Biology subjects

Braver, T. S.

Publications and source records attributed to Braver, T. S..

15 recordsLinked to original sources

bioRxiv2026

Caudate dopamine predicts cognitive effort decisions both in the lab and daily life

Making choices about whether and when to engage cognitive effort is a common feature of human everyday experience with important consequences for academic, career, and health outcomes. Yet, despite its importance, little is understood about the neurochemical mechanisms that underlie individual differences in the willingness to engage in cognitively effortful activities. To examine this question, we employed a multi-modal neuroimaging approach in which participants performed the Cognitive Effort Discounting paradigm (Cog-ED) while simultaneous PET-fMRI was acquired. We assessed both individual differences in dopamine-D2 receptor availability and brain activity during decision-making. In addition, the same participants performed a parametric working memory task (N-Back) and reported the cognitive effort associated with their daily life activities through a seven-day Ecological Momentary Assessment (EMA) protocol. The findings suggest a central role for caudate dopamine-D2 receptors in modulating cognitive effort-related motivation. People with higher caudate dopamine-D2 receptor availability had better working memory function, heightened sensitivity to effort costs, and greater modulation of brain activity during effort valuation. These laboratory findings also translated directly to daily life: higher levels of caudate dopamine-D2 receptors were associated with greater engagement in cognitively effortful activities. Together, the results suggest that how individuals determine the motivational value of cognitively effortful activities strongly depends on the dopamine system.

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bioRxiv2026

Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences

Across the last three decades, functional magnetic resonance imaging (fMRI) research - through both resting-state (rsfMRI) and task-based (tfMRI) studies - has greatly advanced our understanding regarding the neural basis of cognition. Yet the mechanistic relationship between rsfMRI and tfMRI is still poorly understood. In particular, it remains unclear how and why the brain activation patterns observed during the resting state are linked to cognitive functioning and individual differences present during task performance. Here, we test a unifying computational account which postulates that task contexts modulate the nonlinear attractor landscape and associated dynamical properties of the brain present under resting conditions, and further that the nature of this modulation is impacted by meaningful cognitive individual differences. To test this account, we develop a joint rsfMRI-tfMRI modeling and analysis framework called Mesoscale Individualized NeuroDynamics with eXogenous inputs (MINDy-X) and apply it to resting and N-back working memory task data from the Human Connectome Project. We first validated that the joint model can simulate and predict both rsfMRI and tfMRI data accurately, consistent with a common underlying dynamical system. Analyses of this joint model revealed that task-related modulation bifurcated the predominantly multistable attractor dynamics present during the resting state towards a predominantly monostable dynamics observed during N-back task states. This topological shift was also accompanied by a geometric reconfiguration, with the task state characterized by an enrichment of dynamical attractor "motifs" clustered around the frontoparietal (FPN) and default mode (DMN) networks. Task-related modulations of this attractor landscape were further subject to clear individual differences, such that individuals who did not exhibit a shift in attractor topology were more error-prone and less cautious in responding, while closer geometric proximity to the FPN and DMN motifs explained additional aspects of task performance. N-back behavior was best characterized by the combination of topological and geometric properties present in both task and rest states, suggesting that they each account for unique aspects of individual variability. The current work supports a novel computational framework for understanding the whole-brain neural activity patterns observed during rsfMRI and tfMRI as reflecting different states within a common non-linear dynamical system. This framework provides a new vocabulary for characterizing cognitive functioning in terms of the unique geometric and topological configuration of the associated attractor landscapes, with the potential for wide application in many domains of basic and clinical neuroscience research.

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bioRxiv2025

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention

The brain is a complex dynamical system whose activity reflects changes in internal states, such as attention. While prior work has shown that large-scale brain activity reflects attention, the mechanism governing this association in a time-varying and task-dependent manner remains unknown. Here, we tested a hypothesis that the geometry of neural dynamics on the attractor landscape, or the movement along the "hills and valleys", reflects changes in attentional states over time and variations across controlled and naturalistic contexts. We fit a parametric dynamical systems model to fMRI data collected during rest, task performance, and naturalistic movie-watching. The model decomposes neural dynamics into components that are intrinsic versus extrinsically driven by stimuli. Model parameters were biologically meaningful, reflecting both cognitive states and individual differences. Model simulations revealed a set of attractors that mirrored functional brain networks, spanning the canonical gradient from sensorimotor to default mode network regions. The speed and direction of neural trajectories toward these attractors systematically varied across attentional states in a context-dependent manner. When participants were paying attention to effortful tasks, neural dynamics converged directly toward a task-relevant attractor, suggesting that it occupied a steeper region of the attractor landscape. In contrast, when participants were engaged in sitcom episodes, neural dynamics were in a flattened region of the landscape, directed away from the attractors. These findings demonstrate that while the positions of the attractors are largely determined by the cortical organization, the geometry of neural dynamics on the attractor landscape changes systematically across attentional states and situational contexts.

