Brain network analysis reveals affected connectome structure in bipolar I disorder

Different structural connectivity patterns in the subregions of the thalamus, hippocampus, and cingulate cortex between schizophrenia and psychotic bipolar disorder

OBJECTIVE

Schizophrenia (SCZ) and psychotic bipolar disorder (PBD) are two major psychotic disorders with similar symptoms and tight associations on the psychopathological level, posing a clinical challenge for their differentiation. This study aimed to investigate and compare the structural connectivity patterns of the limbic system between SCZ and PBD, and to identify specific regional disruptions associated with psychiatric symptoms.

METHODS

Using sMRI data from 146 SCZ, 160 PBD, and 145 healthy control (HC) participants, we employed a data-driven approach to segment the hippocampus, thalamus, hypothalamus, amygdala, and cingulate cortex into subregions. We then investigated the structural connectivity patterns between these subregions at the global and nodal levels. Additionally, we assessed psychotic symptoms by utilizing the subscales of the Brief Psychiatric Rating Scale (BPRS) to examine correlations between symptom severity and network metrics between groups.

RESULTS

Patients with SCZ and PBD had decreased global efficiency (Eglob) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.003), local efficiency (Eloc) (SCZ and PBD: adjusted P<0.001), and clustering coefficient (Cp) (SCZ and PBD: adjusted P<0.001), and increased path length (Lp) (SCZ: adjusted P<0.001; PBD: adjusted P = 0.004) compared to HC. Patients with SCZ showed more pronounced decreases in Eglob (adjusted P<0.001), Eloc (adjusted P<0.001), and Cp (adjusted P = 0.029), and increased Lp (adjusted P = 0.024) compared to patients with PBD. The most notable structural disruptions were observed in the hippocampus and thalamus, which correlated with different psychotic symptoms, respectively.

CONCLUSION

This study provides evidence of distinct structural connectivity disruptions in the limbic system of patients with SCZ and PBD. These findings might contribute to our understanding of the neuropathological basis for distinguishing SCZ and PBD.

Effects of sleep on the glymphatic functioning and multimodal human brain network affecting memory in older adults

Understanding how sleep affects the glymphatic system and human brain networks is crucial for elucidating the neurophysiological mechanism underpinning aging-related memory declines. We analyzed a multimodal dataset collected through magnetic resonance imaging (MRI) and polysomnographic recording from 72 older adults. A proxy of the glymphatic functioning was obtained from the Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) index. Structural and functional brain networks were constructed based on MRI data, and coupling between the two networks (SC-FC coupling) was also calculated. Correlation analyses revealed that DTI-ALPS was negatively correlated with sleep quality measures [e.g., Pittsburgh Sleep Quality Index (PSQI) and apnea-hypopnea index]. Regarding human brain networks, DTI-ALPS was associated with the strength of both functional connectivity (FC) and structural connectivity (SC) involving regions such as the middle temporal gyrus and parahippocampal gyrus, as well as with the SC-FC coupling of rich-club connections. Furthermore, we found that DTI-ALPS positively mediated the association between sleep quality and rich-club SC-FC coupling. The rich-club SC-FC coupling further mediated the association between DTI-ALPS and memory function in good sleepers but not in poor sleepers. The results suggest a disrupted glymphatic-brain relationship in poor sleepers, which underlies memory decline. Our findings add important evidence that sleep quality affects cognitive health through the underlying neural relationships and the interplay between the glymphatic system and multimodal brain networks.

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.

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

Background

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated.

Methods

A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity.

Results

Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder.

Conclusions

The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

Weighting the structural connectome: Exploring its impact on network properties and predicting cognitive performance in the human brain

Brain function does not emerge from isolated activity, but rather from the interactions and exchanges between neural elements that form a network known as the connectome. The human connectome consists of structural and functional aspects. The structural connectome (SC) represents the anatomical connections, and the functional connectome represents the resulting dynamics that emerge from this arrangement of structures. As there are different ways of weighting these connections, it is important to consider how such different approaches impact study conclusions. Here, we propose that different weighted connectomes result in varied network properties, and while neither superior the other, selection might affect interpretation and conclusions in different study cases. We present three different weighting models, namely, number of streamlines (NOS), fractional anisotropy (FA), and axon diameter distribution (ADD), to demonstrate these differences. The later, is extracted using recently published AxSI method and is first compared to commonly used weighting methods. Moreover, we explore the functional relevance of each weighted SC, using the Human Connectome Project (HCP) database. By analyzing intelligence-related data, we develop a predictive model for cognitive performance based on graph properties and the National Institutes of Health (NIH) toolbox. Results demonstrate that the ADD SC, combined with a functional subnetwork model, outperforms other models in estimating cognitive performance.

Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models

Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.

State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study

BACKGROUND

The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD.

METHODS

We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD.

RESULTS

We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD.

LIMITATIONS

Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study.

CONCLUSION

Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.

Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder

Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

BACKGROUND

Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects.

METHODS

This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.

RESULTS

Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.

CONCLUSIONS

We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders.

Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

Changes in the structural brain connectome over the course of a nonrandomized clinical trial for acute mania

Disrupted topological organization of brain functional networks has been widely reported in bipolar disorder. However, the potential clinical implications of structural connectome abnormalities have not been systematically investigated. The present study included 109 unmedicated subjects with acute mania who were assigned to 8 weeks of treatment with quetiapine or lithium and 60 healthy controls. High resolution 3D-T1 weighted magnetic resonance images (MRI) were collected from both groups at baseline, week 1 and week 8. Brain networks were constructed based on the similarity of morphological features across brain regions and analyzed using graph theory approaches. At baseline, individuals with bipolar disorder illness showed significantly lower clustering coefficient (Cp) (p = 0.012) and normalized characteristic path length (λ) (p = 0.004) compared to healthy individuals, as well as differences in nodal centralities across multiple brain regions. No baseline or post-treatment differences were identified between drug treatment conditions, so change after treatment were considered in the combined treatment groups. Relative to healthy individuals, differences in Cp, λ and cingulate gyrus nodal centrality were significantly reduced with treatment; changes in these parameters correlated with changes in Young Mania Rating Scale scores. Baseline structural connectome matrices significantly differentiated responder and non-responder groups at 8 weeks with 74% accuracy. Global and nodal network alterations evident at baseline were normalized with treatment and these changes associated with symptomatic improvement. Further, baseline structural connectome matrices predicted treatment response. These findings suggest that structural connectome abnormalities are clinically significant and may be useful for predicting clinical outcome of treatment and tracking drug effects on brain anatomy in bipolar disorder.

CLINICAL TRIALS REGISTRATION

Name: Functional and Neurochemical Brain Changes in First-episode Bipolar Mania Following Successful Treatment with Lithium or Quetiapine. URL: https://clinicaltrials.gov/ .

REGISTRATION NUMBER

NCT00609193. Name: Neurofunctional and Neurochemical Markers of Treatment Response in Bipolar Disorder. URL: https://clinicaltrials.gov/ .

REGISTRATION NUMBER

NCT00608075.

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography

Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.

Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum

Functional connectome alterations, including modular network organization, have been related to the experience of hallucinations. It remains to be determined whether individuals with hallucinations across the psychosis continuum exhibit similar alterations in modular brain network organization. This study assessed functional connectivity matrices of 465 individuals with and without hallucinations, including patients with schizophrenia and bipolar disorder, nonclinical individuals with hallucinations, and healthy controls. Modular brain network organization was examined at different scales of network resolution, including (1) global modularity measured as Qmax and Normalised Mutual Information (NMI) scores, and (2) within- and between-module connectivity. Global modular organization was not significantly altered across groups. However, alterations in within- and between-module connectivity were observed for higher-order cognitive (e.g., central-executive salience, memory, default mode), and sensory modules in patients with schizophrenia and nonclinical individuals with hallucinations relative to controls. Dissimilar patterns of altered within- and between-module connectivity were found bipolar disorder patients with hallucinations relative to controls, including the visual, default mode, and memory network, while connectivity patterns between visual, salience, and cognitive control modules were unaltered. Bipolar disorder patients without hallucinations did not show significant alterations relative to controls. This study provides evidence for alterations in the modular organization of the functional connectome in individuals prone to hallucinations, with schizophrenia patients and nonclinical individuals showing similar alterations in sensory and higher-order cognitive modules. Other higher-order cognitive modules were found to relate to hallucinations in bipolar disorder patients, suggesting differential neural mechanisms may underlie hallucinations across the psychosis continuum.

Topological alteration is associated with non-dependent alcohol use in bipolar disorder

INTRODUCTION

Structural alterations in cortical thickness and the microstructural organisation of white matter are independently associated with non-dependent alcohol consumption and bipolar disorder(BD). Identifying their interactive and network level effects on brain topology may identify the impact of alcohol on reward and emotion circuitry, and its contribution to relapse in BD.

METHODS

Thirty-four BD-I (DSM-IV-TR) and 38 healthy controls underwent T1 and diffusion-weighted MRI scanning, and the AUDIT-C to assess alcohol use. Connectomes comprised of 34 cortical and nine subcortical nodes bilaterally (Freesurfer v5.3) connected by fractional anisotropy-weighted edges derived from non-tensor based deterministic constrained spherical deconvolution tractography (ExploreDTI v4.8.6) underwent permutation-based topological analysis (NBS v1.2) and were examined for effects of alcohol use and diagnosis-by-alcohol use accounting for age, sex and diagnosis.

RESULTS

Alcohol was significantly related to a subnetwork, encompassing connections between fronto-limbic, basal ganglia and temporal nodes (Frange=5-8.4, p=0.031) and was not detected to have an effect on global brain integration or segregation. A portion of this network (18%), involving cortico-limbic and basal ganglia connections, was differentially impacted by alcohol in the BD relative to the control group (Frange=5-8.8, p=0.033), despite the groups' consuming similar amounts of alcohol (BD: mean±SD 4.95±3.0; HC 3.62±3.0, T=1.88, p=0.06).

DISCUSSION

Non-dependent alcohol use impacts brain architectural organization and connectivity within salience, reward, and affective circuitry. The relationship between alcohol use and topology of the network in BD suggests an interactive effect between specific biological vulnerability and alcohol use, which may explain susceptibility to increased risk of relapse in the disorder.

