Biology subjects

Poldrack, R. A.

Publications and source records attributed to Poldrack, R. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Area-level context and functional brain organization across the lifespan

The neighborhoods children grow up in are associated with cognitive and mental health outcomes across the lifespan. Yet how neighborhood-level factors are quantified, and how they relate to large-scale functional brain connectivity, remains an emerging area of investigation. This scoping review synthesizes empirical evidence on associations between area-level factors and resting-state functional brain connectivity and network organization. We included studies assessing resting-state fMRI functional connectivity or rs-fMRI-derived intrinsic activity in relation to validated area-level deprivation indices guided by Trinidad et al.s (2022) inventory, the Opportunity Atlas, or geocoded crime exposure from the Uniform Crime Reporting program. Of the 28 studies meeting inclusion criteria, the evidence base was unevenly distributed across the lifespan; 25 of 28 (89.3%) examined perinatal, childhood, or adolescent samples, while only one examined adults exclusively. The literature was also methodologically non-independent, over half drew on the Adolescent Brain and Cognitive Development (ABCD) Study, and the Area Deprivation Index was the most commonly used measure (k = 16, 57.1%). Therefore, apparent convergence partly reflects shared data and instruments rather than independent replication. Where directions could be compared, higher area-level disadvantage was associated predominantly with lower within-network connectivity in association and control systems and higher connectivity in somatomotor systems, with several findings attenuating once area-level exposure was isolated from household socioeconomic status. Geographic resolution varied but was rarely justified. Progress will require greater theoretical intentionality in selecting area-level measures, transparent justification of geographic resolution, treatment of dataset and measure non-independence, and investment in understudied developmental periods and non-US contexts.

neuroscience↗

bioRxiv2026

Early Adversity Selectively Reshapes the Somato-Cognitive Action Network in the Developing Brain

Early adversity is among the most potent environmental influences on the developing brain; yet whether it remodels the areal layout of cortical networks, and along which dimensions, is unknown. In a longitudinal sample of 4,525 youth aged 9-18, we derived individualized cortical parcellations and related the topography of 15 networks to 10 data-driven dimensions of adversity. Greater exposure to threat, but not deprivation or unpredictability, predicted dose-dependent expansion of the somato-cognitive action network (SCAN), an effect nearly seven times larger than for any other network. The SCAN expanded toward the sensorimotor pole, encroached on neighbors, and shifted functional connectivity toward sensorimotor and away from control systems. Expansion mediated the association between threat and poorer cognition, replicating across waves, within children, and out-of-sample. Rather than diffusing across many systems, the imprint of adversity on the developing cortex converges on the territory of a single integrative network that couples brain to body.

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bioRxiv2026

Functional brain organization is stable within individuals across years

Brain regions exhibit dynamic yet highly coordinated activity patterns that form large-scale functional networks measurable through resting-state correlations. While their association with fluctuating activity may intuitively suggest functional networks to be temporally transient and dependent on state, a growing body of literature suggests that they are person-specific and stable across days. If truly person-specific, then functional networks should preserve unique characteristics over extended periods. To test this hypothesis, we collected longitudinal precision fMRI data ([≥]60 minutes per participant per time point) from 10 healthy young adults across 1-3 year intervals, as well as three adults over 8-13 years. We further replicated findings in the MyConnectome dataset and its 10-year follow-up. Functional network organization--when sufficient per-participant data were collected-- remained largely stable within individuals over prolonged periods of up to 13 years, suggesting that individualized brain organization constitutes persistent features of personal identity that may be supported by homeostatic mechanisms. SIGNIFICANCEWhile many aspects of brain function are portrayed as dynamic and malleable, our study provides evidence for the long-term preservation of functional organization of the healthy young adult brain. Whole-brain functional organization exhibited unique individual characteristics that were preserved for years, even over a decade. The stability of such individualized neural architecture suggests that the brain encodes features of personhood that may be invariant across contexts and temporal fluctuations. This observation reframes our understanding of brain resilience and plasticity by offering new insights into the extent that homeostatic mechanisms of brain organization can withstand stressors and environmental changes through time to maintain a constant functional organization. These insights pave the way for a more comprehensive understanding of the neural mechanisms behind this stability, with the possibility of pinpointing critical markers of healthy brain function and its disruptions.

