Impact of exposure to natural versus urban soundscapes on brain functional connectivity, BOLD entropy and behavior.

BACKGROUND: Humans have been moving from rural to urban environments for decades. This process may have important consequences for our health and well-being. Most previous studies have focused on visual input, and the auditory domain has been understudied so far. Therefore, we set out to investigate the influence of exposure to natural vs urban soundscapes on brain activity and behavior. METHODS: Resting-state fMRI data was acquired while participants (N = 35) listened to natural and urban soundscapes. Two affective questionnaires (the Positive and Negative Affect Schedule (PANAS) and the Perceived Stress Scale) and two cognitive tasks (dual n-back (DNB) and the backward digit-span (BDS)) were assessed before and after each soundscape condition. To quantify brain function we used complexity and network measures, namely brain entropy (BEN) and whole brain functional connectivity (FC). To study the link between brain and behavior, changes in BEN and whole brain FC were correlated to changes in cognitive performance and self-reported affect. RESULTS: We found higher BEN when listening to urban sounds in posterior cingulate gyrus, cuneus and precuneus, occipital lobe/calcarine as compared to nature sounds, which was negatively correlated to (post-pre) differences in positive affect (PANAS) in the urban soundscape condition. In addition, we found higher FC between areas in the auditory, cinguloopercular, somatomotor hand and mouth networks when listening to nature as compared to urban sounds which was positively correlated to (post-pre) differences of the of the composite score of Digit span and N-back for nature soundscape. CONCLUSIONS: This study provides a framework for the neural underpinnings of how natural versus urban soundscapes affect both whole brain FC and BEN and bear implications for the understanding of how the physical auditory environment affects brain function and subsequently observed behavior. Moreover, correlations with cognition and affect reveal the meaning that exposure to soundscapes may have on the human brain. To the best of our knowledge this is the first study to analyze BEN and whole brain FC at rest during exposure to nature and urban soundscapes and to explore their relationship to behavior.

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia.

AIM: Recent imaging studies have found significant abnormalities in the brain's functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients. METHODS: We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups. RESULTS: In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01). CONCLUSION: HM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder.

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are two highly prevalent and commonly co-occurring neurodevelopmental disorders. The neural mechanisms underpinning the comorbidity of ASD and ADHD (ASD + ADHD) remain unclear. We focused on the topological organization and functional connectivity of brain networks in ASD + ADHD patients versus ASD patients without ADHD (ASD-only). Resting-state functional magnetic resonance imaging (rs-fMRI) data from 114 ASD and 161 typically developing (TD) individuals were obtained from the Autism Brain Imaging Data Exchange II. The ASD patients comprised 40 ASD + ADHD and 74 ASD-only individuals. We constructed functional brain networks for each group and performed graph-theory and network-based statistic (NBS) analyses. Group differences between ASD + ADHD and ASD-only were analyzed at three levels: nodal, global, and connectivity. At the nodal level, ASD + ADHD exhibited topological disorganization in the temporal and occipital regions, compared with ASD-only. At the global level, ASD + ADHD and ASD-only displayed no significant differences. At the connectivity level, the NBS analysis revealed that ASD + ADHD showed enhanced functional connectivity between the prefrontal and frontoparietal regions, as well as between the orbitofrontal and occipital regions, compared with ASD-only. The hippocampus was the shared region in aberrant functional connectivity patterns in ASD + ADHD and ASD-only compared with TD. These findings suggests that ASD + ADHD displays altered topology and functional connectivity in the brain regions that undertake social cognition, language processing, and sensory processing.

Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy.

OBJECTIVE: Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality. METHODS: Participants including 74 TLE patients (47 male, mean age = 39.2 years) and 45 healthy controls (27 male, mean age = 31.9 years) from the Epilepsy Connectome Project were classified into ADI-defined low and high disadvantage groups. Graph theoretic metrics were applied to multishell connectome diffusion-weighted imaging (DWI) measurements to derive 162 × 162 structural connectivity matrices (SCMs). The SCMs were harmonized using neuroCombat to account for interscanner differences. Threshold-free network-based statistics were used for analysis, and findings were correlated with ADI quintile metrics. A decrease in cross-sectional area (CSA) indicates reduced white matter integrity. RESULTS: Sex- and age-adjusted CSA in TLE groups was significantly reduced compared to controls regardless of disadvantage status, revealing discrete aberrant white matter tract connectivity abnormalities in addition to apparent differences in graph measures of connectivity and network-based statistics. When comparing broadly defined disadvantaged TLE groups, differences were at trend level. Sensitivity analyses of ADI quintile extremes revealed significantly lower CSA in the most compared to least disadvantaged TLE group. SIGNIFICANCE: Our findings demonstrate (1) the general impact of TLE on DWI connectome status is larger than the association with neighborhood disadvantage; however, (2) neighborhood disadvantage, indexed by ADI, revealed modest relationships with white matter structure and integrity on sensitivity analysis in TLE. Further studies are needed to explore this relationship and determine whether the white matter relationship with ADI is driven by social drift or environmental influences on brain development. Understanding the etiology and course of the disadvantage-brain integrity relationship may serve to inform care, management, and policy for patients.

