR) for estimating discrete, dynamic brain states from the fMRI time-series data, going beyond static connectivity measures.
Anatomical & Functional Atlases	Digital brain parcellations (e.g., AAL, Harvard-Oxford) for defining regions of interest and identifying activated brain regions within established networks like the DMN and ECN.
4-(Bromomethyl)-2,5-diphenyloxazole	4-(Bromomethyl)-2,5-diphenyloxazole, CAS:133130-86-6, MF:C16H12BrNO, MW:314.18 g/mol
Benzene, [(1-chloroethyl)thio]-	Benzene, [(1-chloroethyl)thio]-|CAS 13557-24-9

Discussion and Research Implications

The convergence of cognitive psychology and neuroscience provides a powerful lens through which to validate the differences between Design Thinking and traditional problem-solving. The experimental data clearly indicate that insightful problem-solving—a key component of Design Thinking—is not merely a subjective feeling but a distinct neurocognitive process characterized by dynamic activation of the Default Mode Network and increased cognitive flexibility [4] [5].

For research and drug development professionals, these findings have concrete implications:

• Team Composition and Process: When tackling wicked problems that require innovation, structuring teams and workflows to encourage DMN activity (e.g., through brainstorming, empathetic immersion, and breaks for incubation) may be more effective than enforcing continuous, focused analysis.
• Problem Scoping: The neural evidence supports the conceptual advice to apply Traditional Problem-Solving to well-defined, technical problems (engaging the ECN) and to reserve Design Thinking's iterative, human-centered cycle for ambiguous, complex challenges that require the creative potential of the DMN [1] [3].
• Future Research: The role of the "social brain" network in group problem-solving [9] opens a new avenue for exploring the neuroscience of collaborative Design Thinking sessions, potentially measuring the neural synchronization between team members during co-creation.

In conclusion, the choice between these two cognitive frameworks is not a matter of preference but of cognitive fit. The growing body of neuroscientific evidence strengthens the case for a deliberate and informed application of each paradigm, empowering scientists and innovators to select the right tool for the intellectual challenge at hand.

In cognitive neuroscience, the Triple Network Model has emerged as a crucial framework for understanding how large-scale brain networks support complex human thought and their disruption in various neuropsychiatric conditions [10]. This model focuses on three canonical macro-scale brain networks: the Default Mode Network (DMN), the Executive Control Network (ECN), and the Salience Network (SN). These networks, with their dynamic interplay, are fundamental to almost all cognitive functions [10]. Understanding their distinct roles, interactions, and the experimental methods used to study them is essential for research into higher-order cognition, including design thinking versus problem-solving. This guide provides a comparative analysis of these networks' functions, neural correlates, and the key experimental protocols used in their investigation.

Network Profiles and Comparative Analysis

The following table offers a structured comparison of the three core networks, detailing their key nodes, primary functions, and behavioral correlates.

Table 1: Comparative Profile of the Default Mode, Executive Control, and Salience Networks

Feature	Default Mode Network (DMN)	Executive Control Network (ECN)	Salience Network (SN)
Key Brain Regions	Posterior Cingulate Cortex (PCC), Precuneus, Medial Prefrontal Cortex (mPFC) [11]	Dorsolateral Prefrontal Cortex (dlPFC), Lateral Posterior Parietal Cortex [10] [12]	Anterior Insula (AI), Dorsal Anterior Cingulate Cortex (dACC) [10]
Primary Functions	Self-referential thought, autobiographical memory, future imagining, social cognition [11]	Task selection, executive function, working memory, goal-directed problem-solving [10]	Detecting salient stimuli, switching between DMN and ECN, integrating internal and external signals [10]
Typical State	"Task-negative," active during rest and internal mentation [13]	"Task-positive," active during cognitive effort and external focus [13]	"Task-positive," active in response to salient stimuli and cognitive demand [10]
Behavioral Correlates	Mind-wandering, creative incubation, memory retrieval [11]	Dual-task shielding, logical reasoning, cognitive control [12]	Interoceptive awareness, emotional processing, response to reward and pain [10]
Dysfunction Linked To	---	Cocaine dependence, deficits in task shielding [14] [12]	Depression, anxiety, chronic pain (hyperactivity); autism, schizophrenia (hypoactivity) [10] [15]

Experimental Protocols for Network Investigation

Researchers employ various functional magnetic resonance imaging (fMRI) paradigms and analysis techniques to probe the functions and connectivity of these networks. The table below summarizes key experimental methodologies cited in the literature.

Table 2: Key Experimental Protocols for Studying Brain Network Function and Connectivity

Experimental Aim	Protocol / Paradigm	Key Methodology	Measured Outcome
Assess DMN engagement during varying cognitive effort [13]	Stroop Task Block-Design	Participants alternate between rest, low-effort (word reading), and high-effort (color naming) conditions in a block design during fMRI.	BOLD activation and functional connectivity, particularly DMN up-regulation during low effort and rest versus down-regulation during high effort.
Probe ECN function and task shielding [12]	Dual-Task with Backward Crosstalk	Participants perform a visual discrimination task (Task 1) and an auditory discrimination task (Task 2) at varying Stimulus Onset Asynchronies (SOAs).	The Backward Crosstalk Effect (BCE), calculated as the performance difference (reaction time/accuracy) on Task 1 between compatible and incompatible trials.
Investigate visual creativity and divergent thinking [16]	Creative vs. Control Mental Synthesis	Participants perform a creative task (mentally assembling shapes into a recognizable object) and a control task (mentally rotating shapes to form a known shape) during fMRI.	BOLD signal contrast between creative (divergent) and control (convergent) tasks, identifying regions like the left dlPFC and posterior parietal cortex.
Measure functional and effective connectivity [14]	Resting-State fMRI with Dynamic Causal Modeling (DCM)	Independent Component Analysis (ICA) identifies networks (DMN, ECN, SN) from resting-state fMRI data. DCM models directional influence between networks.	Functional connectivity (temporal correlations) and effective connectivity (directional coupling) between and within the LECN, RECN, DMN, and SN.
Identify network topology in individuals [15]	Precision Functional Mapping	Densely sampled, longitudinal resting-state fMRI data is acquired from individuals (dozens of scans over months). Individual-specific network maps are generated.	Cortical surface area occupied by specific networks (e.g., SN), providing a stable, trait-like measure of individual network topology.

Visualizing Network Interactions and Experimental Flow

The Triple Network Model posits a specific functional relationship between the DMN, ECN, and SN. The following diagram illustrates this core interaction, with the SN acting as a dynamic switch.

Figure 1: The Triple Network Interaction Model. The Salience Network (SN) acts as a switch, activating the task-positive Executive Control Network (ECN) and suppressing the task-negative Default Mode Network (DMN). The DMN and ECN are typically anti-correlated [10].

A typical fMRI experiment to investigate these networks, such as the dual-task protocol, follows a structured workflow. The diagram below outlines the key steps from subject preparation to data analysis.

Figure 2: Generic Workflow for a Task-Based fMRI Experiment. This pipeline is adapted from protocols used to study network function during specific cognitive tasks [12] [13].

The Scientist's Toolkit: Essential Research Reagents and Materials

This section details key solutions and tools used in the experimental research on brain networks.

Table 3: Key Research Reagent Solutions and Materials

Tool / Solution	Primary Function in Research	Example Use Case
FMRIB Software Library (FSL)	A comprehensive library of analysis tools for fMRI, MRI, and DTI brain imaging data.	Used for MELODIC Independent Component Analysis (ICA) to identify networks and Dual Regression to assess functional connectivity differences [14].
Dynamic Causal Modeling (DCM)	A statistical framework for inferring effective (directional) connectivity between brain regions or networks.	Models the excitatory/inhibitory directional influence between the ECN, DMN, and SN [14].
Parametric Empirical Bayes (PEB)	An extension of DCM used for group-level analysis and to compare connectivity models across subjects.	Tests for effective connectivity differences between clinical (e.g., cocaine dependent) and control groups [14].
Transcranial Direct Current Stimulation (tDCS)	A non-invasive brain stimulation technique that modulates cortical excitability using a low electrical current.	Anodal tDCS over the left dlPFC is applied to test causal role of ECN in improving task shielding in dual-tasking [12].
Stroop Task Paradigm	A classic cognitive psychology task that induces conflict between automatic and controlled processing.	Used in block-design fMRI to manipulate cognitive effort (word reading vs. color naming) and study DMN/EMN dynamics [13].
Diethyl 2-methyl-3-oxopentanedioate	Diethyl 2-methyl-3-oxopentanedioate, CAS:16631-18-8, MF:C10H16O5, MW:216.23 g/mol	Chemical Reagent
4-Chloro-2-nitrobenzenediazonium	4-Chloro-2-nitrobenzenediazonium, CAS:27165-22-6, MF:C6H3ClN3O2+, MW:184.56 g/mol	Chemical Reagent

The Default Mode, Executive Control, and Salience Networks represent a core system for human cognition. The DMN supports internal self-referential and creative thought, the ECN drives focused problem-solving and executive control, and the SN dynamically arbitrates between them [10]. Their interaction is not merely sequential but is a complex, graded, and context-sensitive dance [11] [13]. Research leveraging the outlined experimental protocols—from classic fMRI paradigms to cutting-edge precision mapping and effective connectivity analyses—continues to validate and refine our understanding of these neural correlates. This foundational knowledge is critical for framing advanced research, such as distinguishing the neural substrates of design thinking, which may rely more heavily on DMN-ECN interplay, from those of pure problem-solving, potentially dominated by the ECN.

Understanding the neurocognitive models of creativity is paramount for advancing research in design thinking and problem solving. This review objectively compares two foundational frameworks: Graham Wallas's stage model and the Geneplore model. We contextualize this comparison within a broader thesis on validating the neural correlates of design thinking, which often involves navigating ill-defined problems, versus traditional problem-solving research, which frequently deals with well-defined domains. The distinction is critical; design thinking is recognized as a core creative behavior in numerous professional fields, including drug development, where generating novel molecular structures and therapeutic approaches requires a deep understanding of creative cognition [17] [18]. This analysis synthesizes historical cognitive theories with contemporary neuroimaging data to provide a robust framework for researchers and scientists aiming to foster innovation within their teams and pipelines.

Wallas's Four-Stage Model

Graham Wallas, in his 1926 work The Art of Thought, proposed one of the first formal models of the creative process. Drawing on accounts from eminent scientists like Hermann von Helmholtz and Henri Poincaré, he dissected creativity into a four-stage sequence [19] [20]:

• Preparation: The initial, conscious stage of intensely investigating a problem "in all directions." This involves gathering information, defining the problem, and attempting initial solutions through systematic analysis [19] [21].
• Incubation: A period where the problem is set aside, and no conscious, directed thought is applied to it. During this stage, unconscious mental processes are believed to operate on the problem [19] [20].
• Illumination: The moment of insight, often sudden, where a promising idea or solution emerges into consciousness. This is the "Aha!" or "Eureka!" experience, characterized by its suddenness and a sense of certainty [19] [4].
• Verification: The final stage, where the illuminated idea is consciously evaluated, tested, refined, and implemented. As Poincaré emphasized, this stage involves rigorous calculation and critical analysis to verify the idea's validity and reduce it to an exact form [19].

The Geneplore Model

Developed by Finke, Ward, and Smith in 1992, the Geneplore model (a portmanteau of "generate" and "explore") offers a more granular, cognitive-process-oriented view. It posits that creativity involves the cyclical and iterative generation and exploration of "preinventive" cognitive structures [22] [23].

• Generative Processes: These processes are used to construct preinventive structures. They include basic operations like memory retrieval and association, as well as more complex ones like synthesis, transformation, analogical transfer, and categorical reduction [23].
• Preinventive Structures: These are incomplete, ambiguous, and potentially promising mental representations formed during the generative phase. They can take the form of visual patterns, object forms, mental blends, or verbal combinations and possess properties like novelty, ambiguity, and emergence [22] [23].
• Explorative Processes: Once a preinventive structure is formed, it is explored and interpreted through processes such as attribute finding, functional inference, and hypothesis testing to discover its potential meanings and uses [22] [23].
• Product Constraints: Throughout the process, constraints related to the desired product's category, features, or functions can guide both generative and exploratory phases [23].

The following diagram illustrates the structure of the Geneplore model, showing the iterative cycle between its core components.

Comparative Analysis: Cognitive Processes and Neural Correlates

A direct comparison of the Wallas and Geneplore models reveals distinct yet complementary perspectives on creative cognition. The table below summarizes their core characteristics.

Table 1: Comparative analysis of Wallas's stages and the Geneplore model

Feature	Wallas's Stages	Geneplore Model
Core Structure	Sequential, stage-based [19]	Cyclical, iterative, and non-linear [22] [23]
Primary Focus	Macroscopic description of the creative process from start to finish [19] [18]	Microscopic, cognitive operations on mental structures [22]
Role of Unconscious Thought	Explicitly defined as the Incubation stage [19]	Implicit in the generation of preinventive structures, which can occur outside full conscious awareness [22]
Defining Characteristics	Describes phenomenological experience; broadly applicable across domains [20]	Specifies cognitive processes (e.g., synthesis, analogical transfer); links to testable mental operations [22] [23]
Key Cognitive Operations	Preparation, Incubation, Illumination, Verification [19]	Generative (e.g., association, transformation) and Exploratory (e.g., functional inference) processes [23]

Neuroimaging studies provide empirical support for the distinct brain networks involved in the types of cognition described by both models. A 2025 fMRI study on insight problem-solving using matchstick arithmetic problems found that different solution strategies (quick, analytical, and insight-based) correlate with distinct patterns of brain network activity [4]. Insight-based solutions were associated with greater activation in the Default Mode Network (DMN), including the right angular gyrus, posterior cingulate cortex (PCC), and precuneus. Conversely, quick and analytical solutions showed increased activation in the Executive Control Network (ECN) [4]. This suggests that the generative, associative thought central to the Geneplore model and the incubation/illumination stages of Wallas's model may rely more heavily on the DMN, while the focused, verificatory stages depend on the ECN.

The following diagram synthesizes the neural correlates associated with key processes from both models, based on current neuroimaging evidence.

Experimental Protocols and Key Findings

Validating these neurocognitive models requires rigorous experimental paradigms. Below, we detail the methodology of a key recent study that provides neural evidence for the cognitive processes underlying creativity.

Insight Problem-Solving fMRI Protocol

A 2025 study published in Scientific Reports investigated the neural dynamics of creative insight using a spatial problem-solving task [4].

• Task: The Matchstick Arithmetic (MA) task was used. Participants were presented with an incorrect arithmetic equation constructed from matchsticks (e.g., in Roman numerals) and were required to correct it by moving a single matchstick. This task requires spatial manipulation and often leads to insight-based solutions [4].
• Participants & Design: Participants underwent fMRI scanning across three sessions while solving MA problems. After each trial, they self-reported their solution strategy as either "Quick" (immediate solution), "Analytical" (step-by-step reasoning), or "Insight" (sudden "Aha!" solution) [4].
• Imaging & Analysis: Functional MRI (fMRI) data were acquired. Analysis employed two primary methods:
  • General Linear Model (GLM): Used to identify brain regions with significantly different activation levels between the three solution types.
  • Hidden Markov Model (HMM): Applied to estimate discrete, dynamic brain states and their transitions during the problem-solving process, offering a temporal view of cognitive shifts [4].
• Key Quantitative Findings:
  • Reaction Times: Insight solutions (114.44 ± 29.6 s) took significantly longer than analytical (36.58 ± 13.7 s) or quick solutions (8.61 ± 1.9 s), supporting the involvement of a prolonged, non-conscious process [4].
  • Brain Activation: The contrast "Insight > Analytical" solutions revealed significantly greater activation in DMN regions, including the right angular gyrus, left superior temporal gyrus, and posterior cingulate cortex [4].
  • Dynamic States: HMM analysis showed that insight solutions were characterized by a higher frequency of occurrence (FO) of specific brain states (States 4 & 5) associated with the DMN and greater overall variability in state transitions, indicating higher cognitive flexibility [4].

The following workflow diagram outlines the experimental protocol and key analytical steps.

Application in Drug Discovery and Development

The principles of these neurocognitive models are increasingly mirrored and augmented by generative artificial intelligence (AI) in drug discovery, a field where creative problem-solving is paramount.

• Generative AI as an Externalized Geneplore Process: AI models operationalize the generate-explore cycle. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) generate vast libraries of novel molecular structures (preinventive structures). Subsequent models then explore these structures for desired properties like binding affinity and synthetic feasibility [24] [25]. This mirrors the internal cognitive process proposed by the Geneplore model but at a scale and speed unattainable by humans alone.
• Validation and the Wallas Framework: The AI-driven drug discovery pipeline closely follows Wallas's stages. The preparation phase involves feeding the AI curated data on known drugs and targets. The incubation phase is the computational process of generating and screening millions of compounds. Illumination occurs when a candidate molecule with high predicted efficacy is identified. Finally, verification takes place through in silico testing, in vitro experiments, and clinical trials [24].
• Quantitative Impact: The use of generative AI in drug discovery is projected to reduce clinical development costs by up to 50% and shorten trial duration by over 12 months. As of 2024, about 15 AI-developed drug candidates had entered various clinical trial stages, demonstrating the real-world efficacy of this computational "creative cognition" [24] [25].