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bioRxiv2026

Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative

Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools currently focused on PET. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET-BIDS), providing a standardized framework for PET datasets and metadata. Building on PET-BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET-BIDS datasets for open sharing, while complementary platforms such as PublicnEUro provide controlled-access pathways designed to support compliance with the European Union General Data Protection Regulation (GDPR). Emerging open-source workflows further support automated, reproducible PET analysis, promoting harmonization across centers. Together, these developments mark an important step toward an open molecular neuroimaging community in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

bioinformatics↗

bioRxiv2025

Anatomical White Matter Tracts Span the Cortical Hierarchy to Support Cognitive Diversity

Long-range white matter (WM) tracts support cognition by enabling communication between distant cortical regions, which are organized along a hierarchy defined by the sensorimotor-to-association (S-A) axis. However, it remains unknown how WM tracts are positioned within the cortical hierarchy to support cognition. Here, we show that WM tracts are differentially positioned in the cortical hierarchy to support specific cognitive functions, and that tracts spanning the hierarchy connect regions with greater cognitive diversity. Moreover, tracts situated within the same hierarchical level connect biologically similar regions, while those crossing the hierarchy bridge distinct biological milieux to support diverse cognitive functions. The placement of tracts in the cortical hierarchy also reflects developmental variation in tract microstructure and individual differences in cognition. Together, these findings provide a framework that moves beyond conventional categories of association or projection tracts and links WM tract anatomy to cortical organization, cognitive function, cortical neurobiology, and neurodevelopment. We anticipate that this cortex-anchored framework for describing WM tracts may aid the interpretation of individual differences in WM structure related to development and behavior.

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bioRxiv2025

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible Brain-wide Motifs

The Research Domain Criteria (RDoC) framework was introduced to guide psychiatric research using biologically grounded, dimensional constructs of mental function. However, its hierarchical domain structure remains largely unvalidated against individual-level brain and behavioral data. Building on prior group-level work, we applied a multi-stage validation framework to Human Connectome Project (HCP) task-fMRI data to test whether individual-level, data-driven models more accurately capture the organization of brain activity and behavior than RDoC-based models. Using confirmatory factor analysis in two independent cohorts, we found that data-driven bifactor models consistently outperformed RDoC-based models across multiple fit indices. The general factor derived from these models revealed a reproducible, low-dimensional axis spanning visual-attentional to default mode-auditory systems, aligning with canonical macroscale cortical gradients. Community detection further identified reproducible spatial motifs whose centroids corresponded to interpretable functional systems and whose alignment predicted individual performance on working memory and relational reasoning tasks. To assess whether these findings extended beyond neural data, we analyzed behavioral measures in HCP and in an independent transdiagnostic dataset (LA5c). In both datasets, data-driven behavioral models outperformed RDoC-based models, although the relative support for bifactor versus specific factor structure differed by dataset. Extending the neural analyses to LA5c, which included healthy controls and individuals with ADHD, bipolar disorder, and schizophrenia, showed that data-driven bifactor models generalized across diagnostic groups and that alignment with data-driven community centroids related to symptom severity, whereas RDoC-based representations showed weaker or no associations. Finally, topological analysis of task-evoked brain activity revealed that data-driven representations better captured the global organization of brain states than RDoC domains. Together, these findings demonstrate that individual-level, empirically derived models provide a more accurate, generalizable, and behaviorally relevant account of brain organization than the current RDoC framework. By integrating neural, behavioral, and clinical validation, this work advances precision neuroscience and supports the empirical refinement of dimensional psychiatric frameworks.

neuroscience↗

bioRxiv2025

Retention and data exclusion challenges for representative longitudinal neuroimaging in the understanding of addiction

Head motion during resting-state functional magnetic resonance imaging (rsfMRI) poses a major challenge for neuroimaging research, often leading to data quality concerns and participant exclusions, particularly among pediatric and clinical populations. Although necessary for ensuring reliable data, motion-related exclusions may inadvertently bias samples by disproportionately excluding certain sociodemographic groups. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we employed both frequentist and Bayesian approaches to examine how head motion-related exclusions and participant retention shape sample composition over time. At baseline (ages 9-10), Black, Hispanic, and Asian youth were significantly more likely than White youth to be excluded due to excessive head motion; these disparities were not observed at the two-year follow-up (ages 11-13). In contrast, disparities in retention persisted; Black participants were less likely to return for follow-up, even after accounting for socioeconomic factors and motion. Together, these findings highlight how both motion-related exclusions and differential retention can systematically influence the representativeness of longitudinal neuroimaging samples, with important implications for the generalizability of research in addiction neuroscience.

