Brain Networks Research Articles

Brain network alterations in mobile phone use problem severity: A multimodal neuroimaging analysis.

Problematic mobile phone use can disrupt social interaction and well-being, potentially influencing cognitive processes. This study investigated whether mobile phone use problem severity is associated with alterations in the topological organization of brain networks. Rs-fMRI and DTI data were collected from 81 healthy participants. Graph theory analyses were applied. The Mobile Phone Problem Use Scale-10 (MPPUS-10) was used to assess mobile phone use problem severity. Correlation analyses were conducted between each graph metric and questionnaire scores. MPPUS-10 scores correlated with global fMRI metrics: higher scores linked to longer shortest path length (reduced integration) and lower global efficiency (reduced information transfer). Conversely, higher MPPUS-10 scores were correlated with a greater clustering coefficient and higher local efficiency, which reflect increased local connectivity. Furthermore, higher MPPUS-10 scores were associated with a higher sigma value from DTI, indicating altered structural network properties. Some specific brain regions also showed significant correlations with MPPUS-10 scores. These findings indicate that higher mobile phone use problem severity is associated with decreased integration and increased segregation of functional networks, alongside enhanced small-worldness in structural networks. Reduced integration aligns with addiction theories suggesting digital overload worsens network dysfunction, disrupting brain connectivity. Additionally, higher severity was correlated with altered connectivity in multiple regions, such as the precentral gyrus, supplementary motor area, and postcentral gyrus. These regions are associated with motor control, sensorimotor processing, and memory function. Further research is needed to explore whether these findings reflect shifts in the integration and integrity of brain information-processing modules.

Reduced Cerebellar Volumes Associate with P300 Amplitude Attenuation in Children with Clinical High Risk for Psychosis and Early Onset Psychosis.

Patients with psychotic illnesses, including early onset psychosis (EOP), often experience cognitive impairment. The cerebellum is critically involved in neurocognitive processes, yet possible regional alterations in the cerebellum and their associations with behavioral parameters remain largely unexplored in EOP. In this preliminary study, we aimed to investigate structural morphological properties of the cerebellum as well as the supratentorial brain, and how morphological changes in the central nervous system relate to neurocognitive performance in children with EOP and clinical high-risk for psychosis (CHR). We performed whole-brain structural magnetic resonance imaging (MRI) and voxel-based morphological analyses in children with EOP (N = 15), children with CHR (N = 11), and healthy controls (Con, N = 13). An auditory event-related potential (ERP) task to elicit a P300 response was also completed by a subset of children (N = 29) as a measure of neurocognitive functioning. Linear regression analyses were performed to explore relationships between cerebellar volume, cortical thickness, and P300 amplitudes. Volumetric reductions (Con > CHR > EOP) in bilateral Crus I, Crus II, lobule VI and VIIIa, left VIIIb, and right lobules V and IX of the cerebellum were observed (p < 0.05). This downward trend across study cohorts was also evident for rostral middle frontal cortical (RMFC) thickness, and for centroparietal P300 amplitudes. Significant positive correlations among P300 amplitudes and cerebellar volumes were observed (p < 0.05). Significant correlations between P300 amplitudes and RMFC thickness were not present. Robust morphological disruptions in cerebellar subdivisions and frontal subdivisions were quantified in children with EOP. Structural abnormalities in these regions, particularly in the cerebellum, may signify broader brain network disruptions, potentially contributing to neurocognitive dysfunction in EOP.