Table 2: AI-driven drug discovery tools and their cognitive model parallels

AI Tool / Model	Function in Drug Discovery	Parallel in Neurocognitive Model
Generative Adversarial Network (GAN)	Generates diverse, novel molecular structures [25]	Generative Processes (Geneplore) [22]
Variational Autoencoder (VAE)	Encodes molecules into a latent space for optimization and novel compound generation [25]	Preinventive Structure formation & mental synthesis (Geneplore) [23]
Multilayer Perceptron (MLP)	Predicts drug-target interactions (DTI) and binding affinity [25]	Explorative Processes / Verification (Geneplore/Wallas) [22] [19]
AlphaFold Database	Provides predicted protein structures, defining the "problem space" for drug design [24]	Preparation & Product Constraints (Wallas/Geneplore) [19] [23]
GraphGPT/ChemSpaceAL	Condition-based molecular generation for creating virtual screening libraries [24]	Analogical Transfer & Conceptual Combination (Geneplore) [23]

The Scientist's Toolkit: Key Research Reagents

This table details essential methodological "reagents" for researching the neural correlates of creativity, as featured in the cited studies.

Table 3: Essential reagents and methodologies for neurocognitive creativity research

Research Reagent / Method	Function in Creativity Research
Functional MRI (fMRI)	Non-invasive measurement of brain activity by detecting changes in blood flow, used to localize activity to networks like the DMN and ECN during creative tasks [4].
Matchstick Arithmetic (MA) Task	A spatial insight problem-solving task used to induce and study the "Aha!" moment in a controlled laboratory setting [4].
Remote Associates Test (RAT)	A verbal insight task requiring participants to find a common associative word for three given words, another standard paradigm for studying creative insight [4].
Hidden Markov Model (HMM)	A statistical model used to analyze fMRI data, identifying discrete, dynamic brain states and their transitions over time, quantifying cognitive flexibility [4].
Transcranial Direct Current Stimulation (tDCS)	A non-invasive brain stimulation technique used to modulate cortical excitability in regions like the DLPFC or ATL, testing causal roles in creative problem-solving [4].
Self-Report Strategy Classification	Participants' post-trial categorization of their own solution process (Quick, Analytical, Insight), providing a behavioral correlate for neuroimaging data [4].
Generative AI Models (GANs/VAEs)	In silico tools that externalize and augment human creative cognition for generating and screening novel drug-like molecules in pharmaceutical research [24] [25].
BindingDB Database	A public database of measured binding affinities, used as a ground-truth dataset for training and validating MLP models for drug-target interaction prediction [25].
4-(Diisopropylamino)benzonitrile	4-(Diisopropylamino)benzonitrile, CAS:282118-97-2, MF:C13H18N2, MW:202.3 g/mol
Methyl (2E,6Z)-dodeca-2,6-dienoate	Methyl (2E,6Z)-dodeca-2,6-dienoate|28369-22-4

Human problem-solving employs distinct cognitive strategies, ranging from methodical analysis to sudden flashes of creative insight—the celebrated "Aha!" moment. Understanding the neural correlates underlying these different approaches is critical for advancing cognitive neuroscience and developing applications in fields from education to drug development. Contemporary research has made significant progress in dissecting the brain dynamics of problem-solving, revealing that creative insight and analytical thought are supported by dissociable, large-scale brain networks [4]. This guide provides a comparative analysis of the neural signatures of these cognitive strategies, synthesizing recent experimental findings to offer researchers a clear framework for differentiating these states based on objective neuroimaging data and methodological protocols.

Comparative Neural Signatures: Key Experimental Findings

Differential Network Engagement

Research consistently demonstrates that creative insight and analytical problem-solving recruit fundamentally distinct brain networks. The table below summarizes the core differences in neural activation patterns associated with each strategy, synthesized from recent neuroimaging studies.

Table 1: Neural Correlates of Insight versus Analytical Problem-Solving

Brain Region/Network	Creative Insight	Analytical Solution	Functional Interpretation
Default Mode Network (DMN)	↑↑ Activation [4]	↓ Activation	Self-referential thought, mental simulation, idea generation
Executive Control Network (ECN)	↓ Activation	↑↑ Activation [4]	Focused attention, logical sequencing, working memory
Anterior Temporal Lobe (right)	↑ Activation [4]	Not significantly active	Semantic integration, distant association formation
Dorsolateral Prefrontal Cortex	Variable (context-dependent)	↑↑ Activation [4]	Cognitive control, rule-based reasoning
Anterior Insula	↑ Activation [4]	↑ Activation (different timing)	Salience detection, attention switching
Visual Network	↓ Activation	↑ Activation [4]	Visual processing, perceptual encoding

Temporal Dynamics and State Transitions

Beyond static localization, the temporal dynamics of brain activity critically differentiate these processes. Hidden Markov Model (HMM) analyses of fMRI data reveal that insight problem-solving is characterized by high-variability brain state dynamics, reflecting increased cognitive flexibility during the prolonged search for a solution [4]. In contrast, analytical reasoning exhibits more stable state transitions, typically dominated by states associated with cognitive control and visual processing [4]. The onset of the "Aha!" moment coincides with a distinct state transition pattern often involving a shift from executive-dominated states to states where the DMN features prominently [4].

Table 2: Dynamic Brain State Characteristics During Problem-Solving

Characteristic	Creative Insight Process	Analytical Process
State Duration	Variable, prolonged searches (mean ~114s) [4]	More consistent, shorter searches (mean ~37s) [4]
State Variability	High variability in HMM states [4]	Lower variability, more predictable sequences
Predominant HMM States	States 4 & 5 show significantly higher fractional occupancy [4]	State 9 shows significantly higher fractional occupancy [4]
Network Interaction	Dynamic DMN-ECN interaction	Stable ECN dominance with visual network
Transition Pattern	Erratic before solution, then sharp transition [4]	Methodical, incremental state progressions

Experimental Protocols and Methodologies

Paradigm for Eliciting Insight: Matchstick Arithmetic Problems

A robust method for investigating insight in neuroimaging settings involves matchstick arithmetic (MA) tasks [4]. These visuospatial problems require participants to correct false arithmetic statements (e.g., "IV = III + III") by moving a single matchstick. The paradigm is particularly effective because it induces a clear mental impasse followed by sudden restructuring of the problem space—the hallmark of insight.

Protocol Implementation:

• Stimulus Presentation: Equations are displayed visually, and participants manipulate solutions mentally or via interface.
• Trial Structure: Each trial begins with a fixation cross, followed by problem presentation until response, culminating with solution type self-report.
• Strategy Classification: Post-trial, participants categorize their solution as:
  • Quick: Almost immediate solution (mean RT ~8.6s).
  • Analytical: Methodical, step-by-step reasoning (mean RT ~36.6s).
  • Insight: Sudden "Aha!" solution after impasse (mean RT ~114.4s) [4].
• fMRI Acquisition: Blood-oxygen-level-dependent (BOLD) signals are recorded throughout task performance, typically with standard parameters (e.g., TR=2000ms, TE=30ms, voxel size=3×3×3mm).

Neuroimaging Analysis Frameworks

Investigating the neural correlates of insight employs complementary analytical approaches:

General Linear Model (GLM) Analysis:

• Purpose: Localize brain regions significantly associated with different solution types.
• Implementation: Contrasts are constructed between insight > analytical, insight > quick, and analytical > quick conditions.
• Statistical Correction: Multiple comparison correction via Gaussian random field theory or false discovery rate (FDR).
• Output: Statistical parametric maps highlighting solution-specific activations [4].

Hidden Markov Model (HMM) Analysis:

• Purpose: Characterize discrete, dynamic brain states during problem-solving.
• Implementation: Models temporal sequences of brain activity patterns as transitions between finite number of hidden states.
• Key Metrics: Fractional occupancy (percentage of time spent in each state) and transition probabilities between states.
• Output: Time courses of brain state dynamics correlated with behavioral measures [4].

Signaling Pathways and Neural Workflows

The cognitive transition from impasse to insight involves coordinated interactions between large-scale brain networks. The following diagram illustrates the proposed neural workflow during creative insight problem-solving:

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Resources for Neural Correlates Research

Resource Category	Specific Examples	Research Function
Neuroimaging Hardware	3T fMRI Scanner, MRI-compatible response devices, eye-tracking systems	High-resolution BOLD signal acquisition, behavioral response recording
Stimulus Presentation	E-Prime, PsychoPy, Presentation	Precise timing control for cognitive paradigms
Analysis Software	SPM, FSL, AFNI, HMM-MAR Toolkit	GLM statistics, brain state modeling, connectivity analysis
Cognitive Paradigms	Matchstick Arithmetic Problems, Remote Associates Test	Validated tasks for eliciting insight and analytical states
Physiological Monitoring	Electrodermal Activity (EDA), heart rate monitoring	Complementary arousal and cognitive effort assessment
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Implications for Research and Development

The neural dissociation between insight and analytical thinking has significant implications for research and drug development. First, the identified neural signatures provide objective biomarkers for assessing cognitive states, potentially useful for evaluating compounds targeting creative cognition or executive function. Second, understanding DMN-ECN dynamics could inform neuroenhancement strategies using techniques like transcranial direct current stimulation (tDCS), which has been shown to modulate insight performance when targeting key regions like the anterior temporal lobe [4]. For drug development professionals, these findings offer a framework for designing target engagement biomarkers for cognitive enhancers and a potential pathway for developing personalized neuromodulation approaches based on individual brain network characteristics [26].

Inside the Black Box: Neuroimaging Methods for Capturing Creative Cognition

Understanding the neural underpinnings of design thinking requires tools that can capture the brain's dynamic, complex activity. Design cognition often involves divergent thinking, mental imagery, and creative insight—processes that are spatially distributed and unfold rapidly over time [16]. In contrast, traditional problem-solving may engage more convergent, analytical thought patterns. Validating the neural correlates that distinguish these processes hinges on selecting the appropriate neuroimaging technology. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) are three non-invasive tools at the forefront of this endeavor, each with unique strengths and limitations. This guide provides an objective comparison of these modalities, equipping researchers with the data needed to align their tool selection with specific experimental goals in design neurocognition.

Technical Comparison: fMRI, EEG, and fNIRS at a Glance

The table below summarizes the core technical specifications and practical considerations for each modality, highlighting their performance trade-offs.

Table 1: Technical and Practical Comparison of fMRI, EEG, and fNIRS

Feature	fMRI	EEG	fNIRS
What It Measures	Blood Oxygenation Level Dependent (BOLD) response [27] [28]	Electrical potentials from synchronized neuronal firing [29] [30]	Concentration changes in oxygenated (HbO) and deoxygenated hemoglobin (HbR) [29] [28]
Spatial Resolution	High (millimeter-level) [27] [31]	Low (centimeter-level) [29] [31]	Moderate (centimeter-level), limited to cortex [27] [30]
Temporal Resolution	Low (seconds), limited by hemodynamic response [27] [31]	Very High (millisecond-level) [29] [30]	Low (seconds), limited by hemodynamic response [29] [30]
Depth of Measurement	Whole brain, including deep structures (e.g., amygdala, hippocampus) [27]	Cortical surface [31] [30]	Outer cortex (~1-2.5 cm deep) [27] [30]
Portability & Environment	Not portable; requires magnetic shielding [27] [28]	High (wearable systems available) [29]	High (wearable systems available) [27] [28]
Tolerance to Motion	Very low; highly sensitive to motion artifacts [27] [28]	Moderate; susceptible to muscle and movement artifacts [30]	High; relatively robust to motion artifacts [28] [30]
Best Suited For	Precise spatial localization of activity in deep and superficial brain regions [27]	Tracking fast neural dynamics (e.g., ERPs), rapid task changes [29] [30]	Naturalistic studies, sustained cognitive states, populations prone to movement [28] [30]
Approximate Cost	High ($1000+/scan) [31]	Generally lower [30]	Generally higher than EEG [30]

Experimental Protocols and Key Findings in Cognition Research

The choice of neuroimaging tool directly influences experimental design and the types of neural correlates that can be reliably detected. Below are detailed methodologies and findings from key studies relevant to design and problem-solving cognition.

Investigating Creative Insight with fMRI

Protocol Summary: A 2025 study investigated the neural dynamics of insight problem-solving using matchstick arithmetic tasks, which require spatial manipulation to find novel solutions [4].

• Participants & Task: Participants solved problems and categorized their solutions as "Quick," "Analytical," or "Insight" after each trial. Insight solutions were characterized by overcoming a mental impasse and a sudden "Aha!" moment [4].
• Data Acquisition: fMRI data were acquired across multiple sessions. The prolonged response times for insight solutions (averaging around 114 seconds) were well-suited to fMRI's slower temporal resolution [4].
• Analysis & Key Findings: Two analytical approaches were used:
  • General Linear Model (GLM): Contrasting insight with analytical solutions revealed greater activation in regions of the Default Mode Network (DMN), including the right angular gyrus and posterior cingulate cortex. Conversely, analytical solutions showed higher activation in the Executive Control Network (ECN) and visual networks [4].
  • Hidden Markov Model (HMM): This analysis identified dynamic brain states. The insight solution was associated with a higher fractional occupancy of distinct brain states, suggesting increased cognitive flexibility and dynamic interactions between large-scale networks during creative breakthroughs [4].

Relevance to Design Thinking: This protocol demonstrates fMRI's power to dissect the distinct brain networks involved in creative insight versus analytical problem-solving, a cornerstone of design cognition.

Uncovering Visual Creativity with fMRI

Protocol Summary: An earlier but foundational fMRI study specifically examined visual creativity, a key component of design thinking [16].

• Participants: Architects and architecture students were recruited, as they were expected to perform well on the visuospatial tasks [16].
• Task Design: Participants completed two tasks in the scanner:
  • Creative Task (Divergent Thinking): Assemble three given shapes into a namable, recognizable object (e.g., using a circle, '8', and 'C' to create a smiley face).
  • Control Task (Convergent Thinking): Mentally rotate trisected pieces of a common shape to reconstruct and name the original shape (e.g., a rectangle) [16].
• Key Findings: Contrary to the hypothesis that visual creativity is a right-hemisphere process, the creative task robustly activated left-hemisphere regions, including the dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex, and premotor cortex. This suggests that even for visuospatial tasks, creative cognition involves goal-directed planning and working memory processes lateralized to the left hemisphere [16].

Relevance to Design Thinking: This study highlights the role of higher-order cognitive control and motor planning in creative improvisation, processes that are central to design.

Capturing Motor Imagery with Simultaneous fNIRS-EEG

Protocol Summary: A 2023 study employed a multimodal approach to study motor execution, observation, and imagery (MI)—processes analogous to the mental simulation used in design [32].

• Participants & Paradigm: Healthy adults participated in a live-action paradigm where they either executed, observed, or imagined moving a cup with their right hand [32].
• Data Acquisition: Simultaneous fNIRS and EEG data were recorded using a custom cap that integrated both modalities. fNIRS optodes were placed over sensorimotor and parietal cortices to measure hemodynamic responses, while EEG electrodes captured electrical activity [32].
• Analysis & Key Findings:
  • Unimodal Analysis: fNIRS and EEG showed differentiated but non-overlapping activation patterns for the different conditions, underscoring that they capture different physiological signals.
  • Multimodal Fusion (ssmCCA): Using structured sparse multiset Canonical Correlation Analysis, the fused data consistently identified activation in the left inferior parietal lobe across all three conditions. This region is part of the Action Observation Network (AON), suggesting a shared neural mechanism for overt and covert action processing [32].

Relevance to Design Thinking: This protocol showcases the synergistic potential of combining fNIRS and EEG. While fNIRS provided better spatial localization of the AON, EEG complemented it with temporal detail, offering a more complete picture of the neural activity during mental imagery.

Decision Workflow: Selecting the Right Neuroimaging Tool

The following diagram outlines a logical workflow for researchers to select the most appropriate neuroimaging modality based on their experimental priorities.

Essential Research Reagent Solutions

The table below lists key materials and analytical solutions commonly used in experiments involving these neuroimaging modalities.

Table 2: Key Research Reagents and Materials for Neuroimaging Studies

Item	Function/Description	Common Use In
fNIRS Optodes	Sources emit near-infrared light, and detectors measure its attenuation after passing through tissue. Often placed using the international 10-20 system.	fNIRS, simultaneous EEG-fNIRS [32]
EEG Electrodes	Sensors (e.g., Ag/AgCl) placed on the scalp to measure voltage fluctuations. High-density caps provide better spatial coverage.	EEG, simultaneous EEG-fNIRS [29] [32]
Conductive Gel/Electrolyte	Improves electrical conductivity between EEG electrodes and the scalp, reducing impedance and improving signal quality.	EEG [31]
3D Magnetic Digitizer	A device (e.g., Polhemus Fastrak) used to record the precise 3D coordinates of fNIRS optodes/EEG electrodes relative to head landmarks.	fNIRS, EEG, simultaneous EEG-fNIRS [32]
Structured Sparse Multiset CCA (ssmCCA)	A advanced data fusion algorithm used to identify correlated components from simultaneously recorded multimodal data (e.g., fNIRS and EEG).	Simultaneous EEG-fNIRS Analysis [32]
Hidden Markov Model (HMM)	A statistical model used to estimate discrete, dynamic brain states from continuous neuroimaging data (e.g., fMRI).	fMRI Analysis [4]

fMRI, EEG, and fNIRS each offer a unique window into the brain processes underlying design thinking and problem-solving. No single tool is universally superior; the optimal choice is dictated by the specific aspect of cognition under investigation. fMRI remains unparalleled for deep, whole-brain spatial localization, making it ideal for mapping the distinct networks of insight versus analysis. EEG excels at capturing the rapid, millisecond-scale dynamics of neural processing. fNIRS offers a compelling balance, providing reasonable spatial resolution for the cortex in naturalistic settings that are often more representative of real-world design activities. For a comprehensive validation of neural correlates in design cognition, a multimodal approach that combines the temporal resolution of EEG with the cortical mapping capability of fNIRS presents a powerful and increasingly accessible path forward [29] [32].

