Biology subjects

Constable, R. T.

Publications and source records attributed to Constable, R. T..

At least 19 recordsLinked to original sources

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

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bioRxiv2025

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediate phenotype supporting cognitive control in individuals with a spectrum of clinical symptoms. We leveraged machine learning and external validation to explore this question in three independent, transdiagnostic datasets (N>600), including participants with anxiety disorders, schizophrenia, mood disorders, substance use disorders, post-traumatic stress disorder, obsessive-compulsive disorder, and neurodevelopmental disorders. To capture cognitive controls multifaceted nature, we assessed two of its components--inhibition and shift-- using both task-based and questionnaire data. Flexible brain state engagement predicted all cognitive control metrics in previously unseen individuals transdiagnostically, regardless of which dataset was used for model training. Connectome-based predictive modeling also revealed that shared brain networks underpinned flexible brain state engagement in a transdiagnostic manner. Leveraging brain network dynamics, we further observed that moments of more flexible brain state engagement aligned with moments of network connectivity related to better cognitive control within the same individual. This temporal alignment was replicated in all three datasets with heterogeneous samples. Altogether, this study suggests flexible engagement of brain states may support both inter- and intra-individual differences in cognitive control across individuals with diverse mental health profiles. Funding sourcesThis work was supported by F31AA032179 and R01MH121095. Data were provided in part by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410) and the Transdiagnostic Connectome Project (TCP) dataset. Datasets were accessed through OpenNeuro (https://openneuro.org/datasets/ds005237 and https://openneuro.org/datasets/ds000030/versions/00016).

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bioRxiv2025

Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth

Each individuals complex, multidimensional environment, known as their "exposome", plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby childrens exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137 - 8,391, rs = 0.34 - 0.44, ps <.001). Results were robust across data collection sites and functional connections common across all significant models were associated with cognitive performance across domains (ps < 0.0009). Collectively, these findings reveal that multidimensional environmental exposures are reflected in patterns of functional connectivity and relate to cognitive functioning among youth. HighlightsO_LIPatterns of functional connectivity reflect multivariate environmental exposures. C_LIO_LIConsistent exposome-related connectivity patterns were observed across brain states. C_LIO_LIThe identified general exposome network was associated with cognitive performance across domains. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/688261v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@b5cc3aorg.highwire.dtl.DTLVardef@172f954org.highwire.dtl.DTLVardef@14f0d01org.highwire.dtl.DTLVardef@deccb5_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotype

Popular methods for analyzing the brains functional connectome examine statistical associations between pairs of atlas-defined brain regions, viewing the strength of these links as independent values. However, edges within a standard connectivity matrix, i.e., correlations between individual regions or nodes, are not independent. They are part of an interconnected system. Here, we propose that consideration of both independent, linear relationships (as in standard approaches such as linear kernel ridge regression and connectome-based predictive modeling) as well as higher order statistical associations - such as tertiary interactions between matrix components and global features of the matrix space - will enhance identification of meaningful individual differences. To test this, we adopt a geometrically grounded measure of similarity that accounts for higher-order local statistical relationships and global interactions, the Wasserstein metric. Results indicate that considering connectivity matrices as representations of their associated Gaussian distributions significantly improves both identification of individuals based on their connectivity matrices (aka, fingerprinting) and prediction of individual differences in phenotypes such as fluid intelligence and openness to experience. Thus, both pairwise local and global brain connectivity properties encode for meaningful individual differences that relate to phenotypic expressions and should be considered in brain-behavior predictive models.

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bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

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bioRxiv2025

Functional connectivity in the social perception pathway at birth is linked with attention to faces at 4 months

BackgroundProcessing faces and speech is supported by the right-lateralized social visual perception pathway (social pathway) involving medial temporal/visual 5 area (MT/V5) and superior temporal sulcus (STS). Little is known about development of the social pathway and its links with later social outcomes. We examined intrinsic functional connectivity (iFC) in the right social pathway in neurotypical neonates, compared it with iFC in the left social and bilateral dorsal attention pathways, and interrogated prospective links between iFC and social attention in neurodiverse neonates. MethodsiFC in the social and dorsal pathways was measured in 517 full-term neonates from the developing Human Connectome Project (dHCP) and 73 full-term Yale neonates [Mage=41.5 weeks (SD=1.9)]. Social attention was assessed in 36 Yale neonates at Mage=4.2 months (SD=0.4) months. ResultsIn the dHCP sample, the iFC indices were positive in all the pathways (all p-values < 0.001) and did not vary by sex [all p>0.275], but the iFC in the right-lateralized social pathway was higher than in the remaining pathways [all p<0.001] and was positively associated with age at scan [r(517)=0.251, p<0.011]. In the prospective neurodiverse Yale sample, the iFC in the right social pathway was positively associated with social attention at 4 months [p=0.007], and greater social attention at 4 months predicted better social functioning in the second year [p=0.010]. ConclusionsThe early development of intrinsic functional connectivity in the right social perception pathway represents an area of interest for identifying neural mechanisms underlying emergence of atypical social attention associated with autism.

