Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia

Large-scale mechanism hypothesis and research prospects of cognitive impairment in schizophrenia based on magnetic resonance imaging

Cognitive impairments in schizophrenia are pivotal clinical issues that need to be solved urgently. However, the mechanism remains unknown. It has been suggested that cognitive impairments in schizophrenia are associated with connectome damage, and are especially relevant to the disrupted hub nodes in the frontal and parietal lobes. Activating the dorsolateral prefrontal cortex (DLPFC) via repetitive transcranial magnetic stimulation (rTMS) could result in improved cognition. Based on several previous magnetic resonance imaging (MRI) studies on schizophrenia, we found that the first-episode patients showed connectome damage, as well as abnormal activation and connectivity of the DLPFC and inferior parietal lobule (IPL). Accordingly, we proposed that DLPFC-IPL pathway destruction might mediate connectome damage of cognitive impairments in schizophrenia. In the meantime, with the help of multimodal MRI and noninvasive neuromodulation tool, we may not only validate the hypothesis, but also find IPL as the potential intervention target for cognitive impairments in schizophrenia.

Rich-club connectivity and structural connectome organization in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia

Brain dysconnectivity has been posited as a biological marker of schizophrenia. Emerging schizophrenia connectome research has focused on rich-club organization, a tendency for brain hubs to be highly-interconnected but disproportionately vulnerable to dysconnectivity. However, less is known about rich-club organization in individuals at clinical high-risk for psychosis (CHR-P) and how it compares with abnormalities early in schizophrenia (ESZ). Combining diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), we examined rich-club and global network organization in CHR-P (n = 41) and ESZ (n = 70) relative to healthy controls (HC; n = 74) after accounting for normal aging. To characterize rich-club regions, we examined rich-club MRI morphometry (thickness, surface area). We also examined connectome metric associations with symptom severity, antipsychotic dosage, and in CHR-P specifically, transition to a full-blown psychotic disorder. ESZ had fewer connections among rich-club regions (ps < .024) relative to HC and CHR-P, with this reduction specific to the rich-club even after accounting for other connections in ESZ relative to HC (ps < .048). There was also cortical thinning of rich-club regions in ESZ (ps < .013). In contrast, there was no strong evidence of global network organization differences among the three groups. Although connectome abnormalities were not present in CHR-P overall, CHR-P converters to psychosis (n = 9) had fewer connections among rich-club regions (ps < .037) and greater modularity (ps < .037) compared to CHR-P non-converters (n = 19). Lastly, symptom severity and antipsychotic dosage were not significantly associated with connectome metrics (ps < .012). Findings suggest that rich-club and connectome organization abnormalities are present early in schizophrenia and in CHR-P individuals who subsequently transition to psychosis.

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.

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.

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study

Growing evidence indicates that disruptions in the brain's functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

Aerobic Exercise Induces Functional and Structural Reorganization of CNS Networks in Multiple Sclerosis: A Randomized Controlled Trial

Objectives: Evidence from animal studies suggests that aerobic exercise may promote neuroplasticity and could, therefore, provide therapeutic benefits for neurological diseases such as multiple sclerosis (MS). However, the effects of exercise in human CNS disorders on the topology of brain networks, which might serve as an outcome at the interface between biology and clinical performance, remain poorly understood.

Methods: We investigated functional and structural networks in patients with relapsing-remitting MS in a clinical trial of standardized aerobic exercise. Fifty-seven patients were randomly assigned to moderate-intensity exercise for 3 months or a non-exercise control group. We reconstructed functional networks based on resting-state functional magnetic resonance imaging (MRI) and used probabilistic tractography on diffusion-weighted imaging data for structural networks.

Results: At baseline, compared to 30 healthy controls, patients exhibited decreased structural connectivity that was most pronounced in hub regions of the brain. Vice versa, functional connectivity was increased in hubs. After 3 months, we observed hub independent increased functional connectivity in the exercise group while the control group presented a loss of functional hub connectivity. On a structural level, the control group remained unchanged, while the exercise group had also increased connectivity. Increased clustering of hubs indicates a better structural integration and internal connectivity at the top of the network hierarchy.

