Structural Brain Networks Research Articles

In Multiple Sclerosis, inflammation and neurodegeneration disrupt structural and functional brain networks. While the association between structural connectivity and disability is rather clear, functional connectivity changes are not yet characterised as a physiological response to the disease, as functionally meaningful adaptation or as a deceptive response. We explored the topology of brain networks of 65 Multiple Sclerosis patients over up to seven years in comparison to 59 controls. Connectomes based on probabilistic tractography from diffusion weighted imaging and resting-state MRI, were analysed with graph theory. The hub disruption index estimated connectivity perturbation in relation to the network hierarchy. In controls, we observed a transient increase in functional hub connectivity in the 5th and 6th age decade as a response to a subtle diffuse loss of structural connectivity, before structural and functional connectomes show a pronounced loss of hub connectivity. In Multiple Sclerosis, structural hub disruption was present from the disease onset while the transient upregulation of functional hub connectivity in the middle age was lacking. Patients seem to transition directly into an exhausted hub connectivity configuration. However, we observed the transient functional reorganisation of hubs in the first years after disease onset. Multiple Sclerosis patients present a probable physiological response to structural connectivity loss very early in the disease, potentially leading to an accelerated hub overload with accelerated neurodegeneration. The onset of chronic progression in the 5th age decade might be partially driven by the absence of the physiological increased hub connectivity observed in healthy individuals.

Cerebral white matter changes and their correlation with cognitive dysfunction and clinical indicators in patients with early coal workers' pneumoconiosis based on MR-diffusion spectrum imaging.

Influences of early sexual trauma on brain structural connectome in panic disorder and their relationship with short- and long-term treatment responses.

Structural connectomic signatures of childhood maltreatment across affective and psychotic disorders.

SCN1A rs3812718 polymorphism modulates structural and functional brain networks in TLE: A multimodal imaging-genomics study.

ABSTRACT Background The alterations in the brain structural network associated with surgical resection of temporal gliomas remain unclear. Methods Based on graph algorithms, we examined neuroimaging network properties from 6 healthy controls and 23 patients with temporal gliomas. We compared the topological network, fractional anisotropy (FA), and graph metrics of each patient before and after surgery. Results In the preoperative analysis, patients showed graph metrics decrease, though FA and FN showed no significant difference. In the postoperative analysis, the topological network mainly changed in the temporal, frontal, and basal ganglia regions, while FA declined mainly in the occipital and frontal regions. Regarding graph metrics, the brain network showed no significant difference in small-worldness, global efficiency, and local efficiency. However, the degree centrality mainly increased on the affected side after surgery and decreased in multiple regions (i.e. amygdala). Similarly, nodal efficiency increased in the frontal region, including middle frontal gyrus (MFG) and superior medial frontal gyrus (SFGmed), and declined mainly in insula (INS), inferior temporal gyrus (ITG), and amygdala (AMYG). Conclusion Surgical resection of temporal gliomas can decrease FA and FN, indicating the damage to the integrity of WM tracts. Though the global structure shows no significant differences after surgery, the decreases in degree centrality and nodal efficiency in some regions may indicate the decline of brain function. The compensatory increases in these graph metrics in some regions may show the recovery from glioma invasion. Our structural analysis may provide a new method to assess post-operative recovery in patients with temporal gliomas.

This study aims to understand the supraspinal regulation of balance control in chronic ankle instability (CAI) by characterizing the large-scale communication and interaction via brain functional network topology in CAI and establish the association between topological properties and dynamic balance performance. In this cross-sectional design study, 40 CAI individuals and 39 healthy control (HC) individuals were enrolled. To assess the dynamic balance, the Y-balance test was utilised. To explore the topological structure of brain networks, graph theory was used to analyse resting-state functional MRI data. The CAI group had lower normalized reach distances in the Y-balance test than HC. Compared to HC, CAI exhibited remarkably lower nodal degree centrality (Dc) and higher nodal shortest path length (NLp) within the sensorimotor network (SMN), particularly in the precentral gyrus, temporal cortex, and pre-supplementary motor area of the right hemisphere. CAI showed reduced NLp and increased nodal efficiency in the posterior cingulate cortex of the left hemisphere, a hub region of the default mode subnetwork (DMN). In CAI, high Dc and low NLp in the precentral gyrus of the right hemisphere were substantially correlated to poor performance of the Y-balance test, but not in HC. CAI individuals demonstrated diminished regional processing capability within the SMN and a potential compensatory increase in nodal efficiency within the DMN, which are critical to maintain safe balance in this cohort. These alterations in supraspinal networks could be an effective target for rehabilitation and management in CAI.