The exponential distance rule based network model predicts topology and reveals functionally relevant properties of the Drosophila projectome

Abstract Studying structural brain networks has witnessed significant advancement in recent decades. Findings revealed a geometric principle, the exponential distance rule (EDR) showing that the number of neurons decreases exponentially with the length of their axons. This neuron-level information was used to build a region-level EDR network model that was able to explain various characteristics of inter-areal cortical networks in macaques, mice, and rats. The complete connectome of the Drosophila has recently been mapped providing information also about the network of neuropils (projectome). A recent study demonstrated the presence of the EDR in Drosophila. In our study we first revisit the EDR itself, and precisely measure the characteristic decay rate. Next, we demonstrate that the EDR model effectively accounts for numerous binary and weighted properties of the projectome. Our study illustrates that the EDR model is a suitable null model for analyzing networks of brain regions, as it captures properties of region-level networks in very different species. The importance of the null model lies in its ability to facilitate the identification of functionally significant features not caused by inevitable geometric constraints, as we illustrate with the pronounced asymmetry of connection weights important for functional hierarchy.

Cortico-subcortical networks that determine behavioral memory renewal are redefined by noradrenergic neuromodulation

During spatial appetitive extinction learning (EL), rodents learn that previously rewarded behavior is no longer rewarded. Renewal of the extinguished behavior is enabled by re-exposure to the context in which rewarded learning occurred. When the renewal response (RR) is unrewarded, it is rapidly followed by response extinction (RE). Although the hippocampus is known to be engaged, whether this dynamic is supported by different brain networks is unclear. To clarify this, male rats engaged in context-dependent spatial memory acquisition, EL and RR testing in a T-Maze. Fluorescence in situ hybridization disambiguated somatic immediate early gene expression in neuronal somata engaged in RR or RE. Graph analysis revealed pronounced hippocampal connectivity with retrosplenial and prefrontal cortex (PFC) during initial RR. By contrast, RE was accompanied by a shift towards elevated coordinated activity within all hippocampal subfields. Given that β-adrenergic receptors (β-AR) regulate spatial memory, we activated β-AR to further scrutinize these network effects. This enhanced RR and prevented RE. Effects were associated with initially increased thalamic-hippocampus activity, followed by a decrease in hippocampal intraconnectivity and the predominance of network activity within PFC. Our findings highlight a critical hippocampal-cortical-thalamic network that underpins renewal behavior, with noradrenergic neuromodulation playing a pivotal role in governing this circuit’s dynamics.

Computational memory capacity predicts aging and cognitive decline

Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

Adding Space to Random Networks of Spiking Neurons: A Method Based on Scaling the Network Size.

Many spiking neural network models are based on random graphs that do not include topological and structural properties featured in real brain networks. To turn these models into spatial networks that describe the topographic arrangement of connections is a challenging task because one has to deal with neurons at the spatial network boundary. Addition of space may generate spurious network behavior like oscillations introduced by periodic boundary conditions or unbalanced neuronal spiking due to lack or excess of connections. Here, we introduce a boundary solution method for networks with added spatial extension that prevents the occurrence of spurious spiking behavior. The method is based on a recently proposed technique for scaling the network size that preserves first- and second-order statistics.

Identification of key brain networks and functional connectivities of successful aging: A surface-based resting-state functional magnetic resonance study.

Successful aging (SA) refers to the ability to maintain high levels of physical, cognitive, psychological, and social engagement in old age, with high cognitive function being the key to achieving SA. To explore the potential characteristics of the brain network and functional connectivity (FC) of SA. Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly, which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination (MMSE). The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf, and the brain functional network was conducted by DPABINet. The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA. The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance. The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score, and the FC of the right supramarginal gyrus and right temporal pole: Middle temporal gyrus was the only one decreased and negatively related to MMSE score. All 17 significantly altered FCs of SA were taken into the support vector machine model, and the area under the curve was 0.895. The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.

