Biology subjects

Whitfield-Gabrieli, S.

Publications and source records attributed to Whitfield-Gabrieli, S..

At least 19 recordsLinked to original sources

bioRxiv2026

HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain-behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project-style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP-style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof-of-concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging-derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi-cohort resource for reproducible mental health neuroimaging research.

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bioRxiv2026

Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion

Individual-specific cortical parcellations enable the characterization of brain network organization that is often obscured by population-level atlases, with broad implications for both basic neuroscience and translational applications. However, existing methods rely primarily on resting-state fMRI and underutilize task-evoked data, which provide complementary information about functional specialization. This limitation partly reflects the challenge of integrating heterogeneous datasets that differ in task design, sample size, and cortical coverage. Here, we present mRBM-HBP, a scalable hierarchical Bayesian framework that incorporates a multinomial restricted Boltzmann machine to model spatial dependencies, enabling efficient and flexible integration of resting-state and task fMRI across diverse datasets and inference of both group-level and individual-level cortical parcellations. We show that mRBM-HBP achieves performance comparable to state-of-the-art resting-state-based parcellation methods while substantially reducing computational cost. By integrating large-scale task-fMRI datasets, we derive a task-based parcellation and demonstrate that resting-state and task conditions reveal largely consistent macroscopic networks, while task data provide state-specific refinements of functional boundaries. Moreover, a fused rest-task group-level atlas improves the accuracy, reliability, and individual specificity of inferred parcellations, particularly when individual-level data are limited. These results indicate that integrating resting-state and task fMRI enhances precision mapping of functional brain organization.

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bioRxiv2025

Real-time fMRI neurofeedback modulates auditory cortex activity and connectivity in schizophrenia patients with auditory hallucinations: A controlled study

Background and HypothesisWe have reported previously a reduction in superior temporal gyrus (STG) activation and in auditory verbal hallucinations (AHs) after real-time fMRI neurofeedback (NFB) in schizophrenia patients with AHs. Study DesignWith this randomized, participant-blinded, sham-controlled trial, we expanded our previous results. Specifically, we examined neurofeedback effects from the STG, an area associated with auditory hallucinations. The effects were compared to Sham-NFB from the motor cortex, a region unrelated to hallucinations. Twenty-three adults with schizophrenia or schizoaffective disorder and frequent medication-resistant hallucinations performed mindfulness meditation to ignore pre-recorded strangers voices while receiving neurofeedback either from the STG (n=10, Real-NFB) or motor cortex (n=13 Sham-NFB). Individuals randomized to Sham-NFB received Real-NFB in a subsequent visit, providing a within-subject Real-after-Sham-NFB comparison. Study ResultsBoth groups showed reduced AHs after NFB, with no group differences. Compared to the Sham-NFB group, the Real-NFB group showed more reduced activation in secondary auditory cortex (AC) and more reduced connectivity between AC and cognitive control regions including dorsolateral prefrontal cortex (DLPFC) and anterior cingulate. The connectivity reduction was also observed in the Real-after-Sham-NFB condition. Secondary AC-DLPFC connectivity reduction correlated with hallucination reduction in the Real-NFB group. Replicating prior results, both groups showed reduced primary auditory cortex activation, suggesting mindfulness meditation may regulate bottom-up processes involved in hallucinations. ConclusionsOur findings emphasize delivering NFB from brain regions involved in medication-resistant AHs. They provide insights into auditory cortex and cognitive control network interactions, highlighting complex processing dynamics and top-down modulation of sensory information.

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bioRxiv2025

Real-time fMRI neurofeedback reduces default mode network and auditory cortex functional connectivity in schizophrenia