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bioRxiv2025

Neural Mechanisms Supporting Proactive Control

Recent prior work suggests a preferential relationship between working memory capacity (WMC) and proactive control, yet the neural mechanisms that support this relationship are still not well understood. We directly addressed this question by leveraging the Dual Mechanisms of Cognitive Control (DMCC) project, as it employed a fMRI neuroimaging design optimized to test for individual differences (sample N > 100), with task variants that independently assessed proactive and reactive control relative to baseline conditions. Behavioral analyses replicated prior work with the AX-CPT paradigm, in which a measure of target preparation based on contextual cues (the A-cue Bias index) was both reliably increased under task conditions encouraging proactive control and positively associated with WMC. Analyses of fMRI activity indicated that A-cue Bias was selectively linked to increased cue-related neural activity in left motor cortex (lMOT). Additionally, WMC was associated with increased cue-related activation in right dorsolateral prefrontal cortex (rDLPFC), even when statistically controlling for baseline and reactive conditions. The relationship between these two effects was supported by a latent path analysis, which suggested that the rDLPFC-lMOT circuit preferentially mediates the WMC-A-cue Bias relationship present under proactive task conditions. The results suggest this neural circuit may translate strategic task goals into active response preparation as a mechanism of proactive control. Individuals high in WMC may be better able to implement proactive task strategies when instructed via contextual cues. The sensitivity of the rDLPFC-lMOT circuit to individual differences suggest it as a potential target for cognitive enhancement.

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bioRxiv2024

Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control

Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior work has pinpointed the source of the issue -- the vast amount of cross-trial variability within these measures -- solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data. Using this framework and longitudinal data from the Dual Mechanisms of Cognitive Control project, we estimated individuals neural responses associated with cognitive control within a color-word Stroop task, then assessed the reliability of these individuals responses across a time interval of several months. We show that in many prefrontal and parietal brain regions, test-retest reliability was near maximal, and that only hierarchical models were able to reveal this state of affairs. Further, when compared to traditional univariate contrasts, multivariate decoding enabled individual-level correlations to be estimated with significantly greater precision. We specifically link these improvements in precision to the optimized suppression of cross-trial variability in decoding. Together, these findings not only indicate that cognitive control-related neural responses individuate people in a highly stable manner across time, but also suggest that integrating hierarchical and multivariate models provides a powerful approach for investigating individual differences in cognitive control, one that can effectively address the issue of high-variability measures.

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bioRxiv2024

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks

Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generates several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

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bioRxiv2023

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms of canonical oscillations

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.

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bioRxiv2021

The Dual Mechanisms of Cognitive Control dataset: A theoretically-guided within-subject task fMRI battery

Cognitive control is a critical higher mental function, which is subject to considerable individual variation, and is impaired in a range of mental health disorders. We describe here the initial release of Dual Mechanisms of Cognitive Control (DMCC) project data, the DMCC55B dataset, with 55 healthy unrelated young adult participants. Each participant performed four well-established cognitive control tasks (AX-CPT, Cued Task-Switching, Sternberg Working Memory, and Stroop) while undergoing functional MRI scanning. The dataset includes a range of state and trait self-report questionnaires, as well as behavioural tasks assessing individual differences in cognitive ability. The DMCC project is on-going and features additional components (e.g., related participants, manipulations of cognitive control mode, resting state fMRI, longitudinal testing) that will be publicly released following study completion. This DMCC55B subset is released early with the aim of encouraging wider use and greater benefit to the scientific community. The DMCC55B dataset is suitable for benchmarking and methods exploration, as well as analyses of task performance and individual differences.

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bioRxiv2020

Enhancing Task fMRI Preprocessing via Individualized Model-Based Filtering of Intrinsic Activity Dynamics

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out" the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; [1]) models built from individualized resting-state data (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction of individual differences in behavior across tasks measuring the same construct (cognitive control).Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural activity.