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bioRxiv2025

Geometric influences on the regional organization of the mammalian brain

The mammalian brain comprises anatomically and functionally distinct regions, yet the principles governing their organization across scales remain unclear. Efforts to map regional boundaries use expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties, but these approaches are often descriptive, have limited generalizability, and do not elucidate generative mechanisms. Here, we develop a geometrically constrained framework for the hierarchical, multiscale regional organization of neocortical and non-neocortical structures. The framework yields parcels at any resolution with high internal homogeneity across hundreds of anatomical, functional, cellular, and molecular properties in humans, macaques, marmosets, and mice, and it generalizes to understudied mammalian species. Finally, we demonstrate that the framework captures the essence of a reaction-diffusion mechanism in which brain geometry shapes the spatial expression of putative patterning molecules that establish a blueprint of regional identity during development. Our findings point to a conserved, universal influence of geometry on mammalian brain organization.

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bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

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bioRxiv2024

Multimodal identification of the mouse brain using simultaneous Ca2+ imaging and fMRI

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.

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bioRxiv2022

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

Alzheimers disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to womens elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n=579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from {rho}=0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between-visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to womens elevated risk of AD.

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bioRxiv2022

Functional network organization of the mouse cortex determined by wide-field fluorescence imaging shares some --but not all-- properties revealed with simultaneous fMRI-BOLD

Work in humans and animals shows that the brain can be decomposed into large-scale functional networks. Whereas most studies, especially in humans, use the blood-oxygenation-level-dependent (BOLD) signal, the relationship between BOLD and neuronal activity is complex and incompletely understood. This limits our ability to interpret and apply measures derived from fMRI-BOLD. Here, we employ wide-field Ca2+ imaging simultaneously recorded with fMRI-BOLD in highly-sampled mice expressing GCaMP6f in excitatory neurons. These unique data enabled us to characterize the similarities and differences between networks discoverable by each modality. Importantly, we applied a network partitioning approach that uses a mixed-membership algorithm, which allows brain regions to participate in multiple networks with varying strengths. This contrasts with assuming regions belong to only one network. Our findings demonstrate that (1) most BOLD networks are detected via Ca2+ signals. (2) There is considerable overlapping--as opposed to disjoint--network organization that is evident from both modalities. (3) Large-scale networks determined by Ca2+ signals at low temporal frequencies (0.01 - 0.5 Hz)--as opposed to higher frequencies (0.5 - 5 Hz)--are more similar to those determined by BOLD. (4) Despite many similarities, differences emerge across modes including the spatial distribution of membership diversity (the extent to which regions affiliate with multiple networks). In sum, Ca2+ imaging of excitatory neurons confirms that the mouse cortex is functionally organized into overlapping large-scale networks in a manner that reflects many, but not all, properties observable with simultaneous fMRI-BOLD; affirming the neural origins of patterns of brain organization that are evident in a clinically accessible neuroimaging modality.

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bioRxiv2022

Identifying dynamic reproducible brain states using a predictive modelling approach

Conceptually brain states reflect some combination of the internal mental process of a person, and the influence of their external environment. Importantly, for neuroimaging, brain states may impact brain-behavior modeling of a persons traits, which should be independent of moment-to-moment changes in behavior. A common way to measure both brain states and traits is to use functional connectivity based on functional MRI data. Brain states can fluctuate in time periods shorter than a typical fMRI scan, and a family of methods called dynamic functional connectivity analyses, have been developed to capture these short time estimates of brain states. There has been a rise in the use of dynamic functional connectivity in order to find temporally specific spatial patterns of connectivity which reflect brain states, that can yield further insight into traits and behaviors. It has previously been shown that brain state can be manipulated through the use of continuous performance tasks that put the brain in a particular configuration while the task is performed. Here we focus on moment-to-moment changes in brain state and test the hypothesis that there are particular brain-states that maximize brain-trait modeling performance. We use a regression-based brain-behavior modelling framework, Connectome-based Predictive Modelling, allied to a resample aggregating approach, to identify behavior and trait related short time brain states, as represented by dynamic functional connectivity maps. We find that there is not a particular brain state that is optimal for trait-based prediction, and drawing data from across the scan is better. We also find that this not the case for in-magnet behavioral prediction where more isolated and temporally specific parts of the scan session are better for building predictive models of behavior. The resample aggregated dynamic functional connectivity models of behavior replicated within sample using unseen HCP data. The modelling framework also showed success in the estimating variance behavior in the ABCD dataset when using data from that dataset. The method detailed here may prove useful for both the study of behaviorally related brain states, and for short time predictive modelling.