The brain is a dynamic system, and its functions are supported by the ever-changing, coordinated activity of different neural populations. Analyzing these brain dynamics is crucial for understanding complex cognitive processes. Two key concepts in this domain are Hidden Markov Models (HMM), which are statistical models that infer hidden brain states from observable data, and functional connectivity (FC), which maps the statistical dependencies between neural time series from different brain regions [33] [34]. While traditional FC analysis often assumes that connectivity is static over time, a paradigm shift towards dynamic Functional Connectivity (dFC) recognizes that these interaction patterns reconfigure on the timescale of seconds [34]. HMMs have emerged as a powerful tool for estimating this dFC, providing a framework to model the brain as a sequence of discrete, recurring states characterized by unique patterns of neural activity or connectivity [35] [36]. This guide compares the performance of several advanced HMM frameworks, evaluating their capabilities in mapping brain dynamics within the research context of validating neural correlates of design thinking versus problem-solving.

Experimental Protocols: Methodologies for Mapping Brain States

To objectively compare different HMM approaches, it is essential to understand the core methodologies and experimental designs used to validate them. The following section details the protocols from key studies that have advanced the application of HMMs in neuroimaging.

Protocol 1: Novel Full Functional Connectivity (FFC) HMM

• Objective: To introduce and validate an HMM that defines its hidden states based on full functional connectivity profiles between brain regions, moving beyond models based solely on activity intensity [35].
• Data: Resting-state fMRI data from the Human Connectome Project (HCP) unrelated 100 dataset was used [35].
• Methodology: The study compared three types of HMMs:
  • Intensity-Based (IB) HMM: States are defined based on activity levels.
  • Summed-FC HMM: States are defined based on connectivity summed across nodes.
  • Full-FC (FFC) HMM: The novel model defining states based on comprehensive connectivity profiles between all region pairs [35].
• Validation Metrics: The models were evaluated on their ability to discover states with distinguishable connectivity patterns and to faithfully recover simulated, ground-truth connectivity states in a computer-based experiment [35].

Protocol 2: HMM with Fisher Kernel for Trait Prediction

• Objective: To leverage HMMs of brain dynamics for accurately and reliably predicting individual behavioral and cognitive traits [36].
• Data: Resting-state fMRI timeseries from 1,001 subjects of the HCP [36].
• Methodology:
  • A group-level HMM was estimated from the concatenated fMRI data of all subjects.
  • Subject-specific HMMs were derived via dual estimation to fit individual timeseries.
  • The HMM parameters, which reside on a Riemannian manifold, were used to construct different kernels for prediction:
    • Naïve Kernel: Treats HMM parameters as Euclidean, ignoring the underlying mathematical structure.
    • Fisher Kernel: Maps parameters into a gradient space that preserves the model's structure [36].
  • Prediction was performed using kernel ridge regression on 35 behavioral variables.
• Validation: The approach used 10-fold nested cross-validation (3,500 iterations) and was compared against models based on time-averaged (static) functional connectivity [36].

Protocol 3: HMM for Characterizing Creative Insight

• Objective: To identify the dynamic brain states associated with different problem-solving strategies, specifically creative insight [4].
• Data: fMRI data was acquired during a matchstick arithmetic (MA) task, a spatial insight problem. Participants self-annotated their solutions as "Quick," "Analytical," or "Insight" [4].
• Methodology:
  • A General Linear Model (GLM) identified brain regions associated with each solution type.
  • An HMM was applied to estimate discrete, recurring brain states during the entire problem-solving process.
  • The fractional occupancy (FO)—the total time spent in each brain state—was compared across the different solution types in the periods leading up to the response [4].
• Validation: The temporal patterns and transition dynamics of the brain states were quantified and linked to behavioral performance and subjective solution categories.

Protocol 4: Factor Analysis HMM for Clinical Reclassification

• Objective: To achieve a data-driven reclassification of Multiple Sclerosis (MS) disease progression using a multimodal approach [37].
• Data: Clinical and radiological data from approximately 8,000 patients across 118,000 visits in the Novartis-Oxford MS database [37].
• Methodology:
  • A Probabilistic Factor Analysis Hidden Markov Model (FAHMM) was used.
  • The model first reduced numerous clinical variables into four latent dimensions (e.g., physical disability, brain damage).
  • The longitudinal trajectories of these composite scores were then modeled by the HMM to identify data-driven disease states.
  • The transition probability matrix between these states was calculated to model disease evolution [37].
• Validation: The model was validated on a holdout sample and externally replicated in an independent clinical trial database and a real-world cohort totaling over 4,000 patients.

Comparative Performance Analysis

The table below synthesizes quantitative data from the reviewed studies, providing a direct comparison of HMM performance across key applications.

Table 1: Comparative Performance of Hidden Markov Model (HMM) Frameworks in Neuroimaging

HMM Framework / Study	Primary Application	Key Performance Metric	Reported Result	Outperformance Versus
Full-FC HMM [35]	Dynamic State Discovery	State Distinguishability & Simulation Recovery	Faithfully recovered simulated states; more distinguishable patterns	Intensity-Based & Summed-FC HMMs
HMM-Fisher Kernel [36]	Brain-Behavior Prediction	Prediction Accuracy (mean correlation)	Mean r = 0.192 (Linear Fisher Kernel)	Naïve Kernel (r=0.05); Static FC Models
HMM for Insight [4]	Cognitive State Characterization	Fractional Occupancy (FO) of States	Significantly higher FO for States 4 & 5 during insight	Quick & Analytical problem-solving
Factor Analysis HMM [37]	Clinical Disease Modeling	Model Fit (Bayesian Information Criterion)	Favored 8+ state models over traditional 3-type classification	Traditional MS subtyping (RRMS, SPMS, PPMS)

The Researcher's Toolkit: Essential Materials and Methods

Table 2: Key Research Reagents and Tools for HMM-based Brain Dynamics Research

Item Name / Category	Specification / Example	Primary Function in Research
Neuroimaging Datasets	Human Connectome Project (HCP) [35] [36] [38]	Provides large-scale, high-quality resting-state and task fMRI data for model development and testing.
Brain Parcellation Atlas	Schaefer 100x7 Atlas [39]	Divides the brain into defined regions of interest (ROIs) for extracting time series and computing functional connectivity.
Pairwise Statistic Library	PySPI package (239 statistics) [39]	Benchmarks and computes a wide array of functional connectivity methods beyond standard correlation.
Computational Framework	Variational Autoencoder (VAE) [40]	Used in some advanced HMMs (e.g., PHMM) for training via evidence lower bound (ELBO) optimization on Riemannian manifolds.
Model Evaluation Metric	Bayesian Information Criterion (BIC) [37]	A criterion for model selection, helping to determine the optimal number of hidden states while penalizing complexity.
Clinical Validation Cohorts	MS PATHS, Roche Trial DB [37]	Independent, real-world or trial datasets used for external validation of data-driven models' generalizability.
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Signaling Pathways and Experimental Workflows

The following diagrams, generated with Graphviz, illustrate the core logical and methodological pathways described in the research.

HMM-Fisher Kernel Prediction Workflow

Factor Analysis HMM for Disease Modeling

The comparative analysis demonstrates that HMMs are a flexible and powerful family of models for dissecting brain dynamics across various research contexts. The Full-FC HMM proves superior for studies focused purely on the temporal evolution of network connectivity [35], whereas the HMM-Fisher kernel combination provides a mathematically principled and highly accurate method for linking these dynamics to individual differences in behavior or traits [36]. For cognitive neuroscience, standard HMMs effectively discriminate between distinct cognitive processes, such as analytical problem-solving and creative insight, by revealing their unique underlying brain states [4]. In clinical translation, more complex frameworks like the FAHMM can integrate massive, multimodal datasets to redefine disease progression in a data-driven manner, moving beyond potentially limiting clinical consensus categories [37].

Within the specific thesis context of validating neural correlates of design thinking versus problem-solving, the protocols and findings herein are highly relevant. The study on insight problem-solving [4] provides a direct experimental blueprint. It shows that HMMs can detect states where default mode network (DMN) activity is predominant during insight, contrasting with executive control network (ECN) dominance during analytical solutions. Applying the more powerful FFC-HMM or HMM-Fisher kernel approaches to a dedicated design-thinking experiment could further refine these neural correlates, offering a dynamic and quantifiable signature of the creative process to distinguish it from more sequential problem-solving. This powerful toolkit allows researchers to move beyond static snapshots of brain activity and truly capture the temporal dynamics that underlie human cognition and its pathologies.

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent two pillars of non-invasive human brain imaging, each with distinct and complementary strengths and limitations. fMRI excels at localizing brain activity with high spatial resolution, often at the scale of millimeters, by measuring the blood-oxygen-level-dependent (BOLD) signal that reflects hemodynamic changes coupled to neural activity [41]. EEG, in contrast, captures the electrical activity of the brain with millisecond temporal precision, directly reflecting postsynaptic potentials of neuronal populations [42]. The fundamental challenge in cognitive neuroscience lies in the fact that many brain processes unfold rapidly across distributed networks, requiring both fine temporal and spatial resolution to be fully understood. This integration is particularly relevant for research aimed at validating the neural correlates of higher-order cognitive processes, such as distinguishing between design thinking and routine problem solving, which likely involve rapidly shifting interactions between multiple brain systems [9].

The convergence of these modalities offers a more complete picture of brain function than either can provide alone. However, simply using them concurrently is not equivalent to true integration. Multimodal data fusion represents a sophisticated set of computational approaches designed to merge these disparate data types at different levels—from the raw signal to the feature or decision level—to create a unified model of brain activity that leverages their complementary advantages [43]. This guide objectively compares the performance characteristics of fMRI and EEG, details the experimental protocols for their integration, and explores how this fusion advances specific research goals in differentiating complex cognitive states.

Technical Performance Comparison of fMRI and EEG

The following tables provide a quantitative and qualitative comparison of the core technical capabilities of fMRI and EEG, highlighting their complementary profiles.

Table 1: Spatial and Temporal Resolution Specifications

Feature	fMRI	EEG
Spatial Resolution	~1-3 mm (3T), <1 mm (7T+) [41] [44]	~5-9 cm for scalp potentials; improved with High-Density EEG and CSD [42] [45]
Temporal Resolution	~1-3 seconds (hemodynamic lag); sub-second with advanced protocols [44]	~1-5 milliseconds [42]
Primary Signal Source	Hemodynamic (BOLD) response, an indirect metabolic correlate of neural activity	Electrical potentials from synchronized postsynaptic neuronal activity
Spatial Coverage	Whole-brain by default	Primarily cortical surfaces, with limited sensitivity to subcortical structures
Key Strength	Localizing neural activity with high spatial specificity	Tracking the dynamics of neural processing in real-time

Table 2: Practical Considerations for Research Applications

Consideration	fMRI	EEG
Noise Sensitivity	Sensitive to motion; physiological noise (cardiac, respiration) limits temporal SNR [46]	Highly sensitive to environmental and physiological artifacts (e.g., muscle, eye movements)
Participant Burden	High: restrictive environment, loud noise, requires supine position	Low to Moderate: portable systems allow for more naturalistic seating
Cost	Very High (scanner purchase, maintenance)	Relatively Low
Use in Drug Development	Pharmacodynamic measures, target engagement, dose-response relationships [47]	Functional target engagement, dose-response on brain rhythms/ERPs [47]
Suitability for Social Interaction Studies	Challenging; requires hyperscanning setups or simulated interaction [9]	Good; easier hyperscanning for real-time, multi-person studies [9]

Methodological Frameworks for Multimodal Fusion

The fusion of fMRI and EEG data can be achieved at different processing stages, each with distinct methodological approaches and goals.

Data-Level Fusion

Data-level fusion involves the direct integration of raw or preprocessed signals. A common approach is using the high temporal precision of EEG to inform the analysis of the slower fMRI signal.

• EEG-Informed fMRI Analysis: This method identifies relationships between EEG features and the BOLD signal. For instance, trial-by-trial variations in the amplitude of a specific event-related potential (ERP) component, such as the P300, are used as regressors in a generalized linear model (GLM) of the fMRI data. This reveals brain regions whose activity fluctuates in tandem with the electrophysiological signature of a cognitive process (e.g., attentional allocation or decision confidence) [47].
• fMRI-Constrained Source Reconstruction: The low spatial resolution of EEG is a major constraint. This technique uses fMRI activation maps as spatial priors to guide the inverse problem—estimating the locations and time courses of the intracranial sources that generated the scalp EEG signals. This results in a source-localized EEG signal with much improved spatial accuracy, allowing researchers to visualize the millisecond-by-millisecond flow of activity through the brain's network hubs identified by fMRI.

Feature-Level Fusion

Feature-level fusion involves extracting relevant features from each modality and combining them into a unified dataset for joint analysis or classification. This is considered one of the most powerful approaches for leveraging complementary information [43].

• Mutual Information-Based Feature Selection: As demonstrated in EEG-fNIRS fusion, this advanced algorithm selects a subset of combined EEG and fMRI features that maximizes their shared information with the experimental condition (e.g., design thinking vs. problem-solving) while minimizing redundancy between the features themselves. This optimizes the feature set for improved classification performance [43].
• Canonical Correlation Analysis (CCA): CCA projects the two feature sets from EEG and fMRI into a new, shared space where their correlation is maximized. This effectively reduces data dimensionality and isolates the common sources of variance, highlighting the neural processes that are robustly captured by both modalities [43].

Decision-Level Fusion

In decision-level fusion, data from each modality are analyzed independently through separate classifiers or models, and their final outcomes are combined.

• Meta-Classifier Fusion: Separate classifiers (e.g., Support Vector Machines) are trained on EEG features and fMRI features. The probabilistic outputs or decisions from these classifiers are then fed into a second-level "meta-classifier" that makes a final integrated decision. This approach benefits from the strengths of each modality's model without the complexity of merging the raw data streams initially [43].

The following diagram illustrates the workflow for the primary fusion levels:

Multimodal Fusion Workflow

Experimental Protocols for Validating Neural Correlates

To rigorously distinguish between complex cognitive states like design thinking and problem solving, carefully controlled experiments with multimodal neuroimaging are essential. Below are detailed protocols for key experimental paradigms.

Protocol 1: The Alternating Divergent-Convergent Thinking Task

This protocol is designed to capture the core cognitive cycle often associated with design thinking.

• Stimuli & Paradigm: Participants are presented with a series of open-ended problems (e.g., "Design a chair for a zero-gravity environment"). The task alternates in blocks: (A) Divergent Thinking Phase: Participants generate as many ideas or solutions as possible. (B) Convergent Thinking Phase: Participants evaluate, critique, and select the most promising idea from their list.
• fMRI Acquisition: Whole-brain BOLD fMRI acquired at 3T or 7T. A T2*-weighted gradient-echo EPI sequence is used (TR=2000 ms, TE=30 ms, voxel size=2 mm isotropic). A high-resolution T1-weighted anatomical scan (MPRAGE) is also collected for co-registration.
• EEG Acquisition: Continuous EEG is recorded simultaneously from a 64- or 128-channel system. Data is sampled at 1000 Hz, with impedances kept below 10 kΩ. Electrooculogram (EOG) and electrocardiogram (ECG) are recorded for artifact removal.
• Analysis Plan:
  • fMRI: GLM analysis contrasting Divergent > Convergent blocks and vice versa. Connectivity analysis (e.g., psychophysiological interaction - PPI) to identify network dynamics.
  • EEG: Time-frequency analysis to track power in the alpha (8-12 Hz) and theta (4-7 Hz) bands, associated with internal attention and cognitive control. ERP analysis locked to the onset of each phase.
  • Fusion: EEG theta power from frontal electrodes can be used as a regressor in the fMRI GLM to identify regions whose BOLD signal co-varies with this electrophysiological index of cognitive control during the convergent phase.

Protocol 2: Insight-Based vs. Analytical Problem Solving

This protocol contrasts two distinct problem-solving modes, one of which (insight) is a key component of design thinking.

