Brain Network Hubs Research Articles

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework.

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder.

Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.

The impact of input node placement in the controllability of structural brain networks

Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.

Prognostic prediction of the longitudinal cognitive trajectory of amyloid‐positive patients with mild dementia

AbstractBackgroundPrediction of longitudinal cognitive decline for patients with mild dementia using baseline characteristics will be useful for designing optimal clinical trials and real‐world patient monitoring. This can be accomplished via machine‐learning models using baseline clinical characteristics. Adding plasma pTau181 and brain region volumes may improve the predictions.MethodThe training cohort (TC) included 905 amyloid‐positive (A+) patients with mild dementia from two clinical trials. The validation cohort (VC) included 230 A+ patients from another clinical trial. Over 85% of these patients had MCI. The longitudinal cognitive decline was defined by the change from baseline in CDR‐SB at months 3, 6, 9, 12, 15, and 18. Plasma pTau181 was measured using Simoa assay in a subset of 159 patients in TC.Structural brain network (SBN) modules and hubs were derived using 207 regional volumetric MRI measures in TC via an algorithm from genomics called “multiscale embedded gene co‐expression network analysis”. A signature for predicting longitudinal cognitive trajectory for each patient was first derived using baseline cognitive assessments and demographics within TC via the Stochastic Gradient Boosting algorithm. The added value of SBNs and plasma pTau181 was then evaluated within this framework. Prediction performance was evaluated in VC via the correlation between predicted versus observed cognitive trajectory.ResultPredictions of the longitudinal cognitive trajectory using baseline cognitive assessments achieved 43.4% and 42.5% correlation with observed values at months 12 and 18 respectively. Key baseline cognitive function predictors were ADAS‐Cog‐14, CDR‐SB, and sub‐scores such as ideational praxis and word recall.Adding baseline SBNs significantly improved the correlation between predicted versus observed cognitive trajectory to 46.3% and 49.7% at months 12 and 18 respectively (p<0.05). Key baseline SBN predictors were middle and inferior temporal, inferior parietal, and superior frontal cortex, along with a module comprising the entorhinal cortex and temporal pole. Adding baseline plasma pTau181 to cognitive assessments yielded similar improvements, but adding both pTau181 and SBNs did not improve the predictions further.ConclusionLongitudinal cognitive decline of A+ patients with mild dementia can be predicted using baseline cognitive assessments. Adding specific structural brain networks or plasma pTau181 significantly improves the predictions.

Identifying network hubs from brain anatomical similarity networks

AbstractBackgroundBrain hubs are highly connected regions important for integrating cognitive processing in the healthy brain. Brain hubs also play role in neurodegeneration causing dementia. However, it is not clear what brain morphometry is most useful in identifying brain regions critical to their involvement in neurodegeneration.MethodWe examined brain network hubs from four measures of cortical morphometry, derived using anatomical T1‐weigher MRI. From a large cohort of healthy participants (N = 260) from 20 to 85 years of age, we generated 200 networks for each measure of cortical morphometry using bootstrapping. We then tested how two measures of brain network hubness – nodal strength and clustering coefficient – were mapped onto brain network hubs across all 200 networks for each morphometric feature. Regions were identified as brain hubs if ranked two standard deviations away from the corresponding group mean.ResultWe mapped out brain hubs for each four measures (features) of brain morphometry. Nodal strength and clustering coefficient showed similarity in the behaviour across the features: gray volume, regional surface area and its thickness and thickness standard deviation. However, the differences were found in what brain region qualify as a network hub for each feature.ConclusionCortical morphometry extracted from anatomical brain scans can be used as a potential imaging marker for identification of brain hubs potentially involved in early differentiation of dementia subtypes.

Science, Medicine, and the Anesthesiologist

Science, Medicine, and the Anesthesiologist

Paradoxical markers of conscious levels: Effects of propofol on patients in disorders of consciousness.

