Human Brain Mapping

Source-based morphometry (SBM) is a powerful multivariate method for identifying covarying structural brain networks. However, standard SBM provides only a single loading value per component for each subject, which limits the characterization of relationships between these components. We propose a novel technical co-modulation approach to derive an individualized, network-like measure of structural brain organization. This method transforms the subject-specific SBM loading vector into a symmetric co-modulation matrix by computing the vectors outer product. Each element of this matrix quantifies the pairwise interaction between structural components, creating a subject-specific fingerprint. Similar to functional connectivity that maps the temporal synchronization between networks, this matrix maps their joint structural prominence, reflecting how strongly two networks co-occur within an individual. To demonstrate the utility of this method, we applied it to structural MRI data from 210 patients with schizophrenia (SZ) and 195 healthy controls (HC) from the fBIRN psychosis dataset using functional networks as priors for SBM. We observed widespread reductions in structural co-modulation in the SZ group, particularly within and between visual, default-mode, and cognitive control networks. Furthermore, co-modulation patterns were significantly correlated with cognitive performance and clinical symptom severity in patients. Structural co-modulation provides a robust framework for quantifying individualized relationships between structural brain features, overcoming key limitations of standard SBM and offering a new avenue for integrating structural and functional brain analyses.

Fundamental and clinical neuroscience has benefited from the development of automated computational analyses of Magnetic Resonance Imaging (MRI) data, such as Voxel-based Morphometry (VBM). VBM determines regional gray matter variations with high spatial resolution and results are commonly interpreted in a regional-specific manner, for instance with respect to which specific brain regions differ in volume between women and men. In excess of 600 papers using VBM are now published every year and a number of different automated VBM processing pipelines are frequently used in analyses although it remains to be fully and systematically assessed whether they come up with the same answers. Here we have therefore examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity, reproducibility and reliability of the processed gray matter brain maps was generally low between pipelines. Examination of sex-differences and age-related changes in gray matter volumes revealed considerable differences between the pipelines in terms of the specific regions identified as well as meta-analytic characterization of their function. In contrast, applying machine learning-based multivariate analyses allowed an accurate prediction of sex or age based on the gray matter maps across pipelines, although prediction accuracy differed strongly between them. Together the findings suggest that the choice of pipeline alone leads to considerable variability in brain structural analyses which poses a serious challenge for reproducibility as well as interpretation.

Understanding the dynamic intrinsic interactions between brain regions has been advanced by functional magnetic resonance imaging (fMRI), particularly through the connectivity analysis used to characterize reoccurring patterns in the brain known as brain states. However, previous studies have primarily focused on unimodal models, which can hinder optimal dynamic state estimation, especially in cognitive disorders, cases where there may be both structure and function disruption. To better estimate intrinsic brain interactions, it is important to account for the factors shaping this estimation not only in terms of time resolved variability of the connectivity but also regarding the underlying physical pathways between brain regions. However current approaches mostly use deterministic weighting of structural connectivity. To address this, we propose a flexible multimodal connectivity constrained independent component analysis (ICA) model, termed structural connectivity-informed dynamic state estimation (STRiDE), that enhances stability and sensitivity by leveraging white matter structural connectivity and dynamic functional connectivity information. Using this model, we decompose brain interactions into independent, reoccurring multimodal patterns or structural-functional states, guided by maximally independent structural connectivity priors derived from the group level data. We first evaluate our proposed model using a simulation pipeline, showing the approach works as design and improves sensitivity to group differences and enhances robustness to noise. Next, we applied the proposed multimodal model to real dataset including a cohort of subjects with schizophrenia (SZ) and healthy controls (HC). Results demonstrated its potential to enhance group-differences in both connectivity domain and temporal dynamics parameters. Specifically results highlighted disruption within and between sensory and trans-modal domains, through a SZ vs HC comparison. Symptoms severity and cognitive scores statistical analysis specifies their significant association with default mode domain, offering insights into the disrupted functional and neural mechanisms underlying schizophrenia. In addition, temporal interplay of the estimated STRiDEs reveals that visual-related STRiDE is significantly impacted in SZ, regarding the speed of processing score, underscoring the link between visual system and speed of processing. In sum, the STRiDE approach provides a flexible way to link structural and functional connectivity at the network level and represents a general approach for studying multimodal dynamic patterns and leveraging these to study the typical and disordered brain.

Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results revealed that the directed functional connectivity of large-scale brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore trajectories of development in more fine-grained network parcellations and in different populations.

Brain age algorithms, which estimate biological aging from neuroimaging data, are increasingly used as biomarkers for health and disease. However, most algorithms are trained on datasets with limited racial and ethnic diversity, raising concerns about potential algorithmic bias that could exacerbate health disparities. To probe this potential, we evaluated six popular brain age algorithms using data from the Health and Aging Brain Study-Health Disparities (HABS-HD), comprising 1,123 White American, 1,107 Hispanic American, and 678 African American participants, ages [≥]50. Comparing correlations between brain age and chronological age across racial/ethnic groups, relations were consistently weaker for African American participants compared to White and Hispanic American participants across most algorithms (ranging from r=0.51-0.85 for African Americans vs. r=0.57-0.89 for other groups). We also examined error for brain age v. chronological age and found significant differences in median errors across racial/ethnic groups, though specific patterns varied by algorithm. Sensitivity models weighting for age, sex, and scan quality noted similar patterns, with all algorithms maintaining significant differences in correlation or median prediction error between groups. Our findings reveal systematic performance differences in brain age algorithms across racial and ethnic groups, with most algorithms consistently showing reduced algorithm accuracy for African American and/or Hispanic-American participants. These biases, which are likely introduced at multiple stages of algorithm development, could impact clinical utility and diagnostic accuracy. Results highlight the urgent need for more inclusive algorithm development and validation to ensure equitable healthcare applications of neuroimaging biomarkers.

Nearly 7.5% U.S. adults have long COVID. Recent epidemiological studies indicated that long COVID, is significantly associated with subsequent brain structure changes. However, it remains unknown if long COVID is causally associated with brain structure change. Here we applied two Mendelian Randomization (MR) methods - Inverse Variance Weighting MR method (IVW) for correlated instrument variables and Component analysis-based Generalized Method of Moments (PC-GMM) - to examine the potential causal relationships from long COVID to brain structure changes. The MR study was based on an instrumental variable analysis of data from a recent long COVID genome-wide association study (GWAS) (3,018 cases and 994,582 controls), the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) (Global and regional cortical measures, N = 33,709; combined hemispheric subcortical volumes, N = 38,851), and UK Biobank (left/right subcortical volumes, N = 19,629). We found no significant causal relationship between long COVID and brain structure changes. As we gain more insights into long COVID and its long-term health outcomes, future works are necessary to validate our findings and understand the mechanisms underlying the observed associations, though not causal, of long COVID with subsequent brain structure changes.

The diagnosis of neurological disorders requires comprehensive frameworks that incorporate multimodal neuroimaging data while ensuring clinical interpretability. Recent neuroimaging research is focusing on the integration of brain structure and function to reveal some of the prominent alteration caused by a brain disorder at the system level. This work presents a fresh multiscale graph convolutional network (GCN) framework that integrates structural connectivity (SC) from diffusion tensor imaging and functional connectivity (FC) from resting-state fMRI on three different anatomical scales. We present softmax-based decision fusion for cross-modal multiscale integration in our architecture. The preprocessing pipeline improves connectivity representations by means of graph diffusion, topological sparsification, and noise augmentation. Using five-fold cross-valuation, evaluated on a schizophrenia classification dataset, our model achieves 71.59% accuracy, outperforming single-scale methods and conventional machine learning bench-marks. Explainability analysis reveals that different dysconnectivity patterns in schizophrenia patients overlapping with biomarkers reported in literature. The multiscale approach shows complementing insights: coarse scales capture global network changes while finer scales identify localized subcortical disruptions. Combining diagnostic precision with biologically interpretable modeling, this work creates a new paradigm for interpretable brain network analysis.