Conclusion: Increased functional connectivity of hubs contrasts a loss of structural connectivity in relapsing-remitting MS. Under an exercise condition, a further hub independent increase of functional connectivity seems to translate in higher structural connectivity of the whole brain.

Good neighbors, bad neighbors: the frequent network neighborhood mapping of the hippocampus enlightens several structural factors of the human intelligence on a 414-subject cohort

The human connectome has become the very frequent subject of study of brain-scientists, psychologists and imaging experts in the last decade. With diffusion magnetic resonance imaging techniques, united with advanced data processing algorithms, today we are able to compute braingraphs with several hundred, anatomically identified nodes and thousands of edges, corresponding to the anatomical connections of the brain. The analysis of these graphs without refined mathematical tools is hopeless. These tools need to address the high error rate of the MRI processing workflow, and need to find structural causes or at least correlations of psychological properties and cerebral connections. Until now, structural connectomics was only rarely able of identifying such causes or correlations. In the present work we study the frequent neighbor sets of the most deeply investigated brain area, the hippocampus. By applying the Frequent Network Neighborhood mapping method, we identified frequent neighbor-sets of the hippocampus, which may influence numerous psychological parameters, including intelligence-related ones. We have found "Good Neighbor" sets, which correlate with better test results and also "Bad Neighbor" sets, which correlate with worse test results. Our study utilizes the braingraphs, computed from the imaging data of the Human Connectome Project's 414 subjects, each with 463 anatomically identified nodes.

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.

Impaired brain network architecture in Cushing's disease based on graph theoretical analysis

To investigate the whole functional brain networks of active Cushing disease (CD) patients about topological parameters (small world and rich club et al.) and compared with healthy control (NC). Nineteen active CD patients and twenty-two healthy control subjects, matched in age, gender, and education, underwent resting-state fMRI. Graph theoretical analysis was used to calculate the functional brain network organizations for all participants, and those for active CD patients were compared for and NCs. Active CD patients revealed higher global efficiency, shortest path length and reduced cluster efficiency compared with healthy control. Additionally, small world organization was present in active CD patients but higher than healthy control. Moreover, rich club connections, feeder connections and local connections were significantly decreased in active CD patients. Functional network properties appeared to be disrupted in active CD patients compared with healthy control. Analyzing the changes that lead to abnormal network metrics will improve our understanding of the pathophysiological mechanisms underlying CD.

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.

Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury

The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.

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.

Network structural dependency in the human connectome across the life-span

Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.

Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study

Higher polygenic risk score for schizophrenia (szPGRS) has been associated with lower cognitive function and might be a predictor of decline in brain structure in apparently healthy populations. Age-related declines in structural brain connectivity-measured using white matter diffusion MRI -are evident from cross-sectional data. Yet, it remains unclear how graph theoretical metrics of the structural connectome change over time, and whether szPGRS is associated with differences in ageing-related changes in human brain connectivity. Here, we studied a large, relatively healthy, same-year-of-birth, older age cohort over a period of 3 years (age ∼ 73 years, N = 731; age ∼76 years, N = 488). From their brain scans we derived tract-averaged fractional anisotropy (FA) and mean diffusivity (MD), and network topology properties. We investigated the cross-sectional and longitudinal associations between these structural brain variables and szPGRS. Higher szPGRS showed significant associations with longitudinal increases in MD in the splenium (β = 0.132, pFDR = 0.040), arcuate (β = 0.291, pFDR = 0.040), anterior thalamic radiations (β = 0.215, pFDR = 0.040) and cingulum (β = 0.165, pFDR = 0.040). Significant declines over time were observed in graph theory metrics for FA-weighted networks, such as mean edge weight (β = -0.039, pFDR = 0.048) and strength (β = -0.027, pFDR = 0.048). No significant associations were found between szPGRS and graph theory metrics. These results are consistent with the hypothesis that szPGRS confers risk for ageing-related degradation of some aspects of structural connectivity.