Brain network architecture is anticipated to influence future grey matter loss in individuals at Clinical High Risk (CHR) for psychosis. However, existing studies on grey matter structural network properties in CHR are scarce and constrained by small sample sizes. Here, we examined network topology differences comparing a) CHR versus healthy controls (HC); b) CHR who transitioned to psychosis (CHR-T) versus those who did not (CHR-NT); and c) different subsyndromes. We included structural scans from 1842 CHR individuals and 1417 HC individuals from 31 sites within the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. At the global level, CHR individuals exhibited lower structural covariance (q < 0.001; Cohen's d = 0.164) and less optimal structural network configuration than HC (lower global efficiency and clustering coefficient, d = 0.100,0.087, qs <= 0.027). Though no global difference between CHR-T and CHR-NT, network distinctiveness of the frontal and temporal surface area networks was higher in CHR-T than CHR-NT (d = 0.223,0.237) and HC (d = 0.208,0.219) (qs < 0.001). Network distinctiveness of the frontal cortical thickness network was lower in CHR-T (d = 0.218, q < 0.001) than CHR-NT and HC (d = 0.165, q < 0.001). Importantly, higher network distinctiveness was associated with worse positive symptoms in CHR-NT (frontal surface area, q = 0.008, R2 = 0.013) and at trend with worse negative symptoms in CHR-T (frontal thickness, q = 0.063, R2 = 0.049). Further, the brief intermittent psychotic syndrome subgroup showed more severe network alterations. Together, brain structural networks inform symptoms and the risk of transition to psychosis in CHR individuals.

ABSTRACTEpilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges. Seizure detection via EEG signals has progressed, but two main challenges are still encountered. First, EEG data can be distorted by physiological factors and external variables, resulting in noisy brain networks. Static adjacency matrices are typically used in current mainstream methods, which neglect the need for dynamic updates and feature refinement. The second challenge stems from the strong reliance on long‐range dependencies through self‐attention in current methods, which can introduce redundant noise and increase computational complexity, especially in long‐duration data. To address these challenges, the Attention‐based Adaptive Graph ProbSparse Hybrid Network (AA‐GPHN) is proposed. Brain network structures are dynamically optimised using variational inference and the information bottleneck principle, refining the adjacency matrix for improved epilepsy classification. A Linear Graph Convolutional Network (LGCN) is incorporated to focus on first‐order neighbours, minimising the aggregation of distant information. Furthermore, a ProbSparse attention‐based Informer (PAT) is introduced to adaptively filter long‐range dependencies, enhancing efficiency. A joint optimisation loss function is applied to improve robustness in noisy environments. Experimental results on both patient‐specific and cross‐subject datasets demonstrate that AA‐GPHN outperforms existing methods in seizure detection, showing superior effectiveness and generalisation.

The multidimensional nature of schizophrenia requires a comprehensive exploration of the functional and structural brain networks. While prior research has provided valuable insights into these aspects, our study goes a step further to investigate the reconfiguration of the hierarchy of brain dynamics, which can help understand how brain regions interact and coordinate in schizophrenia. We applied an innovative thermodynamic framework, which allows for a quantification of the degree of functional hierarchical organisation by analysing resting state fMRI-data. Our findings reveal increased hierarchical organisation at the whole-brain level and within specific resting-state networks in individuals with schizophrenia, which correlated with negative symptoms, positive formal thought disorder and apathy. Moreover, using a machine learning approach, we showed that hierarchy measures allow a robust diagnostic separation between healthy controls and schizophrenia patients. Thus, our findings provide new insights into the nature of functional connectivity anomalies in schizophrenia, suggesting that they could be caused by the breakdown of the functional orchestration of brain dynamics.

BackgroundNeurological conditions account for millions of deaths per year and induce long-lasting cognitive impairments. The disruption of structural brain networks predicts the emergence of cognitive impairments in stroke cases, but the role of the white matter in modeling longitudinal behavioral trajectories in glioma patients is understudied.MethodsWe analyzed 486 intracranial brain stimulations from 297 patients (age range 37–40, male ratio 53–64% depending on the functional categories) along with functional and structural brain connectivity data from over 1750 healthy individuals, to create a network mapping method able to identify the neural substrate causally involved in language production. We tested the validity of our procedure by (i) quantifying the spatial correspondence between white matter metabolic and hemodynamic spontaneous activity, measured via resting-state functional Magnetic Resonance Imaging and [18 F]-fluorodeoxyglucose functional Positron Emission Tomography (respectively); (ii) predicting unseen intracranial stimulations points; (iii) modeling the severity of stroke-induced aphasia (n = 105) and the longitudinal recovery of language abilities in glioma patients (n = 42, 3 timepoints).ResultsWe show that spontaneous white matter hemodynamic oscillations map into metabolic fluctuations. We also demonstrate that the integration of patient-specific intracranial stimulation points and normative human connectivity data (i) is predictive of unseen stimulation points; (ii) provides better estimates than total lesion volume in predicting the severity of stroke-induced aphasia symptoms; (iii) models post-operative language recovery trajectories better than state-of-the-art clinical measures in glioma patients.ConclusionsThis work presents a data-driven and neurobiologically grounded tool for modeling cognitive and neurological impairments in terms of network disruption, demonstrating improved precision over existing approaches.