• Stimuli & Paradigm: Participants solve compound remote associate tasks (CRATs), where they must find a word that forms a compound word with three given words (e.g., "pine", "crab", "sauce" -> "apple"). Trials are categorized based on participant self-report into "Insight" (sudden, 'Aha!' moment) and "Analytical" (step-by-step reasoning) solutions.
• Data Acquisition: fMRI and EEG parameters are similar to Protocol 1.
• Analysis Plan:
  • fMRI: GLM contrasting the hemodynamic response in the seconds leading up to and including the solution moment for Insight vs. Analytical trials. This targets regions like the anterior cingulate cortex and temporal gyri, previously implicated in insight.
  • EEG: ERP analysis time-locked to the solution moment. A key marker is the "frontal negativity" reported for insight solutions around 300-500 ms pre-solution. Spectral power analysis focusing on gamma-band activity (>30 Hz) potentially signaling the formation of new associative links.
  • Fusion: fMRI-constrained source reconstruction of the EEG frontal negativity to precisely localize its neural generators within the network identified by the fMRI contrast.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Reagents and Equipment for Multimodal fMRI-EEG Research

Item Name	Function/Brief Explanation
High-Density EEG System (64-128+ channels)	Captures neural electrical activity with high temporal resolution. Higher density improves spatial sampling and source localization [45].
Ultra-High Field MRI Scanner (7T+)	Provides the increased signal-to-noise ratio (SNR) necessary for high-resolution (sub-millimeter) fMRI, enabling layer-specific imaging [44] [48].
MRI-Compatible EEG Amplifier & Headcap	Allows for safe and artifact-free simultaneous recording of EEG inside the MRI scanner. Electrodes are made of non-ferromagnetic materials.
Current Source Density (CSD) Toolbox	Computational tool to transform scalp EEG potentials into reference-free current source density estimates. This reduces volume conduction effects and significantly improves both spatial and temporal resolution of EEG [42].
Mutual Information Feature Selection Algorithm	A computational filter method used in feature-level fusion to select an optimized subset of multimodal features that maximizes relevance and minimizes redundancy [43].
Cortical Layer Atlas	A parcellation map derived from high-resolution histology or structural imaging, used to define regions of interest (ROIs) for analyzing laminar fMRI data [48] [46].
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Application in Differentiating Design Thinking and Problem Solving

The fusion of fMRI and EEG provides a unique lens to dissect the neural correlates of design thinking versus more routine problem solving. The following diagram models the proposed distinct neural pathways:

Neural Correlates of Cognitive Processes

Research suggests that creative generation and evaluation—core components of design thinking—engage a distributed network. ALE meta-analysis has shown that creative generation involves the dorsomedial prefrontal cortex (DMPFC), inferior frontal gyrus (IFG), and middle temporal gyrus (MTG), while evaluation engages the anterior cingulate cortex (ACC) and superior frontal gyrus (SFG) [49]. The temporal dynamics of how these regions interact likely differentiates design thinking. fMRI can localize these hubs, while EEG can track the rapid, oscillatory communication between them—for instance, via synchrony in the theta (4-7 Hz) or gamma (>30 Hz) frequency bands.

For drug development professionals, this fused approach is critical for de-risking clinical trials for disorders affecting creativity and executive function. It allows for the development of pharmacodynamic biomarkers that are sensitive to both the spatial location and temporal dynamics of a drug's effect on these critical brain networks [47].

The quest to understand the neural underpinnings of human creativity requires carefully constructed experimental paradigms that can reliably evoke and measure distinct cognitive processes. Research in this domain seeks to dissect the neural correlates of design thinking—characterized by generative and exploratory processes—from those of more analytical problem-solving. This guide objectively compares the performance of key experimental paradigms used in cognitive neuroscience research, evaluating their efficacy in isolating specific components of creative cognition through functional magnetic resonance imaging (fMRI) and other neuroimaging techniques. The comparison focuses on how effectively each paradigm engages large-scale brain networks, particularly the executive control network (ECN) associated with focused analytical thinking and the default mode network (DMN) linked with spontaneous creative insight and generative thinking [4].

Comparative Analysis of Experimental Paradigms

The following table summarizes the key characteristics, neural correlates, and experimental outputs of three prominent paradigms used in creativity and problem-solving research.

Table 1: Comparison of Experimental Paradigms in Creativity and Problem-Solving Research

Experimental Paradigm	Primary Cognitive Process Measured	Key Brain Regions/Networks Identified	Typical Task Duration/Design	Key Behavioral Metrics	Major Neural Findings
Matchstick Arithmetic Problems [4]	Insightful problem-solving, spatial manipulation	DMN (Insight); ECN (Analytical); Right STP, Left SFG [4]	~114s (Insight); ~37s (Analytical); Self-reported strategy classification [4]	Solution time, accuracy, solution type (Quick, Analytical, Insight) [4]	DMN more active during insight; ECN more active during quick/analytical solutions; High state variability in insight [4]
Pictionary-based Drawing Task [50]	Spontaneous improvisation, figural creativity	Cerebellum, Prefrontal Cortex, Cingulate, Parietal Cortex [50]	30-second drawing blocks; Word vs. control (zigzag) contrast [50]	Expert-rated creativity of drawings, self-reported difficulty [50]	Cerebral-cerebellar interaction facilitates improvisation; Left PFC activity negatively correlated with performance [50]
Chunk Decomposition (Chinese Characters) [51]	Processing of novelty and appropriateness in insight	ACC, Visual-Spatial Regions, Memory Areas (e.g., Hippocampus) [51]	2x2 design (Familiar/Novel x Appropriate/Inappropriate); Hint-guided decomposition [51]	Response time, accuracy in creating real characters [51]	Novelty processing linked to conflict monitoring (ACC); Appropriateness linked to memory and visual-spatial processing [51]

Detailed Experimental Protocols and Methodologies

Matchstick Arithmetic Problems for Insight Problem-Solving

The matchstick arithmetic paradigm is a spatial insight task where participants view false arithmetic statements composed of roman numerals (e.g., 'IV = III + III') and operations made from matchsticks. The goal is to mentally manipulate the equation by moving only one matchstick to create a true statement [4].

• Procedure: Participants are typically placed in an fMRI scanner and presented with problems via a visual display. The problem remains on screen until the participant formulates a solution. After each trial, participants provide a self-report categorizing their solution process as "Quick" (known solution), "Analytical" (step-by-step reasoning), or "Insight" (sudden "Aha!" experience) [4].
• Data Acquisition: fMRI data are collected throughout the problem-solving period. The prolonged solution times for insight-based solutions (averaging ~114 seconds) compared to analytical (~37 seconds) and quick (~9 seconds) solutions allow for the application of dynamic brain analysis methods like hidden Markov models (HMM) to track state transitions [4].
• Analysis Methods: Researchers employ both the general linear model (GLM) to identify brain regions associated with different strategies and HMM to estimate discrete, dynamic brain states during the problem-solving process [4].

Pictionary-Based Figural Creativity Paradigm

This game-like paradigm assesses spontaneous improvisation and figural creativity in an ecological setting without explicit instructions to "be creative," reducing performance anxiety that can confound results [50].

• Procedure: Participants use an MR-safe drawing tablet to draw representations of a given word (e.g., "angel," "hunger") within 30-second blocks, with the goal that others could later guess the word from their drawing alone. Control conditions involve drawing simple zigzag patterns to account for basic motor planning and execution [50].
• Data Acquisition: fMRI data are collected during drawing blocks. The drawings are later independently scored by experts for creativity and representation quality on standardized scales [50].
• Analysis Methods: The primary contrast compares word-drawing versus zigzag-drawing blocks to identify neural correlates of spontaneous improvisation. Parametric analyses then correlate brain activity with expert-rated creativity scores and participant-reported difficulty ratings [50].

Chunk Decomposition Task for Novelty and Appropriateness

This paradigm systematically manipulates two core components of creativity—novelty and appropriateness—using Chinese character decomposition [51].

• Stimuli and Design: The task employs a 2x2 within-subjects design crossing Familiarity (Familiar vs. Novel) with Appropriateness (Appropriate vs. Inappropriate). For example:
  • Familiar-Appropriate: Decomposing "冷" (lÄ›ng, cold) into "令" (lìng, order) by removing the radical "冫".
  • Novel-Appropriate: Decomposing "æ±—" (hàn, sweat) into "汁" (zhÄ«, juice) by removing a stroke.
  • Familiar-Inappropriate/Novel-Inappropriate: Similar decompositions that result in false/non-real characters [51].
• Procedure: Participants are shown a target character and a specific part to remove (the "hint"). They mentally perform the decomposition and indicate whether the resulting character is real (appropriate) or not [51].
• Analysis Methods: fMRI analyses focus on contrasts between the four conditions to dissociate brain activity related to overcoming familiar perceptual chunks (novelty) from activity related to evaluating the meaningfulness of the outcome (appropriateness) [51].

Neural Signaling Pathways and Workflows

The following diagram illustrates the core neural networks and their dynamic interactions during creative problem-solving, as identified by the featured paradigms.

Figure 1: Neural Networks in Problem-Solving and Design Thinking

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Methods for Creativity Research

Research 'Reagent'	Function in Experimental Paradigm	Key Utility in Isolving Neural Correlates
fMRI with HMM Analysis [4]	Tracks dynamic brain state transitions during prolonged problem-solving	Captures temporal dynamics of network switching between ECN and DMN during insight
Matchstick Arithmetic Stimuli [4]	Provides spatial manipulation problems with multiple solution pathways	Enables trial-by-trial classification of insight vs. analytical strategies via self-report
MR-Safe Drawing Tablet [50]	Enables naturalistic drawing during fMRI acquisition	Facilitates study of figural creativity and improvisation in ecologically valid game context
Chinese Character Decomposition [51]	Allows systematic manipulation of novelty and appropriateness factors	Dissociates neural processing of creative originality (novelty) from utility (appropriateness)
Self-Report Strategy Classification [4]	Participants categorize their own problem-solving approach	Provides behavioral validation for distinguishing insight from analytical neural correlates
Expert Creativity Ratings [50]	Independent assessment of creative output quality	Offers objective metric for parametric fMRI analysis of creative content generation
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Bridging the Gap: Challenges in Translating Neural Data to Design Practice

In the pursuit of validating neural correlates of complex cognitive processes like design thinking versus problem-solving, researchers face a fundamental neuroimaging paradox: the inescapable trade-off between temporal resolution and spatial precision. Functional Magnetic Resonance Imaging (fMRI), while offering detailed spatial maps of brain activity, suffers from inherently slow measurement of the hemodynamic response, which lags behind neural activity by several seconds. Simultaneously, all neuroimaging techniques grapple with signal noise that can obscure subtle neural patterns critical for distinguishing between sophisticated cognitive states. This methodological challenge becomes particularly acute when investigating nuanced differences between design thinking—characterized by non-linear, iterative ideation processes—and more structured problem-solving approaches. The contamination of neural signals by noise from physiological sources, scanner artifacts, and subject motion further compounds the interpretative challenge, potentially leading to false positives or masked effects. This article examines the primary sources of these limitations, evaluates emerging technological and analytical solutions, and provides a structured framework for optimizing neuroimaging protocols to enhance the validity of cognitive neuroscience research.

The Temporal Resolution Challenge in fMRI

Understanding the Hemodynamic Lag

The blood oxygenation level-dependent (BOLD) signal, the primary contrast mechanism in fMRI, provides an indirect measure of neural activity through cerebral blood flow changes. This neurovascular coupling introduces a fundamental temporal delay, with the peak BOLD response occurring 4-6 seconds after neural firing. This sluggish response function effectively low-pass filters the underlying neural signals, making it difficult to resolve rapid cognitive sequences such as those occurring during iterative design thinking processes, where idea generation, evaluation, and refinement may occur within sub-second timescales.

The Multiband Acquisition Dilemma

Multiband (MB) or simultaneous multi-slice acquisition sequences have emerged as a popular solution for improving temporal resolution by accelerating volume acquisition through simultaneous excitation of multiple slices. The Human Connectome Project (HCP) popularized this approach using an MB factor of 8 with 2mm isotropic voxels and a TR of 0.72 seconds [52]. However, this approach presents significant trade-offs for smaller-scale studies investigating specialized cognition:

• Reduced Signal-to-Noise Ratio (SNR): MB sequences introduce additional noise with characteristic striped or checkerboard patterns and "slice-leakage" effects where signal from one slice appears in another [52].
• Signal Dropout in Critical Regions: High MB acceleration factors cause significant signal dropout in medial and ventral brain regions, including subcortical structures (thalamus, striatum) and medial-temporal areas (amygdala, hippocampus) [52]. This is particularly problematic for design thinking research, as these regions are implicated in emotional processing, memory retrieval, and reward anticipation—potentially key differentiators from analytical problem-solving.
• Exacerbated Motion Sensitivity: MB sequences can interact with head motion to produce additional artefacts in a non-linear manner, complicating the interpretation of results [52].

Table 1: Impact of Multiband Acceleration Factors on fMRI Data Quality

MB Factor	TR (seconds)	Voxel Size (mm³)	Key Advantages	Key Limitations
1 (Single-band)	2.0-4.0	3×3×3	High SNR, minimal artefacts	Long TR limits temporal resolution
4	1.0-1.5	2×2×2	Good balance of speed and quality	Moderate SNR reduction
8 (HCP-style)	0.7-1.0	2×2×2	High temporal resolution	Significant SNR loss, ventral dropout
≥12	<0.5	<2×2×2	Ultra-fast sampling	Severe artefacts, limited coverage

Emerging Solutions for Enhanced Temporal Resolution

gSLIDER-SWAT for High-Resolution fMRI

Novel pulse sequences are addressing temporal resolution limitations while maintaining spatial precision. The gSLIDER-SWAT (generalized Slice Dithered Enhanced Resolution with Sliding Window Accelerated Temporal resolution) technique enables high spatial-temporal resolution fMRI (≤1mm³) at 3T, providing an approximate 2× gain in temporal SNR over traditional spin-echo EPI and a 5-fold improvement in effective temporal resolution [53]. This approach is particularly valuable for investigating design thinking, as it enables better characterization of smaller subcortical structures like the amygdala and hippocampal subfields that may exhibit differential engagement during creative cognition versus analytical problem-solving.

Multi-Modal Integration Approaches

The inherent limitations of any single neuroimaging modality have spurred interest in multi-modal integration. A promising approach uses transformer-based encoding models that combine magnetoencephalography (MEG) and fMRI data collected during naturalistic stimulation to estimate latent cortical source responses with high spatiotemporal resolution [54]. This method effectively decomposes the complementary strengths of each modality: MEG's millisecond temporal precision and fMRI's millimeter spatial resolution. For design thinking research, which often employs ecologically valid complex tasks, this approach enables more precise tracking of the rapid neural dynamics supporting creative cognition within spatially defined networks.

Table 2: Temporal and Spatial Resolution Characteristics of Neuroimaging Techniques

Technique	Temporal Resolution	Spatial Resolution	Best Applications in Design Thinking Research
Standard fMRI (3T)	1-3 seconds	2-3 mm	Localizing sustained cognitive states
High-speed fMRI (gSLIDER-SWAT)	0.5-1.5 seconds	0.8-1.5 mm	Subcortical engagement, network dynamics
MEG	1-10 milliseconds	5-10 mm	Tracking rapid idea evolution
MEG-fMRI Fusion	~100 milliseconds	2-3 mm	Comprehensive spatiotemporal mapping
ECoG	1-5 milliseconds	1-2 mm	Ground truth for specific regions

Neuroimaging signals are contaminated by multiple noise sources that can mask subtle neural correlates differentiating cognitive states:

• Thermal Noise: Arises from random thermal motion of electrons in receiver coils and biological tissues, following a Rician distribution in magnitude images [55]. This noise source is particularly problematic at lower field strengths.
• Physiological Noise: Cardiac (0.6-1.2 Hz) and respiratory (0.1-0.5 Hz) cycles introduce rhythmic signal fluctuations that can confound resting-state and task-based analyses [56].
• Motion Artifacts: Head movement during scanning creates complex signal distortions, particularly problematic for populations likely to exhibit more movement during engaging tasks.
• Scanner Drift: Slow signal changes over time can introduce spurious correlations in functional connectivity analyses.

Denoising Methodologies and Their Efficacy

Preprocessing Pipelines: fMRIPrep

fMRIPrep has emerged as a standardized preprocessing pipeline that addresses multiple noise sources through a transparent, automated workflow [57] [58]. Key denoising components include:

• Head-motion correction via volume realignment to a reference image
• Susceptibility distortion correction using fieldmap data or reverse phase-encoded images
• Slice-timing correction to address acquisition time differences across slices
• Confound regressor estimation including motion parameters, physiological signals, and tissue-based regressors

Notably, fMRIPrep remains agnostic to subsequent denoising approaches, providing comprehensive confound matrices for flexible noise modeling in downstream analyses [57].

Data-Driven Denoising Approaches

Independent Component Analysis (ICA) has proven particularly effective for separating neural signals from noise sources in resting-state and task-based fMRI [56]. This data-driven approach identifies spatially independent components without strong a priori hypotheses, allowing for the identification and removal of noise-related components characterized by:

• Spatial patterns dominated by edge, ventricular, or vascular regions
• Temporal profiles dominated by high frequencies or sudden signal spikes
• Association with physiological recording data when available

The effectiveness of ICA is highly dependent on model order selection, with higher dimensionality improving the segregation of neural signals from noise [56].