Aberrant Subnetwork and Hub Dysconnectivity in Adult Bipolar Disorder: A Multicenter Graph Theory Analysis

Neuroimaging evidence implicates structural network-level abnormalities in bipolar disorder (BD); however, there remain conflicting results in the current literature hampered by sample size limitations and clinical heterogeneity. Here, we set out to perform a multisite graph theory analysis to assess the extent of neuroanatomical dysconnectivity in a large representative study of individuals with BD. This cross-sectional multicenter international study assessed structural and diffusion-weighted magnetic resonance imaging data obtained from 109 subjects with BD type 1 and 103 psychiatrically healthy volunteers. Whole-brain metrics, permutation-based statistics, and connectivity of highly connected nodes were used to compare network-level connectivity patterns in individuals with BD compared with controls. The BD group displayed longer characteristic path length, a weakly connected left frontotemporal network, and increased rich-club dysconnectivity compared with healthy controls. Our multisite findings implicate emotion and reward networks dysconnectivity in bipolar illness and may guide larger scale global efforts in understanding how human brain architecture impacts mood regulation in BD.

An updated reappraisal of synapsins: structure, function and role in neurological and psychiatric disorders

Synapsins (Syns) are phosphoproteins strongly involved in neuronal development and neurotransmitter release. Three distinct genes SYN1, SYN2 and SYN3, with elevated evolutionary conservation, have been described to encode for Synapsin I, Synapsin II and Synapsin III, respectively. Syns display a series of common features, but also exhibit distinctive localization, expression pattern, post-translational modifications (PTM). These characteristics enable their interaction with other synaptic proteins, membranes and cytoskeletal components, which is essential for the proper execution of their multiple functions in neuronal cells. These include the control of synapse formation and growth, neuron maturation and renewal, as well as synaptic vesicle mobilization, docking, fusion, recycling. Perturbations in the balanced expression of Syns, alterations of their PTM, mutations and polymorphisms of their encoding genes induce severe dysregulations in brain networks functions leading to the onset of psychiatric or neurological disorders. This review presents what we have learned since the discovery of Syn I in 1977, providing the state of the art on Syns structure, function, physiology and involvement in central nervous system disorders.

Decline of anterior cingulate functional network efficiency in first-episode, medication-naïve somatic symptom disorder and its relationship with catastrophizing

The high prevalence of somatic symptom disorder (SSD) led to cumulative burdens to the medical system. However, the pathogenesis of this disease still remains unclear. Graph theoretical analysis discovered altered network topology across various psychiatric disorders, yet alteration in the topological structure of brain functional network in SSD patients is still unexplored. Catastrophizing is a common cognitive distortion in SSD. We hypothesize that the network topological metrics of SSD should be altered, and should correlate with catastrophizing scales. 32 medication-naïve, first-episode SSD patients and 30 age, gender matched HCs were recruited. The 17-item Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA) and Cognitive Emotion Regulation Questionnaire (CERQ) were accessed. Functional MRI were scanned and brain functional networks were constructed based on 166 anatomically cerebrum regions from the automated anatomical labeling 3 (AAL3) template. Network topological metrics were calculated and compared between the two groups. Correlation between these metrics and clinical scales were also calculated. Network global efficiency of SSD was significantly lower than that of HC. Nodal global efficiency of the left subgenual anterior cingulate cortex (sgACC) of SSD was significantly lower than that of HC. FCs between the left sgACC and other 21 seed nodes were significantly declined in SSD in comparison with HC. In SSD group, HAMD total score was significantly negatively correlated with the connection between the left medial superior frontal gyrus and the left sgACC. CERQ catastrophizing score was significantly negatively correlated with nodal global efficiency of left sgACC and with the FCs between the left sgACC and other 13 seed nodes. Catastrophizing could reflect the specific sgACC-centered dysfunction of brain network global efficiency of SSD. The left sgACC may be a future treatment target of dealing with catastrophizing, which is a core cognitive distortion of SSD.

Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are "easily" reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks

A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.

Predicting MEG resting-state functional connectivity from microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

Structural brain network differences in bipolar disorder using with similarity-based approach

Objective: Previous studies have shown differences in the regional brain structure and function between patients with bipolar disorder (BD) and healthy subjects, but little is known about the structural connectivity between BD patients and healthy subjects. In this study, we evaluated the disease-related changes in regional structural connectivity derived from gray matter magnetic resonance imaging (MRI) scans. Methods: The subjects were 73 patients with BD and 80 healthy volunteers who underwent 3-Tesla MRI. Network metrics, such as the small world properties, were computed. We also performed rendering of the network metric images such as the degree, betweenness centrality, and clustering coefficient, on individual brain image. Then, we estimated the differences between them, and evaluate the relationships between the clinical symptoms and the network metrics in the patients with BD. Results: BD patients showed a lower clustering coefficient in the right parietal region and left occipital region, compared with healthy subjects. A weak negative correlation between Young mania rating scale and clustering coefficient was found in left anterior cingulate cortex. Conclusions: We found differences in gray matter structural connectivity between BD patients and healthy subjects by a similarity-based approach. These points may provide objective biological information as an adjunct to the clinical diagnosis of BD.