Adolescent depression presented higher risk of suicide than adult depression. However, the neurophysiological mechanisms underlying this phenomenon have not been elucidated. We aimed to identify structural covariance network alterations in depressed adolescents with suicidal behaviors to provide novel neuroimaging evidence for this condition. 64 first-episode, treatment-naïve depressed adolescent patients with suicidal behaviors and 48 healthy controls were enrolled. Nonnegative matrix factorization was used to identify the structural covariance networks. The Kullback-Leibler divergence method was applied to estimate the interregional relationships between the altered brain networks. Correlation analyses were conducted between altered brain networks and clinical characteristics. Patients had lower gray matter volumes in the anterior default mode network (DMN), visual network, sensorimotor network, and right executive control network than healthy controls. Morphological connections were altered in the anterior DMN, visual network, and right executive control network in patients. Correlation analyses revealed negative associations between morphological connections in anterior DMN-visual networks and illness duration in the patient group. This study revealed abnormal gray matter attributes in the anterior DMN, visual network, sensorimotor network, and executive control network in first-episode and treatment-naïve adolescent depression with suicide, which might reflect disease traits and provide essential neurobiological evidence for behavioral disturbances in depression.

Abstract Our understanding of brain function has shifted from a focus on localized processing to an emphasis on dynamic interactions among distributed neuronal assemblies connected through structural networks. The resting state provides an ideal condition to study these processes, free from task-related influences. Early work highlighted the role of electrophysiological oscillations in facilitating long-range synchronization, while later neuroimaging studies revealed large-scale networks characterized by correlated hemodynamic activity. Multimodal approaches have linked these hemodynamic signals to their electrophysiological origins, offering insights into the neural basis of resting-state connectivity. Electrophysiological studies also show that synchronization patterns evolve rapidly, underscoring the brain’s dynamic nature. These oscillatory changes in distributed networks are thought to support behavioral flexibility by modulating task representations in real time. Importantly, altered oscillatory dynamics are implicated in a range of neuropsychiatric and neurological disorders, and neuromodulatory interventions often aim to normalize oscillatory patterns. This review synthesizes evidence from electrophysiology and neuroimaging on resting-state brain dynamics, with a focus on synchronized oscillatory activity. We will examine how oscillations contribute to long-range neuronal communication, discuss models describing mechanisms underlying functional interactions between distant regions, and highlight the promise of multimodal approaches for clarifying the temporal structure of brain networks and their relevance to flexible cognition.

Neuroimaging study on brain structural network topological properties affected by obesity in children and adolescents.

Integrating structural and functional brain features to classify major depressive disorder: a multi modal approach.

The functional and structural language-associated brain network in patients with temporal lobe epilepsy and atypical language organization.

Neuroimaging studies reveal correlated brain activity across distant regions, suggesting underlying mechanisms that constrain brain function beyond the complex interactions between neurons. Despite these findings, the origins of these patterns and their alterations in neurological disorders remain unclear. Current literature suggests that these patterns could be explained by standing waves resonating within the brain, similar to the vibrational modes observed in musical instruments. Studies have successfully reconstructed brain activity by superimposing resonance modes predicted from the brain's surface mesh or network structure. However, the role of the brain's mechanical properties beyond mere geometry and connectivity-such as tissue rigidity and viscosity-, remains largely unexplored. This work aims to fill that gap by demonstrating that the shape of brain modes is also influenced by the physical properties of brain materials, providing a possible mechanistic explanation for alterations observed across cognitive states and mental conditions, when brain shape and connectivity remain unchanged. The brain's eigenmodes and corresponding eigenfrequencies were analyzed through finite element simulations in Abaqus, incorporating distinct mechanical properties for various brain structures. This study confirms that the brain's resonance modes are influenced by these properties and highlights similarities between the simulated eigenmodes and fMRI patterns observed in human brains.