Functional disruptions of the brain network in low back pain: a graph-theoretical study.

PURPOSE: The aim of this study was to investigate alterations in the topological organization of whole-brain functional networks in patients with chronic low back pain (CLBP) and characterize the relationship of these alterations with pain characteristics. METHODS: Thirty-three CLBP patients and 34 matched healthy controls (HCs) underwent fMRI scans. A graph-theoretical approach was applied to identify brain network changes in patients suffering from chronic low back pain given its nonspecific etiology and complexity. Graph theory-based analysis was used to construct functional connectivity matrices and extract the features of small-world networks of the brain in both groups. Then, the whole-brain functional connectivity differences were characterized by network-based statistics (NBS) analysis, and the relationship between the altered brain features and clinical measures was explored. RESULTS: At the global level, patients with CLBP showed significantly decreased gamma, sigma, global efficiency, and local efficiency and increased lambda and shortest path length compared with HCs. At the regional level, there were deficits in nodal efficiency within the default mode network and salience network. NBS analysis demonstrated that decreased functional connectivity was present in the CLBP patients, mainly in the frontolimbic circuit and temporal regions. Furthermore, aspects of topological dysfunctions in CLBP were correlated with pain severity. CONCLUSION: This study highlighted the aberrant topological organization of functional brain networks in CLBP, which may shed light on the pathophysiology of CLBP and support the development of pain management approaches.

Longitudinal functional connectivity patterns of the default mode network in healthy older adults.

Cross-sectional studies have consistently identified age-associated alterations in default mode network (DMN) functional connectivity (FC). Yet, research on longitudinal trajectories of FC changes of the DMN in healthy aging is less conclusive. For the present study, we used a resting state functional MRI dataset drawn from the Longitudinal Healthy Aging Brain Database Project (LHAB) collected in 5 occasions over a course of 7 years (baseline N = 232, age range: 64-87 y, mean age = 70.85 y). FC strength changes within the DMN and its regions were investigated using a network-based statistical method suitable for the analysis of longitudinal data. The average DMN FC strength remained stable, however, various DMN components showed differential age- and time-related effects. Our results revealed a complex pattern of longitudinal change seen as decreases and increases of FC strength encompassing the majority of DMN regions, while age-related effects were negative and present in select brain areas. These findings testify to the growing importance of longitudinal studies using more sophisticated fine-grained tools needed to highlight the complexity of the functional reorganization of DMN with healthy aging.

Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis.

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.

Dysconnectivity of a brain functional network was associated with blood inflammatory markers in depression.

OBJECTIVE: There is increasing evidence for a subgroup of major depressive disorder (MDD) associated with heightened peripheral blood inflammatory markers. In this study, we aimed to understand the mechanistic brain-immune axis in inflammation-linked depression by investigating associations between functional connectivity (FC) of brain networks and peripheral blood immune markers in depression. METHODS: Resting-state functional magnetic resonance imaging (fMRI) and peripheral blood inflammatory markers (C-reactive protein; CRP, interleukin-6; IL-6 and immune cells) were collected on N = 46 healthy controls (HC; CRP ≤ 3 mg/L) and N = 83 cases of depression, stratified further into low CRP cases (loCRP cases; ≤ 3 mg/L; N = 50) and high CRP cases (hiCRP cases; > 3 mg/L; N = 33). In a two-part analysis, network-based statistics (NBS) was firstly used to ascertain whole-brain FC differences in HC vs hiCRP cases. Secondly, we investigated the association between this network of interconnected brain regions and continuous measures of peripheral CRP (N = 83), IL-6 (N = 72), neutrophils and CD4+ T-cells (N = 36) in depression cases only. RESULTS: Case-control NBS testing revealed a single network of abnormally attenuated FC in the high CRP depression cases compared to healthy controls. Connections within this network were mainly between brain regions located in the left insula/frontal operculum and posterior cingulate cortex, which were assigned to ventral attention and default mode canonical fMRI networks respectively. Within-group analysis across all depression cases, secondarily demonstrated that FC within the identified network significantly negatively scaled with CRP, IL-6 and neutrophils. CONCLUSIONS: The findings suggest that inflammation is associated with disruption of functional connectivity within a brain network deemed critical for interoceptive signalling, e.g. accurate communication of peripheral bodily signals such as immune states to the brain, with implications for the pathogenesis of inflammation-linked depression.

Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data.

Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra-hemispheric networks of healthy controls (HCs) and patients with left- or right-hemispheric temporal lobe or temporal plus extra-temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra-hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve-Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network-based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra-hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra-hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.

Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder.

Social anxiety disorder (SAD) is a common and disabling condition characterized by excessive fear and avoidance of public scrutiny. Psychoradiology studies have suggested that the emotional and behavior deficits in SAD are associated with abnormalities in regional brain function and functional connectivity. However, little is known about whether intrinsic functional brain networks in patients with SAD are topologically disrupted. Here, we collected resting-state fMRI data from 33 drug-naive patients with SAD and 32 healthy controls (HC), constructed functional networks with 34 predefined regions based on previous meta-analytic research with task-based fMRI in SAD, and performed network-based statistic and graph-theory analyses. The network-based statistic analysis revealed a single connected abnormal circuitry including the frontolimbic circuit (termed the "fear circuit", including the dorsolateral prefrontal cortex, ventral medial prefrontal cortex and insula) and posterior cingulate/occipital areas supporting perceptual processing. In this single altered network, patients with SAD had higher functional connectivity than HC. At the global level, graph-theory analysis revealed that the patients exhibited a lower normalized characteristic path length than HC, which suggests a disorder-related shift of network topology toward randomized configurations. SAD-related deficits in nodal degree, efficiency and participation coefficient were detected in the parahippocampal gyrus, posterior cingulate cortex, dorsolateral prefrontal cortex, insula and the calcarine sulcus. Aspects of abnormal connectivity were associated with anxiety symptoms. These findings highlight the aberrant topological organization of functional brain network organization in SAD, which provides insights into the neural mechanisms underlying excessive fear and avoidance of social interactions in patients with debilitating social anxiety.

Diffusion tensor imaging reveals abnormal brain networks in elderly subjects with subjective cognitive deficits.

PURPOSE: Some elders with subjective cognitive deficits (SCD) develop prodromal phase of dementia over time; however, little is known about how they differ from those with normal cognition (NC). Thus, we aim to distinguish the differences in the brain network of elders with SCD and NC. METHODS: Multiple diffusion-weighted images (DWI) and T1-weighted images were obtained from 18 subjects with NC and 26 subjects with SCD. Using network-based statistics (NBS) analysis, we extracted abnormal brain subnetworks and localized abnormal brain connectivity. We also ran correlation analysis to compare the affected regions and the results of the neurocognitive assessments. RESULTS: Altered subnetworks were found in the superior parietal gyrus, angular gyrus, precuneus, posterior cingulum, putamen, precentral gyrus, postcentral gyrus, and paracentral lobule. They were also associated with scores on the word list recall, word list recognition, and Boston naming test. CONCLUSIONS: Elders with SCD had distinctive brain network alterations when compared with those of elders with NC. The results are also in line with the previously identified characteristics of mild cognitive impairment (MCI) and of Alzheimer's disease (AD) in a milder form. We speculate that it may be possible to predict AD progression early in the SCD stage using NBS analysis.

Expertise-related functional brain network efficiency in healthy older adults.