Structural connectome with high angular resolution diffusion imaging MRI: assessing the impact of diffusion weighting and sampling on graph-theoretic measures

Purpose

Advances in computational network analysis have enabled the characterization of topological properties of human brain networks (connectomics) from high angular resolution diffusion imaging (HARDI) MRI structural measurements. In this study, the effect of changing the diffusion weighting (b value) and sampling (number of gradient directions) was investigated in ten healthy volunteers, with specific focus on graph theoretical network metrics used to characterize the human connectome.

Methods

Probabilistic tractography based on the Q-ball reconstruction of HARDI MRI measurements was performed and structural connections between all pairs of regions from the automated anatomical labeling (AAL) atlas were estimated, to compare two HARDI schemes: low b value (b = 1000) and low direction number (n = 32) (LBLD); high b value (b = 3000) and high number (n = 54) of directions (HBHD).

Results

LBLD and HBHD data sets produced connectome images with highly overlapping hub structure. Overall, the HBHD scheme yielded significantly higher connection probabilities between cortical and subcortical sites and allowed detecting more connections. Small worldness and modularity were reduced in HBHD data. The clustering coefficient was significantly higher in HBHD data indicating a higher level of segregation in the resulting connectome for the HBHD scheme.

Conclusion

Our results demonstrate that the HARDI scheme as an impact on structural connectome measures which is not automatically implied by the tractography outcome. As the number of gradient directions and b values applied may introduce a bias in the assessment of network properties, the choice of a given HARDI protocol must be carefully considered when comparing results across connectomic studies.

A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children

Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4-13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8-13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157-170, 2018. © 2017 Wiley Periodicals, Inc.

Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia

Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (p_FDR  < 0.05). All metrics across networks were significantly associated with intelligence (p_FDR  < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc.

Reduced rich-club connectivity is related to disability in primary progressive MS

OBJECTIVE

To investigate whether the structural connectivity of the brain's rich-club organization is altered in patients with primary progressive MS and whether such changes to this fundamental network feature are associated with disability measures.

METHODS

We recruited 37 patients with primary progressive MS and 21 healthy controls for an observational cohort study. Structural connectomes were reconstructed based on diffusion-weighted imaging data using probabilistic tractography and analyzed with graph theory.

RESULTS

We observed the same topological organization of brain networks in patients and controls. Consistent with the originally defined rich-club regions, we identified superior frontal, precuneus, superior parietal, and insular cortex in both hemispheres as rich-club nodes. Connectivity within the rich club was significantly reduced in patients with MS (p = 0.039). The extent of reduced rich-club connectivity correlated with clinical measurements of mobility (Kendall rank correlation coefficient τ = -0.20, p = 0.047), hand function (τ = -0.26, p = 0.014), and information processing speed (τ = -0.20, p = 0.049).

CONCLUSIONS

In patients with primary progressive MS, the fundamental organization of the structural connectome in rich-club and peripheral nodes was preserved and did not differ from healthy controls. The proportion of rich-club connections was altered and correlated with disability measures. Thus, the rich-club organization of the brain may be a promising network phenotype for understanding the patterns and mechanisms of neurodegeneration in MS.

Structural Network Disorganization in Subjects at Clinical High Risk for Psychosis

Previous network studies in chronic schizophrenia patients revealed impaired structural organization of the brain's rich-club members, a set of highly interconnected hub regions that play an important integrative role for global brain communication. Moreover, impaired rich-club connectivity has also been found in unaffected siblings of schizophrenia patients, suggesting that abnormal rich-club connectivity is related to familiar, possibly reflecting genetic, vulnerability for schizophrenia. However, no study has yet investigated whether structural rich-club organization is also impaired in individuals with a clinical risk syndrome for psychosis. Diffusion tensor imaging and probabilistic tractography was used to construct structural whole-brain networks in 24 healthy controls and 24 subjects with an at-risk mental state (ARMS). Graph theory was applied to quantify the structural rich-club organization and global network properties. ARMS subjects revealed a significantly altered structural rich-club organization compared with the control group. The disruption of rich-club organization was associated with the severity of negative psychotic symptoms and led to an elevated level of modularity in ARMS subjects. This study shows that abnormal structural rich-club organization is already evident in clinical high-risk subjects for psychosis and further demonstrates the impact of rich-club disorganization on global network communication. Together with previous evidence in chronic schizophrenia patients and unaffected siblings, our findings suggest that abnormal structural rich-club organization may reflect an endophenotypic marker of psychosis.