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bioRxiv2020

A representational similarity analysis of cognitive control during color-word Stroop

Progress in understanding the neural bases of cognitive control has been supported by the paradigmatic color-word Stroop task, in which a target response (color name) must be selected over a more automatic, yet potentially incongruent, distractor response (word). For this paradigm, models have postulated complementary coding schemes: dorsomedial frontal cortex (DMFC) is proposed to evaluate the demand for control via incongruency-related coding, whereas dorsolateral prefrontal cortex (DLPFC) is proposed to implement control via goal and target-related coding. Yet, mapping these theorized schemes to measured neural activity within this task has been challenging. Here, we tested for these coding schemes relatively directly, by decomposing an event-related color-word Stroop task via representational similarity analysis (RSA). Three neural coding models were fit to the similarity structure of multi-voxel patterns of human fMRI activity, acquired from 65 healthy, young-adult males and females. Incongruency coding was predominant in DMFC, whereas both target and incongruency coding were present with indistinguishable strength in DLPFC. In contrast, distractor coding was strongly encoded within early visual cortex. Further, these coding schemes were differentially related to behavior: individuals with stronger DLPFC (and lateral posterior parietal cortex) target coding, but weaker DMFC incongruency coding, exhibited less behavioral Stroop interference. These results highlight the utility of the RSA framework for investigating neural mechanisms of cognitive control and point to several promising directions to extend the Stroop paradigm. Significant StatementHow the human brain enables cognitive control -- the ability to override behavioral habits to pursue internal goals -- has been a major focus of neuroscience research. This ability has been frequently investigated by using the Stroop color-word naming task. With the Stroop as a test-bed, many theories have proposed specific neuroanatomical dissociations, in which medial and lateral frontal brain regions underlie cognitive control by encoding distinct types of information. Yet providing a direct confirmation of these claims has been challenging. Here, we demonstrate that representational similarity analysis (RSA), which estimates and models the similarity structure of brain activity patterns, can successfully establish the hypothesized functional dissociations within the Stroop task. RSA may provide a useful approach for investigating cognitive control mechanisms.

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bioRxiv2020

Frontoparietal pattern similarity analyses of cognitive control in monozygotic twins

The ability to flexibly adapt thoughts and actions in a goal-directed manner appears to rely on cognitive control mechanisms that are strongly impacted by individual differences. A powerful research strategy for investigating the nature of individual variation is to study monozygotic (identical) twins. Evidence of twin effects have been observed in prior behavioral and neuroimaging studies, yet within the domain of cognitive control, it remains to be demonstrated that the neural underpinnings of such effects are specific and reliable. Here, we utilize a multi-task, within-subjects event-related neuroimaging design with functional magnetic resonance imaging to investigate twin effects through multivariate pattern similarity analyses. We focus on fronto-parietal brain regions exhibiting consistently increased activation associated with cognitive control demands across four task domains: selective attention, context processing, multi-tasking, and working memory. Healthy young adult monozygotic twin pairs exhibited increased similarity of within- and cross-task activation patterns in these fronto-parietal regions, relative to unrelated pairs. Twin activation pattern similarity effects were clearest under high control demands, were not present in a set of task-unrelated parcels or due to anatomic similarity, and were primarily observed during the within-trial timepoints in which the control demands peaked. Together, these results indicate that twin similarity in the neural representation of cognitive control may be domain-general but also functionally and temporally specific in relation to the level of control demand. The findings suggest a genetic and/or environmental basis for individual variation in cognitive control function, and highlight the potential of twin-based neuroimaging designs for exploring heritability questions within this domain.

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bioRxiv2020

The Dual Mechanisms of Cognitive Control (DMCC) Project

The Dual Mechanisms of Cognitive Control (DMCC) project provides an ambitious and rigorous empirical test of a theoretical framework that posits two key cognitive control modes: proactive and reactive. The frameworks central tenets are that proactive and reactive control reflect domain-general dimensions of individual variation, with distinctive neural signatures, involving lateral prefrontal cortex (PFC) in interactions with other brain networks and circuits (e.g., frontoparietal, cingulo-opercular). In the DMCC project, each participant is scanned while performing theoretically-targeted variants of multiple well-established cognitive control tasks (Stroop, Cued Task-Switching, AX-CPT, Sternberg Working Memory) in three separate imaging sessions, that each encourage utilization of different control modes, plus also completes an extensive out-of-scanner individual differences battery. Additional key features of the project include a high spatio-temporal resolution (multiband) acquisition protocol, and a sample that includes a substantial subset of monozygotic twin pairs and participants recruited from the Human Connectome Project. Although data collection is still continuing (target N=200), we provide an overview of the study design and protocol, planned analytic approaches and methodological development, along with initial results (N=80) revealing novel evidence of a domain-general neural signature of reactive control. In the interests of scientific community building, the dataset will be made public at project completion, so it can serve as a valuable resource.