Background and HypothesisAuditory verbal hallucinations (AHs) are a cardinal symptom of schizophrenia that can cause distress but are not always responsive to antipsychotic medications. There is a critical need to develop novel interventions that target neural mechanisms underlying AHs. We developed a real-time fMRI neurofeedback (NFB) paradigm for AHs that aims at modulating default mode network (DMN) functional connectivity. Study DesignPatients with schizophrenia or schizoaffective disorders who were experiencing AHs (N = 25) attempted to decrease brain activation while listening to sentences recorded in another persons voice and increase brain activation while listening to sentences recorded in their own voice. Participants randomly assigned to the real group (n = 12) received neurofeedback based on signals from their auditory cortex in the superior temporal gyrus (STG) and those assigned to the sham group (n = 13) received neurofeedback based on motor cortex signals. Study ResultsAnalyzing resting state fMRI data collected pre- and post-NFB, we found that: (1) at baseline, stronger within-DMN connectivity between the medial prefrontal cortex (MPFC) and posterior cingulate cortex was associated with higher AHs severity; (2) after NFB, participants in the real group, compared to those in the sham group, showed greater reduction in functional connectivity between the MPFC and auditory cortices in the STG and middle temporal gyrus (MTG). Notably, the reduction in MPFC-STG/MTG connectivity was found in all participants in the real group. ConclusionsThese findings suggest that NFB can effectively and non-invasively modulate functional connectivity in regions associated with AHs in psychosis.

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bioRxiv2024

Shared and unique lifetime stressor characteristics and brain networks predict adolescent anxiety and depression

BackgroundExposure to major life stressors and aberrant brain functioning have been related to anxiety and depression, especially during adolescence. However, whether these associations differ based on the specific characteristics of the stressors experienced, and/or the functional networks engaged, remains unclear. MethodsWe used baseline lifetime stressor exposure and resting-state functional magnetic resonance imaging data from a longitudinal sample of 150 adolescents enriched for anxiety and depressive disorders. We examined the cumulative lifetime stressor frequency and severity of five stressor characteristics: physical danger, interpersonal loss, humiliation, entrapment, and role change/disruption. Anxiety and depression symptoms were assessed at three time points: baseline, 6-month and 12-month follow-ups. Linear mixed-effect models tested if the lifetime frequency and severity of these stressor characteristics and functional connectivity within and between frontoparietal, default, and ventral attention networks at baseline predicted anxiety and depression symptoms at three time points. ResultsLifetime frequency and severity of humiliation and entrapment predicted both anxiety and depression symptoms. Lifetime frequency and severity of entrapment exposures predicted anxiety and depression symptoms after accounting for baseline depression and anxiety symptoms, respectively. Resting-state functional connectivity between default, frontoparietal and ventral attention networks did not predict either anxiety or depression symptoms after correcting for multiple comparisons. ConclusionsOur study highlights lifetime exposures to humiliation and entrapment stressors as central stressor characteristics predictive of prospective anxiety and depression symptoms in adolescence. Our results also suggest that resting-state functional connectivity within and between default, frontoparietal and ventral attention networks may be relatively weak predictors of prospective anxiety and depression symptoms in adolescence.

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bioRxiv2024

Dissociable Default Mode Network Connectivity Patterns Underlie Distinct Symptoms in Psychosis Risk

The Clinical High Risk (CHR) stage of psychosis is characterized by subthreshold symptoms of schizophrenia including negative symptoms, dysphoric mood, and functional deterioration. Hyperconnectivity of the default-mode network (DMN) has been observed in early schizophrenia, but the extent to which hyperconnectivity is present in CHR, and the extent to which such hyperconnectivity may underlie transdiagnostic symptoms, is not clear. As part of the Shanghai At-Risk for Psychosis (SHARP) program, resting-state fMRI data were collected from 251 young adults (158 CHR and 93 controls, M = 18.72, SD = 4.68, 129 male). We examined functional connectivity of the DMN by performing a whole-brain seed-to-voxel analysis with the MPFC as the seed. Symptom severity across a number of dimensions, including negative symptoms, positive symptoms, and affective symptoms were assessed. Compared to controls, CHRs exhibited significantly greater functional connectivity (p < 0.001 uncorrected) between the MPFC and 1) other DMN nodes including the posterior cingulate cortex (PCC), and 2) auditory cortices (superior and middle temporal gyri, STG/MTG). Furthermore, these two patterns of hyperconnectivity were differentially associated with distinct symptom clusters. Within CHR, MPFC-PCC connectivity was significantly correlated with anxiety (r= 0.23, p=0.006), while MPFC-STG/MTG connectivity was significantly correlated with negative symptom severity (r=0.26, p=0.001). Secondary analyses using item-level symptom scores confirmed a similar dissociation. These results demonstrate that two dissociable patterns of DMN hyperconnectivity found in the CHR stage may underlie distinct dimensions of symptomatology.