Changes in the brain structural connectome after a prospective randomized clinical trial of lithium and quetiapine treatment in youth with bipolar disorder

The goals of the current study were to determine whether topological organization of brain structural networks is altered in youth with bipolar disorder, whether such alterations predict treatment outcomes, and whether they are normalized by treatment. Youth with bipolar disorder were randomized to double-blind treatment with quetiapine or lithium and assessed weekly. High-resolution MRI images were collected from children and adolescents with bipolar disorder who were experiencing a mixed or manic episode (n = 100) and healthy youth (n = 63). Brain networks were constructed based on the similarity of morphological features across regions and analyzed using graph theory approaches. We tested for pretreatment anatomical differences between bipolar and healthy youth and for changes in neuroanatomic network metrics following treatment in the youth with bipolar disorder. Youth with bipolar disorder showed significantly increased clustering coefficient (Cp) (p = 0.009) and characteristic path length (Lp) (p = 0.04) at baseline, and altered nodal centralities in insula, inferior frontal gyrus, and supplementary motor area. Cp, Lp, and nodal centrality of the insula exhibited normalization in patients following treatment. Changes in these neuroanatomic parameters were correlated with improvement in manic symptoms but did not differ between the two drug therapies. Baseline structural network matrices significantly differentiated medication responders and non-responders with 80% accuracy. These findings demonstrate that both global and nodal structural network features are altered in early course bipolar disorder, and that pretreatment alterations in neuroanatomic features predicted treatment outcome and were reduced by treatment. Similar connectome normalization with lithium and quetiapine suggests that the connectome changes are a downstream effect of both therapies that is related to their clinical efficacy.

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

In the present work, we show that by using a myelin-sensitive measure we can complement the diffusion MRI-based connectivity and provide a different picture of the brain organization. We show that the R1-weighted average distribution does not follow the same trend as the number of streamlines strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions have higher R1-weighted average but fewer streamlines. This could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions rely on connections with higher myelination.

Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if they do, how these effects could contribute to the disease course of MDD.

METHODS

This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic patients with MDD and 539 healthy control subjects.

RESULTS

Polygenic risk score for T2D across MDD patients and healthy control subjects was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this fractional anisotropy variation may mediate the association between polygenic risk score and cognitive performance.

CONCLUSIONS

Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in patients with MDD and healthy control subjects, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments.

Structural dysconnectivity in offspring of individuals with bipolar disorder: The effect of co-existing clinical-high-risk for bipolar disorder

BACKGROUND

Bipolar disorder (BD) might be associated in disturbances in brain networks. However, little is known about the abnormalities in structural brain connectivity which might be related to vulnerability to BD and predictive of the emergence of manic symptoms. No previous study has investigated the effect of subthreshold syndromes on structural dysconnectivity in offspring of parents with BD (BDoff).

METHODS

We investigated diffusion weighted images of 70 BDoff and 48 healthy controls (HC). Nineteen of the 70 BDoff had presented with subthreshold syndromes indicating a clinical high-risk (BDoff-CHR) and other 51 BDoff had no such history (BDoff-non-CHR). Global and regional network properties, rich club organization and inter-regional connectivity in BDoff and healthy controls were investigated using graph analytical methods and network-based-statistics (NBS).

RESULTS

Global properties of WM networks appeared to be intact in BDoff-CHR and BDoff-non-CHR. However, decreased regional connectivity in right occipito-parietal areas and cerebellum was a common feature of both BDoff groups. Importantly, decreased interregional connectivity between nodes in right and left prefrontal regions, nodes in right prefrontal lobe and right temporal lobe and nodes in left occipital area and left cerebellum were evident in BDoff-CHR but not BDoff-non-CHR.

LIMITATIONS

The cross-sectional nature of the study was the main consideration.

CONCLUSION

Decreased regional connectivity in right posterior brain regions might be related to vulnerability to BD. On the other hand, interregional dysconnectivity in anterior frontal and limbic regions and left posterior brain regions might be evident in individuals genetically at risk for developing BD who had experienced subthreshold mood symptoms.

Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored.

METHODS

An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD.

RESULTS

Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations.

CONCLUSION

These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.

Functional connectome differences in individuals with hallucinations across the psychosis continuum

Hallucinations may arise from an imbalance between sensory and higher cognitive brain regions, reflected by alterations in functional connectivity. It is unknown whether hallucinations across the psychosis continuum exhibit similar alterations in functional connectivity, suggesting a common neural mechanism, or whether different mechanisms link to hallucinations across phenotypes. We acquired resting-state functional MRI scans of 483 participants, including 40 non-clinical individuals with hallucinations, 99 schizophrenia patients with hallucinations, 74 bipolar-I disorder patients with hallucinations, 42 bipolar-I disorder patients without hallucinations, and 228 healthy controls. The weighted connectivity matrices were compared using network-based statistics. Non-clinical individuals with hallucinations and schizophrenia patients with hallucinations exhibited increased connectivity, mainly among fronto-temporal and fronto-insula/cingulate areas compared to controls (P < 0.001 adjusted). Differential effects were observed for bipolar-I disorder patients with hallucinations versus controls, mainly characterized by decreased connectivity between fronto-temporal and fronto-striatal areas (P = 0.012 adjusted). No connectivity alterations were found between bipolar-I disorder patients without hallucinations and controls. Our results support the notion that hallucinations in non-clinical individuals and schizophrenia patients are related to altered interactions between sensory and higher-order cognitive brain regions. However, a different dysconnectivity pattern was observed for bipolar-I disorder patients with hallucinations, which implies a different neural mechanism across the psychosis continuum.