Deep Learning Denoising Approaches

Recent advances in deep learning have produced powerful denoising frameworks applicable across imaging modalities:

• Native Noise Denoising (NND): This approach leverages the inherent noise characteristics of acquired data, modeling noise directly from image corner patches and applying it to train denoising autoencoders. NND has demonstrated SNR improvements of 19.02% for in vivo brain MRI at 0.05T [55].
• FAST Denoising: The FrAme-multiplexed SpatioTemporal learning strategy employs an ultra-lightweight convolutional neural network for real-time denoising at speeds exceeding 1000 frames per second, balancing spatial and temporal redundancy to prevent over-smoothing of rapidly evolving signals [59].
• Joint Denoising and Motion Correction (JDAC): This iterative framework combines adaptive denoising with motion artifact correction, using noise level estimation based on gradient map variance to progressively improve image quality [60].

Evaluating Denoising Impact on Tractometry

The practical impact of denoising methods on neuroscientific conclusions must be critically evaluated. A recent study examining denoising effects on diffusion MRI tractometry in glaucoma patients found that while MPPCA and Patch2Self denoising improved image quality and reduced residuals in voxelwise model fitting, they had limited impact on the ability to detect tissue abnormalities between patients and controls [61]. This highlights the importance of validating that denoising methods preserve biologically relevant signals while removing noise, particularly for clinical or cognitive applications seeking to identify subtle between-group differences.

Integrated Experimental Protocols for Optimized Neuroimaging

Protocol for High-Fidelity Naturalistic Paradigm Imaging

Naturalistic paradigms involving complex stimuli such as design challenges or problem-solving tasks present particular challenges for neuroimaging. The following integrated protocol optimizes data quality for such studies:

• Acquisition Parameters:

  • Implement gSLIDER-SWAT sequence at 3T with 1mm³ isotropic resolution [53]
  • Use moderate multiband acceleration (factor 4-6) balanced with TR ~1.2s
  • Employ simultaneous multi-echo acquisition for enhanced BOLD sensitivity and denoising

• Preprocessing Pipeline:

  • Process through fMRIPrep for standardized preprocessing and confound estimation [57]
  • Apply ICA-based denoising (e.g., FSL FIX or AROMA) for aggressive noise removal
  • Incorporate physiological monitoring data (cardiac, respiration) into noise models

• Multi-Modal Validation:

  • Collect parallel MEG data during similar naturalistic tasks [54]
  • Implement cross-modal encoding models to validate temporal dynamics
  • Use model-based approaches to estimate latent neural sources

Diagram: Multi-Modal Neuroimaging Pipeline for Naturalistic Paradigms

Protocol for Resting-State Connectivity Studies

Resting-state functional connectivity analyses require particular attention to denoising approaches to avoid spurious correlations:

• Acquisition Optimization:

  • Use standard (3-3.5mm) voxel sizes rather than high-resolution protocols to maintain SNR [52]
  • Employ TR = 2-2.5s for optimal balance of T1 effects and sampling rate
  • Collect 15+ minutes of data per subject to improve reliability [52]

• Comprehensive Denoising:

  • Apply volume censoring (e.g., FSL FIX) for motion-contaminated volumes
  • Incorporate anatomical CompCor to remove noise from white matter and CSF regions
  • Implement global signal regression selectively based on research question
  • Use bandpass filtering (0.008-0.1 Hz) to focus on relevant frequencies

• Validation and Reliability:

  • Quantify frame-wise displacement and DVARS for quality assessment
  • Apply scan-rescan protocols to establish reliability of connectivity measures
  • Compare denoising approaches through goodness-of-fit metrics at the subject level

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Essential Tools for High-Quality Neuroimaging Research

Tool/Resource	Primary Function	Application Context	Key Considerations
fMRIPrep [57]	Standardized fMRI preprocessing	All BOLD fMRI studies	Provides reproducible pipeline; generates comprehensive confound matrices
MRTrix3 [61]	Diffusion MRI processing	Tractometry studies	Includes MPPCA denoising for dMRI; enables advanced tractography
DIPY (Patch2Self) [61]	Self-supervised dMRI denoising	Diffusion studies with limited data	Does not require noise estimation; preserves signal integrity
ICA-AROMA	Motion artifact removal	Resting-state and task fMRI	Identifies motion-related components; superior to standard regression
gSLIDER-SWAT [53]	High-resolution fMRI	3T studies of subcortical regions	Reduces vein bias and signal dropout; improves temporal resolution
FAST Denoising [59]	Real-time image enhancement	High-speed fluorescence imaging	Ultra-lightweight network; >1000 FPS processing
JDAC Framework [60]	Joint denoising and motion correction	Structural MRI with artifacts	Iterative learning; preserves anatomical details
MNE-Python [54]	MEG/EEG processing	Multi-modal integration	Source estimation; morphing to template spaces

Overcoming the dual challenges of low temporal resolution and signal noise in neuroimaging requires a multifaceted approach that combines optimized acquisition sequences, sophisticated denoising algorithms, and appropriate analytical frameworks. For researchers investigating nuanced distinctions between cognitive processes such as design thinking and problem-solving, these methodological considerations are not merely technical details but fundamental determinants of validity. The emerging toolkit of high-temporal resolution fMRI sequences, advanced denoising approaches, and multi-modal integration methods provides powerful resources for enhancing the sensitivity and precision of neuroimaging investigations. By thoughtfully implementing these solutions and transparently reporting methodological choices, the field can progress toward more reliable characterization of the neural underpinnings of complex human cognition, ultimately strengthening the evidence base for neuroscientific theories of creative and analytical thinking.

The quest to validate the neural correlates of creative processes, such as design thinking and problem-solving, is fundamentally complicated by the Variability Problem: the high degree of inter-subject differences in creative cognition and its underlying neurophysiology. While creativity is universally recognized as a critical driver of innovation in fields like drug discovery, its measurement and neural validation are plagued by inconsistent and poorly replicated findings across individuals [62]. This variability presents a significant obstacle for researchers and pharmaceutical professionals seeking to harness creativity for developing patient-centered therapies. Emerging evidence suggests that this heterogeneity is not mere noise but arises from a complex interaction of stable anatomical differences, variable state-based factors, and the specific methodologies employed to probe creative thought [4] [62]. This guide objectively compares the performance of different experimental approaches in accounting for this variability, providing a framework for selecting and interpreting research on the neural correlates of design thinking versus problem-solving.

Neural Correlates of Creativity: Networks, Dynamics, and Individual Differences

Creative cognition is not localized to a single brain region but emerges from the dynamic interaction of large-scale brain networks. Understanding the roles of these networks and their variable engagement across individuals is crucial for interpreting neuroimaging data.

• Default Mode Network (DMN): Associated with internal mentation, mind-wandering, and incubation. Studies consistently show greater DMN activation (particularly in the precuneus, angular gyrus, and posterior cingulate cortex) during insight-based solutions and creative idea generation [4] [63]. Its activity is crucial for the incubation and "Aha!" moment stages of the creative process.
• Executive Control Network (ECN): Involved in focused attention, cognitive control, and deliberate analysis. The ECN (including the dorsolateral prefrontal cortex - DLPFC) is more active during analytical problem-solving and the preparation/verification stages of creativity [4] [63].
• Salience Network: Acts as a switch between the DMN and ECN, facilitating the transition between spontaneous idea generation and controlled evaluation [9].

The key to creative ability may lie not merely in the activation of these networks but in the dynamic flexibility with which the brain switches between them [63]. Research using Hidden Markov Models (HMM) of fMRI data reveals that insight problem-solving is characterized by a high-variability sequence of brain states, indicating greater cognitive flexibility compared to the more stable state patterns observed during analytical problem-solving [4]. This dynamic interplay provides a more nuanced neural signature than static network activation alone.

Individual differences in brain morphology significantly influence responses to neuromodulation techniques commonly used in creativity research. For instance, skull thickness and scalp-to-cortex distance can shape the electric field distribution of transcranial Direct Current Stimulation (tDCS), leading to variable effects on cortical excitability and, consequently, creative performance [62]. These anatomical factors contribute to the classification of individuals as "responders" or "non-responders" to stimulation, a critical consideration for experimental design and interpretation [62].

Figure 1: Dynamic interaction between creative stages and brain networks during insight problem-solving.

Comparative Analysis of Experimental Methodologies

Different experimental protocols measure distinct facets of creativity and are susceptible to inter-subject variability in different ways. The table below compares key methodological approaches used in creativity research.

Table 1: Comparison of Experimental Protocols in Creativity Research

Methodology	Measured Creativity Component	Key Experimental Tasks	Strengths	Susceptibility to Inter-Subject Variability
fMRI with HMM Analysis [4]	Dynamic brain states during insight; Network switching flexibility	Matchstick Arithmetic Problems; Remote Associates Test (RAT)	High spatial resolution; Captures temporal dynamics of network interaction	High; influenced by anatomical differences, strategy selection, and baseline cognitive flexibility
tDCS [62]	Causal role of specific regions (e.g., DLPFC) in creative performance	Alternative Uses Task; n-back tasks; Insight problems	Causal inference; portable and low-cost	Very High; strongly influenced by skull anatomy, neural architecture, and genetic profile
Divergent Thinking Tests [64] [65]	Ideational fluency, flexibility, and originality	Egg Task [64]; Torrance Tests of Creative Thinking (TTCT) [65]	Standardized scoring; easy to administer in large groups	Moderate; influenced by task instruction interpretation, motivational state, and cultural background
Self-Report & Learning Journals [65]	Self-regulated learning (SRL) phases; metacognitive awareness	Zimmerman's SRL model phases (forethought, performance, self-reflection) [65]	Provides insight into subjective creative process	High; subject to recall bias and individual differences in metacognitive ability

Detailed Experimental Protocols

fMRI with Hidden Markov Model (HMM) for Insight Problem-Solving [4]

• Purpose: To characterize the dynamic, time-varying brain states associated with different problem-solving strategies (quick, analytical, insight).
• Procedure:
  • Participants undergo fMRI while solving matchstick arithmetic problems, which require spatial manipulation.
  • After each trial, participants annotate their solution as "Quick," "Analytical," or "Insight."
  • fMRI data is analyzed using a General Linear Model (GLM) to identify brain regions associated with each strategy.
  • HMM is applied to estimate discrete, transient brain states and their frequency of occurrence (FO) relative to the solution type.
• Key Metrics: Reaction Time (RT), accuracy, BOLD signal changes, HMM-derived state frequency, and transition patterns.
• Variability Consideration: This method directly accounts for variability by classifying strategies at the single-trial level and linking them to distinct neural dynamics, rather than averaging across all trials [4].

Transcranial Direct Current Stimulation (tDCS) [62]

• Purpose: To modulate cortical excitability and establish a causal link between brain function and creative performance.
• Procedure:
  • A weak electrical current (1-2 mA) is delivered via scalp electrodes (anode and cathode) positioned over target regions (e.g., left DLPFC) and a reference region.
  • Stimulation can be administered before (offline) or during (online) a creativity task.
  • Performance on tasks (e.g., divergent thinking) is compared between active (real) and sham (placebo) stimulation conditions.
• Key Metrics: Changes in task performance (fluency, originality, reaction times).
• Variability Consideration: Response is highly variable, with 40-85% of participants classified as "non-responders." Accounting for factors like skull thickness, genetics, and baseline task performance is essential for interpretability [62].

The Egg Task for Divergent Thinking and Fixation [64]

• Purpose: To quantitatively assess fixation bias and original idea generation (conceptual expansion).
• Procedure:
  • Participants are prompted: "Ensure that a hen's egg dropped from a height of 10 m does not break."
  • All generated ideas are recorded and later mapped into ten predefined conceptual categories.
  • Three categories (cushion the fall, protect the egg, slow the fall) are classified as the "fixation path," representing conventional ideas.
  • Ideas in the remaining seven categories are classified as "expansion" ideas, representing creative solutions.
• Key Metrics: Fluency (total ideas), fixation score (ideas in fixation categories), expansivity score (ideas in expansion categories).
• Variability Consideration: The task systematically quantifies a universal fixation bias (≈80% of ideas fall in fixation categories), providing a baseline against which individual differences in expansivity can be measured [64].

The Scientist's Toolkit: Essential Research Reagents and Materials

This section details key materials and their functions for conducting rigorous creativity research, with a focus on managing variability.

Table 2: Key Research Reagents and Solutions for Creativity Studies

Item	Function in Research	Specific Application Example	Considerations for Variability
fMRI Scanner	Measures brain activity via blood oxygenation (BOLD signal).	Mapping DMN and ECN activity during insight vs. analytical problem-solving [9] [4].	Use HMM analysis to model dynamic states rather than relying solely on static GLM contrasts [4].
tDCS Device	Non-invasively modulates cortical excitability.	Investigating causal role of DLPFC in overcoming cognitive fixation [62].	Include anatomical MRI to model electric field distribution for each subject [62].
Divergent Association Task (DAT)	Assesses divergent thinking via semantic distance.	Quick, automated assessment of verbal creativity for large cohorts [63].	Benchmarks AI vs. human creativity; sensitive to language and cultural background.
Egg Task Protocol	Quantifies fixation bias and conceptual expansion.	Testing the impact of cognitive inhibition training on overcoming design fixation [64].	Provides objective categorization, minimizing scorer bias in divergent thinking assessment.
Self-Report SRL Instruments	Assesses self-regulated learning phases.	Tracking planning, performance, and reflection in sustained creative projects [65].	Subject to bias; should be triangulated with behavioral or neurophysiological data.

Addressing the variability problem in creativity research is not about eliminating differences but about systematically measuring, understanding, and accounting for them. The most robust experimental approaches move beyond group-level analyses to embrace individual differences in strategy, neuroanatomy, and cognitive style [4] [62]. For researchers and drug development professionals, this means:

• Selecting Multi-Method Protocols: Relying on a single measure is insufficient. Combining neuroimaging (fMRI), neuromodulation (tDCS), and behavioral tasks (Egg Task) provides a more complete picture.
• Adopting Dynamic Analysis Frameworks: Techniques like HMM are better suited to capture the fluid nature of creative cognition than traditional static models.
• Accounting for Moderating Variables: Pre-screening for anatomical factors, baseline creativity, and genetic markers can improve the signal-to-noise ratio in studies aiming to validate neural correlates.

The path forward for validating the neural correlates of design thinking and problem-solving lies in protocols that are not confounded by, but are instead designed to elucidate, the rich tapestry of inter-subject variability.

Ecological validity—the extent to which research findings generalize to real-world settings—presents a fundamental challenge in neuroscience research applied to design. The field of neurodesign seeks to understand how brain function influences and responds to design elements, but this pursuit is complicated by the inherent artificiality of laboratory environments. As research increasingly attempts to identify neural correlates distinguishing complex cognitive processes like design thinking versus problem-solving, the methodological rigor of experimental protocols becomes paramount. The translational gap between controlled laboratory settings and real-world application persists as a significant hurdle, potentially limiting the practical impact of neuroscientific discoveries in design contexts.

Laboratory assessments of cognitive processes frequently suffer from oversimplification, stripping away the rich contextual factors that shape human experience in natural environments. This is particularly problematic for neurodesign research, which inherently deals with how humans interact with complex, multisensory environments in daily life. The challenge is further compounded when studying subtle distinctions between related but distinct cognitive processes, such as the iterative, abductive reasoning characteristic of design thinking versus the more linear, deductive processes often employed in problem-solving. Understanding these limitations is essential for researchers aiming to produce findings that genuinely inform design practice, drug development protocols, and therapeutic environments.

Theoretical Framework: Ecological Validity in Neuroscience Research

Ecological validity is specifically defined as the extent to which research findings generalize to settings typical of everyday life [66]. This concept moves beyond general external validity (generalizability across people, places, and time) to focus specifically on the representativeness of the research environment and tasks. In neurodesign, this translates to assessing whether brain activity measured in sterile laboratory settings accurately reflects the neural processes engaged when individuals interact with designs in their natural habitats—be it workplaces, healthcare settings, or personal living spaces.

Theoretical work suggests that achieving ecological validity requires careful consideration at both the study design and data analysis stages [66]. Common research approaches like Ecological Momentary Assessment (EMA) aim to capture data in real-world contexts, but their ecological validity can be compromised during analysis if statistical models fail to account for the nested, dynamic structure of real-life experiences. Furthermore, reactivity to assessment—where participants change their behavior simply because they are being observed—represents a persistent threat to ecological validity, even in studies conducted outside traditional laboratory settings [66]. This is particularly relevant for neurodesign studies incorporating brain imaging or physiological monitoring, where the measurement apparatus itself may fundamentally alter the naturalistic experience researchers seek to understand.

Experimental Approaches and Methodologies

Ecological Experimental Paradigms

Innovative research paradigms are emerging to better capture neural processes in contexts that balance experimental control with real-world relevance. One promising approach involves using immersive scenarios that simulate everyday environments while maintaining measurement precision. A 2025 EEG study exemplifies this method by having participants watch a movie simulating being at home on their sofa, then subsequently maintaining and executing intentions related to everyday activities like "virtual cooking" while their neurophysiological activity was recorded using a high-density EEG system [67].