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bioRxiv2024

Mindfulness-based Neurofeedback: A Systematic Review of EEG and fMRI studies

Neurofeedback concurrent with mindfulness meditation may reveal meditation effects on the brain and facilitate improved mental health outcomes. Here, we systematically reviewed EEG and fMRI studies of mindfulness meditation with neurofeedback (mbNF) and followed PRISMA guidelines. We identified 10 fMRI reports, consisting of 177 unique participants, and 9 EEG reports, consisting of 242 participants. Studies of fMRI focused primarily on downregulating the default-mode network (DMN). Although studies found decreases in DMN activations during neurofeedback, there is a lack of evidence for transfer effects, and the majority of studies did not employ adequate controls, e.g. sham neurofeedback. Accordingly, DMN decreases may have been confounded by general task-related deactivation. EEG studies typically examined alpha, gamma, and theta frequency bands, with the most robust evidence supporting the modulation of theta band activity. Both EEG and fMRI mbNF have been implemented with high fidelity in clinical populations. However, the mental health benefits of mbNF have not been established. In general, mbNF studies would benefit from sham-controlled RCTs, as well as clear reporting (e.g. CRED-NF).

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bioRxiv2024

Connectome predictive modeling of trait mindfulness

IntroductionTrait mindfulness refers to ones disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. MethodsTo address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. ResultsIn the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. ConclusionsWe present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

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bioRxiv2023

Cortical and subcortical mapping of the allostatic-interoceptive system in the human brain: replication and extension with 7 Tesla fMRI

The brain continuously anticipates the energetic needs of the body and prepares to meet those needs before they arise, called allostasis. In support of allostasis, the brain continually models the sensory state of the body, called interoception. We replicated and extended a large-scale system supporting allostasis and interoception in the human brain using ultra-high precision 7 Tesla functional magnetic resonance imaging (fMRI) (N = 90), improving the precision of subgenual and pregenual anterior cingulate topography combined with extensive brainstem nuclei mapping. We observed over 90% of the anatomical connections published in tract-tracing studies in non-human animals. The system also included regions of dense intrinsic connectivity broadly throughout the system, some of which were identified previously as part of the backbone of neural communication across the brain. These results strengthen previous evidence for a whole-brain system supporting the modeling and regulation of the internal milieu of the body.

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bioRxiv2022

Targeting default mode network connectivity with mindfulness-based fMRI neurofeedback: A pilot study among adolescents with affective disorder history

Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed a mindfulness-based fMRI neurofeedback (mbNF) for adolescents that targets default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and then, each participants DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks known to be associated with the emergence and persistence of depressive symptoms during adolescence.

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bioRxiv2021

Brain structure and function predict adherence to an exercise intervention in older adults

Individualized and precision medicine approaches to exercise for cognitive and brain health in aging have the potential to improve intervention efficacy. Predicting adherence to an exercise intervention in older adults prior to its commencement will allow for adaptive and optimized approaches that could save time (no need to demonstrate failure before changing course) and money (cost of de-implementing approaches that do not work for certain individuals) which ultimately could improve health outcomes (e.g., preventative medicine approaches prior to the onset of symptoms). Individual differences in brain structure and function in older adults are potential proxies of brain and brain reserve or maintenance and may provide strong predictions of adherence. We hypothesized that brain-based measures would predict adherence to a six-month randomized controlled trial of exercise in older adults, alone and in combination with psychosocial, cognitive and health measures. In 131 older adults (aged 65.79 {+/-} 4.65 years, 63% female) we found, using regularized elastic net regression within a nested cross-validation framework, that brain structure (cortical thickness and cortical surface area) in somatosensory, inferior temporal, and inferior frontal regions and functional connectivity (degree count) in primary information processing (somatosensory, visual), executive control, default, and attentional networks, predicted exercise adherence (R2 = 0.15, p < 0.001). Traditional survey and clinical measures such as gait and walking self-efficacy, biological sex and perceived stress also predicted adherence (R2 = 0.06, p = 0.001) but a combined multimodal model achieved the highest predictive strength (R2 = 0.22, p <0.001). Neuroimaging features alone can predict adherence to a structured group-based exercise intervention in older adults which suggests there is substantial utility of these measures for future research into precision medicine approaches. The best performing model contained multimodal features suggesting that each modality provided independent relevant information in the prediction of exercise adherence.

