Biology subjects

Mill, R. D.

Publications and source records attributed to Mill, R. D..

12 recordsLinked to original sources

bioRxiv2026

Hierarchical and non-hierarchical network flows generate complementary representational dynamics in human visual cortex

Hierarchy is considered a fundamental organizing principle of visual cortex, but its functional implications remain debated given the presence of direct (non-hierarchical) connections. Building on recent advances in measuring direct region-to-region functional connectivity in the human brain, and in using that connectivity (rather than, e.g., visual classification training) to construct deep neural network models, we tested the hypothesis that hierarchical and direct connectivity pathways make distinct contributions to the generation of visual functionality. Detailed measurement of visual functionality, connectivity, and their interaction was achieved using 7T MRI and empirical neural network (ENN) models parameterized by empirical connectivity estimates. The classic V1 to V4 hierarchy was recovered in terms of (i) network distance from V1 along the human brains direct region-to-region resting-state functional connectome and (ii) on-task representational transformation distance (visual representation dissimilarity) from V1. In silico ENN lesion experiments revealed that hierarchical pathways (V1{leftrightarrow}V2{leftrightarrow}V3{leftrightarrow}V4) reduce the dimensionality of neural representations relative to more rapid and high-dimensional representational contributions from direct pathways (e.g., V1{leftrightarrow}V4). These findings reveal distinct but complementary roles of hierarchical and direct pathways in generating cortical functionality. Significance StatementHierarchy is a foundational organizing principle of cortex, yet its functional consequences remain unclear because of direct, non-hierarchical connections. The visual system, often portrayed as the clearest example of cortical hierarchy, provides a testbed for dissociating hierarchical and non-hierarchical contributions. Using high-resolution 7T MRI with recent advances in measuring direct region-to-region functional connectivity, we mapped the classic V1 to V4 hierarchy in the human brain. Using empirical neural network (ENN) models parameterized by these empirical connections, we determined that hierarchical pathways (V1{leftrightarrow}V2{leftrightarrow}V3{leftrightarrow}V4) reduce representational dimensionality relative to more rapid, high-dimensional contributions from direct pathways (e.g., V1{leftrightarrow}V4). These results reveal complementary hierarchical and direct contributions and establish ENN modeling as a general approach for determining pathway-specific functions throughout the brain.

neuroscience↗

bioRxiv2024

Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation

Animal and computational work indicate that phase resetting of theta oscillations (4-12 Hz) in the parahippocampal gyrus (PHG) by salient events (e.g., reward, landmarks) facilitates the encoding of goal-oriented information during navigation. Although well-studied in animals, this mechanism has not been empirically substantiated in humans. In the present article, we present data from two studies (Study 1: asynchronous EEG-MEG | Study 2: simultaneous EEG-fMRI) to investigate theta phase resetting and its relationship to PHG BOLD activation in healthy adults (aged 18-34 years old) navigating a virtual T-maze to find rewards. In the first experiment, both EEG and MEG data revealed a burst of theta power over right-posterior scalp locations following feedback onset (termed right-posterior theta, RPT), and RPT power and measures of phase resetting were sensitive to the subjects spatial trajectory. In Experiment 2, we used probabilistic tractography data from the human connectome project to segment the anterior and posterior PHG based on differential connectivity profiles to other brain regions. This analysis resulted in a PHG subdivision consisting of four distinct anterior and two posterior PHG clusters. Next, a series of linear mixed effects models based on simultaneous EEG-fMRI data revealed that single-trial RPT peak power significantly predicted single-trial hemodynamic responses in two clusters within the posterior PHG and one in the anterior PHG. This coupling between RPT power and PHG BOLD was exclusive to trials performed during maze navigation, and not during a similar task devoid of the spatial context of the maze. These findings highlight a role of PHG theta phase resetting for the purpose of encoding salient information during goal-directed spatial navigation. Taken together, RPT during virtual navigation integrates experimental, computational, and theoretical research of PHG function in animals with human cognitive electrophysiology studies and clinical research on memory-related disorders such as Alzheimers disease.

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bioRxiv2023

Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding

Functional connectivity (FC) has been invaluable for understanding the brains communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives - graphical lasso, graphical ridge, and principal component regression - against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion - common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

neuroscience↗

bioRxiv2023

Neural representation dynamics reveal computational principles of cognitive task learning

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass cognitive task learning. The strengthening of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain. HighlightsO_LILearning shifts multi-task representations from compositional to conjunctive formats C_LIO_LICortical conjunctions uniquely associate with improved behavior and pattern separation C_LIO_LIThese conjunctions strengthen over separated learning events and index switch costs C_LIO_LISubcortical regions are critical for cross-region binding of task rule information C_LI

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bioRxiv2022

Distributed network processes account for the majority of variance in localized visual category selectivity

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N=352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating - in a highly empirically constrained manner - the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each regions unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brains intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses. Author SummaryA fundamental question in neuroscience has persisted for over a century: to what extent do distributed processes drive brain function? The existence of category-selective regions within visual cortex provides long-standing evidence supporting localized computations, wherein specialized functions (e.g., selective responsiveness to face images) are thought to be primarily generated by within-region processes. This account was recently updated to include category selectivity dispersed across visual cortex, in the absence of category-selective regions. Here we provide groundwork evidence demonstrating that locally-exhibited visual-category-selective responses can be accurately generated via distributed activity flowing over globally connected systems. These processes were simulated via empirically-based computational models initialized by stimulus-evoked activity patterns and empirical connectivity matching each category-selective regions unique intrinsic functional connectivity fingerprint. Results demonstrate that activity flowing over the human brains distributed network architecture can account for the generation of category selectivity in visual cortex regions.