Identification of topological alterations using microstate dynamics in patients with infantile epileptic spasms syndrome

Infantile epileptic spasm syndrome (IESS) is characterized by clustered epileptic spasms and hypsarrhythmia on electroencephalography (EEG). This study aimed to investigate the temporal dynamics and dynamic synchronization of neural networks in IESS using EEG microstate analysis of interictal recordings from 49 healthy controls (HC) and 42 patients with IESS. Five microstate maps were identified, and features including the global explained variance (GEV), mean correlation, occurrence, time coverage, mean time duration, and transition probabilities were extracted. Significant differences were observed in patients with IESS compared to HCs, with increased microstate features and transition probabilities in microstates A and B, and reduced values in microstates D and E. Furthermore, in patients with structural/metabolic etiologies, microstate A demonstrated heightened microstate features and transition probabilities compared to genetic/unknown etiologies. These microstate characteristics enabled accurate classification of IESS versus HCs and differentiation between structural/metabolic and genetic/unknown etiologies. The altered microstate topologies in IESS reflect disruptions in brain network dynamics, suggesting that specific microstate features and transition probabilities could serve as potential diagnostic biomarkers. This study underscores the potential of EEG microstate analysis in understanding neural dysfunction, particularly in structural/metabolic subtypes of IESS.

Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.

Central alterations of brain networks in patients with optic neuritis: a resting state fMRI study.

To assess the alterations in the resting-state function connections between the two cerebral hemispheres in patients with optic neuritis (ON) and healthy controls (HCs). A total of 12 ON patients (six males and six females) and 12 HCs (six males and six females) who were highly matched for sex, age, and educational level were recruited. They underwent functional magnetic resonance imaging (fMRI), testing and brain activities were assessed using the degree centrality (DC) method. Correlation analysis between the mean DC values in specific brain areas and behavior performances was analyzed as well. Linear correlations between A anxiety scale (AS) and depression scale (DS) values and DC values in brain regions of patients with ON were also analyzed. The areas that showed a higher DC value in ON patients were the right angular gyrus and bilateral precuneus, while the left insula and left superior temporal gyrus (LSTG) were regions that presented a lower DC value in ON patients. A receiver operating characteristic (ROC) curve analysis confirmed the accuracy of the area under the curve (AUC) assessment. Linear analysis showed anxiety scale (AS) and depression scale (DS) values in the left insula were both negatively correlated with DC values, while best corrected visual acuity logMAR-R (BCVA logMAR-R) showed a negative correlation with DC in the LSTG. The study explores altered brain activities of specific regions in patients with ON. The results provide clues for revealing the underlying mechanism of ON development.

A Case Report of Dyke-Davidoff-Masson Syndrome in the Philippines: Review of the Literature and a Discussion on Neuroplasticity and Brain Network Connectivity

A Case Report of Dyke-Davidoff-Masson Syndrome in the Philippines: Review of the Literature and a Discussion on Neuroplasticity and Brain Network Connectivity

Over-integration of visual network in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant functional connectivity in large-scale brain networks. However, it is unclear how the network dysfunction is characterized by imbalance or derangement of network modular interaction in MDD patients and whether this disruption is associated with gene expression profiles. We included 262 MDD patients and 297 healthy controls, embarking on a comprehensive analysis of intrinsic brain activity using resting-state functional magnetic resonance imaging (R-fMRI). We assessed brain network integration by calculating the Participation Coefficient (PC) and conducted an analysis of intra- and inter-modular connections to reveal the dysconnectivity patterns underlying abnormal PC manifestations. Besides, we explored the potential relationship between the above graph theory measures and clinical symptoms severity in MDD. Finally, we sought to uncover the association between aberrant graph theory measures and postmortem gene expression data sourced from the Allen Human Brain Atlas (AHBA). Relative to the controls, alterations in systemic functional connectivity were observed in MDD patients. Specifically, increased PC within the bilateral visual network (VIS) was found, accompanied by elevated functional connectivities (FCs) between VIS and both higher-order networks and Limbic network (Limbic), contrasted by diminished FCs within the VIS and between the VIS and the sensorimotor network (SMN). The clinical correlations indicated positive associations between inter-VIS FCs and depression symptom, whereas negative correlations were noted between intra-VIS FCs with depression symptom and cognitive disfunction. The transcriptional profiles explained 21–23.5% variance of the altered brain network system dysconnectivity pattern, with the most correlated genes enriched in trans-synaptic signaling and ion transport regulation. These results highlight the modular connectome dysfunctions characteristic of MDD and its linkage with gene expression profiles and clinical symptomatology, providing insight into the neurobiological underpinnings and holding potential implications for clinical management and therapeutic interventions in MDD.