Human consciousness is widely understood to be underpinned by rich and diverse functional networks, whose breakdown results in unconsciousness. Candidate neural correlates of anesthetic-induced unconsciousness include: (1) disrupted frontoparietal functional connectivity; (2) disrupted brain network hubs; and (3) reduced spatiotemporal complexity. However, emerging counterexamples have revealed that these markers may appear outside of the state they are associated with, challenging both their inclusion as markers of conscious level, and the theories of consciousness that rely on their evidence. In this study, we present a case series of three individuals in disorders of consciousness (DOC) who exhibit paradoxical brain responses to exposure to anesthesia. High-density electroencephalographic data were recorded from three patients with unresponsive wakefulness syndrome (UWS) while they underwent a protocol of propofol anesthesia with a targeted effect site concentration of 2 μg/ml. Network hubs and directionality of functional connectivity in the alpha frequency band (8–13 Hz), were estimated using the weighted phase lag index (wPLI) and directed phase lag index (dPLI). The spatiotemporal signal complexity was estimated using three types of Lempel-Ziv complexity (LZC). Our results illustrate that exposure to propofol anesthesia can paradoxically result in: (1) increased frontoparietal feedback-dominant connectivity; (2) posterior network hubs; and (3) increased spatiotemporal complexity. The case examples presented in this paper challenge the role of functional connectivity and spatiotemporal complexity in theories of consciousness and for the clinical evaluation of levels of human consciousness.

Network hub centrality and working memory performance in schizophrenia

Cognitive impairment, and working memory deficits in particular, are debilitating, treatment-resistant aspects of schizophrenia. Dysfunction of brain network hubs, putatively related to altered neurodevelopment, is thought to underlie the cognitive symptoms associated with this illness. Here, we used weighted degree, a robust graph theory metric representing the number of weighted connections to a node, to quantify centrality in cortical hubs in 29 patients with schizophrenia and 29 age- and gender-matched healthy controls and identify the critical nodes that underlie working memory performance. In both patients and controls, elevated weighted degree in the default mode network (DMN) was generally associated with poorer performance (accuracy and reaction time). Higher degree in the ventral attention network (VAN) nodes in the right superior temporal cortex was associated with better performance (accuracy) in patients. Degree in several prefrontal and parietal areas was associated with cognitive performance only in patients. In regions that are critical for sustained attention, these correlations were primarily driven by between-network connectivity in patients. Moreover, a cross-validated prediction analysis showed that a linear model using a summary degree score can be used to predict an individual’s working memory accuracy (r = 0.35). Our results suggest that schizophrenia is associated with dysfunctional hubs in the cortical systems supporting internal and external cognition and highlight the importance of topological network analysis in the search of biomarkers for cognitive deficits in schizophrenia.

Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer's disease.

Alzheimer's disease (AD) phenotypes might result from differences in selective vulnerability. Evidence from preclinical models suggests that tau pathology has cell-to-cell propagation properties. Therefore, here, we tested the cell-to-cell propagation framework in the amnestic, visuospatial, language, and behavioral/dysexecutive phenotypes of AD. We report that each AD phenotype is associated with a distinct network-specific pattern of tau aggregation, where tau aggregation is concentrated in brain network hubs. In all AD phenotypes, regional tau load could be predicted by connectivity patterns of the human brain. Furthermore, regions with greater connectivity displayed similar rates of longitudinal tau accumulation in an independent cohort. Connectivity-based tau deposition was not restricted to a specific vulnerable network but was rather a general property of brain organization, linking selective vulnerability and transneuronal spreading models of neurodegeneration. Together, this study indicates that intrinsic brain connectivity provides a framework for tau aggregation across diverse phenotypic manifestations of AD.