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bioRxiv2022

Developmental trajectories of the default mode, executive control, and salience networks from the third trimester through the newborn period

Social cognition is critical to early learning. Functional imaging studies in adults and older children suggest the involvement of the default mode (DMN), executive control (ECN), and salience (SAL) networks in social cognition. These networks are vulnerable to environmental insults, and abnormalities of intra- and inter-network connectivity of the three are emerging as biomarkers of neurobehavioral disorders. However, the developmental trajectories of the DMN, ECN, and SAL across the third trimester of gestation and perinatal transition remain largely unknown. Employing resting-state functional MRI studies at 30-32, 34-36, and 40-44 weeks postmenstrual age (PMA), we tested the hypothesis that both intra- and inter-network functional connectivity in the DMN, ECN, and SAL develop across the 30-46 weeks PMA time interval in a longitudinal/cross-sectional sample of 84 fetuses and neonates. A secondary analysis addressed the impact of maternal mental health assessed at 28 weeks PMA on tri-network development from 30-46 weeks PMA. The DMN, ECN, and SAL develop across the third trimester of gestation and the first postnatal month. At the intra-network level, significant increases occurred between 36 to 44 weeks PMA for all three, with network strength values significantly different from 0 beginning at 40 weeks PMA for all. Functional connectivity increased less rapidly in the DMN than in the ECN and SAL networks, suggesting slower maturation of the network subserving social interactions. In contrast, significant inter-network DMN - ECN connectivity greater than 0 was found from 36 weeks PMA through the first postnatal month, suggesting the emergence of inter-network functional connectivity in the fetal brain. Finally, higher maternal mental health symptoms measured at the beginning of the third trimester negatively affected the developmental trajectory of the SAL network across the critical time interval of 30 weeks to 44 weeks PMA. Together, these data provide a framework to compare fetuses and neonates at risk for neurobehavioral disorders and assess the impact of the environment on the developing brain.

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bioRxiv2022

Sex differences in default mode network connectivity in healthy aging adults

Women show an increased lifetime risk of Alzheimers disease (AD) compared to men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project - Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated increased connectivity with age in posterior DMN nodes and decreased connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed increased connectivity in women from the posterior cingulate to angular gyrus and parahippocampal gyrus, which correlated with neuropsychological measures of declarative memory. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

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bioRxiv2022

A preprocessing and functional connectivity analysis pipeline for mesoscale calcium imaging

To improve bench-to-bedside translation, it is integral that knowledge flow bidirectionally--from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data--an emerging mode applicable in animal models--as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.

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bioRxiv2022

Hypoconnectivity between anterior insula and amygdala in neonates with familial history of autism

BackgroundAltered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. MethodHere we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD=1.5) who due to family history have high likelihood (HL) for developing autism (n=12) and in controls (n=41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M=17.3 months, SD=2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). ResultsCompared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -.561, p=.003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n=14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. ConclusionsThe study identifies left lateralized anterior insula - amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.

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bioRxiv2021

Brain networks in human conscious visual perception

Consciousness is not explained by a single mechanism, rather it involves multiple specialized neural systems overlapping in space and time. We hypothesize that synergistic, large-scale subcortical and cortical attention and signal processing networks encode conscious experiences. To identify brain activity in conscious perception without overt report, we classified visual stimuli as perceived or not using eye measurements. Report-independent event-related potentials and functional magnetic resonance imaging (fMRI) signals both occurred at early times after stimuli. Direct recordings revealed a novel thalamic awareness potential linked to conscious visual perception based on report. fMRI showed thalamic and cortical detection, arousal, attentional salience, task-positive, and default mode networks were involved independent of overt report. These findings identify a specific sequence of neural mechanisms in human conscious visual perception. One-Sentence SummaryHuman conscious visual perception engages large-scale subcortical and cortical networks even without overt report.

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bioRxiv2021

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications.

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