Altered resting-state network connectivity in internet gaming disorder

BackgroundThe growing popularity of internet gaming among adolescents and young adults has driven an increase in both casual and excessive gaming behavior. Nevertheless, it remains unclear how progressive increases in internet gaming engagement led to changes within and between brain networks. This study aims to investigate these connectivity alterations across varying levels of gaming involvement.MethodsIn this cross-sectional study, 231 participants were recruited and classified into three groups according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for Internet Gaming Disorder (IGD): IGD group, highly engaged gaming(HEG) group, and lowly engaged gaming (LEG) group. Resting-state fMRI data from 217 participants (143 males, 74 females) were included in the final analysis. Independent component analysis was used to examine differences in intra- and inter-network functional connectivity (FC)across the three groups.ResultsNo significant differences were found in intra-network FC across the three groups. However, significant inter-network differences between the dorsal attention network(dAN)and the visual network (VN) among the three groups were observed. The HEG group exhibited significantly higher dAN-VN functional network connectivity (FNC) compared to the LEG group. Linear correlation analyses showed no significant correlation between the dAN-VN FNC values and IGD-20T scores.ConclusionThroughout the development of IGD, increasing levels of engagement are associated with a rise and subsequent decline in FNC of DAN-VN. This pattern may reflect top-down attentional regulation in the early stages of addiction, followed by attentional bias as addiction progresses.

Exploring Human Brain Metabolism via Genome-Scale Metabolic Modeling with Highlights on Multiple Sclerosis.

Cerebral dysfunctions give rise to a wide range of neurological diseases due to the structural and functional complexity of the human brain stemming from the interactive cellular metabolism of its specific cells, including neurons and glial cells. In parallel with advances in isolation and measurement technologies, genome-scale metabolic models (GEMs) have become a powerful tool in the studies of systems biology to provide critical insights into the understanding of sophisticated eukaryotic systems. In this study, brain cell-specific GEMs were reconstructed for neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte precursor cells by integrating single-cell RNA-seq data and global Human1 via a task-driven integrative network inference for tissues (tINIT) algorithm. Then, intercellular reactions among neurons, astrocytes, microglia, and oligodendrocytes were added to generate a combined brain model, iHumanBrain2690. This brain network was used in the prediction of metabolic alterations in glucose, ketone bodies, oxygen change, and reporter metabolites. Glucose supplementation increased the subsystems' activities in glycolysis, and ketone bodies elevated those in the TCA cycle and oxidative phosphorylation. Reporter metabolite analysis identified L-carnitine and arachidonate as the top reporter metabolites in gray and white matter microglia in multiple sclerosis (MS), respectively. Carbamoyl-phosphate was found to be the top reporter metabolite in primary progressive MS. Taken together, single and integrated iHumanBrain2690 metabolic networks help us elucidate complex metabolism in brain physiology and homeostasis in health and disease.

Brief sleep disruption alters synaptic structures among hippocampal and neocortical somatostatin-expressing interneurons.

Brief sleep loss alters cognition and synaptic structures of principal neurons in hippocampus and neocortex. However, while in vivo recording and bioinformatic data suggest that inhibitory interneurons are more strongly affected by sleep loss, it is unclear how sleep and sleep deprivation affect interneurons' synapses. Disruption of the SST+ interneuron population seems to be a critical early sign of neuropathology in Alzheimer's dementia, schizophrenia, and bipolar disorder - and the risk of developing all three is increased by habitual sleep loss. We aimed to test how the synaptic structures of SST+ interneurons in various brain regions are affected by brief sleep disruption. We used Brainbow 3.0 to label SST+ interneurons in the dorsal hippocampus, prefrontal cortex, and visual cortex of male SST-CRE transgenic mice, then compared synaptic structures in labeled neurons after a 6-h period of ad lib sleep, or gentle handling sleep deprivation (SD) starting at lights on. Dendritic spine density among SST+ interneurons in both hippocampus and neocortex was altered in a subregion-specific manner, with increased overall and thin spine density in CA1, dramatic increases in spine volume and surface area in CA3, and small but significant changes (primarily decreases) in spine size in CA1, PFC and V1. Our suggest that the synaptic connectivity of SST+ interneurons is significantly altered in a brain region-specific manner by a few hours of sleep loss. This suggests a cell type-specific mechanism by which sleep loss disrupts cognition and alters excitatory-inhibitory balance in brain networks.