Sex differences in the size of specific brain structures have been extensively studied but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences brains of males and females. On the other hand, multivariate statistical or machine learning methods that analyse MR images of the whole brain have reported respectable accuracies for the task of distinguishing males from females. However, most existing studies lacked a careful control for brain volume differences between sexes and, if done, their accuracy often declined to 70% or below. This raises questions on the relevance of accuracies achieved without careful control of overall volume. Also the potential applicability is uncertain insofar as the robustness of methods had rarely been tested or they suffered from poor accuracy when applied on a different cohort. We examined how accurately sex can be classified with multivariate methods from gray matter properties of the human brain when correcting for overall brain volume. We also tested, how robust machine learning classifiers are when predicting cross-cohort, i.e. when they are used on a different cohort than they were trained on. Further, we studied how their accuracy depends on the size of the training set. MRI data was used from two population based data sets of 3308 mostly older adults from the Study of Health in Pomerania (SHIP) and 1113 mostly younger adults from the Human Connectome Project (HCP), respectively. Our new open source program BraiNN is based on a 3D convolutional neural network and was compared with a simple logistic regression approach. When using the gold standard method of matching male and female participants for total intracranial volume, BraiNN achieved 86% accuracy when predicting sex on the same (SHIP) cohort and 73% accuracy when cross-predicting on the HCP cohort. Logistic regression achieved an accuracy >90% on the SHIP cohort, but required a large number of training examples to perform well and did not generalize well across cohorts. On the other hand, BraiNN lost less than 2% accuracy when the cohort size was reduced from 3308 to 1274.

Sex-specific characteristics demonstrate a substantial influence on the brain white matter (WM), suggesting distinct structural connectivity patterns between females and males. Diffusion MRI (dMRI) tractography is an important tool in assessing WM connectivity and brain tissue microstructure across different populations. Whole brain tractography analysis for group statistical comparison is a challenging task due to the large number of WM connections. This work studies whole-brain WM connectivity differences between females and males using dMRI tractography. We study a large cohort of 707 subjects from the Human Connectome Project Young Adult dataset. By applying a well-established fiber clustering pipeline and a suprathreshold fiber cluster statistical method, we analyze tracts in the cerebral cortex and understudied pathways like those connecting to the cerebellum. We identify several tracts with significant sex differences in terms of their fractional anisotropy and/or mean diffusivity. These include deep tracts like the arcuate fasciculus, corticospinal tract, and corpus callosum, superficial tracts in the frontal lobe, and cerebellar tracts. Finally, correlation analysis reveals that these WM differences are linked to a range of neurobehavioral measures, with the strongest and most consistent associations observed for motor function, suggesting motor circuits as a potential key focus for future research.

Brain functional networks identified from resting fMRI data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale. In this work, we present an approach using scICA to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales (ICA model orders), which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N>1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into spatially constrained ICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. We also used the msFNC features as input to a classification model in cross-validated hold-out data and also in an independent test data. Visualization of predictive features was performed by evaluating their feature weights. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.

Total intracranial volume (TIV) is a major confounding factor in neuroimaging studies, particularly when studying sex differences in the brain. Different methods have been proposed to adjust for this effect, however, their impact has not been directly studied and compared. In this study, we sought to evaluate the impact of four most commonly used adjustment methods in the literature on the estimations of neuroanatomical sex differences. These methods included: the proportions method, the residuals method, the power corrected proportions method, and adding TIV as a covariate in a regression analysis. Leveraging data from the UK Biobank, we employed a matching approach to devise a gold standard as reference for comparing these methods. To achieve this, we matched the male and female participants based on age and TIV to remove the impact of TIV differences between sexes. We further modeled aging trajectories at the regional level, vertexwise, and voxelwise, using raw and adjusted values, and compared the obtained estimates against the gold standard. We found that across different metrics, adding TIV as a covariate was the best-performing method for removing the effect of TIV, in terms of the correlation between the estimates of the different subsamples and the gold standard as well as the degree of estimation bias. Furthermore, we showed that the commonly used smoothing of the morphometric measures can result in biased estimation of sex differences in these measures. Finally, we showed that while small in effect size, there still remains some neuroanatomically specific uncorrected effects for all adjustment methods.