This paradigm specifically investigated prospective memory (remembering to perform future intentions) through both time-based ("execute action after 10 minutes") and event-based ("execute action when specific event occurs") tasks, mimicking real-world cognitive demands. The study analyzed power spectral density across theta (4-8 Hz), alpha (8-13 Hz), and high beta (20-30 Hz) frequency bands, revealing distinct neural activation patterns for different memory types [67]. Such approaches represent significant advances over traditional laboratory tasks that often lack the complexity and variability of real-world cognitive demands.

Assessing Spontaneous Thought in Laboratory vs. Real-World Settings

Research has also examined how well laboratory assessments of spontaneous thought processes predict cognitive experiences in daily life. One study employed the Amsterdam Resting State Questionnaire (ARSQ 2.0) to assess ten dimensions of spontaneous thought during laboratory resting states, then used electronic ambulatory assessment to track mind wandering, repetitive negative thought, and affect in participants' daily lives [68]. The study found that several spontaneous thought dimensions measured in the lab showed moderate stability across a one-week interval and demonstrated plausible associations with categories of self-generated thought and mood in daily life [68].

This line of research provides important methodological insights for neurodesign studies aiming to capture creative states and design cognition, suggesting that carefully designed laboratory assessments can indeed predict aspects of real-world cognitive experience. However, the findings also highlight that the relationship is not uniform across all cognitive dimensions, emphasizing the need for domain-specific validation of laboratory paradigms.

Comparative Analysis: Neural Correlates of Design Thinking vs. Problem-Solving

The distinction between design thinking and problem-solving represents a particularly challenging domain for ecological validity in neurodesign research. While both involve complex cognition, they differ in fundamental ways: problem-solving typically works toward a known solution, while design thinking is more exploratory, iterative, and abductive. The following table summarizes key methodological considerations for studies aiming to identify neural correlates distinguishing these processes.

Table 1: Methodological Approaches for Studying Design Thinking vs. Problem-Solving

Research Aspect	Traditional Laboratory Approach	Ecologically-Valid Approach	Measurement Considerations
Task Environment	Highly controlled, minimalist stimuli	Context-rich, immersive scenarios	Balance between experimental control and real-world relevance
Task Structure	Discrete trials with clear correct/incorrect answers	Open-ended, iterative tasks with ambiguous solutions	Capturing process versus outcome measures
Temporal Dynamics	Short time frames (seconds to minutes)	Extended sessions allowing for incubation and insight	Monitoring neural dynamics across different creative phases
Response Measures	Simple behavioral responses (accuracy, RT)	Multimodal assessment (verbal reports, psychophysiology, behavior)	Integrating complementary data streams
Contextual Factors	Deliberately minimized	Strategically incorporated as variables of interest	Assessing environmental influences on neural processes

Research indicates that ecologically-valid paradigms reveal distinct neural dynamics that might be missed in traditional laboratory tasks. The aforementioned prospective memory study found that time-based tasks (more analogous to design thinking's temporal aspects) were characterized by widespread and sustained fronto-temporal activation with pronounced engagement of high beta frequencies in prefrontal areas [67]. In contrast, event-based tasks (more similar to structured problem-solving) were associated with theta and alpha power localized to focal occipito-parietal areas [67]. These findings suggest that ecologically-valid tasks can reveal distinct neural signatures for different cognitive modes relevant to the design thinking/problem-solving distinction.

Experimental Protocols and Methodological Details

Protocol 1: Ecological Prospective Memory Assessment

This protocol adapts the methodology from the 2025 EEG study on prospective memory for application in neurodesign research [67]:

• Participant Preparation: Participants are fitted with a high-density EEG system (e.g., 128-channel) and seated in a comfortable, environment-designed space resembling a living area rather than a clinical laboratory.

• Familiarization Phase: Participants watch a movie segment (10-15 minutes) simulating a relaxing home environment (e.g., on a sofa) to establish baseline measures and acclimate to the setting.

• Instruction Phase: Participants receive instructions for both time-based and event-based prospective memory tasks embedded within the ongoing movie-watching activity. Example instructions include: "When 10 minutes have passed, adjust the lighting to your preference" (time-based) and "When you see a specific symbol appear, make a note about the room's color scheme" (event-based).

• Execution Phase: Participants continue watching the movie while simultaneously maintaining and executing the instructed intentions. The total phase duration is approximately 30-45 minutes to allow for sufficient trials.

• Data Acquisition: EEG is recorded continuously throughout all phases, with specific markers for instruction delivery, task execution, and stimulus events. Power spectral density is analyzed across theta (4-8 Hz), alpha (8-13 Hz), and high beta (20-30 Hz) frequency bands.

• Data Analysis: Cluster-based permutation analysis is used to identify significant differences in oscillatory power between conditions while controlling for multiple comparisons. Focus is placed on fronto-temporal regions for time-based tasks and occipito-parietal regions for event-based tasks.

This protocol offers high ecological validity by embedding cognitive tasks within a naturalistic viewing activity, capturing aspects of real-world multitasking and intention maintenance.

Protocol 2: Ambulatory Assessment of Spontaneous Thought

This protocol adapts methods from research on spontaneous thought for tracking design cognition in daily life [68]:

• Laboratory Baseline Assessment: Participants complete a 5-minute resting-state EEG recording in the laboratory, followed by the Amsterdam Resting State Questionnaire (ARSQ 2.0) which assesses ten dimensions of spontaneous thought: Discontinuity of Mind, Theory of Mind, Self, Planning, Sleepiness, Comfort, Somatic Awareness, Health Concern, Visual Thought, and Verbal Thought.

• Ambulatory Assessment Phase: For 5-7 days following the laboratory session, participants carry mobile devices that prompt them 8-10 times per day at random intervals to complete brief questionnaires about their current cognitive and affective states.

• Ambulatory Measures: At each prompt, participants rate: (1) intensity of mind wandering (task-unrelated thought), (2) intensity of repetitive negative thought (rumination), (3) positive and negative affect, and (4) current context (activity, location, social situation). For neurodesign applications, additional items could assess engagement in design-related thinking.

• Compliance Monitoring: Real-time data transfer enables monitoring of protocol compliance and provision of motivational support if needed.

• Data Analysis: Hierarchical linear models (multilevel modeling) are used to account for the nested structure of the data (repeated measures within individuals). Laboratory measures of spontaneous thought dimensions are tested as predictors of daily life cognitive and affective states.

This approach bridges laboratory and real-world measures, allowing researchers to test which laboratory assessments best predict meaningful cognitive experiences in natural contexts.

Visualization of Research Approaches

The following diagram illustrates the conceptual relationship between experimental control and ecological validity in different methodological approaches, highlighting how emerging methods attempt to balance these competing demands:

Neurodesign Research Methodology Spectrum

The methodological landscape of neurodesign research spans a continuum from traditional laboratory studies with high experimental control but limited ecological validity to real-world assessments with high ecological validity but reduced control. Emerging approaches like immersive paradigms attempt to optimize both dimensions simultaneously.

The Scientist's Toolkit: Essential Research Solutions

Table 2: Essential Tools for Ecologically-Valid Neurodesign Research

Tool/Category	Specific Example	Function in Research	Ecological Validity Consideration
High-Density EEG	128-channel systems	Records brain oscillations with high temporal resolution	Portable systems allow testing in naturalistic settings
Ambulatory Assessment Platforms	Mobile experience sampling apps	Captures real-time cognitive and affective states in daily life	Minimizes recall bias; assesses cognition in context
Immersive Virtual Reality	VR environments simulating real spaces	Presents controlled yet realistic environments	Balances experimental control with contextual richness
Resting State Questionnaires	Amsterdam Resting State Questionnaire (ARSQ 2.0)	Assesses dimensions of spontaneous thought	Links laboratory measures to real-world cognitive traits
Wearable Physiological Monitors	EDA, ECG, HRV sensors	Tracks autonomic nervous system activity	Enables continuous monitoring during natural behavior
Cluster-Based Permutation Analysis	FieldTrip, MNE-Python	Statistical method for neuroimaging data	Controls multiple comparisons without assumptions of precise spatiotemporal focus
Power Spectral Density Analysis	Custom MATLAB/Python scripts	Quantifies oscillatory power in frequency bands	Reveals neural mechanisms of different cognitive processes

The pursuit of ecological validity in neurodesign research requires continued methodological innovation. Promising directions include the development of unobtrusive monitoring technologies that reduce participant burden and reactivity effects [66], the creation of more sophisticated digital brain models that can simulate neural processes across different contexts [69], and the adoption of person-specific analysis approaches that better account for individual differences in neural functioning [66]. The BRAIN Initiative 2025 report emphasizes the importance of integrating new technological and conceptual approaches to discover "how dynamic patterns of neural activity are transformed into cognition, emotion, perception, and action in health and disease" [70]—a goal that necessitates addressing ecological validity challenges.

For researchers specifically interested in distinguishing the neural correlates of design thinking versus problem-solving, future studies should develop tasks that authentically capture the iterative, abductive nature of design thinking while maintaining the measurement precision needed for neuroscientific analysis. This might involve creating extended design challenges that unfold over multiple sessions, incorporating real stakeholder feedback, and measuring neural dynamics across different phases of the design process. Such approaches could potentially reveal neural signatures unique to design thinking, such as specific patterns of network connectivity or oscillatory activity that emerge during particularly creative or insightful moments.

As the field advances, the development of standardized evaluation frameworks specifically for assessing ecological validity in neurodesign research will be crucial [71]. Currently, the NeuroDesign/NeuroArchitecture Index (NDIX) represents one promising approach, operationalizing six domains relevant to how built environments impact mental health: safety and accessibility, cognitive capacity, sensory experiences, emotional states, social experiences, and naturalness [71]. Similar frameworks could be adapted specifically for evaluating the ecological validity of experimental paradigms in neurodesign research, ultimately strengthening the connection between laboratory findings and their application in real-world design contexts.

A growing body of cognitive neuroscience research reveals distinct neural correlates between design thinking and conventional problem-solving, necessitating optimized experimental protocols for their valid investigation. Neuroimaging studies consistently demonstrate that design thinking elicits distinct patterns of brain activation, particularly in prefrontal regions, compared to standard problem-solving tasks [72]. This emerging field of design neurocognition integrates traditional design research with cognitive neuroscience methodologies to understand the complex higher-order thinking processes underlying creative design [72]. As regulatory requirements for safety and efficacy become more stringent across multiple fields, researchers are increasingly seeking adaptive approaches to optimize study designs, control duration and costs, and demonstrate intervention effectiveness [73]. This article examines best practices for experimental protocol design focused specifically on validating neural correlates of design thinking, comparing methodological approaches, and providing frameworks for enhancing participant engagement in complex cognitive studies.

Neural Correlates of Design Thinking Versus Problem Solving

Distinct Neurocognitive Signatures

Advanced neuroimaging techniques have begun to identify differentiable neural signatures associated with design thinking compared to general problem solving. Functional magnetic resonance imaging (fMRI) studies reveal that design tasks preferentially engage the left cingulate gyrus and exhibit greater activity in prefrontal cortical regions compared to standard problem-solving tasks [72] [74]. These findings suggest that design thinking involves specialized cognitive processes beyond general problem solving, potentially related to the integration of technical constraints with creative generation.

The emerging dual-process model of creative ideation suggests that design thinking involves complex interactions between three core brain networks: (1) the default mode network, supporting idea generation through spontaneous memory retrieval; (2) the executive control network, enabling evaluation and modification of ideas against task constraints; and (3) the salience network, responsible for identifying promising ideas for further development [74]. This network interaction differentiates design cognition from more linear problem-solving approaches and underscores the need for specialized experimental protocols to capture its neural basis accurately.

Comparative Neural Activation Patterns

Table 1: Neural Correlates of Design Thinking Versus Problem Solving

Brain Region	Design Thinking Activation	Problem Solving Activation	Associated Cognitive Process
Prefrontal Cortex	Significant engagement [72]	Lesser engagement [72]	Higher-order executive functions
Left Cingulate Gyrus	Greater activity [74]	Not significantly active	Integration of technical and creative elements
Default Mode Network	Highly engaged [74]	Moderately engaged	Self-generated thought and memory retrieval
Executive Control Network	Highly engaged [74]	Highly engaged	Evaluation against constraints
Salience Network	Highly engaged [74]	Moderately engaged	Identification of promising ideas

Electroencephalography (EEG) studies provide additional temporal resolution, demonstrating that design cognition can be distinguished through specific neural signatures. Research shows that higher alpha-band activity over temporal and occipital regions distinguishes between open-ended problem descriptions and close-ended, decision-focused problems in expert designers [72]. Furthermore, different design expertise profiles (e.g., mechanical engineers versus industrial designers) exhibit distinguishable patterns of local activity and temporal distribution across prefrontal and occipitotemporal regions [72], highlighting the importance of careful participant selection and characterization in experimental design.

Optimized Experimental Protocols for Neuroscience Research

Protocol Complexity Assessment Framework

Well-designed study protocols are fundamental to research quality and integrity, particularly in complex neuroscience investigations. A standardized complexity assessment model can help researchers allocate appropriate resources and identify potential operational challenges before study initiation [73]. The following framework adapts clinical trial complexity parameters for neuroscience research on design cognition.

Table 2: Experimental Protocol Complexity Assessment Scoring Model

Protocol Parameter	Routine/Standard (0 points)	Moderate Complexity (1 point)	High Complexity (2 points)
Study Arms/Groups	One or two study arms	Three or four study arms	Greater than four study arms
Participant Population	Routinely available population	Population with uncommon characteristics	Vulnerable populations or highly selective criteria
Data Collection Complexity	Standard reporting	Expedited reporting	Real-time reporting with central review
Technical Requirements	Single modality	Combined modality	High-risk technologies requiring special training
Team Composition	One discipline/clinical service	Moderate number of practices/services involved	Multiple medical disciplines requiring complex coordination
Follow-up Requirements	Up to 3-6 months of follow-up	1-2 years of follow-up	3-5 years or >5 years of follow-up

Methodological Approaches for Neural Correlate Validation

Different neuroscience methodologies offer complementary advantages for investigating design thinking, each with distinct protocol requirements. Functional MRI (fMRI) provides excellent spatial resolution for localizing brain activity but limited temporal resolution and ecological validity due to constrained laboratory settings [72]. Electroencephalography (EEG) offers millisecond temporal resolution to capture rapidly evolving design processes but suffers from poorer spatial resolution [72]. Functional near-infrared spectroscopy (fNIRS) represents a promising middle ground, allowing more natural participant movement and interaction while measuring cortical activation patterns [72].

Recent research has employed innovative protocol designs to address methodological challenges. For example, studies have contrasted conceptual design problem-solving with and without inspirational stimuli [72], alternated between design generation and evaluation phases [72], and employed both open-ended and constrained problem types [74]. These approaches reveal how neural processing differs based on task parameters and constraints, providing important insights for protocol optimization.

Diagram 1: Experimental protocol workflow for neural correlates research.

Participant Engagement and Recruitment Optimization

Strategic Engagement Frameworks

Successful participant engagement in neuroscience research requires multifaceted strategies tailored to specific study populations and protocol demands. Research indicates that five key strategies significantly enhance recruitment and retention outcomes: (1) thoughtful trial design and location selection; (2) comprehensive global feasibility assessment; (3) proactive risk reduction planning; (4) stakeholder alignment and expectation management; and (5) integrated recruitment, engagement, and retention strategies [75].

Input from participants and care teams is critical to understanding perspectives that affect protocol design [75]. This is particularly relevant for design neuroscience studies where participant expertise level (e.g., professional designers versus novices) significantly impacts neural activation patterns [74]. Early engagement with potential participant populations during protocol development helps identify burdensome procedures that may hinder recruitment and allows researchers to streamline protocols before implementation.

Feasibility Assessment and Risk Mitigation

Comprehensive feasibility assessment should occur both during initial trial design and after protocol finalization [75]. Effective feasibility analysis includes examining population characteristics, geographical considerations, site feedback, participant perspectives, operational considerations, and risk mitigation strategies [75]. For design neuroscience studies, this includes careful consideration of the specific design expertise required, availability of specialized populations, and accessibility of appropriate neuroimaging facilities.

Each study requires a customized risk management plan addressing participant enrollment and retention risks [75]. Researchers should define key risk indicators before study initiation, establish specific measurement timepoints, and develop contingency plans for implementation when needed [75]. Alignment among all research team members on plans, timelines, and costs is essential for successful participant engagement, requiring clear communication about risks and mitigation strategies [75].

Essential Research Reagent Solutions and Methodologies

Neuroimaging and Assessment Tools

Table 3: Essential Research Reagents and Methodologies for Design Neuroscience

Research Tool	Primary Function	Protocol Considerations
fMRI	Localizes neural activity with high spatial resolution	Constrained laboratory settings; limited ecological validity [72]
EEG/ERP	Captures neural timing with millisecond resolution	Poor spatial resolution; motion artifacts during design tasks [72]
fNIRS	Measures cortical activation in naturalistic settings	Limited to cortical surface measurements; good for design prototyping studies [72]
Protocol Analysis	Captures design thinking processes behaviorally	Requires transcription and coding; complements neural data [72]
Complexity Assessment Model	Evaluates protocol implementation challenges	Scores 10 parameters affecting site workload and resources [73]

Experimental Paradigms for Design Cognition

Research-validated experimental paradigms are essential reagents for reliably investigating neural correlates of design thinking. Studies have successfully employed contrasting conditions such as: open-ended versus constrained design problems [74]; design generation versus evaluation phases [72]; and professional designers versus novice comparisons [74]. These paradigms enable researchers to isolate specific components of design thinking for neuroscientific investigation.