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bioRxiv2021

Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from todays state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

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bioRxiv2021

Cerebello-cerebral Functional Connectivity Networks in Major Depressive Disorder: A CAN-BIND-1 Study Report

ObjectiveNeuroimaging studies have demonstrated aberrant structure and function of the "cognitive-affective cerebellum" in Major Depressive Disorder (MDD), although the specific role of the cerebello-cerebral circuitry in this population remains largely uninvestigated. The objective of this study was to delineate the role of cerebellar functional networks in depression. MethodsA total of 308 unmedicated participants completed resting-state functional magnetic resonance imaging scans, of which 247 (148 MDD; 99 Healthy Controls, HC) were suitable for this study. Seed-based resting-state functional connectivity (RsFc) analysis was performed using three cerebellar regions of interest (ROIs): ROI1 corresponded to default mode network (DMN) / inattentive processing; ROI2 corresponded to attentional networks including frontoparietal, dorsal attention, and ventral attention; ROI3 corresponded to motor processing. These ROIs were delineated based on prior functional gradient analyses of the cerebellum. A general linear model was used to perform within-group and between-group comparisons. ResultsIn comparison to HC, participants with MDD displayed increased RsFc within the cerebello-cerebral DMN (ROI1) and significantly elevated RsFc between the cerebellar ROI1 and bilateral angular gyrus at a voxel threshold (p < 0.001, two-tailed) and at a cluster level (p < 0.05, FDR-corrected). Group differences were non-significant for ROI2 and ROI3. ConclusionsThese results contribute to the development of a systems neuroscience approach to the diagnosis and treatment of MDD. Specifically, our findings confirm previously reported associations between MDD, DMN, and cerebellum, and highlight the promising role of these functional and anatomical locations for the development of novel imaging-based biomarkers and targets for neuromodulation therapies.

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bioRxiv2021

Abnormal function in dentate nuclei precedes the onset of psychosis: a resting-state fMRI study in high-risk individuals

ObjectiveThe cerebellum serves a wide range of functions and is suggested to be composed of discrete regions dedicated to unique functions. We recently developed a new parcellation of the dentate nuclei (DN), the major output nuclei of the cerebellum, which optimally divides the structure into three functional territories that contribute uniquely to default-mode, motor-salience, and visual processing networks as indexed by resting-state functional connectivity (RsFc). Here we test for the first time whether RsFc differences in the DN precede the onset of psychosis in individuals at risk of developing schizophrenia. MethodsWe used the MRI dataset from the Shanghai At Risk for Psychosis study that included subjects at high risk to develop schizophrenia (N=144), with longitudinal follow-up to determine which subjects developed a psychotic episode within one year of their fMRI scan (converters N=23). Analysis used the three functional parcels (default-mode, salience-motor, and visual territory) from the DN as seed regions of interest for whole-brain RsFc analysis. ResultsRsFc analysis revealed abnormalities at baseline in high-risk individuals who developed psychosis, compared to high-risk individuals who did not develop psychosis. The nature of the observed abnormalities was found to be anatomically specific such that abnormal RsFc was localized predominantly in cerebral cortical networks that matched the three functional territories of the DN that were evaluated. ConclusionsWe show for the first time that abnormal RsFc of the DN may precede the onset of psychosis. This new evidence highlights the role of the cerebellum as a potential target for psychosis prediction and prevention.

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bioRxiv2021

Resting State Functional Connectivity Predicts Future Changes in Sedentary Behavior

Information about a persons available energy resources is integrated in daily behavioral choices that weigh motor costs against expected rewards. It has been posited that humans have an innate attraction towards effort minimization and that executive control is required to overcome this prepotent disposition. With sedentary behaviors increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths, understanding the predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. In 64 healthy older adults participating in a 6-month aerobic exercise intervention, we use neuroimaging (resting state functional connectivity), baseline measures of executive function and accelerometer measures of time spent sedentary to predict future changes in objectively measured time spent sedentary in daily life. Using cross-validation and bootstrap resampling, our results demonstrate that functional connectivity between 1) the anterior cingulate cortex and the supplementary motor area and 2) the right anterior insula and the left temporoparietal/temporooccipital junction, predict changes in time spent sedentary, whereas baseline cognitive, behavioral and demographic measures do not. Previous research has shown activation in and between the anterior cingulate and supplementary motor area as well as in the right anterior insula during effort avoidance and tasks that integrate motor costs and reward benefits in effort-based decision making. Our results add important knowledge toward understanding mechanistic associations underlying complex sedentary behaviors.

