Biology subjects

Cole, M. W.

Publications and source records attributed to Cole, M. W..

At least 19 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↗

bioRxiv2025

Distributed Cortical Network Dynamics of Binocular Convergent Eye Movements in Humans

Neuroimaging studies in humans have localized brain functions to specific brain regions, but a recent shift toward distributed network-based models of brain function promises deeper insights into the network processes that generate brain functionality. Resting-state functional connectivity provides a rich mapping of the brains network architecture, linking with both underlying structure and task-evoked responses across the whole brain. In this study, we utilized a model based on propagation of task-evoked activations over resting-state functional connectivity networks to identify cortical contributions to localized functional brain activations associated with binocular convergent eye movements. Binocular vision is crucial for daily routine activities, with its impairment leading to significant challenges in daily life. The distributed network-level mechanisms of binocular convergent eye movements remain unknown. Results showed that mapping activity flow over brain connections accurately generated actual brain activations associated with convergent eye movements, which were distinct from those observed during control tasks. The visual and dorsal attention networks dominated the propagation of activations through resting-state connections during convergent eye movements. Submodel analyses further revealed that restricting activity flow to individual networks, such as the visual or dorsal attention systems alone, substantially reduced model accuracy, underscoring the necessity of distributed, whole-brain contributions. In conclusion, highly distributed network pathways are involved in convergent eye movements, with some pathways contributing much more than others, providing important implications for future clinical models of binocular dysfunction.

neuroscience↗

bioRxiv2025

Network geometry shapes multi-task representational transformations across human cortex

Flexible cognition requires the brain to generalize across tasks while preserving task-specific information. Previous work showed that task representations are compressed in association cortex and expanded in sensory and motor systems, but the mechanisms underlying these transformations are unclear. Here we test whether the geometry of intrinsic brain connectivity constrains how representations change between cortical regions. We analyzed fMRI acquired during 16 diverse cognitive tasks together with activity flow modelling, finding that connectivity dimensionality predicts representational changes across cortex. Low-dimensional connectivity compressed task representations and increased similarities across tasks, whereas high-dimensional connectivity expanded representations and supported conjunctive coding of task features. Activity flow modelling reproduced the observed compression to expansion pattern along the sensory-association-motor hierarchy and generated representational geometries that more closely matched targets than sources, consistent with transformation rather than transfer of information. These findings identify network geometry as a systems-level principle that shapes cortical representations and supports flexible cognition.

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.

neuroscience↗

bioRxiv2024

Integrity of arousal-regulating brain circuitry predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

neuroscience↗

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

neuroscience↗

bioRxiv2022

Thalamocortical contribution to cognitive task activity

Thalamocortical interaction is a ubiquitous functional motif in the mammalian brain. Previously (Hwang et al., 2021), we reported that lesions to network hubs in the human thalamus are associated with multi-domain behavioral impairments in language, memory, and executive functions. Here we show how task-evoked thalamic activity and thalamocortical interactions are organized to support these broad cognitive abilities. To address this question, we analyzed functional MRI data from human subjects that performed 127 tasks encompassing a broad range of cognitive representations. We first investigated the spatial organization of task-evoked activity and found that multi-task thalamic activity converged onto a low-dimensional structure, through which a basis set of activity patterns are evoked to support processing needs of each task. Specifically, the anterior, medial, and posterior-medial thalamus exhibit hub-like activity profiles that are suggestive of broad functional participation. These thalamic task hubs overlapped with network hubs interlinking cortical systems. To further determine the cognitive relevance of thalamocortical interactions, we built a data-driven thalamocortical interaction model to test whether thalamocortical functional connectivity transformed thalamic activity to cortical task activity. The thalamocortical model predicted task-specific cortical activity patterns, and outperformed comparison models built on cortical, hippocampal, and striatal regions. Simulated lesions to low-dimensional, multi-task thalamic hub regions impaired task activity prediction. This simulation result was further supported by profiles of neuropsychological impairments in human patients with focal thalamic lesions. In summary, our results suggest a general organizational principle of how thalamocortical interactions support cognitive task activity. Impact StatementHuman thalamic activity transformed via thalamocortical functional connectivity to support task representations across functional domains.

neuroscience↗

bioRxiv2022

Functional Redundancy of the Posterior Hippocampi, but not Anterior Hippocampi or Left Frontal Cortex, is Disrupted in Pathological Brain Aging