Shared and distinct structure-function substrates of heterogenous distractor suppression ability between high and low working memory capacity individuals

Salient stimuli can capture attention in a bottom-up manner; however, this attentional capture can be suppressed in a top-down manner. It has been shown that individuals with high working memory capacity (WMC) can suppress salient‑but-irrelevant distractors better than those with low WMC; however, neural substrates underlying this difference remain unclear. To examine this, participants with high or low WMC (high-/low-WMC, n = 44/44) performed a visual search task wherein a color singleton item served as a salient distractor, and underwent structural and resting-state functional magnetic resonance imaging scans. Behaviorally, the color singleton distractor generally reduced the reaction time (RT). This RT benefit (ΔRT) was higher in the high-WMC group relative to the low-WMC group, indicating the superior distractor suppression ability of the high-WMC group. Moreover, leveraging voxel-based morphometry analysis, gray matter morphology (volume and deformation) in the ventral attention network (VAN) was found to show the same, positive associations with ΔRT in both WMC groups. However, correlations of the opposite sign were found between ΔRT and gray matter morphology in the frontoparietal (FPN)/default mode network (DMN) in the two WMC groups. Furthermore, resting-state functional connectivity analysis centering on regions with a structural-behavioral relationship found that connections between the left orbital and right superior frontal gyrus (hubs of DMN and VAN, respectively) was correlated with ΔRT in the high-WMC group (but not in the low-WMC group). Collectively, our work present shared and distinct neuroanatomical substrates of distractor suppression in high- and low-WMC individuals. Furthermore, intrinsic connectivity of the brain network hubs in high-WMC individuals may account for their superior ability in suppressing salient distractors.

Epilepsy and brain network hubs.

Epilepsy is a disorder of brain networks. A better understanding of structural and dynamic network properties may improve epilepsy diagnosis, treatment, and prognostics. Hubs are brain regions with high connectivity to other parts of the brain and are typically situated along the brain's most efficient communication pathways, supporting large-scale brain wiring and many higher order neural functions. The visualization and analysis of hubs offers a perspective on regional and global network organization and can provide novel insights into brain disorders and epilepsy. By notably supporting the interaction between various brain networks, hubs may be implicated in seizure spread and in epilepsy-related phenotypes. In this review, we will discuss the growing literature on atypical hub organization in common epilepsy syndromes, both related to neuroimaging of brain structure and function, and related to neurophysiological data from magneto- and electroencephalographic measures of neural dynamics. With studies increasingly exploring the clinical utility of network neuroscience approaches, we highlight the potential of hub mapping as a candidate biomarker of cognitive dysfunction and postsurgical seizure outcome. We will conclude the review with a discussion of current limitations and outlook for future research.

Subcortical Volumes as Early Predictors of Fatigue in Multiple Sclerosis

Fatigue is a frequent and severe symptom in multiple sclerosis (MS), but its pathophysiological origin remains incompletely understood. We aimed to examine the predictive value of subcortical gray matter volumes for fatigue severity at disease onset and after 4 years by applying structural equation modeling (SEM). This multicenter cohort study included 601 treatment-naive patients with MS after the first demyelinating event. All patients underwent a standardized 3T magnetic resonance imaging (MRI) protocol. A subgroup of 230 patients with available clinical follow-up data after 4 years was also analyzed. Associations of subcortical volumes (included into SEM) with MS-related fatigue were studied regarding their predictive value. In addition, subcortical regions that have a central role in the brain network (hubs) were determined through structural covariance network (SCN) analysis. Predictive causal modeling identified volumes of the caudate (s [standardized path coefficient]=0.763, p= 0.003 [left]; s=0.755, p= 0.006 [right]), putamen (s=0.614, p= 0.002 [left]; s=0.606, p= 0.003 [right]) and pallidum (s=0.606, p= 0.012 [left]; s=0.606, p= 0.012 [right]) as prognostic factors for fatigue severity in the cross-sectional cohort. Moreover, the volume of the pons was additionally predictive for fatigue severity in the longitudinal cohort (s=0.605, p= 0.013). In the SCN analysis, network hubs in patients with fatigue worsening were detected in the putamen (p= 0.008 [left]; p= 0.007 [right]) and pons (p= 0.0001). We unveiled predictive associations of specific subcortical gray matter volumes with fatigue in an early and initially untreated MS cohort. The colocalization of these subcortical structures with network hubs suggests an early role of these brain regions in terms of fatigue evolution. ANN NEUROL 2022;91:192-202.