Functional localizer tasks are particularly valuable for identifying individual differences in neural network engagement. Studies suggest that core hubs of the default mode network (left posterior cingulate cortex), salience network (left anterior insula), and executive control network (right dorsolateral prefrontal cortex) form important connectivity points during creative ideation [74]. Protocol designs that incorporate these localizers alongside domain-specific design tasks strengthen the validity of neural correlate investigations.

Diagram 2: Neural network interactions in design thinking.

Validating neural correlates of design thinking requires carefully optimized experimental protocols that balance methodological rigor with participant engagement. The distinctive neural signatures associated with design thinking—including preferential engagement of prefrontal regions, the left cingulate gyrus, and dynamic interactions between large-scale brain networks—necessitate specialized approaches beyond standard problem-solving paradigms. By implementing structured complexity assessments, selecting appropriate neuroimaging methodologies, designing ecologically valid experimental tasks, and employing strategic participant engagement frameworks, researchers can significantly enhance the validity and reliability of their findings. As the field of design neurocognition continues to evolve, these optimized protocols will be essential for advancing our understanding of the complex neural architecture supporting human design thinking.

A Tale of Two Processes: Validating the Neural Distinction Between Design and Analytical Thinking

Creative cognition is not a monolithic process but rather a dynamic interplay between distinct brain states. Two large-scale brain networks—the Default Mode Network (DMN) and the Executive Control Network (ECN)—have been identified as central to different thinking styles. The DMN, active during rest and internal thought, supports spontaneous, associative processes crucial for insight. In contrast, the ECN, engaged during focused, goal-directed tasks, underpins analytical problem-solving [76] [77]. This guide objectively compares the performance of these "neural paradigms" by synthesizing current experimental data, providing a foundational resource for research into the neurobiology of design thinking versus conventional problem-solving.

Experimental Comparisons: Quantitative Contrast of Brain States

Neural Activation and Behavioral Performance

Recent studies have directly contrasted the neural correlates of insight-based and analytical problem-solving strategies. The table below summarizes key findings from a spatial problem-solving task (Matchstick Arithmetic) that quantified these differences [4].

Table 1: Contrasting Insight and Analytical Problem-Solving

Feature	Insight-Based Solutions	Analytical Solutions
Defining Characteristic	Sudden "Aha!" moment; overcoming a mental impasse [4]	Step-by-step, conscious application of rules [4]
Average Response Time	114.44 ± 29.6 seconds [4]	36.58 ± 13.7 seconds [4]
Key Activated Networks	Default Mode Network (DMN) [4]	Executive Control Network (ECN) [4]
Specific Activated Regions	Right Angular Gyrus, Left Superior Frontal Gyrus, Right Posterior Cingulate Cortex (PCC), Left Precuneus [4]	Right Middle Frontal Gyrus, Left Insula, Left Middle Occipital Gyrus [4]
Brain State Dynamics	High variability; frequent switching between states [4]	Lower variability; sustained state dominance [4]
Accuracy (%)	78.6 ± 27.8 [4]	96.7 ± 4.1 [4]

Large-Scale Network Dynamics and Creative Ability

Beyond task-specific contrasts, large-scale resting-state studies show that the dynamic interplay between the DMN and ECN predicts general creative ability. The following table summarizes findings from a multi-center study of over 2,400 individuals [78].

Table 2: DMN-ECN Dynamics as a Predictor of Creative Ability

Metric	Correlation with Creativity	Correlation with Intelligence
Frequency of DMN-ECN Switching	Positive, reliable predictor [78]	Not a reliable predictor [78]
Balance of DMN-ECN Switching	Inverted-U relationship (optimal balance is key) [78]	Not reported
Static DMN-ECN Connectivity	Less effective than dynamic measures [78]	Not reported

Detailed Experimental Protocols

To ensure reproducibility and critical evaluation, this section outlines the methodologies of the key experiments cited.

This fMRI study investigated the neural dynamics of different problem-solving strategies.

• Participants: Healthy adults (sample size detailed in the original publication).
• Task: Matchstick Arithmetic (MA) Task. Participants were presented with incorrect Roman numeral equations constructed from matchsticks and were required to correct them by moving a single matchstick.
• Procedure: Inside the fMRI scanner, participants viewed equations and provided their solution via a response box. After each trial, they retrospectively reported their solution strategy by categorizing it as:
  • Quick: The solution was immediately apparent.
  • Analytical: The solution was reached through a conscious, step-by-step process.
  • Insight: The solution was reached with a sudden "Aha!" moment after an impasse.
• Data Acquisition: Functional MRI data were acquired across three scanning sessions using a standard protocol.
• Data Analysis:
  • General Linear Model (GLM): Used to identify brain regions with significantly different activation levels during the different solution types (e.g., Insight > Analytical).
  • Hidden Markov Model (HMM): Applied to resting-state and task-based data to estimate discrete, dynamic brain states and calculate their fractional occupancy (FO) and transitions during different solution types.

This large-scale study examined whether intrinsic brain network dynamics predict creative ability.

• Samples: 10 independent datasets from 5 countries (Austria, Canada, China, Japan, USA), totaling N = 2433 healthy individuals.
• Creative Ability Assessment: Administered the Alternate Uses Task (AUT) outside the scanner. Participants generate novel uses for common objects. Responses were scored by multiple trained raters for fluency, flexibility, and originality.
• Data Acquisition: Resting-state fMRI data were collected at each site according to local protocols.
• Data Analysis:
  • Time-Resolved Network Analysis: Resting-state fMRI data were analyzed to identify dichotomous states of DMN-ECN interaction: segregated (networks operate independently) and integrated (networks cooperate).
  • Dynamic Switching Metric: The number of switches between segregated and integrated states over time was calculated for each individual.
  • Validation Study: An independent task-fMRI study (N=31) was conducted to confirm that DMN-ECN switching increases during creative idea generation compared to a control condition.

Visualizing Neural Dynamics and Workflows

Brain Network Dynamics During Problem-Solving

The following diagram illustrates the dynamic brain states and their transitions during different problem-solving strategies, as revealed by Hidden Markov Model analysis [4].

Diagram 1: Problem-Solving Brain State Transitions. FO = Fractional Occupancy. Insight solving is characterized by high variability in state transitions, while analytical solving involves more sustained states [4].

DMN-ECN Interaction in Creative Cognition

This diagram outlines the hypothesized workflow of how DMN and ECN interact during creative cognition enhanced by aesthetic experience, integrating findings from multiple studies [78] [76].

Diagram 2: DMN-ECN-Creativity Workflow. A proposed model of network interaction across creative stages, modulated by the Salience Network [76].

The Scientist's Toolkit: Key Research Reagents & Materials

For researchers aiming to replicate or build upon these findings, the following table details essential tools and their applications in this field.

Table 3: Essential Reagents and Materials for Creativity Neuroscience Research

Item Name	Function/Application	Example Use Case
Functional MRI (fMRI)	Measures brain activity by detecting changes in blood flow.	Mapping DMN and ECN activation during cognitive tasks [78] [4].
Alternate Uses Task (AUT)	A standardized behavioral test of divergent thinking creativity.	Assessing creative ability outside the scanner; scoring for fluency, flexibility, originality [78].
Matchstick Arithmetic (MA) Task	A spatial insight problem-solving task.	Eliciting and comparing insight vs. analytical problem-solving strategies in the scanner [4].
Hidden Markov Model (HMM)	A statistical model used to estimate discrete, dynamic states from time-series data.	Identifying and characterizing transient brain states from fMRI data [4].
Time-Resolved Functional Connectivity Analysis	A method to analyze how functional connections between brain regions change over time.	Quantifying the frequency of switching between DMN-ECN segregated/integrated states [78].
General Linear Model (GLM)	A standard statistical approach for analyzing fMRI data.	Identifying brain regions with significant activation differences between task conditions (e.g., Insight > Analytical) [4].

Within cognitive neuroscience, the comparative analysis of creative insight versus analytical problem-solving provides a robust framework for validating the neural correlates of design thinking. Creative insight is characterized by its sudden, "Aha!" nature, involving the overcoming of mental impasses to discover novel solutions, whereas analytical problem-solving follows a more structured, incremental progression [4]. Neuroimaging studies reveal these processes engage distinct, large-scale brain networks: the Default Mode Network (DMN) is prominently active during insight-based solutions, whereas the Executive Control Network (ECN) dominates during analytical and quick solutions [4]. This guide objectively compares the temporal dynamics and neural underpinnings of these cognitive modes, presenting quantitative experimental data to elucidate their unique profiles for researchers and drug development professionals investigating neurocognitive function.

Experimental Data and Comparative Neural Dynamics

Table 1: Neural Correlates of Insight vs. Analytical Problem-Solving

Cognitive Process	Key Associated Brain Regions/Networks	Representative fMRI Activation Contrast	Temporal Profile (Mean Response Time)
Creative Insight	Default Mode Network (DMN), Left Superior Frontal Gyrus, Right Angular Gyrus, Right Superior Temporal Pole, Left Caudate, Hippocampus, Dopaminergic Midbrain (VTA, NAcc) [4] [79]	Insight > Analytical [4]	Extended, Variable (114.44 ± 29.6 s) [4]
Analytical Problem-Solving	Executive Control Network (ECN), Visual Network, Left Middle Occipital Gyrus, Right Middle Frontal Gyrus, Thalamus [4]	Analytical > Insight [4]	Focused, Sustained (36.58 ± 13.7 s) [4]
Quick Solutions	Executive Control Network (ECN), Visual Network [4]	Quick > Insight [4]	Rapid (8.61 ± 1.9 s) [4]

Table 2: Quantitative Brain State Dynamics from Hidden Markov Model (HMM) Analysis

Problem-Solving Strategy	High-Fractional Occupancy (FO) Brain States	Key Characteristic of State Transition Dynamics
Creative Insight	States 4 and 5 [4]	High variability; reflects increased cognitive flexibility and dynamic interaction between networks [4]
Analytical Problem-Solving	State 9 [4]	More stable and linear progression of brain states [4]
Quick Solutions	States 2, 6, and 8 [4]	N/A

Experimental Protocols and Methodologies

1. fMRI Protocol for Matchstick Arithmetic Task This protocol investigated spatial insight problem-solving [4].

• Task Design: Participants solved matchstick arithmetic problems, requiring spatial manipulation to correct invalid equations. Following each trial, they self-annotated their solution strategy as "Quick," "Analytical," or "Insight" [4].
• Data Acquisition: fMRI data were acquired across three scanning sessions, capturing BOLD signals during task performance and rest [4].
• Analysis Methods:
  • General Linear Model (GLM): Used to identify brain regions with significantly different activation levels between the three solution types (e.g., Insight > Analytical) [4].
  • Hidden Markov Model (HMM): Applied to estimate discrete, dynamic brain states and their temporal evolution (fractional occupancy, state transitions) during the prolonged problem-solving process [4].

2. Ultra-High-Field fMRI Protocol for Remote Associates Test (RAT) This protocol focused on the affective components of verbal insight [79].

• Task Design: Participants were presented with word triplets (e.g., "HOUSE–BARK–APPLE") and instructed to find a solution word that forms a compound noun with each (e.g., "TREE"). They pressed a button immediately upon feeling confident in their solution to capture the exact "Aha!" moment [79].
• Data Acquisition: Employed 7 Tesla fMRI for high spatial resolution (1.5×1.5×1 mm³ voxels), crucial for imaging small subcortical structures [79].
• Analysis Focus: Targeted robust subcortical activity changes in the bilateral thalamus, hippocampus, ventral tegmental area (VTA), nucleus accumbens (NAcc), and caudate nucleus, linked to reward processing and the "Aha!" experience [79].

Visualizing Workflows and Neural Dynamics

Diagram 1: Insight creative cognition stages and neural correlates.

Diagram 2: Linear progression of analytical problem-solving.

Diagram 3: Dynamic network interactions in insight versus analytical thinking.

The Scientist's Toolkit: Key Research Reagents and Materials

Table 3: Essential Materials for Neuroimaging Research on Cognition

Item	Function/Application in Research
Functional Magnetic Resonance Imaging (fMRI)	Non-invasively measures brain activity by detecting changes in blood flow (BOLD signal). The primary tool for localizing neural correlates of cognitive tasks [4] [79].
Ultra-High-Field 7T fMRI Scanner	Provides high spatial resolution, essential for robustly imaging small subcortical structures like the nucleus accumbens and dopaminergic midbrain involved in insight and reward [79].
Hidden Markov Model (HMM)	A computational analysis method used to estimate discrete, dynamic brain states from fMRI data, characterizing the temporal evolution of brain network activity during complex cognition [4].
General Linear Model (GLM)	A standard statistical approach for analyzing fMRI data to identify brain regions with significant activation changes correlated with specific task conditions or behaviors [4].
Remote Associates Test (RAT)	A validated verbal insight problem-solving task where subjects find a word connecting three given words. Elicits reliable "Aha!" moments for study [79].
Matchstick Arithmetic Problems	A spatial insight problem-solving task requiring cognitive manipulation to correct equations, used to study spatial insight and problem-solving strategies [4].

The medial Prefrontal Cortex (mPFC), Dorsolateral Prefrontal Cortex (DLPFC), and Temporal Lobes constitute a core neural triad orchestrating human social and creative cognition. Understanding the functional specialization and interaction of these regions provides a critical framework for validating the distinct neural correlates of design thinking—a generative, socially-informed creative process—against those of traditional problem-solving. Neuroimaging evidence reveals that creative cognition engages a distributed network, with the DLPFC and mPFC playing central, complementary roles: the DLPFC is frequently implicated in goal-directed planning and novel solution organization, while the mPFC is involved in self-referential and social evaluation processes [16] [80]. The temporal lobes, particularly the Anterior Temporal Lobe (ATL) and temporoparietal junction (TPJ), contribute semantic knowledge and social perception vital for both domains [81] [4]. This review synthesizes comparative functional profiles, experimental data, and methodological protocols to objectively delineate how this neural triad supports cognitive processes essential for advanced design thinking and problem-solving research.

Regional Functional Profiles and Comparative Analysis

Functional Specialization of Key Brain Regions

• Medial Prefrontal Cortex (mPFC): The mPFC is a hub for social cognition and self-referential thought. It is functionally segregated along a ventral-dorsal axis [80]. The ventral mPFC (vmPFC) is more strongly connected to limbic structures such as the nucleus accumbens and hippocampus, is selectively associated with reward processing, and evaluates the personal significance or importance of stimuli [80] [82]. In contrast, the dorsal mPFC (dmPFC) shows stronger connectivity with the inferior frontal gyrus and temporo-parietal junction, and is involved in perspective-taking and metacognition [80]. Crucially, the mPFC's role in social cognition emerges early in development, processing socially relevant information like faces and voices in infancy, and predicting later sociability [83] [84].

• Dorsolateral Prefrontal Cortex (DLPFC): The DLPFC is a key node for executive control and goal-directed planning. It supports convergent thinking and analytical problem-solving, as evidenced by its activation during mental rotation tasks and "analytical" solutions in insight problems [16] [4]. During creative tasks, the left DLPFC, in particular, is thought to orchestrate the top-down planning of novel solutions and modulate value representations [16] [81]. Its interaction with subcortical regions like the nucleus accumbens is also linked to the subjective "Aha!" experience of insight [4].

• Temporal Lobe (Anterior & Posterior Regions): The temporal lobe contributes domain-specific processing for both social and creative cognition. The Anterior Temporal Lobe (ATL), particularly the right hemisphere, is critically involved in insight-based problem-solving and the formation of novel, distant associations [4]. The Temporoparietal Junction (TPJ) and superior temporal sulcus are fundamental to mentalizing—the ability to infer others' beliefs and intentions—a core component of social cognition and cooperation [81]. Furthermore, the middle temporal gyrus and angular gyrus show increased activation during creative insight, potentially supporting semantic and conceptual integration [4].