Brain size measures are well-studied and often treated as a confound in volumetric neuroimaging analyses. Yet their relationship with body anthropometric measures and demographics remains underexplored. In this study, we examined those relationships alongside age- and sex-related differences in global brain volumes. Using brain magnetic resonance imaging (MRI) of healthy participants in the UK Biobank, we derived global measures of brain morphometry, including total intracranial volume (TIV), total brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid (CSF). We extracted these measures using the Computational Anatomy Toolbox (CAT) and FreeSurfer. Our analyses were structured in three approaches: across-sex analysis, sex-specific analysis, and impact of age analysis. Employing machine learning (ML), we found that TIV was strongly predicted by sex (across-sex r = 0.68), reflecting sexual dimorphism. On the other hand, TBV, GMV, WMV, and CSF were more sensitive to age, with higher prediction accuracy when age was included as a feature, highlighting age-related changes in the brain structure, such as fluid expansion. Sex-specific models showed reduced TIV prediction (r {approx} 0.25) but improved TBV accuracy (r {approx} 0.44), underscoring sex-specific body-brain relationships. Anthropometrics enhanced prediction but only subsidiary to age and sex. These findings advance our understanding of brain-body scaling relationships and underscore the necessity of accounting for age and sex in neuroimaging studies of brain morphology.

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

Significant changes occur in brain structure and cognitive abilities during adolescence. Investigating their association can provide insight into brain-based cognitive development, yet previous studies were limited by narrow brain measures, small samples, and lacking focus on age-related variation. Here, we analyzed a large cohort (N = 8,534, age 9-15) from the Adolescent Brain Cognitive Development dataset. Using structural MRI and diffusion imaging, we derived 16 regional structural measures and integrated them via morphometric similarity networks to characterize 16,563 regional, connectivity, and hub features. We applied large-scale computational models to investigate their associations with performance on seven cognitive subtests and general intelligence (g), as well as age-related changes. Brain areas most strongly associated with cognitive ability also showed the greatest age-related variability in these associations, located primarily in the frontal, temporal, and occipital lobes. Structural MRI measures exhibited stronger associations with cognition and greater age-related variability than diffusion-derived metrics, while global hub measures showed stronger and more variable associations than local measures. Overall, our study provides a comprehensive and reliable understanding of brain structure-cognition associations during adolescence.

The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across-MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.

Most psychiatric disorders are highly heritable and associated with altered brain structural and functional patterns. Data fusion analyses on brain imaging and genetics, one of which is parallel independent component analysis (pICA), enable the link of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants in psychiatric disorders, it is usually challenging to reliably separate disorder-related genetic factors from the rest of the genome with the typical size of clinical samples. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources. The sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that the proposed spICA yields improved detection of independent sources and imaging-genomic associations compared to pICA. We applied spICA to gray matter volume (GMV) and single nucleotide polymorphism (SNP) data of 341 unrelated adults, including 127 controls, 167 attention-deficit/hyperactivity disorder (ADHD) cases, and 47 unaffected siblings. We identified one SNP source significantly and positively associated with a GMV source in superior/middle frontal regions. This association was replicated with a smaller effect size in 317 adolescents from ADHD families, including 188 individuals with ADHD and 129 unaffected siblings. The association was found to be more significant in ADHD families than controls, and stronger in adults and older adolescents than younger ones. The identified GMV source in superior/middle frontal regions was not correlated with head motion parameters and its loadings (expression levels) were reduced in adolescent (but not adult) individuals with ADHD. This GMV source was associated with working memory deficits in both adult and adolescent individuals with ADHD. The identified SNP component highlights SNPs in genes encoding long non-coding RNAs and SNPs in genes MEF2C, CADM2, and CADPS2, which have known functions relevant for modulating neuronal substrates underlying high-level cognition in ADHD.

The human claustrum is a bilateral, thin, irregularly shaped gray matter structure located between the striatum and insula. While previous research demonstrated the effect of distinct medical conditions, such as prematurity, schizophrenia, and Alzheimers disease, on claustrum function and structure, it is poorly understood how non-pathologic biological conditions effect the claustrum. This study aimed to investigate the effect of age, hemisphere, and sex on claustrum volume. We used T1-weighted 3 Tesla MRI scans of 3,474 healthy participants ranging from 1 to 80 years of age, deep learning-based automated claustrum segmentation, and a normative modeling approach to delineate lifespan trajectories of claustrum volumes for both hemispheres and sexes. Additionally, ordinary least squares regression analyses were applied to further characterize age, hemisphere, and sex effect. Lifespan analysis revealed a trajectory of rapid claustrum volume increase from infancy to adolescence ([~] 1-15 years, annual growth 39.300 mm3/year), a plateau phase from early to middle adulthood ([~] 15-40 years, annual change 0.153 mm3/year), and a subsequent decline from middle adulthood to old age ([~] 40-80 years, annual decrease 10.325 mm3/year). The right claustrum was on average larger than the left one across all ages. Finally, overall, females had larger total intracranial volume-adjusted claustrum volumes than males across the lifespan. Results demonstrate a distinct effect of age, hemisphere, and sex on claustrum volume. Data provide a comprehensive framework for sex- and hemisphere-sensitive claustrum structure lifespan trajectories relevant for studying neurodevelopmental and neurodegenerative effects on the claustrum.