Neurometabolite correlates with personality and stress in healthy emerging adults: A focus on sex differences

Personality traits have been linked with both brain structure and function. However, the exact relationship between personality traits and other behavioural measures with neurometabolites, measured with proton magnetic resonance spectroscopy, is not clear. Here we investigated the association between behavioural measures (i.e., personality traits, resilience, perceived stress, self-esteem, hopelessness, psychological distress) and metabolite ratios (i.e., of choline-containing compounds [Cho], creatine and phosphocreatine [Cr], and N-acetyl-aspartate [NAA]) in the posterior cingulate cortex (pCC) and the dorsal anterior cingulate cortex (dACC) and surrounding white matter (WM) regions in healthy emerging adults (N = 57, 26 women, mean age=23.40 years, SD=2.50). The pCC and the dACC were selected for their known involvement as important brain network hubs and their association to five factor personality dimensions and other psychological measures. Spectral analysis as well as statistics for demographic, clinical, and imaging data were performed. Correlation and multiple regression analyses were used to test the relationship between metabolite ratios and behavioural scores in the entire sample as well as in female and male participants separately. The entire sample showed significant (p<0.05) negative correlates of stress with the NAA/Cr ratio in the pCC, and of extraversion with WM metabolite ratios. In regards of sex differences, a significantly higher NAA/Cho ratio in the pCC (p<0.05), the dACC (p<0.01), and in the left and right posterior WM matter (p<0.05), and a lower Cho/Cr ratio in the dACC (p<0.01) was detected in women. Moreover, the two sexes differed in regards of metabolite correlates of openness, conscientiousness, extraversion, agreeableness, neuroticism, stress, hopelessness, and self-esteem, and in multiple regression model predictions. Our results point to a role of the ACC in conscientiousness through its involvement in higher-order cognitive control as part of the salience network and internally directed thoughts as part of the default mode network (DMN). Furthermore, the two sexes differ in terms of metabolite correlates of openness and conscientiousness in the pCC, suggesting mental process involvement through the DMN, and of agreeableness in the dACC, possibly through involvement in social cognitive processes, particularly in women. Additionally, our results suggest that the ACC is linked to the so-called Alpha-factor of personality. Our findings on stress correlates contribute to the existing literature of the involvement of the ACC as part of the limbic system. In addition, our results suggest a possible role of the pCC in stress-regulatory processes through a possible co-involvement of stress, hopelessness, and self-esteem in the pCC in men, where higher self-esteem may help to cope with stress.

Degeneration of basal and limbic networks is a core feature of behavioural variant frontotemporal dementia.

The behavioural variant of frontotemporal dementia is a clinical syndrome characterized by changes in behaviour, cognition and functional ability. Although atrophy in frontal and temporal regions would appear to be a defining feature, neuroimaging studies have identified volumetric differences distributed across large parts of the cortex, giving rise to a classification into distinct neuroanatomical subtypes. Here, we extended these neuroimaging studies to examine how distributed patterns of cortical atrophy map onto brain network hubs. We used baseline structural magnetic resonance imaging data collected from 213 behavioural variant of frontotemporal dementia patients meeting consensus diagnostic criteria and having definite evidence of frontal and/or temporal lobe atrophy from a global clinical trial conducted in 70 sites in Canada, United States of America, Australia, Asia and Europe. These were compared with data from 244 healthy elderly subjects from a well-characterized cohort study. We have used statistical methods of hierarchical agglomerative clustering of 68 regional cortical and subcortical volumes (34 in each hemisphere) to determine the reproducibility of previously described neuroanatomical subtypes in a global study. We have also attempted to link the structural findings to clinical features defined systematically using well-validated clinical scales (Addenbrooke’s Cognitive Examination Revised, the Mini-Mental Status Examination, the Frontotemporal Dementia Rating Scale and the Functional Assessment Questionnaire) and subscales derived from them. Whilst we can confirm that the subtypes are robust, they have limited value in explaining the clinical heterogeneity of the syndrome. We have found that a common pattern of degeneration affecting a small number of subcortical, limbic and frontal nodes within highly connected networks (most previously identified as rich club members or functional binding nodes) is shared by all the anatomical subtypes. Degeneration in these core regions is correlated with cognitive and functional impairment, but less so with behavioural impairment. These findings suggest that degeneration in highly connected basal, limbic and frontal networks is a core feature of the behavioural variant of frontotemporal dementia phenotype irrespective of neuroanatomical and clinical heterogeneity, and may underly the impairment of integration in cognition, function and behaviour responsible for the loss of insight that characterizes the syndrome.