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bioRxiv2025

Frontostriatal salience network expands as executive networks contract in Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD), marked by intrusive thoughts (obsessions) and repetitive behaviours (compulsions), is linked to dysfunction in frontostriatal circuits. However, neural differences potentially contributing to these alterations are often small, and conflicting evidence obscures the directionality and underlying mechanisms of these alterations. Like many psychiatric conditions, OCD follows a fluctuating symptom trajectory, with symptoms shifting dramatically over months--either naturally or due to treatment. Yet, the absence of longitudinal neuroimaging studies limits our understanding of the neural mechanisms driving these changes. Here, we used precision functional mapping in highly sampled individuals with OCD, uncovering a striking imbalance within frontostriatal networks. We identified a twofold expansion of the salience network in these individuals and a concomitant contraction of the frontoparietal network. Notably, salience network expansion was driven by border shifts, encroaching on adjacent executive networks and leading to their contraction. This imbalance within frontostriatal networks may relate to excessive attention to internal stimuli and lack of goal-directed control, which are classically observed in OCD. Longitudinal analyses of neuroimaging data collected over several months revealed frontostriatal connectivity changes tracking symptom severity. Overall, these findings pinpoint network-level features that may confer risk for individuals with OCD and highlight dynamic connectivity shifts tied to severity of OCD symptoms over time. By isolating frontostriatal abnormalities at the individual level in OCD and their relationship to symptom severity for the first time, this work paves the way for more targeted, personalized treatment strategies and identifies precision functional mapping as a model for precision psychiatry.

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bioRxiv2025

fMRIPrep Lifespan: Extending A Robust Pipeline for Functional MRI Preprocessing to Developmental Neuroimaging

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

bioinformatics↗

bioRxiv2025

Unintended bias in the pursuit of collinearity solutions in fMRI analysis

In task functional magnetic resonance imaging (fMRI), collinearity between task regressors in time series models may impact power. When collinearity is identified after data collection, researchers often modify the model in an effort to reduce collinearity. However, some model adjustments are suboptimal and may introduce bias into parameter estimates. Although relevant to many task-fMRI studies, we highlight these issues using the Monetary Incentive Delay (MID) task data from the Adolescent Brain Cognitive Development (ABCD(R)) study. We introduce a procedure to more directly quantify the impact of collinearity on task-relevant measures: a contrast-based variance inflation factor (cVIF). We also show that collinearity reduction strategies--such as omitting regressors for specific task components, using impulse regressors for extended activations, and ignoring response time variability--can bias contrast estimates. Finally, we present a "Saturated" model that includes all task components, including response times, aiming to reduce these biases while maintaining comparable levels of collinearity, as assessed by cVIF.

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bioRxiv2024

Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the "rating widget" - a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged. RELATED LINKSO_ST_ABSKey reference(s) using this protocolC_ST_ABSEsteban, O. et al. (2017), PLoS ONE 12(9): e0184661. [10.1371/journal.pone.0184661] Esteban, O. et al. (2019), Sci Data 6, 30. [10.1038/s41597-019-0035-4] Esteban, O. et al. (2020), Nat Prot 15, 2186-2202. [10.1038/s41596-020-0327-3] Provins, C. et al. (2023), Front. Neuroinform. 1, 2813-1193. [10.3389/fnimg.2022.1073734] Bissett P. et al. (2024) Sci Data 11: 809. [10.1038/s41597-024-03636-y] Key data used in this protocolAmsterdam Open MRI Collection: Population Imaging of Psychology1 (AOMIC-PIOP1; ds002785 [https://openneuro.org/datasets/ds002785]).

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bioRxiv2024

Defacing biases visual quality assessments of structural MRI

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N=185). By comparing two linear mixed-effects models, we determined that four trained human raters perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQCs quality metrics were mostly insensitive to defacing.

bioinformatics↗

bioRxiv2024

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning

Deep learning models have demonstrated the potential to predict task-evoked brain activation from resting-state fMRI, offering a pathway toward individualized brain mapping without requiring task-based data. In this study, we systematically evaluate architectural strategies for improving the efficiency and scalability of such models. Using data from the Human Connectome Project, we replicate the BrainSurfCNN framework and introduce two extensions: BrainSERF, which incorporates channel-wise attention through squeeze-and-excitation modules, and BrainSurfGCN, a graph-based model that leverages cortical mesh topology for efficient message passing. Across multiple evaluation metrics, including spatial correlation, Dice score, Dice AUC, and subject identification accuracy, all models achieve comparable predictive performance. Despite similar accuracy, the proposed models offer distinct advantages. BrainSERF provides modest improvements in capturing individual-specific features, while BrainSurfGCN achieves substantial reductions in model size and training time, highlighting a favorable trade-off between performance and computational efficiency. Beyond architectural comparisons, we investigate factors driving variability in prediction accuracy. We find that behavioral task performance, resting-state data quality, and inter-subject variability in task activation jointly constrain prediction fidelity. In particular, contrasts with lower signal reliability and higher variability exhibit reduced predictability across all models. Together, these findings demonstrate that incorporating topological and functional structural priors can improve the efficiency of deep learning models without sacrificing accuracy, while also emphasizing that prediction performance is fundamentally limited by the reliability of the underlying neural signals.