Recent advances in neuroimaging modeling highlight the importance of accounting for subgroup heterogeneity in population-based neuroscience research through various investigations in large scale neuroimaging data collection. To integrate survey methodology with neuroscience research, we present an imaging data analysis aiming to achieve population generalizability with screened subsets of data. The Adolescent Brain Cognitive Development (ABCD) Study has enrolled a large cohort of participants to reflect the individual variation of the U.S. population in adolescent development. To ensure population representation, the ABCD Study has released the base weights. We estimated the associations between brain activities and cognitive performance using the functional Magnetic Resonance Imaging (fMRI) data from the ABCD Studys n-back working memory task. Notably, the imaging subsample exhibits differences from the baseline cohort in key child characteristics, and such discrepancies cannot be addressed simply by applying the ABCD base weights. We developed new population weights specific to the subsample and included the adjusted weights in the image-on-scalar regression model. We validated the approach through synthetic simulations and applications to fMRI data from the ABCD Study. Our findings indicate that population weighting adjustments influence association estimates between brain activities and cognition, emphasizing the importance of evaluating validity and generalizability in population neuroscience research.

BackgroundSex differences in brain development are well-documented, yet the dynamic coupling between structure and function remains underexplored. We introduce dynamic inter-modality source coupling (dIMSC), extending our previous work to link structural MRI source-based morphometry (SBM) with dynamic functional network connectivity (dFNC). MethodsWe used data from the Adolescent Brain Cognitive Development (ABCD) study (ages 9-11) and combined SBM-derived gray matter sources with sliding-window dFNC. dIMSC was computed as the time-resolved cross-correlation between these modalities to quantify structure-function coupling strength. We evaluated sex differences in these profiles and their interaction with cognitive performance. ResultsSignificant sex-specific patterns emerged: males exhibited stronger positive coupling in sensorimotor regions (postcentral gyrus), while females showed stronger coupling in higher-order associative regions (inferior parietal lobule). These configurations were functionally distinct: higher positive coupling occupancy predicted better crystallized cognition (vocabulary) in females, whereas it predicted better fluid cognition (working memory) in males. ConclusionTogether, these findings suggest that males and females utilize distinct structural-functional configurations to support cognitive processing, males relying on a sensorimotor-anchored organization and females on an associative-anchored one. The dIMSC method advances our earlier work by enabling time-resolved analysis of brain coupling, providing a powerful framework for investigating sex-specific neurodevelopmental mechanisms.

Prader-Willi Syndrome (PWS) is a rare genetic condition with multifaceted physical, behavioural and cognitive difficulties that is characterized by hyperphagia and low executive functioning. Food-seeking behaviours may be moderated by hormonal, cognitive, and psychological factors, and are thought to be mediated in part by functional brain abnormalities. Here, we used an experimental protocol integrating eyes opens resting state magnetoencephalography (MEG) - a high-resolution neurophysiological imaging technique - and neuropsychological profiling to understand the relationship between executive functioning, and intrinsic brain activity & functional connectivity in a prospective, cross-sectional cohort with PWS, and a sex-, age- and BMI-matched control group. We observed lower executive functioning in PWS as well as functional dysconnectivity across multiple channels of brain synchrony - in other words, across multiple frequency bands that mediate communication within and between brain networks - in the visual, attentional, and the default mode networks. Moreover, we found brain-wide changes in the topological structure of brain networks in those with PWS, with increased hubness of functional networks, but decreased centrality. However, none of these measures survived multiple comparison correction after correlating with neuropsychological outcomes, although there were moderate effect sizes (degree of association). This is the first study to combine neuropsychology and neurophysiological imaging to show that functional synchrony in multiple brain networks is dysregulated in PWS.