BackgroundThe thalamus, along with its component nuclei, possesses extensive connections with various brain regions and is engaged in diverse functions. However, it is unknown whether the gray matter volume (GMV) covariance networks of thalamic subfields are selectively affected in chronic capsular stroke.MethodsWe recruited 45 patients with chronic right capsular strokes (CS) and 93 normal controls (NC) from three centers. The thalamus was segmented into 25 subfields using FreeSurfer (v7.1.1). A general linear model was applied to investigate intergroup differences in the GMV covariance network of each thalamic subfield with each voxel of the entire brain between CS and NC, correcting for confounders such as age, gender, total intracranial volume (TIV), and scanners (voxel-wise p < 0.001, cluster-wise FWE corrected p < 0.05).ResultsOur findings revealed that all 25 ipsilesional thalamic subfields in CS were atrophied (p < 0.05, FDR correction). Among these, 16 ipsilesional thalamic subfields (including AV, LD, LP, VLa, VLp, VPL, VM, CeM, CL, MDm, LGN, PuM, PuI, CM, Pf, and Pt) exhibited significantly subfield-specific increased GMV covariance connectivity with the anterior orbital gyrus, superior occipital gyrus, calcarine, anterior cingulate cortex, precentral gyrus, and other regions. Additionally, although none of the contralesional thalamic subfields demonstrated regional GMV changes, 3/25 showed subfield-specific increased GMV covariance connectivity with the ipsilesional anterior orbital gyrus and subcortex.ConclusionThe GMV covariance networks of thalamic subfields are selectively involved in patients with chronic capsular stroke, which affect not only the ipsilesional thalamic subfields but also the contralesional ones.

ABSTRACTBackgroundMigraine is a neurovascular disease associated with significant morbidity and disability, but its underlying pathophysiology remains elusive. Functional alterations within the brain are frequently observed in individuals with migraine, whereas structural changes are less frequently documented. This study was primarily designed to investigate topological abnormalities in brain structural networks in patients with migraine using structural magnetic resonance imaging.MethodsGraph theoretical analysis was used to compare global and regional topological properties of grey matter structural networks in 37 migraine patients and 44 age‐, gender‐, and education‐matched healthy controls. Structural correlation networks were constructed for both groups on the basis of measurements of grey matter volume.ResultsA statistically significant difference was observed in the scores of Self‐rating Anxiety Scales (SAS) and Self‐rating Depression Scales (SDS) between migraine patients and healthy controls. The brain networks of patients exhibited significantly increased path length, decreased clustering coefficient, and small‐worldness at the global level. At the regional level, brain regions with changes in node degree/betweenness centrality in migraine patients were predominantly located in the left cuneus, the left fusiform gyrus, the left precuneus, the right precentral gyrus, the right middle frontal gyrus, and the bilateral lingual gyrus.ConclusionThe findings of this research indicate that the topological organisation is less efficient in individuals who experience migraine. This may provide new insight into the pathogenesis of migraine from a structural perspective.Trial Registration: Chinese Clinical Trial Registry, ChiCTR2000033995SignificanceThe present study addresses the central pathogenesis of migraine and shows at a structural level that global brain properties are altered in migraine patients and that regional properties of specific brain regions also show abnormalities. This provides new ideas and an objective basis for the future diagnosis and treatment of migraine.

Sleep-related problems (SRP) in childhood are common and clinically relevant yet their underlying neural mechanisms and links to future mental health outcomes remain poorly understood. Here, we investigated how distinct dimensions of SRP relate to multimodal brain structure and function in preadolescents, and whether these neural signatures predict trajectories of mental health difficulties. We employed multivariate mapping to investigate the relationship between structural and functional brain network patterns and various dimensions of SRP in the Adolescent Brain Cognitive Development (ABCD) dataset. Moreover, we explored whether and how the identified multimodal brain signatures could predict the trajectory of internalizing and externalizing behavior difficulties over a two-year follow-up. Our multivariate analysis revealed two robust dimensions of SRP: a general sleep disturbance dimension and a hypersomnolence and parasomnia dimension. Each was associated with partially distinct patterns of brain morphology and functional connectivity, consistent with their differential alignment along the hierarchical organization of cortical neurodevelopment maps. However, both dimensions shared common disruptions in the somatosensory, attention, and default mode networks. We further observed that only these neural patterns associated with the general sleep disturbance dimension predict the longitudinal trajectories of internalizing/externalizing symptoms. Our findings enhance the understanding of the neurobiological mechanisms underlying dimensions of SRP in preadolescence and could inform brain-based intervention and treatment programs to improve sleep-related and mental health–related outcomes across development.