BACKGROUND: In view of age-related brain changes, identifying factors that are associated with healthy aging are of great interest. In the present study, we compared the functional brain network characteristics of three groups of healthy older participants aged 61-75 years who had a different cognitive and motor training history (multi-domain group: participants who had participated in a multi-domain training; visuomotor group: participants who had participated in a visuomotor training; control group: participants with no specific training history). The study's basic idea was to examine whether these different training histories are associated with differences in behavioral performance as well as with task-related functional brain network characteristics. Based on a high-density electroencephalographic measurement one year after training, we calculated graph-theoretical measures representing the efficiency of functional brain networks. RESULTS: Behaviorally, the multi-domain group performed significantly better than the visuomotor and the control groups on a multi-domain task including an inhibition domain, a visuomotor domain, and a spatial navigation domain. In terms of the functional brain network features, the multi-domain group showed significantly higher functional connectivity in a network encompassing visual, motor, executive, and memory-associated brain areas in the theta frequency band compared to the visuomotor group. These brain areas corresponded to the multi-domain task demands. Furthermore, mean connectivity of this network correlated positively with performance across both the multi-domain and the visuomotor group. In addition, the multi-domain group showed significantly enhanced processing efficiency reflected by a higher mean weighted node degree (strength) of the network as compared to the visuomotor group. CONCLUSIONS: Taken together, our study shows expertise-dependent differences in task-related functional brain networks. These network differences were evident even a year after the acquisition of the different expertise levels. Hence, the current findings can foster understanding of how expertise is positively associated with brain functioning during aging.

Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.

The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013).

Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks.

Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.

Gender transition affects neural correlates of empathy: A resting state functional connectivity study with ultra high-field 7T MR imaging.

Sex-steroid hormones have repeatedly been shown to influence empathy, which is in turn reflected in resting state functional connectivity (rsFC). Cross-sex hormone treatment in transgender individuals provides the opportunity to examine changes to rsFC over gender transition. We aimed to investigate whether sex-steroid hormones influence rsFC patterns related to unique aspects of empathy, namely emotion recognition and description as well as emotional contagion. RsFC data was acquired with 7Tesla magnetic resonance imaging in 24 male-to-female (MtF) and 33 female-to-male (FtM) transgender individuals before treatment, in addition to 33 male- and 44 female controls. Of the transgender participants, 15 MtF and 20 FtM were additionally assessed after 4 weeks and 4 months of treatment. Empathy scores were acquired at the same time-points. MtF differed at baseline from all other groups and assimilated over the course of gender transition in a rsFC network around the supramarginal gyrus, a region central to interpersonal emotion processing. While changes to sex-steroid hormones did not correlate with rsFC in this network, a sex hormone independent association between empathy scores and rsFC was found. Our results underline that 1) MtF transgender persons demonstrate unique rsFC patterns in a network related to empathy and 2) changes within this network over gender transition are likely related to changes in emotion recognition, -description, and -contagion, and are sex-steroid hormone independent.

Edgewise and subgraph-level tests for brain networks.

Resting-state functional magnetic resonance image is a useful technique for investigating brain functional connectivity at rest. In this work, we develop flexible regression models and methods for determining differences in resting-state functional connectivity as a function of age, gender, drug intervention, or neuropsychiatric disorders. We propose two complementary methods for identifying changes of edges and subgraphs. (i) For detecting changes of edges, we select the optimal model at each edge and then conduct contrast tests to identify the effects of the important variables while controlling the familywise error rate. (ii) We adopt the network-based statistics method to improve power by incorporating the graph topological structure. Both methods have wide applications for low signal-to-noise ratio data. We propose stability criteria for the choice of threshold in the network-based statistics procedure and utilize efficient massive parallel procedure to speed up the estimation and inference procedure. Results from our simulation studies show that the thresholds chosen by the proposed stability criteria outperform the Bonferroni threshold. To demonstrate applicability, we use both methods in the context of the Oxytocin and Aging Study to determine effects of age, gender, and drug treatment on resting-state functional connectivity, as well as in the context of the Autism Brain Imaging Data Exchange Study to determine effects of autism spectrum disorder on functional connectivity at rest. Copyright © 2016 John Wiley & Sons, Ltd.

Structural connectivity differences in left and right temporal lobe epilepsy.