As prevalence rates of Alzheimers disease (AD), the leading cause of dementia, are projected to more than double by 2050, emphasis has been placed on early intervention strategies that target resilience mechanisms to delay or prevent the onset of clinical symptoms. Several neural mechanisms underlying brain resilience to AD have been proposed, including redundant neural connections between the posterior hippocampi (HC) and all other brain regions, and global functional connectivity of the left frontal cortex (LFC). It remains unknown, however, if regional redundancy of the HC and LFC underscores neural resilience in the presence of AD pathologies. From the ADNI database, 363 cognitively normal older adults (CN) (N = 220; 36% A{beta}+) and patients with Mild Cognitive Impairment (MCI) (N = 143; 51% A{beta}+) were utilized. Regional redundancy was calculated from resting state fMRI data using a graph theoretical approach by summing the direct and indirect paths (path lengths=1-4) between each ROI and its 262 functional connections. The results showed that A{beta}-status significantly disrupted posterior HC, but not anterior HC or LFC, redundancy. A{beta}- groups showed higher redundancy of the bilateral posterior HC than A{beta}+. In regard to redundancy-cognition relationships, higher posterior HC redundancy was related to better episodic memory performance, an effect which was primarily driven by the A{beta}- group. Despite the positive relationship between posterior HC redundancy and cognition, we did not find compelling evidence that redundancy of the posterior HC serves in a resilience manner, as posterior HC redundancy did not moderate the potentially deleterious relationship between A{beta} deposition and cognition. No relationships were found between anterior HC or LFC redundancy and cognitive performance. Together, these findings suggest that redundancy of the LFC does not underpin its role in resilience and that posterior HC redundancy may capture disruptions to network connectivity that occur as a result of A{beta} deposition.

neuroscience↗

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.

neuroscience↗

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

Latent functional connectivity underlying multiple brain states

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brains intrinsic network architecture, which is thought to be broadly relevant because it persists across brain states (i.e., is state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in 7 highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared to resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflects state-general connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

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.

neuroscience↗

bioRxiv2021

The human brain's intrinsic network architecture is organized to represent diverse cognitive task information

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brains intrinsic network architecture. Specifically, we tested the possibility that each brain regions domain generality is reflected in its level of global (hub-like) intrinsic connectivity, as well as its particular global connectivity pattern (connectivity fingerprint). Consistent with prior work, we found that cognitive control networks exhibited domain generality, as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state fMRI) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control regions unique rule response profile (information fingerprint) could be predicted based on its unique intrinsic connectivity fingerprint and the information content in non-cognitive control regions. Together these results suggest that the human brains intrinsic network architecture supports its ability to represent diverse cognitive task information, largely via the location of multiple-demand regions within the brains global network organization.

neuroscience↗

bioRxiv2020

Constructing neural network models from brain data reveals representational transformations underlying adaptive behavior

The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in conjunction hubs - brain regions that selectively integrate sensory, cognitive, and motor activations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These empirically-specified simulations produced above-chance task performance (motor responses) by integrating sensory and task rule activations in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.

neuroscience↗

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.

neuroscience↗

bioRxiv2020

The CoREST Repressor Complex Mediates Phenotype Switching and Therapy Resistance in Melanoma

Virtually all patients with BRAF-mutant melanoma develop resistance to MAPK inhibitors largely through non-mutational events1,2. Although the epigenetic landscape has been shown to be altered in therapy-resistant melanomas and other cancers3,4, a specific targetable epigenetic mechanism regulating treatment resistance has not been validated to date. Here we evaluate the CoREST repressor complex and the novel inhibitor, corin5, within the context of melanoma phenotype plasticity and therapeutic resistance in order to define epigenetic mechanisms underlying these processes. We find that CoREST is a critical mediator of the major distinct melanoma phenotypes and that corin treatment of melanoma cells leads to phenotype reprogramming. We further demonstrate that treatment of BRAF inhibitor (BRAFi)-resistant melanomas with corin leads to resensitization of tumor cells to BRAFi. Among the transcriptional targets of CoREST in melanoma are the dual-specificity phosphatases (DUSPs). DUSP1 is shown to be consistently downregulated in BRAFi-resistant melanomas which can be reversed by corin treatment, thereby leading to downstream inhibition of p38 MAPK activity and resensitization of resistant cells to targeted BRAFi therapies. These findings identify the CoREST repressor complex as a central mediator of melanoma phenotype plasticity and resistance to targeted therapy and suggest that CoREST inhibitors may prove beneficial to patients with BRAF-mutant melanomas who have acquired BRAFi-resistance.

cancer biology↗

bioRxiv2020

Brain Network Mechanisms of Visual Shape Completion

Visual shape completion represents object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the processs underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form visually completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 34 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across all 20 observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal, dorsal attention, default mode, and cingulo-opercular networks. Each parcels task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10-9), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modeling activation differences in the secondary visual network and across all remaining networks. Taken together, these results suggest that shape completion relies upon a distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks. HighlightsO_LIShape completion differentially activates regions distributed across five networks C_LIO_LIThe secondary visual network plays the clearest role in shape completion C_LIO_LIDorsal attention functional connections likely coordinate activity across networks C_LIO_LIPosterior temporal region, PH, played a highly consistent role in completion C_LI

neuroscience↗