Decoupling of Structural and Functional Connectivity in Hubs and Cognitive Impairment After Mild Traumatic Brain Injury.

Introduction: Mild traumatic brain injury (mild TBI) exhibited abnormal brain network topologies associated with cognitive dysfunction. However, it was still unclear which aspects of network organization were critical underlying the key pathology of mild TBI. Here, a multi-imaging strategy was applied to capture dynamic topological features of both structural and functional connectivity networks (SCNs and FCNs), to provide more sensitive detection of altered FCN from its anatomical backbone and identify novel biomarkers of mild TBI outcomes. Methods: A total of 62 mild TBI patients (30 subjects as an original sample with 3-12 months of follow-up, 32 subjects as independent replicated sample) and 37 healthy controls were recruited. Both diffusion tensor imaging and resting-state functional magnetic resonance imaging were used to create global connectivity matrices in the same individuals. Global and regional network analyses were applied to identify group differences and correlations with clinical assessments. Results: Most global network properties were conserved in both SCNs and FCNs in subacute mild TBI, whereas SCNs presented decreased global efficiency and characteristic path length at follow-up. Specifically, some hubs in healthy brain networks typically became nonhubs in patients and vice versa, such as the medial prefrontal cortex, superior temporal gyrus, and middle frontal gyrus. The relationship between structural and functional connectivity (SC and FC) in patients also showed salient decoupling as a function of time, primarily located in the hubs. Conclusions: These results suggested mild TBI influences the relationship between SCN and FCN, and the SC-FC coupling strength may be used as a potential biomarker to predict long-term outcomes after injury. Impact statement The current study was the first study to explore the coupling of structural-functional connectivity (SC-FC) from large-scale whole-brain networks in patients with mild traumatic brain injury. The results showed that the global properties homeostatically conserved in functional connectivity networks while altered in structural connectivity networks. The SC-FC coupling strength showed salient decoupling as a function of time and primarily occurred in hub regions.

Genetic influences on hub connectivity of the human connectome

Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.

Brain network hubs and cognitive performance of survivors of childhood infratentorial tumors

BackgroundChildhood infratentorial tumor patients frequently suffer from long-term cognitive deficits. As each constituent of their treatment can lead to neurotoxicity, cascade effects can lead to profound reorganization of the underlying brain network, the so-called ‘connectome’. However, to date, few studies have assessed the relationship between brain network topology, the functional role of network hubs (i.e. highly connected regions), and neurocognitive outcomes in adult survivors of childhood infratentorial tumors. MethodsIn this cross-sectional study, childhood infratentorial tumor survivors (n = 21: pilocytic astrocytoma (n = 8), ependymoma (n = 1) and medulloblastoma (n = 12)) and healthy controls (n = 21) were recruited. Using multishell diffusion-weighted MRI, microstructural organization and topology of supratentorial white matter was investigated; using a voxel-based approach, a fixel-based analysis, and a graph theoretical approach. In addition, neurocognitive subscales of the WAIS-IV intelligence test, and their relationship with nodal strength and network efficiency metrics were assessed. ResultsSimilar to earlier studies, we observed widespread decreases in fractional anisotropy (FA) in patients compared to controls, based on voxel-based analyses. In addition, the fixel-based analyses dissociated macro- from microstructural changes, which were encountered in in infratentorial versus supratentorial brain areas, respectively. Finally, regional reorganization (i.e. differences in local efficiency) occurred mainly in hubs, which suggests a specific vulnerability of these areas. These hubs were not only mostly affected, but also most strongly correlated with the intelligence subscales. ConclusionThis study suggests that network hubs are functionally important for intellectual outcomes in infratentorial tumor survivors. Furthermore, these regions could be the primary targets of treatment toxicity. Validation of this specific hypothesis in larger samples is required.