Prolonged functional cerebral asymmetry as a consequence of dysfunctional parvocellular paraventricular hypothalamic nucleus signaling: An integrative model for the pathophysiology of bipolar disorder

Approximately 45 million people worldwide are diagnosed with bipolar disorder (BD). While there are many known risk factors and models of the pathologic processes influencing BD, the exact neurologic underpinnings of BD are unknown. We attempt to integrate the existing literature and create a unifying hypothesis regarding the pathophysiology of BD with the hope that a concrete model may potentially facilitate more specific diagnosis, prevention, and treatment of BD in the future. We hypothesize that dysfunctional signaling from the parvocellular neurons of the paraventricular hypothalamic nucleus (PVN) results in the clinical presentation of BD. Functional damage to this nucleus and its signaling pathways may be mediated by myriad factors (e.g. immune dysregulation and auto-immune processes, polygenetic variation, dysfunctional interhemispheric connections, and impaired or overactivated hypothalamic axes) which could help explain the wide variety of clinical presentations along the BD spectrum. The neurons of the PVN regulate ultradian rhythms, which are observed in cyclic variations in healthy individuals, and mediate changes in functional hemispheric lateralization. Theoretically, dysfunctional PVN signaling results in prolonged functional hemispheric dominance. In this model, prolonged right hemispheric dominance leads to depressive symptoms, whereas left hemispheric dominance correlated to the clinical picture of mania. Subsequently, physiologic processes that increase signaling through the PVN (hypothalamic-pituitaryadrenal axis, hypothalamic- pituitary-gonadal axis, and hypothalamic-pituitary-thyroid axis activity, suprachiasmatic nucleus pathways) as well as, neuro-endocrine induced excito-toxicity, auto-immune and inflammatory flairs may induce mood episodes in susceptible individuals. Potentially, ultradian rhythms slowing with age, in combination with changes in hypothalamic axes and maturation of neural circuitry, accounts for BD clinically presenting more frequently in young adulthood than later in life.

Functional brain networks in the schizophrenia spectrum and bipolar disorder with psychosis

Psychotic experiences have been proposed to lie on a spectrum, ranging from subclinical experiences to treatment-resistant schizophrenia. We aimed to characterize functional connectivity and brain network characteristics in relation to the schizophrenia spectrum and bipolar disorder with psychosis to disentangle neural correlates to psychosis. Additionally, we studied antipsychotic medication and lithium effects on network characteristics. We analyzed functional connectivity strength and network topology in 487 resting-state functional MRI scans of individuals with schizophrenia spectrum disorder (SCZ), bipolar disorder with a history of psychotic experiences (BD), treatment-naïve subclinical psychosis (SCP), and healthy controls (HC). Since differences in connectivity strength may confound group comparisons of brain network topology, we analyzed characteristics of the minimum spanning tree (MST), a relatively unbiased backbone of the network. SCZ and SCP subjects had a lower connectivity strength than BD and HC individuals but showed no differences in network topology. In contrast, BD patients showed a less integrated network topology but no disturbances in connectivity strength. No differences in outcome measures were found between SCP and SCZ, or between BD patients that used antipsychotic medication or lithium and those that did not. We conclude that functional networks in patients prone to psychosis have different signatures for chronic SCZ patients and SCP compared to euthymic BD patients, with a limited role for medication. Connectivity strength effects may have confounded previous studies, as no functional network alterations were found in SCZ after strict correction for connectivity strength.

Disrupted Rich Club Organization of Hemispheric White Matter Networks in Bipolar Disorder

Neuroimaging studies suggest disrupted connections of the brain white matter (WM) network in bipolar disorder (BD). A group of highly interconnected high-density structures, termed the 'rich club,' represents an important network for brain functioning. Recent works have revealed abnormal rich club organization in brain networks in BD. However, little is known regarding changes in the rich club organization of the hemispheric WM network in BD. Forty-nine BD patients and fifty-five age- and sex-matched normal controls (NCs) underwent diffusion tensor imaging (DTI). Graph theory approaches were applied to quantify group-specific rich club organization and nodal degree of hemispheric WM networks. We demonstrated that rich club organization of hemispheric WM networks in BD was disrupted, with disrupted feeder and local connections among hub and peripheral regions located in the default mode network (DMN) and the control execution network (CEN). In addition, BD patients showed abnormal asymmetry in the feeder and local connections, involving the hub and peripheral regions associated with emotion regulation and visuospatial functions. Moreover, the clinical symptoms of BD showed a significant correlation with the aberrant asymmetry in the regional degree of peripheral regions. These findings reveal that BD is closely associated with disrupted feeder and local connections but no alteration in rich-club connections in the rich club organization of hemispheric WM networks and provide novel insight into the changes of brain functions in BD.

Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.