Serotonin and the Brain's Rich Club-Association Between Molecular Genetic Variation on the TPH2 Gene and the Structural Connectome

The rich club comprises a densely mutually connected set of hub regions in the brain, thought to serve as a processing and integration core. We assessed the impact of normal variation of the tryptophane hydroxylase 2 gene's promotor region (TPH2 rs4570625) on structural connectivity of the rich club pathways by means of a candidate gene association design. Tryptophane hydroxylase 2 (TPH2) is a rate-limiting enzyme in the biosynthesis of serotonin and is known to inhibit, in addition to its role as a trans-synaptic messenger, axonal and dendritic growth. The TPH2 T-variant has been associated with reduced mRNA expression and reduced serotonin levels, which may particularly influence the development of macroscale anatomical connectivity. Here, we show larger mean connectivity in the rich club in carriers of the T-variant, suggesting potential effects of upregulation of neural connectivity growth in this central core system. In addition, by edge-removal statistics, we show that the TPH2-associated higher levels of rich club connectivity are of importance for the functioning of the total structural network. The observed association is speculated to result from an effect of serotonin levels on brain development, potentially leading to stronger structural connectivity in heavily interconnected hubs.

Optimal trajectories of brain state transitions

The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.

Disruption of rich club organisation in cerebral small vessel disease

Cerebral small vessel disease (SVD) is an important cause of vascular cognitive impairment. Recent studies have demonstrated that structural connectivity of brain networks in SVD is disrupted. However, little is known about the extent and location of the reduced connectivity in SVD. Here they investigate the rich club organisation-a set of highly connected and interconnected regions-and investigate whether there is preferential rich club disruption in SVD. Diffusion tensor imaging (DTI) and cognitive assessment were performed in a discovery sample of SVD patients (n = 115) and healthy control subjects (n = 50). Results were replicated in an independent dataset (49 SVD with confluent WMH cases and 108 SVD controls) with SVD patients having a similar SVD phenotype to that of the discovery cases. Rich club organisation was examined in structural networks derived from DTI followed by deterministic tractography. Structural networks in SVD patients were less dense with lower network strength and efficiency. Reduced connectivity was found in SVD, which was preferentially located in the connectivity between the rich club nodes rather than in the feeder and peripheral connections, a finding confirmed in both datasets. In discovery dataset, lower rich club connectivity was associated with lower scores on psychomotor speed (β = 0.29, P < 0.001) and executive functions (β = 0.20, P = 0.009). These results suggest that SVD is characterized by abnormal connectivity between rich club hubs in SVD and provide evidence that abnormal rich club organisation might contribute to the development of cognitive impairment in SVD. Hum Brain Mapp 38:1751-1766, 2017. © 2017 Wiley Periodicals, Inc.

Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting-state functional connectivity

The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large-scale region-to-region resting-state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M1 ) and inhibitory (GABAA , M2 ) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting-state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting-state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting-state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103-3113, 2016. © 2016 Wiley Periodicals, Inc.

The fragmented self: imbalance between intrinsic and extrinsic self-networks in psychotic disorders

Self-disturbances are among the core features of schizophrenia and related psychotic disorders. The basic structure of the self could depend on the balance between intrinsic and extrinsic self-processing. We discuss studies on self-related processing in psychotic disorders that provide converging evidence for disrupted communication between neural networks subserving the so-called intrinsic self and extrinsic self. This disruption might be mainly caused by impaired integrity of key brain hubs. The intrinsic self has been associated with cortical midline structures involved in self-referential processing, autobiographical memory, and emotional evaluation. Additionally, we highlight central aspects of the extrinsic self in its interaction with the environment using sensorimotor networks, including self-experience in sensation and actions. A deficient relationship between these self-aspects because of disrupted between-network interactions offers a framework to explain core clinical features of psychotic disorders. In particular, we show how relative isolation and reduced modularity of networks subserving intrinsic and extrinsic self-processing might trigger the emergence of hallucinations and delusions, and why patients with psychosis typically have difficulties with self-other relationships and do not recognise mental problems.

Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis

Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies.

Serum S100B Is Related to Illness Duration and Clinical Symptoms in Schizophrenia-A Meta-Regression Analysis

S100B has been linked to glial pathology in several psychiatric disorders. Previous studies found higher S100B serum levels in patients with schizophrenia compared to healthy controls, and a number of covariates influencing the size of this effect have been proposed in the literature. Here, we conducted a meta-analysis and meta-regression analysis on alterations of serum S100B in schizophrenia in comparison with healthy control subjects. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guarantee a high quality and reproducibility. With strict inclusion criteria 19 original studies could be included in the quantitative meta-analysis, comprising a total of 766 patients and 607 healthy control subjects. The meta-analysis confirmed higher values of the glial serum marker S100B in schizophrenia if compared with control subjects. Meta-regression analyses revealed significant effects of illness duration and clinical symptomatology, in particular the total score of the Positive and Negative Syndrome Scale (PANSS), on serum S100B levels in schizophrenia. In sum, results confirm glial pathology in schizophrenia that is modulated by illness duration and related to clinical symptomatology. Further studies are needed to investigate mechanisms and mediating factors related to these findings.

Connectomics in Schizophrenia: From Early Pioneers to Recent Brain Network Findings

Schizophrenia has been conceptualized as a brain network disorder. The historical roots of connectomics in schizophrenia go back to the late 19th century, when influential scholars such as Theodor Meynert, Carl Wernicke, Emil Kraepelin, and Eugen Bleuler worked on a theoretical understanding of the multifaceted syndrome that is currently referred to as schizophrenia. Their work contributed to the understanding that symptoms such as psychosis and cognitive disorganization might stem from abnormal integration or dissociation due to disruptions in the brain's association fibers. As methods to test this hypothesis were long lacking, the claims of these early pioneers remained unsupported by empirical evidence for almost a century. In this review, we revisit and pay tribute to the old masters and, discussing recent findings from the developing field of disease connectomics, we examine how their pioneering hypotheses hold up in light of current evidence.

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

Chromosome 22q11.2 deletion syndrome (22q11DS) is a genetic disease known to lead to cerebral structural alterations, which we study using the framework of the macroscopic white-matter connectome. We create weighted connectomes of 44 patients with 22q11DS and 44 healthy controls using diffusion tensor magnetic resonance imaging, and perform a weighted graph theoretical analysis. After confirming global network integration deficits in 22q11DS (previously identified using binary connectomes), we identify the spatial distribution of regions responsible for global deficits. Next, we further characterize the dysconnectivity of the deficient regions in terms of sub-network properties, and investigate their relevance with respect to clinical profiles. We define the subset of regions with decreased nodal integration (evaluated using the closeness centrality measure) as the affected core (A-core) of the 22q11DS structural connectome. A-core regions are broadly bilaterally symmetric and consist of numerous network hubs — chiefly parietal and frontal cortical, as well as subcortical regions. Using a simulated lesion approach, we demonstrate that these core regions and their connections are particularly important to efficient network communication. Moreover, these regions are generally densely connected, but less so in 22q11DS. These specific disturbances are associated to a rerouting of shortest network paths that circumvent the A-core in 22q11DS, “de-centralizing” the network. Finally, the efficiency and mean connectivity strength of an orbito-frontal/cingulate circuit, included in the affected regions, correlate negatively with the extent of negative symptoms in 22q11DS patients, revealing the clinical relevance of present findings. The identified A-core overlaps numerous regions previously identified as affected in 22q11DS as well as in schizophrenia, which approximately 30–40% of 22q11DS patients develop.

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Graph theory confirms reduced integration in 22q11.2 deletion syndrome (22q11DS).

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An “affected core” (A-core) of hub regions drives global integration deficits.

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The A-core is generally densely connected, but less so in 22q11DS.

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Shortest network paths are rerouted around the A-core in 22q11DS.

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Connectivity of a subset of A-core regions correlates with negative symptoms.