Global and local brain connectivity changes associated with sudden unilateral sensorineural hearing loss.

Recent studies suggest that even moderate sudden sensorineural hearing loss (SSNHL) causes reduction of gray matter volume in the primary auditory cortex, diminishing its ability to react to sound stimulation, as well as reorganization of functional brain networks. We employed resting-state functional MRI (rs-fMRI), in conjunction with graph-theoretical analysis and a newly developed functional "disruption index," to study whole-brain as well as local functional changes in patients with unilateral SSNHL. We also assessed the potential of graph-theoretical measures as biomarkers of disease, in terms of their relationship to clinically relevant audiological parameters. Eight patients with moderate or severe unilateral SSNHL and 15 healthy controls were included in this prospective pilot study. All patients underwent rs-fMRI to study potential changes in brain connectivity. From rs-fMRI data, global and local graph-theoretical measures, disruption index, and audiological examinations were estimated. Mann-Whitney U tests were used to study the differences between SSNHL patients and healthy controls. Associations between brain metrics and clinical variables were studied using multiple linear regressions, and the presence or absence of brain network hubs was assessed using Fisher's exact test. No statistically significant differences between SSNHL patients and healthy controls were found in global or local network measures. However, when analyzing brain networks through the disruption index, we found a brain-wide functional network reorganization (p < 0.001 as compared with controls), whose extent was associated with clinical impairment (p < 0.05). We also observed several functional hubs in SSNHL patients that were not present in healthy controls and vice versa. Our results demonstrate a brain involvement in SSNHL patients, not detectable using conventional graph-theoretical analysis, which may yield subtle disease clues and possibly aid in monitoring disease progression in clinical trials.

Genetic Influences on Brain Network Hubs

Highly connected brain network hubs play a vital role in integrated brain function. They are metabolically costly and implicated as dysfunctional in diverse brain disorders. How do brain hubs emerge? Here, I present converging evidence from heritability, transcriptomic, and modelling analyses to indicate that genes play an important role in shaping the hub architecture of the brain.

Nonhuman primate meso-circuitry data: a translational tool to understand brain networks across species.

The foundation for understanding brain connections and related psychiatric diseases lies in human and animal circuitry studies. In rodents and nonhuman primates (NHPs), axonal tracing methods provide the ground-truth connectivity information of brain circuits, coupled with the ability to experimentally manipulate them when combined with other methods. In humans, neuroimaging approaches have taken the lead for studying connectivity patterns in vivo and the changes in network profiles associated with disease. To integrate knowledge from animal models and humans, a critical question is how similar the animal brains and circuits are to the humans'. In this review, we demonstrate the use of meso-circuitry information from tracing studies in NHPs to understand common network connections across species. We show that the meso-circuitry information help establish homologies of cortical and striatal regions and fiber pathways between rodents and NHPs, facilitate the translation of connections that are detailed in animal models to humans, and can locate critical hubs in large-scale brain networks. This review combines anatomic studies across animal models and imaging studies across NHPs and humans to provide a more comprehensive understanding of the hard-wired connectivity that underlies neuroimaging-derived brain networks.