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bioRxiv2021

Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain. Highlights- Activity flow models provide insight into how neurocognitive effects are generated from brain network interactions. - Functional connectivity methods grounded in statistical causal principles facilitate mechanistic interpretations of task activity flow models. - Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.

neuroscience↗

bioRxiv2021

Causal emergence of task information from dynamic network interactions in the human brain

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when"), and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to non-invasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography with multivariate pattern analysis. A newly developed network modeling approach - dynamic activity flow modeling - then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity approaches) resting-state functional connections (dynamic, lagged, direct and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model towards simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

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bioRxiv2020

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

Cognitive dysfunction is a core feature of many brain disorders such as schizophrenia (SZ), and has been linked to both aberrant brain functional connectivity (FC) and aberrant cognitive brain activations. We propose that aberrant network activity flow over FC pathways leads to altered cognitive activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping - an approach that models the movement of task-related activity between brain regions as a function of FC. Using fMRI data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in independent patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to the emergence of cognitive dysfunction in SZ.

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bioRxiv2019

Predicting dysfunctional age-related task activations from resting-state network alterations

Alzheimers disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC network alterations associated with Alzheimers disease disrupt the ability for activations to flow between brain regions, leading to aberrant task activations. We apply this activity flow modeling framework in a large sample of clinically unimpaired older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) risk factors for AD. We identified healthy task activations in individuals at low risk for AD, and then by estimating activity flow using at-risk AD restFC data we were able to predict the altered at-risk AD task activations. Thus, modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy aged activations. These results provide evidence that activity flow over altered intrinsic functional connections may act as a mechanism underlying Alzheimers-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights linking restFC with cognitive task activations, this approach has potential clinical utility as it enables prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.\n\nSignificance StatementDeveloping analytic approaches that can reliably predict features of Alzheimers disease is a major goal for cognitive and clinical neuroscience, with particular emphasis on identifying such diagnostic features early in the timeline of disease. We demonstrate the utility of an activity flow modeling approach, which predicts fMRI cognitive task activations in subjects identified as at-risk for Alzheimers disease. The approach makes activation predictions by transforming a healthy aged activation template via the at-risk subjects individual pattern of fMRI resting-state functional connectivity (restFC). The observed prediction accuracy supports activity flow as a mechanism linking age-related alterations in restFC and task activations, thereby providing a theoretical basis for incorporating restFC into imaging biomarker and personalized medicine interventions.

neuroscience↗

bioRxiv2019

Task-evoked activity quenches neural correlations and variability in large-scale brain systems

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

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bioRxiv2017

Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in undiagnosed individuals

We all vary in our mental health, even among healthy (undiagnosed) individuals. Understanding this variability may reveal factors driving the onset of mental illness, as well as factors driving sub-clinical mental health problems that can still influence quality of life. To better understand the large-scale brain network mechanisms underlying this variability in mental health we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. The frontoparietal cognitive control network (FPN) consists of flexible hubs that can regulate distributed systems depending on current goals, and dysfunction in the FPN has been identified in a variety of psychiatric disorders. Alterations in FPN connectivity may influence mental health by disrupting the ability to regulate symptoms in a goal-directed manner. This suggests that the FPN may play an important role in the promotion and maintenance of mental health generally. Here we test the hypothesis that disruptions in FPN connectivity are related to mental health (depression) symptoms even among healthy individuals. This hypothesis is consistent with a general role of FPN in the regulation of mental health symptoms. We found that depression symptoms were negatively correlated with between-network global connectivity (BGC) of the FPN as well as the default mode network (DMN). This suggests that decreased connectivity between the FPN (and, separately, DMN) and the rest of the brain is related to increased depression symptoms among undiagnosed individuals. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both mentally healthy and unhealthy individuals.

neuroscience↗

bioRxiv2017

Cognitive task information is transferred between brain regions via resting-state network topology

Resting-state network connectivity has been associated with a variety of cognitive abilities, yet it remains unclear how these connectivity properties might contribute to the neurocognitive computations underlying these abilities. We developed a new approach - information transfer mapping - to test the hypothesis that resting-state functional network topology describes the computational mappings between brain regions that carry cognitive task information. Here we report that the transfer of diverse, task-rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections. Further, we find that these task-rule information transfers are coordinated by global hub regions within cognitive control networks. Activity flow over resting-state connections thus provides a large-scale network mechanism for cognitive task information transfer and global information coordination in the human brain, demonstrating the cognitive relevance of resting-state network topology.

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