Rich-club in the brain's macrostructure: Insights from graph theoretical analysis

The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder

Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can help in understanding the brain mechanisms of schizophrenia and bipolar disorder. We investigated cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder. T1-weighted magnetic resonance images were obtained from 39 patients with schizophrenia, 37 patients with bipolar disorder type I, and 32 healthy controls, and cortical thickness was analyzed via a surface-based morphometry analysis. The structural covariance of cortical thickness was calculated at the individual level, and covariance networks were analyzed based on graph theoretical indices: strength, clustering coefficient (CC), path length (PL) and efficiency. At the global level, both patient groups showed decreased strength, CC and efficiency, and increased PL, compared to healthy controls. In bipolar disorder, we found intermediate network measures among the groups. At the nodal level, schizophrenia patients showed decreased CCs in the left suborbital sulcus and the right superior frontal sulcus, compared to bipolar disorder patients. In addition, patient groups showed decreased CCs in the right insular cortex and the left superior occipital gyrus. Global-level network indices, including strength, CCs and efficiency, positively correlated, while PL negatively correlated, with the positive symptoms of the Positive and Negative Syndrome Scale for patients with schizophrenia. The nodal-level CC of the right insular cortex positively correlated with the positive symptoms of schizophrenia, while that of the left superior occipital gyrus positively correlated with the Young Mania Rating Scale scores for bipolar disorder. Altered cortical structural networks were revealed in patients, and particularly, the prefrontal regions were more altered in schizophrenia. Furthermore, altered cortical structural networks in both patient groups correlated with core pathological symptoms, indicating that the insular cortex is more vulnerable in schizophrenia, and the superior occipital gyrus is more vulnerable in bipolar disorder. Our individualized structural covariance network indices might be promising biomarkers for the evaluation of patients with schizophrenia and bipolar disorder.

Effects of COMT rs4680 and BDNF rs6265 polymorphisms on brain degree centrality in Han Chinese adults who lost their only child

Losing one's only child is a major traumatic life event that may lead to posttraumatic stress disorder (PTSD); however, not all parents who experience this trauma develop PTSD. Genetic variants are associated with the risk of developing PTSD. Catechol-O-methyltransferase (COMT) rs4680 and brain-derived neurotrophic factor (BDNF) rs6265 are two most well-described single-nucleotide polymorphisms that relate to stress response; however, the neural mechanism underlying their effects on adults who lost an only child remains poorly understood. Two hundred and ten Han Chinese adults who had lost their only child (55 with PTSD and 155 without PTSD) were included in this imaging genetics study. Participants were divided into subgroups according to their COMT rs4680 and BDNF rs6265 genotypes. Degree Centrality (DC)-a resting-state fMRI index reflecting the brain network communication-was compared with a three-way (PTSD diagnosis, COMT, and BDNF polymorphisms) analysis of covariance. Diagnosis state had a significant effect on DC in bilateral inferior parietal lobules and right middle frontal gyrus (MFG), where PTSD adults showed weaker DC. BDNF × diagnosis interaction effect was found in the right MFG and hippocampus, and these two regions were reversely modulated. Also, there was a significant COMT × BDNF interaction effect in left cuneus, middle temporal gyrus, right inferior occipital gyrus, and bilateral putamen, independent of PTSD diagnosis. These findings suggest that the modulatory effect of BDNF polymorphism on the MFG and hippocampus may contribute to PTSD development in bereaved adults. Interactions of COMT × BDNF polymorphisms modulate some cortices and basal ganglia, irrespective of PTSD development.

Abnormal topology of brain functional networks in unipolar depression and bipolar disorder using optimal graph thresholding

Two popular debilitating illness, unipolar depression (UD) and bipolar disorder (BD), have the similar symptoms and tight association on the psychopathological level, leading to a clinical challenge to distinguish them. In order to figure out the underlying common and different mechanism of both mood disorders, resting-state functional magnetic resonance imaging (rs-fMRI) data derived from 36 UD patients, 42 BD patients (specially type I, BD-I) and 45 healthy controls (HC) were analyzed retrospectively in this study. Functional brain networks were firstly constructed on both group and individual levels with a density 0.2, which was determined by a network thresholding approach based on modular similarity. Then we investigated the alterations of modular structure and other topological properties of the functional brain network, including global network characteristics and nodal network measures. The results demonstrated that the functional brain networks of UD and BD-I groups preserved the modularity and small-worldness property. However, compared with HC, reduced number of modules was observed in both patients' groups with shared alterations occurring in hippocampus, para hippocampal gyrus, amygdala and superior parietal gyrus and distinct changes of modular composition mainly in the caudate regions of basal ganglia. Additionally, for the network characteristics, compared to HC, significantly decreased global efficiency and small-worldness were observed in BD-I. For the nodal metrics, significant decrease of local efficiency was found in several regions in both UD and BD-I, while a UD-specified increase of participant coefficient was found in the right paracentral lobule and the right thalamus. These findings may contribute to throw light on the neuropathological mechanisms underlying the two disorders and further help to explore objective biomarkers for the correct diagnosis of UD and BD.

Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

BACKGROUND

Pathologies of schizophrenia and bipolar disorder have been poorly understood. Brain network analysis could help understand brain mechanisms of schizophrenia and bipolar disorder. This study investigates the source-level brain cortical networks using resting-state electroencephalography (EEG) in patients with schizophrenia and bipolar disorder.

METHODS

Resting-state EEG was measured in 38 patients with schizophrenia, 34 patients with bipolar disorder type I, and 30 healthy controls. Graph theory based source-level weighted functional networks were evaluated: strength, clustering coefficient (CC), path length (PL), and efficiency in six frequency bands.