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Changes in the functional brain networks and graph theory analysis of patients with epilepsy and comorbid migraine without aura.

This study aims to evaluate the changes in the functional brain networks and graph theory analysis of patients with epilepsy and comorbid migraine without aura (EM). We included 30 patients with EM, 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. Resting-state functional magnetic resonance imaging was performed to obtain imaging data. The fractional amplitude of low-frequency fluctuations (fALFF) value of the left superior temporal gyrus was higher in the EM group than in the HC group. The regional homogeneity (ReHo) values of the right orbital superior frontal gyrus were higher in the EM group than in the EC group. The functional connectivity between left superior occipital gyrus and left orbital part of the superior frontal gyrus, left middle temporal gyrus, right orbital part of middle frontal gyruss, left medial superior frontal gyrus (cluster 2), right middle frontal gyrus, left middle frontal gyrus, left median cingulate and paracingulate gyri was enhanced in the EM group compared with the HC group; the functional connectivity between the left superior occipital and left medial superior frontal gyri (cluster 1) was weakened. The functional connectivity was weakened between the posterior default mode network (pDMN) and dorsal sensorimotor network (dSMN) and the left frontoparietal network (LFPN) and right frontoparietal network. The functional connectivity between pDMN and LFPN was enhanced in the EM group compared with the EC group. Graph theory analysis revealed that the area under the curve of the standardized characteristic path length of the EM group was smaller than that of the HC and EM groups. The abnormal functional brain networks associated with pain regulation, and changes in topological properties may be involved in the mechanisms underlying migraine without aura occurrence in patients with epilepsy.

Ventral tegmental area dopamine neural activity switches simultaneously with rule representations in the medial prefrontal cortex and hippocampus.

Multiple brain regions need to coordinate activity to support cognitive flexibility and behavioral adaptation. Neural activity in both the hippocampus (HPC) and medial prefrontal cortex (mPFC) is known to represent spatial context and is sensitive to reward and rule alterations. Midbrain dopamine (DA) activity is key in reward seeking behavior and learning. There is abundant evidence that midbrain DA modulates HPC and PFC activity. However, it remains underexplored how these networks engage dynamically and coordinate temporally when animals must adjust their behavior according to changing reward contingencies. In particular, is there any relationship between DA reward prediction change during rule switching, and rule representation changes in mPFC and CA1? We addressed these questions using simultaneous recording of neuronal population activity from the hippocampal area CA1, mPFC and ventral tegmental area (VTA) in male TH-Cre rats performing two spatial working memory tasks with frequent rule switches in blocks of trials. CA1 and mPFC ensembles showed rule-specific activity both during maze running and at reward locations, with mPFC rule coding more consistent across animals compared to CA1. Optogenetically tagged VTA DA neuron firing activity responded to and predicted reward outcome. We found that the correct prediction in DA emerged gradually over trials after rule-switching in coordination with transitions in mPFC and CA1 ensemble representations of the current rule after a rule switch, followed by behavioral adaptation to the correct rule sequence. Therefore, our study demonstrates a crucial temporal coordination between the rule representation in mPFC/CA1, the dopamine reward signal and behavioral strategy.Significance Statement This study examines neural activity in mammalian brain networks that support the ability to respond flexibly to changing contexts. We use a rule-switching spatial task to examine whether the key reward-responsive and predictive dopamine (DA) activity changes in coordination with changes in rule representations in key cognitive regions, the medial prefrontal cortex (mPFC) and hippocampus. We first established distinct rule representations in mPFC and hippocampus, and predictive coding of reward outcomes by DA neuronal activity. We show that the rule-specific DA reward prediction after a rule switch develops in temporal coordination with changes in rule representations in mPFC, eventually leading to behavioral changes. These results thus provide an integrated understanding of reward prediction, cognitive representations of rules and behavioral adaptation.