neuroscience↗

bioRxiv2024

hyve, a compositional visualisation engine for brain imaging data

Visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries--such as volumes, surfaces, and networks--have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.

bioinformatics↗

bioRxiv2024

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

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bioRxiv2024

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria (RDoC) Framework

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns, we employed a latent variable approach using bifactor analysis. We examined 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6,192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set, and the remaining held-out maps formed the internal validation set. External validation was conducted using 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Here, we show that a bifactor model incorporating a task-general domain and splitting the cognitive systems domain better fits the examined corpus of tfMRI data than the current RDoC framework. We also identify the domain of arousal and regulatory systems as underrepresented. Our data-driven validation supports revising the RDoC framework to reflect underlying brain circuitry more accurately.

neuroscience↗

bioRxiv2023

When multi-voxel pattern similarity and global activation are intertwined: Approaches to disentangling correlation from activation

Pattern similarity analysis, which uses correlation to examine similarities between neural activation patterns evoked by different trials or conditions, is often leveraged to test hypotheses not easily answerable with univariate comparisons, such as how events are represented or processed and the relationships between representations or processing of events. In principle, univariate analyses of global activation and multivariate analyses of pattern similarity can be used to answer substantively different questions about psychological and neural processing. For this to hold, it is necessary that pattern similarity estimates are not contaminated by differences in global activation across experimental events. Here, we report simulated data that demonstrate that global activation and pattern similarity (as assessed by correlation), although theoretically independent, are often intertwined. We present two plausible scenarios that illustrate how condition-specific changes in global activation can elicit condition-specific increases in pattern similarity by interacting with underlying across-voxel activation patterns. First, we consider a scenario in which a target region contains subpopulations of voxels such that only some voxels in a region are sensitive to a psychological variable and the remaining voxels are not modulated by this variable. In this scenario, this spatial pattern of responsive and unresponsive voxels adds new, shared across-voxel variability for events in the active condition, thereby increasing pattern similarity between these events. Second, we consider a scenario in which trials from all conditions elicit a shared across-voxel pattern of activation, but this shared across-voxel pattern is amplified for trials within one condition due to greater global activation. In this scenario, the change in activation for a given condition increases the ability to detect pre-existing, shared across-voxel variability across events in that condition, thereby increasing pattern similarity between these events. Given the observed influence of global activation on pattern similarity, we then assess whether it is possible to statistically separate the contributions of global activation and pattern similarity to observed activation patterns (using regression approaches, matching activation across conditions, and inclusion of control conditions). Additional simulations demonstrate that use of these techniques is not always effective in removing the influence of global activation on pattern similarity --the efficacy of these techniques depends on a variety of signal parameters that will likely vary across experiments and participants, highlighting the need for tailored control analyses that are targeted at addressing the particular hypotheses and potential global activation confounds of a given experiment. [Note: The reported simulations and this resulting white paper were generated in 2014. We share, without update, this paper given the continued relevance of understanding and controlling for global activation confounds when conducting multi-variate pattern analyses.]

neuroscience↗

bioRxiv2023

The response time paradox in functional magnetic resonance imaging analyses

The functional MRI (fMRI) signal is a proxy for an unobservable neuronal signal, and differences in fMRI signals on cognitive tasks are generally interpreted as reflecting differences in the intensity of local neuronal activity. However, changes in either intensity or duration of neuronal activity can yield identical differences in fMRI signals. When conditions differ in response times (RTs), it is thus impossible to determine whether condition differences in fMRI signals are due to differences in the intensity of neuronal activity or to potentially spurious differences in the duration of neuronal activity. The most common fMRI analysis approach ignores RTs, making it difficult to interpret condition differences that could be driven by RTs and/or intensity. Because differences in response time are one of the most important signals of interest for cognitive psychology, nearly every task of interest for fMRI exhibits RT differences across conditions of interest. This results in a paradox, wherein the signal of interest for the psychologist is a potential confound for the fMRI researcher. We review this longstanding problem, and demonstrate that the failure to address RTs in the fMRI time series model can also lead to spurious correlations at the group level related to RTs or other variables of interest, potentially impacting the interpretation of brain-behavior correlations. We propose a simple approach that remedies this problem by including RT in the fMRI time series model. This model separates condition differences from RT differences, retaining power for detection of unconfounded condition differences while also allowing the identification of RT-related activation. We conclude by highlighting the need for further theoretical development regarding the interpretation of fMRI signals and their relationship to response times.

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