RESULTS

At the global level, patients with schizophrenia or bipolar disorder showed higher strength, CC, and efficiency, and lower PL in the theta band, compared to healthy controls. At the nodal level, patients with schizophrenia or bipolar disorder showed higher CCs, mostly in the frontal lobe for the theta band. Particularly, patients with schizophrenia showed higher nodal CCs in the left inferior frontal cortex and the left ascending ramus of the lateral sulcus compared to patients with bipolar disorder. In addition, the nodal-level theta band CC of the superior frontal gyrus and sulcus (cognition-related region) correlated with positive symptoms and social and occupational functioning scale (SOFAS) scores in the schizophrenia group, while that of the middle frontal gyrus (emotion-related region) correlated with SOFAS scores in the bipolar disorder group.

CONCLUSIONS

Altered cortical networks were revealed and these alterations were significantly correlated with core pathological symptoms of schizophrenia and bipolar disorder. These source-level cortical network indices could be promising biomarkers to evaluate patients with schizophrenia and bipolar disorder.

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

BACKGROUND

White matter network alterations have increasingly been implicated in major depressive disorder, bipolar disorder and schizophrenia. The aim of this study was to identify shared and distinct white matter network alterations among the 3 disorders.

METHODS

We used analysis of covariance, with age and gender as covariates, to investigate white matter network alterations in 123 patients with schizophrenia, 123 with bipolar disorder, 124 with major depressive disorder and 209 healthy controls.

RESULTS

We found significant group differences in global network efficiency (F = 3.386, p = 0.018), nodal efficiency (F = 8.015, p < 0.001 corrected for false discovery rate [FDR]) and nodal degree (F = 5.971, pFDR < 0.001) in the left middle occipital gyrus, as well as nodal efficiency (F = 6.930, pFDR < 0.001) and nodal degree (F = 5.884, pFDR < 0.001) in the left postcentral gyrus. We found no significant alterations in patients with major depressive disorder. Post hoc analyses revealed that compared with healthy controls, patients in the schizophrenia and bipolar disorder groups showed decreased global network efficiency, nodal efficiency and nodal degree in the left middle occipital gyrus. Furthermore, patients in the schizophrenia group showed decreased nodal efficiency and nodal degree in the left postcentral gyrus compared with healthy controls.

LIMITATIONS

Our findings could have been confounded in part by treatment differences.

CONCLUSION

Our findings implicate graded white matter network alterations across the 3 disorders, enhancing our understanding of shared and distinct pathophysiological mechanisms across diagnoses and providing vital insights into neuroimaging-based methods for diagnosis and research.

Neuroanatomical Dysconnectivity Underlying Cognitive Deficits in Bipolar Disorder

BACKGROUND

Graph theory applied to brain networks is an emerging approach to understanding the brain's topological associations with human cognitive ability. Despite well-documented cognitive impairments in bipolar disorder (BD) and recent reports of altered anatomical network organization, the association between connectivity and cognitive impairments in BD remains unclear.

METHODS

We examined the role of anatomical network connectivity derived from T1- and diffusion-weighted magnetic resonance imaging in impaired cognitive performance in individuals with BD (n = 32) compared with healthy control individuals (n = 38). Fractional anisotropy- and number of streamlines-weighted anatomical brain networks were generated by mapping constrained spherical deconvolution-reconstructed white matter among 86 cortical/subcortical bilateral brain regions delineated in the individual's own coordinate space. Intelligence and executive function were investigated as distributed functions using measures of global, rich-club, and interhemispheric connectivity, while memory and social cognition were examined in relation to subnetwork connectivity.

RESULTS

Lower executive functioning related to higher global clustering coefficient in participants with BD, and lower IQ performance may present with a differential relationship between global and interhemispheric efficiency in individuals with BD relative to control individuals. Spatial recognition memory accuracy and response times were similar between diagnostic groups and associated with basal ganglia and thalamus interconnectivity and connectivity within extended anatomical subnetworks in all participants. No anatomical subnetworks related to episodic memory, short-term memory, or social cognition generally or differently in BD.

CONCLUSIONS

Results demonstrate selective influence of subnetwork patterns of connectivity in underlying cognitive performance generally and abnormal global topology underlying discrete cognitive impairments in BD.

Bipolar Disorder and Gender Are Associated with Frontolimbic and Basal Ganglia Dysconnectivity: A Study of Topological Variance Using Network Analysis