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bioRxiv2021

Generalizable prediction of stimulus-independent, task-unrelated thought from functional brain networks

Neural substrates of "mind wandering" have been widely reported, yet experiments have varied in their contexts and their definitions of this psychological phenomenon, limiting generalizability. We aimed to develop and test the generalizability, specificity, and clinical relevance of a functional brain network-based marker for a well-defined feature of mind wandering--stimulus-independent, task-unrelated thought (SITUT). Combining functional MRI (fMRI) with online experience sampling in healthy adults, we defined a connectome-wide model of inter-regional coupling--dominated by default-frontoparietal control subnetwork interactions--that predicted trial-by-trial SITUT fluctuations within novel individuals. Model predictions generalized in an independent sample of attention-deficit/hyperactivity disorder (ADHD) adults. In three additional resting-state fMRI studies (total n=1,115), including healthy and ADHD populations, we demonstrated further prediction of SITUT (at modest effect sizes) defined using multiple trait-level and in-scanner measures. Our findings suggest that SITUT is represented within a common pattern of brain network interactions across time scales, populations, and contexts.

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bioRxiv2020

What is an adaptive pattern of brain activity for a child? It depends on their environment

Prior research indicates that lower resting-state functional coupling between two brain networks, lateral frontoparietal network (LFPN) and default mode network (DMN), relates to better cognitive test performance. However, most study samples skew towards wealthier individuals--and what is adaptive for one population may not be for another. In a pre-registered study, we analyzed resting-state fMRI from 6839 children ages 9-10 years. For children above poverty, we replicated the prior finding: better cognitive performance correlated with weaker LFPN-DMN coupling. For children in poverty, the slope of the relation was instead positive. This significant interaction related to several features of a childs environment. Future research should investigate the possibility that leveraging internally guided cognition is a mechanism of resilience for children in poverty. In sum, "optimal" brain function depends in part on the external pressures children face, highlighting the need for more diverse samples in research on the human brain and behavior.

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bioRxiv2019

Improvising at Rest: Differentiating Jazz and Classical Music Training with Resting State Functional Connectivity

Jazz improvisation offers a model for creative cognition, as it involves the real-time creation of a novel, information-rich product. Previous research has shown that when musicians improvise, they recruit regions in the Default Mode Network (DMN) and Executive Control Network (ECN). Here, we ask whether these findings from task-fMRI studies might extend to intrinsic differences in resting state functional connectivity. We compared Improvising musicians, Classical musicians, and Minimally Musically Trained (MMT) controls in seed-based functional connectivity and network analyses in resting state functional MRI. We also examined the functional correlates of behavioral performance in musical improvisation and divergent thinking. Seed-based analysis consistently showed higher connectivity in ventral DMN (vDMN) and bilateral ECN in both groups of musically trained individuals as compared to MMT controls, with additional group differences in primary visual network, precuneus network, and posterior salience network. In particular, primary visual network connectivity to DMN and ECN was highest in Improvisational musicians, whereas within-network connectivity of vDMN and precuneus network was higher in both Improvisational and Classical musicians than in MMT controls; in contrast, connectivity between posterior salience network and superior parietal lobule was highest in Classical musicians. Furthermore, graph-theoretical analysis indicated heightened betweenness centrality, clustering, and local efficiency in Classical musicians. Taken together, results suggest that heightened functional connectivity among musicians can be explained by higher within-network connectivity (more tight-knit cortical networks) in Classical musicians, as opposed to more disperse, globally-connected cortical networks in Improvisational musicians.\n\nHighlightsO_LIMusic training is associated with higher resting state connectivity\nC_LIO_LIHigher connectivity in Improvisational musicians from visual network to ECN and DMN\nC_LIO_LIClassical musicians show higher vDMN and Precuneus within-network connectivity\nC_LIO_LIImprovisation and divergent thinking performance correlate with similar connectivity patterns\nC_LI

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