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bioRxiv2020

Dorsal Anterior Cingulate Cortex Encodes the Subjective Motivational Value of Cognitive Task Performance

Humans can seamlessly combine value signals from diverse motivational incentives, yet it is not well-understood how these signals are "bundled" in the brain to modulate cognitive control. The dorsal anterior cingulate cortex (dACC) is theorized to integrate motivational value dimensions in the service of goal-directed action, though this hypothesis has yet to receive rigorous confirmation. In the present study, we examined the role of human dACC in motivational incentive integration. Healthy young adult men and women were scanned with fMRI while engaged in an experimental paradigm that quantifies the combined effects of liquid (e.g., juice, neutral, saltwater) and monetary incentives on cognitive task performance. Monetary incentives modulated trial-by-trial dACC activation, whereas block-related effects of liquid incentives on dACC activity were observed. When bundled together, incentive-related dACC modulation predicted fluctuations in both cognitive performance and self-report motivation ratings. Statistical mediation analyses suggest that dACC encoded the incentives in terms of their integrated subjective motivational value, and that this value signal was most proximally associated with task performance. Finally, we confirmed that these incentive integration effects were selectively present in dACC. Together, the results support an account in which dACC integrates motivational signals to compute the expected value of goal-directed cognitive control. Significance StatementHow are primary and secondary incentives integrated in the brain to influence goal-directed behavior? Using an innovative experimental fMRI paradigm that combines motivational incentives that have historically been studied independently between species (e.g., monetary rewards for humans, food rewards for animals), we examine the relationship between incentive motivational value and cognitive control allocation. We find evidence that the integrated incentive motivational value of combined incentives is encoded in human dorsal anterior cingulate cortex (dACC). Further, self-reported motivational shifts mediated the effects of incentive-modulated dACC activity on task performance, revealing convergence in how self-reported and experimentally-induced motivation are encoded in the human brain. Our findings may inform future translational studies examining affective/motivational and cognitive impairments in psychopathology (e.g., anxiety, depression, addiction).

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bioRxiv2020

Scalable Surrogate Deconvolution for Identification of Partially-Observable Systems and Brain Modeling

For many biophysical systems, direct measurement of all state-variables, in - vivo is not-feasible. Thus, a key challenge in biological modeling and signal processing is to reconstruct the activity and structure of interesting biological systems from indirect measurements. These measurements are often generated by approximately linear time-invariant (LTI) dynamical interactions with the hidden system and may therefore be described as a convolution of hidden state-variables with an unknown kernel. In the current work, we present an approach termed surrogate deconvolution, to directly identify such coupled systems (i.e. parameterize models). Surrogate deconvolution reframes certain nonlinear partially-observable identification problems, which are common in neuroscience/biology, as analytical objectives that are compatible with almost any user-chosen optimization procedure. We show that the proposed technique is highly scalable, low in computational complexity, and performs competitively with the current gold-standard in partially-observable system estimation: the joint Kalman Filters (Unscented and Extended). We show the benefits of surrogate deconvolution for model identification when applied to simulations of the Local Field Potential and blood oxygen level dependent (BOLD) signal. Lastly, we demonstrate the empirical stability of Hemodynamic Response Function (HRF) kernel estimates for Mesoscale Individualized NeuroDynamic (MINDy) models of individual human brains. The recovered HRF parameters demonstrate reliable individual variation as well as a stereotyped spatial distribution, on average. These results demonstrate that surrogate deconvolution promises to enhance brain-modeling approaches by simultaneously and rapidly fitting large-scale models of brain networks and the physiological processes which generate neuroscientific measurements (e.g. hemodynamics for BOLD fMRI).

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bioRxiv2019

Pattern similarity analyses of frontoparietal task coding: Individual variation and genetic influences

Pattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task fMRI dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 MZ (monozygotic) twin, 78 DZ (dizygotic) twin, 99 non-twin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically-selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding, and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.

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