Well-established structural abnormalities, mostly involving the limbic system, have been associated with disorders of emotion regulation. Understanding the arrangement and connections of these regions with other functionally specialized cortico-subcortical subnetworks is key to understanding how the human brain's architecture underpins abnormalities of mood and emotion. We investigated topological patterns in bipolar disorder (BD) with the anatomically improved precision conferred by combining subject-specific parcellation/segmentation with nontensor-based tractograms derived using a high-angular resolution diffusion-weighted approach. Connectivity matrices were constructed using 34 cortical and 9 subcortical bilateral nodes (Desikan-Killiany), and edges that were weighted by fractional anisotropy and streamline count derived from deterministic tractography using constrained spherical deconvolution. Whole-brain and rich-club connectivity alongside a permutation-based statistical approach was used to investigate topological variance in predominantly euthymic BD relative to healthy volunteers. BP patients (n = 40) demonstrated impairments across whole-brain topological arrangements (density, degree, and efficiency), and a dysconnected subnetwork involving limbic and basal ganglia relative to controls (n = 45). Increased rich-club connectivity was most evident in females with BD, with frontolimbic and parieto-occipital nodes not members of BD rich-club. Increased centrality in females relative to males was driven by basal ganglia and fronto-temporo-limbic nodes. Our subject-specific cortico-subcortical nontensor-based connectome map presents a neuroanatomical model of BD dysconnectivity that differentially involves communication within and between emotion-regulatory and reward-related subsystems. Moreover, the female brain positions more dependence on nodes belonging to these two differently specialized subsystems for communication relative to males, which may confer increased susceptibility to processes dependent on integration of emotion and reward-related information.

Evolutionary modifications in human brain connectivity associated with schizophrenia

The genetic basis and human-specific character of schizophrenia has led to the hypothesis that human brain evolution may have played a role in the development of the disorder. We examined schizophrenia-related changes in brain connectivity in the context of evolutionary changes in human brain wiring by comparing in vivo neuroimaging data from humans and chimpanzees, one of our closest living evolutionary relatives and a species with which we share a very recent common ancestor. We contrasted the connectome layout between the chimpanzee and human brain and compared differences with the pattern of schizophrenia-related changes in brain connectivity as observed in patients. We show evidence of evolutionary modifications of human brain connectivity to significantly overlap with the cortical pattern of schizophrenia-related dysconnectivity (P < 0.001, permutation testing). We validated these effects in three additional, independent schizophrenia datasets. We further assessed the specificity of effects by examining brain dysconnectivity patterns in seven other psychiatric and neurological brain disorders (including, among others, major depressive disorder and obsessive-compulsive disorder, arguably characterized by behavioural symptoms that are less specific to humans), which showed no such associations with modifications of human brain connectivity. Comparisons of brain connectivity across humans, chimpanzee and macaques further suggest that features of connectivity that evolved in the human lineage showed the strongest association to the disorder, that is, brain circuits potentially related to human evolutionary specializations. Taken together, our findings suggest that human-specific features of connectome organization may be enriched for changes in brain connectivity related to schizophrenia. Modifications in human brain connectivity in service of higher order brain functions may have potentially also rendered the brain vulnerable to brain dysfunction.

Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis

Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of euthymic PBD patients with psychosis, a variant of PBD, and matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients with psychosis in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed, revealing trends of association between patients’ IQ and affective psychotic symptoms with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD with psychosis is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which show trends of association with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.

Structural connectivity in schizophrenia and bipolar disorder: Effects of chronicity and antipsychotic treatment

Previous studies based on graph theory parameters applied to diffusion tensor imaging support an alteration of the global properties of structural connectivity network in schizophrenia. However, the specificity of this alteration and its possible relation with chronicity and treatment have received small attention. We have assessed small-world (SW) and connectivity strength indexes of the structural network built using fractional anisotropy values of the white matter tracts connecting 84 cortical and subcortical regions in 25 chronic and 18 first episode (FE) schizophrenia and 24 bipolar patients and 28 healthy controls. Chronic schizophrenia and bipolar patients showed significantly smaller SW and connectivity strength indexes in comparison with controls and FE patients. SW reduction was driven by increased averaged path-length (PL) values. Illness duration but not treatment doses were negatively associated with connectivity strength, SW and PL in patients. Bipolar patients exposed to antipsychotics did not differ in SW or connectivity strength from bipolar patients without such an exposure. Executive functions and social cognition were related to SW index in the schizophrenia group. Our results support a role for chronicity but not treatment in structural network alterations in major psychoses, which may not differ between schizophrenia and bipolar disorder, and may hamper cognition.

Optimization of graph construction can significantly increase the power of structural brain network studies

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm²). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.

Aberrant brain structural-functional connectivity coupling in euthymic bipolar disorder

Aberrant structural (diffusion tensor imaging [DTI]) and resting-state functional magnetic resonance imagining connectivity are core features of bipolar disorder. However, few studies have explored the integrity agreement between structural and functional connectivity (SC-FC) in bipolar disorder. We examine SC connectivity coupling index whether could potentially provide additional clinical predictive value for bipolar disorder spectrum disorders besides the intramodality network measures. By examining the structural (DTI) and resting-state functional network properties, as well as their coupling index, among 57 euthymic bipolar disorder patients (age 13-28 years, 18 females) and 42 age- and gender-matched healthy controls (age 13-28 years, 16 females), we found that compared to controls, bipolar disorder patients showed increased structural rich-club connectivity as well as decreased functional modularity. Importantly, the coupling strength between structural and functional connectome was decreased in patients compared to controls, which emerged as the most powerful feature discriminating the two groups. Our findings suggest that structural-functional coupling strength could serve as a valuable biological trait-like feature for bipolar disorder over and above the intramodality network measures. Such measure can have important clinical implications for early identification of bipolar disorder individuals, and inform strategies for prevention of bipolar disorder onset and relapse.

Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.

Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder

BACKGROUND

Bipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD.

METHODS

Forty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters.

RESULTS

Compared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density.

CONCLUSIONS

Our results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.