Our knowledge on temporal lobe epilepsy (TLE) with hippocampal sclerosis has evolved towards the view that this syndrome affects widespread brain networks. Diffusion weighted imaging studies have shown alterations of large white matter tracts, most notably in left temporal lobe epilepsy, but the degree of altered connections between cortical and subcortical structures remains to be clarified. We performed a whole brain connectome analysis in 39 patients with refractory temporal lobe epilepsy and unilateral hippocampal sclerosis (20 right and 19 left) and 28 healthy subjects. We performed whole-brain probabilistic fiber tracking using MRtrix and segmented 164 cortical and subcortical structures with Freesurfer. Individual structural connectivity graphs based on these 164 nodes were computed by mapping the mean fractional anisotropy (FA) onto each tract. Connectomes were then compared using two complementary methods: permutation tests for pair-wise connections and Network Based Statistics to probe for differences in large network components. Comparison of pair-wise connections revealed a marked reduction of connectivity between left TLE patients and controls, which was strongly lateralized to the ipsilateral temporal lobe. Specifically, infero-lateral cortex and temporal pole were strongly affected, and so was the perisylvian cortex. In contrast, for right TLE, focal connectivity loss was much less pronounced and restricted to bilateral limbic structures and right temporal cortex. Analysis of large network components revealed furthermore that both left and right hippocampal sclerosis affected diffuse global and interhemispheric connectivity. Thus, left temporal lobe epilepsy was associated with a much more pronounced pattern of reduced FA, that included major landmarks of perisylvian language circuitry. These distinct patterns of connectivity associated with unilateral hippocampal sclerosis show how a focal pathology influences global network architecture, and how left or right-sided lesions may have differential and specific impacts on cerebral connectivity.

Transcutaneous Auricular Vagus Nerve Stimulation Modulating the Brain Topological Architecture of Functional Network in Major Depressive Disorder: An fMRI Study.

BACKGROUND: Transcutaneous auricular vagus nerve stimulation (taVNS) is effective in regulating mood and high-level cognition in patients with major depressive disorder (MDD). This study aimed to investigate the efficacy of taVNS treatment in patients with MDD and an altered brain topological organization of functional networks. METHODS: Nineteen patients with MDD were enrolled in this study. Patients with MDD underwent 4 weeks of taVNS treatments; resting-state functional magnetic resonance imaging (rs-fMRI) data of the patients were collected before and after taVNS treatment. The graph theory method and network-based statistics (NBS) analysis were used to detect abnormal topological organizations of functional networks in patients with MDD before and after taVNS treatment. A correlation analysis was performed to characterize the relationship between altered network properties and neuropsychological scores. RESULTS: After 4 weeks of taVNS treatment, patients with MDD had increased global efficiency and decreased characteristic path length (Lp). Additionally, patients with MDD exhibited increased nodal efficiency (NE) and degree centrality (DC) in the left angular gyrus. NBS results showed that patients with MDD exhibited reduced connectivity between default mode network (DMN)-frontoparietal network (FPN), DMN-cingulo-opercular network (CON), and FPN-CON. Furthermore, changes in Lp and DC were correlated with changes in Hamilton depression scores. CONCLUSIONS: These findings demonstrated that taVNS may be an effective method for reducing the severity of depressive symptoms in patients with MDD, mainly through modulating the brain's topological organization. Our study may offer insights into the underlying neural mechanism of taVNS treatment in patients with MDD.

Mixed anxiety-depressive disorder in Parkinson's disease associated with worse resting state functional response to deep brain stimulation of subthalamic nucleus.

Background: Parkinson's disease (PD), even though generally perceived as a dominantly motor disorder, is associated with a wide range of non-motor symptoms, including mixed anxiety-depressive disorder (MADD). Objectives: The aim of the presented study was to determine whether deep brain stimulation (DBS) of the subthalamic nucleus (STN) brings the functional characteristics of non-motor networks closer to the condition detected in healthy population and whether pre-DBS presence of MADD in PD patients was associated with different reaction to this therapeutic modality. Methods: Resting-state fMRI signature elicited by STN DBS activation and deactivation in 81 PD patients was compared against healthy controls, with the focus on measures of efficiency of information processing and localised subnetwork differences. Results: While all the MRI metrics showed statistically significant differences between PD patients in DBS OFF condition and healthy controls, none were detected in such a comparison against DBS ON condition. Furthermore, in the post-DBS evaluation, PD patients with MADD in the pre-DBS stage showed no differences in depression scales compared to pre-DBS psychiatrically intact PD patients, but still exhibited lower DBS-related connectivity in a subnetwork encompassing anterior and posterior cingulate, dorsolateral prefrontal and medial temporal cortices. Conclusions: STN DBS improved all the metrics of interest towards the healthy state, normalising the resting-state MRI signature of PD. Furthermore, pre-DBS presence of MADD, even though clinically silent at post-DBS MRI acquisition, was associated with lower DBS effect in areas highly relevant for depression. This finding points to a possibly latent nature of post-DBS MADD, calling for caution in further follow-up of these patients.

Functional network segregation is associated with higher functional connectivity in endurance runners.

Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.