Comparative Quantitative Profiles

Table 1: Functional and Connective Profiles of Key Brain Regions

Brain Region	Primary Cognitive Domains	Key Functional Subspecializations	Characteristic Network Affiliations & Connectivity
Medial Prefrontal Cortex (mPFC)	Social Cognition, Self-Reference, Valuation	• Ventral: Reward, Bottom-Up Evaluation, Personal Significance [80] [82]• Dorsal: Perspective-Taking, Metacognition, Top-Down Control [80]	• Connected with limbic system (ventral) [80]• Connected with mentalizing network (dorsal) [80] [81]• Default Mode Network (DMN) [4]
Dorsolateral Prefrontal Cortex (DLPFC)	Executive Control, Working Memory, Planning	• Goal-Directed Planning of Novel Solutions [16]• Convergent/Analytical Thinking [4]• Cognitive Control & Modulation of Value [81]	• Executive Control Network (ECN) [4]• Co-activated with fronto-parietal network [16]
Temporal Lobe	Semantic Memory, Social Perception, Insight	• Anterior Temporal Lobe (ATL): Insight, Conceptual Expansion [4]• Temporoparietal Junction (TPJ): Mentalizing, Belief Attribution [81]	• Language Network (LAN) [4]• Social Brain Network [81]

Table 2: Neural Correlates of Cognitive Processes in Design Thinking vs. Problem-Solving

Cognitive Process	Associated Brain Region/Network	Experimental Task Paradigm	Key Functional Neuroimaging Finding
Divergent Thinking (Creative)	Left DLPFC, mPFC, Angular Gyrus [16] [4]	Visual Creativity Task (assemble shapes into novel object) [16]	Robust bilateral activation, with left DLPFC and mPFC more active in creative vs. control task [16]
Insight (Aha! Moment)	Right ATL, Left DLPFC, Nucleus Accumbens [4]	Matchstick Arithmetic Problem (spatial insight) [4]	Right ATL and left DLPFC more active during insight vs. analytical solutions; DLPFC-NAcc connectivity linked to insight intensity [4]
Analytical Problem-Solving	DLPFC, Inferior Parietal Lobule [4]	Mental Rotation Task; Matchstick "Analytical" solutions [16] [4]	Executive Control Network (ECN) and visual networks exhibit greater activation during analytical solutions [4]
Social Evaluation & Self-Reference	vmPFC, Dorsal mPFC, PCC/Precuneus [82] [80]	Trait Evaluation Task (rating self/other on physical, academic, prosocial traits) [82]	vmPFC activation for positive/important self-traits; dmPFC/PCC for academic traits; Temporal/TPJ for prosocial traits [82]
Perspective-Taking & Mentalizing	Dorsal mPFC, TPJ [80] [81]	Theory of Mind/Perspective-Taking Tasks [80]	Dorsal mPFC and TPJ show convergent activity when attributing mental states to others [80] [81]

Experimental Protocols and Methodologies

Key Experimental Tasks for Eliciting Neural Activity

1. Visuospatial Creativity and Control Task Protocol

• Objective: To dissociate neural activity related to divergent visual creativity from basic visuospatial processing [16].
• Stimuli & Design:
  • Creative Condition: Participants are presented with three distinct shapes (e.g., 'C', '0', '8') and must mentally assemble them into a namable, recognizable object (e.g., a smiley face). This requires divergent thinking.
  • Control Condition: Participants view a familiar shape (e.g., a rectangle) that has been trisected into rotated pieces. They must mentally rotate the pieces to reconstruct and name the original shape. This is a convergent thinking task with one correct answer.
• Procedure: Trials are randomly intermixed. Participants indicate solution via button press. Stimuli are piloted to ensure equal difficulty and completion time between conditions, controlling for confounds like effort and time-on-task [16].
• fMRI Analysis: Contrasting creative > control task activation reveals regions specific to generative creativity, notably left DLPFC and mPFC, beyond basic visuospatial and naming processes [16].

2. Matchstick Arithmetic (MA) Task for Insight

• Objective: To capture the neural dynamics of insightful problem-solving with spatial manipulation [4].
• Stimuli & Design: Participants solve incorrect arithmetic equations constructed from matchsticks (e.g., IV = III + III). A correct solution requires moving one matchstick to create a true equation (e.g., VI = III + III).
• Procedure: Post-trial, participants annotate their solution strategy: "Quick" (immediately obvious), "Analytical" (step-by-step reasoning), or "Insight" (sudden "Aha!" after an impasse). This self-report is crucial for classifying neural correlates.
• fMRI Analysis:
  • General Linear Model (GLM): Contrasts between insight, analytical, and quick solutions identify strategy-specific regions. Insight solutions robustly engage the Default Mode Network (dDMN), including angular gyri and precuneus, while analytical solutions activate the Executive Control Network (ECN) [4].
  • Hidden Markov Model (HMM): This models dynamic brain states during the prolonged problem-solving process. Insight solutions are characterized by high-variability state transitions, reflecting cognitive flexibility, unlike the more stable states of analytical solving [4].

3. Trait Evaluation Task for Social and Self-Referential Cognition

• Objective: To dissociate domain- and valence-specific neural patterns during evaluation of self and a close other [82].
• Stimuli & Design: Participants evaluate positive and negative trait sentences across three domains:
  • Physical (e.g., "I have beautiful eyes.")
  • Academic (e.g., "I am smart.")
  • Prosocial (e.g., "I am helpful.")
• Procedure: In the fMRI scanner, participants rate how well these traits describe themselves and their mother on a scale (e.g., 1-4). This design allows separation of activation related to target (self/other), domain, and valence.
• fMRI Analysis: Reveals dissociable activation patterns: physical traits engage dlPFC and inferior parietal lobule; academic traits activate precuneus/PCC and vmPFC; prosocial traits recruit temporal lobes and TPJ. Valence effects show vmPFC is more active for positive traits, potentially signaling personal significance [82].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Materials for Neuroscientific Research

Reagent/Material	Primary Function in Research	Exemplary Application Context
Functional MRI (fMRI)	Non-invasive mapping of brain activity by measuring blood-oxygen-level-dependent (BOLD) signals.	Locating regional activation during cognitive tasks (e.g., mPFC during social evaluation; DLPFC during analytical problem-solving) [80] [82] [4].
Functional Near-Infrared Spectroscopy (fNIRS)	Portable optical imaging of cortical hemodynamic responses, suitable for naturalistic settings and populations like infants.	Studying mPFC function during infant-adult social interaction and face processing [83] [84].
Antisense Oligonucleotides (ASOs)	Molecularly targeted platforms that alter gene expression to address root causes of CNS disorders.	Investigating and treating genetic CNS disorders (e.g., Spinal Muscular Atrophy); often delivered intrathecally due to BBB [85].
Adeno-Associated Viruses (AAVs)	Viral vector-based system for efficient and long-term gene delivery to specific neural cell types.	Used in animal models for targeted gene expression manipulation in specific brain regions (e.g., serotype AAV9 for crossing BBB) [85].
Stem Cells	Biologically derived therapies that can differentiate into specialized cell types and integrate into neural circuits.	Modeling neurodevelopment and disease (e.g., iPSC-derived cerebral organoids for Alzheimer's research) and potential regenerative strategies [85] [86].
Transcranial Direct Current Stimulation (tDCS)	Non-invasive brain stimulation to modulate cortical excitability and enhance cognitive functions.	Testing causal role of regions like DLPFC and right ATL in enhancing insight problem-solving performance [4].

Signaling Pathways and Neural Workflows

mPFC Functional Segregation and Network Connectivity

The following diagram summarizes the functional segregation of the medial Prefrontal Cortex (mPFC) and its distinct brain-wide connectivity patterns, which underpin its role in social and evaluative cognition.

Network Dynamics in Creative Insight

The process of creative insight involves a dynamic interplay between large-scale brain networks. The diagram below illustrates the distinct roles and interactions of the Executive Control Network (ECN) and the Default Mode Network (DMN) during different problem-solving strategies.

Integrated Discussion: Implications for Neural Correlate Validation

The synthesized data robustly validates distinct, though interacting, neural correlates for the cognitive processes underlying design thinking and problem-solving. A core differentiator lies in the dynamic interplay between the DLPFC-driven Executive Control Network (ECN) and the mPFC-anchored Default Mode Network (DMN). Traditional problem-solving, characterized by analytical and convergent approaches, relies heavily on stable, sustained activation of the ECN [4] [16]. In contrast, design thinking, which involves divergent idea generation, social perspective-taking, and sudden insight, necessitates a more flexible dynamic. This is marked by the recruitment of the DMN—integrating mPFC-based social valuation, angular gyrus semantic processing, and ATL-mediated conceptual insight—and high-variability transitions between brain states [4] [80] [82].

This triad's functional architecture reveals why social context is pivotal for design thinking. The mPFC does not operate in isolation; it evaluates ideas based on personal and social significance [82], informs the DLPFC of social goals and norms to guide planning [81], and is primed by temporal lobe inputs about others' mental states and semantic knowledge [81] [4]. Therefore, validating neural correlates for design thinking requires protocols that move beyond non-social problem-solving to incorporate tasks involving social evaluation, co-creation, and open-ended generation, precisely the domains where the mPFC and temporal lobes show maximal engagement.

These insights are critically informative for CNS drug development, where targeting specific cognitive processes is paramount. The outlined experimental protocols and "Scientist's Toolkit" provide a framework for evaluating therapeutic efficacy. For instance, a compound aiming to enhance cognitive flexibility in disorders like schizophrenia could be tested using the insight task, with success defined by a shift towards more DMN-associated, variable brain states during problem-solving [4]. Similarly, therapies for social impairment could employ trait evaluation or interactive paradigms to measure functional normalization of mPFC and TPJ responses [82] [81] [84]. The clear functional dissociations between these regions provide a solid foundation for developing biomarkers and targeted interventions that modulate specific nodes of this integrated social-creative cognitive network.

Within cognitive neuroscience, a central pursuit is to bridge the gap between observable behavior and its underlying neural mechanisms. This guide provides a structured comparison of how different behavioral performance metrics are linked to specific neural correlates, with a particular focus on dissecting the neural foundations of design thinking—a creative, open-ended process—versus problem-solving—a more analytical, goal-directed process. Advances in neuroimaging and computational modeling have enabled researchers to move beyond simple performance measures, such as accuracy and reaction time, to more nuanced metrics that capture the dynamics of decision-making, exploration, and creativity. Framing this research is the active inference framework, which posits that the brain minimizes uncertainty about the world by balancing the exploration of new information (novelty) with the exploitation of known rewards, a trade-off central to creative and analytical pursuits [87]. This guide synthesizes experimental data and methodologies to objectively compare the behavioral metrics and neural signatures of these distinct cognitive modes, providing a resource for researchers and drug development professionals aiming to quantify complex cognitive processes.

Comparative Analysis of Behavioral Metrics and Neural Correlates

The table below summarizes key behavioral metrics and their associated neural correlates across different cognitive domains, from basic perceptual decisions to higher-order creative cognition.

Table 1: Behavioral Metrics and Their Neural Correlates

Cognitive Domain	Behavioral Metric	Quantitative Measure	Key Neural Correlates	Functional Significance
Perceptual Decision-Making [88]	Choice Probability (CP)	Trial-to-trial correlation between neuronal response and perceptual choice.	V1, MT, VIP; strength of correlation depends on signal reliability for the task.	Measures contribution of sensory neurons to decisions; readout is flexibly determined by signal value.
Exploration-Exploitation Trade-off [87]	Expected Free Energy (EFG) Minimization	Computational model parameter quantifying information gain (novelty) and reward (value).	Frontal Pole & Middle Frontal Gyrus (EFG); separate but overlapping regions for novelty vs. variability.	Guides exploration (resolving novelty) and exploitation (maximizing reward) under active inference.
Creative Cognition [89]	Semantic Relatedness Judgement	Rating of relatedness for word pairs with varying semantic distance (e.g., 1-6 steps).	Default Mode Network (DMN), Executive Control Network (ECN), Salience, and Semantic Control Networks.	Higher creativity linked to judging remote concepts as more related; reflects flexible semantic memory.
Group vs. Individual Problem-Solving [9]	Collective Intelligence / Group Performance	Performance on matrix problems (e.g., Raven's) in group vs. individual settings.	Medial Prefrontal Cortex, Cingulate, Precuneus, Frontal/Temporal Poles ("Social Brain" regions).	Neural substrate for real-time social interaction and collaborative problem-solving.
Emotion Regulation [90]	Emotion Regulation Task Performance	Performance on validated ER tasks (e.g., response to negative stimuli).	Dorsolateral/Ventrolateral Prefrontal Cortex (dlPFC/vlPFC), Amygdala.	PFC implicated in cognitive control; amygdala hyperactivity linked to emotion regulation deficits.

Experimental Protocols and Methodologies

Electrophysiology in Perceptual Decision-Making

This protocol is designed to quantify how neurons in different visual cortical areas contribute to a perceptual decision [88].

• Objective: To examine trial-to-trial correlations (Choice Probability) between neuronal responses and behavioral choices in different visual areas (V1, MT, VIP) during a direction-change detection task.
• Subjects: Two adult male rhesus monkeys were used as subjects.
• Behavioral Task (Direction-Change Detection): Monkeys were trained to maintain fixation on a spot while a Gabor patch flashed inside the receptive field of the recorded neuron. On most flashes, the Gabor drifted in a "reference" direction, but on a pseudo-randomly chosen "target" flash, the direction changed. The monkey was rewarded for making a saccade to the target location within 150-600 ms of its onset.
• Visual Stimuli: Two sets of achromatic Gabor patches were used, differing in size and spatial frequency ("Set L" and "Set S"). The parameters were scaled with receptive field eccentricity to manipulate the reliability of neuronal signals in V1 relative to higher areas.
• Electrophysiological Recordings: Extracellular recordings were made from individual neurons in V1, MT, and VIP while the monkeys performed the task. The timing and rate of neuronal action potentials were analyzed relative to the stimulus and the monkey's choice.
• Data Analysis: Choice Probability was calculated by comparing the distribution of neuronal responses on trials where the monkey made a correct detection versus incorrect or miss trials. This correlation was compared across cortical areas and for the two different stimulus sets.

fMRI in Social and Creative Cognition

This protocol uses fMRI to investigate the neural basis of group problem-solving and creative semantic judgments [9] [89].

• Objective (Group Problem-Solving): To compare brain activation when participants solve problems in a small group versus individually [9].
• Objective (Creative Cognition): To identify brain regions where activity is modulated by the semantic relatedness of word pairs and how this relates to individual creativity [89].
• Participants (Creative Cognition): A large cohort (N=93) of human participants.
• Task (Group Problem-Solving): Participants solved Raven-like matrix problems in two conditions: a) in a small group, discussing the problem via a communication device, and b) individually, while thinking aloud in the silent presence of others [9].
• Task (Creative Cognition - Relatedness Judgement Task, RJT): During fMRI, participants judged the relatedness of word pairs on a continuous scale. The word pairs were pre-classified by their "theoretical semantic distance" (1 to 6 steps) within a semantic network [89].
• Creativity Assessment: Outside the scanner, participants completed a battery of tests to measure different facets of creativity, including divergent thinking (Alternative Uses Task), convergent thinking (Combination of Associates Task), and real-life creative activities and achievements [89].
• fMRI Data Acquisition and Analysis: Whole-brain BOLD signals were acquired while participants performed the tasks. For the RJT, a parametric modulation analysis identified regions whose activity increased or decreased with the participant's relatedness rating. Functional connectivity between networks was also analyzed [9] [89].

Signaling Pathways and Workflow Visualization

Cortical Hierarchy and Behavioral Readout

The following diagram illustrates the flexible readout mechanism from the visual cortical hierarchy during a perceptual decision-making task, based on findings that the brain can access signals from different levels depending on their reliability [88].

Active Inference in Decision-Making

This diagram outlines the core process of decision-making under the active inference framework, where agents minimize expected free energy by balancing novelty and variability [87].

The Scientist's Toolkit: Research Reagent Solutions

This table details key materials and tools essential for conducting research in this field.

Table 2: Essential Research Materials and Tools

Item Name	Function / Application	Example Use Case	Key Considerations
Rhesus Monkey Model	Animal model for invasive electrophysiology.	Recording single-neuron activity from visual cortex during perceptual tasks [88].	Allows for causal manipulations; ethical considerations and cost are significant factors.
Electrophysiology Setup	Records action potentials from single neurons or neuronal populations.	Measuring choice probability in visual areas V1, MT, and VIP [88].	High temporal resolution; technically challenging; requires head fixation.
Functional MRI (fMRI)	Measures brain-wide blood oxygenation (BOLD) signals.	Mapping neural correlates of group problem-solving and semantic relatedness [9] [89].	High spatial resolution; poor temporal resolution; sensitive to motion artifacts.
Electroencephalogram (EEG)	Records electrical activity from the scalp.	Tracking neural correlates of decision variables (e.g., novelty) with high temporal precision [87].	Excellent temporal resolution (milliseconds); limited spatial resolution.
Contextual Multi-Armed Bandit Task	A behavioral paradigm to study exploration-exploitation.	Testing predictions of the active inference framework under novelty and variability [87].	Provides rich behavioral data for computational modeling.
Relatedness Judgement Task (RJT)	Assesses semantic memory structure and flexibility.	Investigating the link between semantic relatedness and creative cognition during fMRI [89].	Can be parametrically varied by theoretical semantic distance.
Active Inference Model	A computational model of perception, learning, and decision-making.	Fitting behavioral choice data to quantify how subjects balance novelty and reward [87].	Provides a unified framework for explaining and predicting behavior.

Conclusion

The validation of distinct neural correlates for design thinking and traditional problem-solving provides a robust biological framework for understanding human cognition. The evidence consistently points to a model where creative, insight-based design thinking is supported by the dynamic interplay of the Default Mode Network, while analytical problem-solving reliably engages the Executive Control Network. For biomedical research, these findings open avenues for developing neuromodulation therapies targeting specific networks to alleviate deficits in cognitive flexibility seen in conditions like addiction or dementia. Future work must focus on longitudinal studies, refining non-invasive biomarkers, and building a stronger bridge between neuroscientific discovery and practical application in clinical and organizational settings.

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