Parsing the heterogeneity of social motivation in autism

BackgroundSocial motivation is posited as a key factor in the expression of the autism phenotype. However, lack of precision in both conceptualization and measurement has impeded a thorough understanding of its diverse presentation and associated outcomes. This study addresses this gap by identifying subgroups of autism characterized by deficits in distinct facets of social motivation, relative to normative benchmarks.MethodsData were from 509 participants with autism, aged 5‐to‐21 years (M = 10.43, SD = 3.67; 81% male), enrolled in the Healthy Brain Network. Latent profile analysis was employed to identify subgroups characterized by unique configurations of reticence, seeking, and maintaining facets of social motivation, derived from a comprehensive multi‐instrument factor analysis of symptom and screening measures. Pearson's chi‐square tests and one‐way analysis of variance were performed to explore subgroup differences in demographic characteristics, cognitive abilities, co‐occurring psychopathologies, and other aspects of social functioning.ResultsFour distinct subgroups were identified: Engaged (n = 247), exhibiting the fewest challenges across each area; Inhibited (n = 143), characterized by high reticence, mild challenges in seeking, and few challenges in maintaining; Aloof (n = 68), characterized by challenges with seeking and maintaining but relatively low reticence; and Avoidant (n = 52), characterized by the highest challenges across all areas. Subgroups did not differ in terms of chronological age or sex. The Engaged subgroup exhibited the fewest challenges in other aspects of social functioning and co‐occurring psychopathologies, while the Avoidant subgroup exhibited the greatest challenges, and with the Inhibited and Aloof profiles falling in between.ConclusionsThis study highlights the heterogeneous nature of deficits in social motivation in autism relative to normative benchmarks, suggesting potential avenues for tailored interventions aimed at addressing the specific challenges experienced by individuals within each subgroup. Nevertheless, there remains a need to develop more refined measurement tools capable of capturing even finer‐grained aspects and diverse expressions of social motivation, facilitating further characterization of individual differences across diagnostic boundaries.

Brain Imaging Phenotypes Associated with Polygenic Risk for Essential Tremor.

Essential tremor (ET) is a common movement disorder with a strong genetic basis. Magnetic resonance imaging (MRI), particularly diffusion-weighted MRI (dMRI) and T1 MRI, have been used to identify brain abnormalities of ET patients. However, the mechanisms by which genetic risk affects the brain to render individuals vulnerable to ET remain unknown. We aimed to understand how ET manifests by identifying presymptomatic brain vulnerabilities driven by ET genetic risk. We probed the vulnerability of healthy people towards ET by investigating the association of morphometry, and white and grey matter dMRI with ET in polygenic risk scores (PRS) in roughly 30,000 individuals from the UK Biobank (UKB). Our results indicate significant effects of ET-PRS with mean diffusivity, fractional anisotropy, free water, radial diffusivity, and axial diffusivity in white matter tracts implicated in movement control. We found significant associations between ET-PRS and grey matter tissue microstructure, including the red nucleus, caudate, putamen, and motor thalamus. ET-PRS was associated with reduced grey matter volumes in several cortical and subcortical areas including the cerebellum. Identified anomalies included networks connected to surgical sites effective in ET treatment. Finally, in a secondary analysis, low PRS individuals compared with a small number of patients with ET (N = 49) in the UKB revealed many structural differences. Brain structural vulnerabilities in healthy people at risk of developing ET correspond to areas known to be involved in the pathology of ET. High genetic risk of ET seems to disrupt ET brain networks even in the absence of overt symptoms of ET. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.