Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

2020 ◽  
Vol 30 (09) ◽  
pp. 2050047
Author(s):  
Lubin Wang ◽  
Xianbin Li ◽  
Yuyang Zhu ◽  
Bei Lin ◽  
Qijing Bo ◽  
...  
Keyword(s):  
High Risk ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Support Vector ◽  
Healthy Controls ◽  
Intrinsic Functional Connectivity ◽  
Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

scholarly journals Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Reema Shafi ◽  
Adrian P. Crawley ◽  
Maria Carmela Tartaglia ◽  
Charles H. Tator ◽  
Robin E. Green ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Healthy Controls ◽  
Postconcussion Syndrome ◽  
Resting State Functional Connectivity ◽  
Post Injury ◽  
Network Nodes ◽  
Specific Impact

AbstractConcussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

scholarly journals Genetic liability in individuals at ultra-high risk of psychosis: A comparison study of 9 psychiatric traits

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243104
Author(s):  
Keane Lim ◽  
Max Lam ◽  
Hailiang Huang ◽  
Jianjun Liu ◽  
Jimmy Lee
Keyword(s):  
High Risk ◽  
Large Scale ◽  
Association Studies ◽  
Predictive Ability ◽  
Risk Scores ◽  
Psychotic Symptoms ◽  
Genome Wide Association Studies ◽  
Healthy Controls ◽  
Genetic Liability ◽  
Ultra High Risk

Individuals at ultra-high risk (UHR) of psychosis are characterised by the emergence of attenuated psychotic symptoms and deterioration in functioning. In view of the high non-psychotic comorbidity and low rates of transition to psychosis, the specificity of the UHR status has been called into question. This study aims to (i) investigate if the UHR construct is associated with the genetic liability of schizophrenia or other psychiatric conditions; (ii) examine the ability of polygenic risk scores (PRS) to discriminate healthy controls from UHR, remission and conversion status. PRS was calculated for 210 youths (nUHR = 102, nControl = 108) recruited as part of the Longitudinal Youth at Risk Study (LYRIKS) using nine psychiatric traits derived from twelve large-scale psychiatric genome-wide association studies as discovery datasets. PRS was also examined to discriminate UHR-Healthy control status, and healthy controls from UHR remission and conversion status. Result indicated that schizophrenia PRS appears to best index the genetic liability of UHR, while trend level associations were observed for depression and cross-disorder PRS. Schizophrenia PRS discriminated healthy controls from UHR (R2 = 7.9%, p = 2.59 x 10−3, OR = 1.82), healthy controls from non-remitters (R2 = 8.1%, p = 4.90 x 10−4, OR = 1.90), and converters (R2 = 7.6%, p = 1.61 x 10−3, OR = 1.82), with modest predictive ability. A trend gradient increase in schizophrenia PRS was observed across categories. The association between schizophrenia PRS and UHR status supports the hypothesis that the schizophrenia polygenic liability indexes the risk for developing psychosis.

scholarly journals Altered Temporal Variability of Local and Large-scale Resting-state Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder

2020 ◽  
Author(s):  
Yicheng Long ◽  
Zhening Liu ◽  
Calais Kin-yuen Chan ◽  
Guowei Wu ◽  
Zhimin Xue ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Clinical Features ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Type I ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Brain Functional Connectivity

AbstractSchizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53) and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder.

scholarly journals A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 129
Author(s):  
Giuseppe Varone ◽  
Wadii Boulila ◽  
Michele Lo Lo Giudice ◽  
Bilel Benjdira ◽  
Nadia Mammone ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Support Vector ◽  
Phase Lag ◽  
Healthy Controls ◽  
Distributed Information ◽  
Linear Discriminant ◽  
And Gender ◽  
Mlp Classifier

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

scholarly journals The connectome predicts resting state functional connectivity across the Drosophila brain

2020 ◽  
Author(s):  
Maxwell H Turner ◽  
Kevin Mann ◽  
Thomas R. Clandinin
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Neural Circuit ◽  
Brain Networks ◽  
Brain Regions ◽  
Central Complex ◽  
Resting State Functional Connectivity ◽  
Brain Structure And Function ◽  
And Function

Connectomic datasets have emerged as invaluable tools for understanding neural circuits in many systems. What constraints does the connectome place on information processing and routing in a large scale neural circuit? For mesoscale brain networks, the relationship between cell and synaptic level connectivity and brain function is not well understood. Here, we use data from the Drosophila connectome in conjunction with whole-brain in vivo imaging to relate structural and functional connectivity in the central brain. We find that functional connectivity is strongly associated with the strength of both direct and indirect anatomical pathways. We also show that some brain regions, including the mushroom body and central complex, show considerably higher functional connectivity to other brain regions than is predicted based on their direct anatomical connections. We find several key topological similarities between mesoscale brain networks in flies and mammals, revealing conserved principles relating brain structure and function.

scholarly journals Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chandra Sripada ◽  
Mike Angstadt ◽  
Aman Taxali ◽  
D. Angus Clark ◽  
Tristan Greathouse ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cognitive Development ◽  
Functional Connectivity ◽  
Cognitive Ability ◽  
Large Scale ◽  
Strong Predictor ◽  
Brain Networks ◽  
Brain Regions ◽  
General Cognitive Ability ◽  
Connectivity Patterns

AbstractGeneral cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η2). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10−16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.

scholarly journals Cerebral Energy Metabolism and the Brain’s Functional Network Architecture: An Integrative Review

2013 ◽  
Vol 33 (9) ◽  
pp. 1347-1354 ◽  
Author(s):  
Louis-David Lord ◽  
Paul Expert ◽  
Jeremy F Huckins ◽  
Federico E Turkheimer
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Network Architecture ◽  
Large Scale ◽  
Brain Networks ◽  
Glucose Consumption ◽  
Brain Regions ◽  
Functional Brain ◽  
Neurobiological Substrates ◽  
Functional Brain Networks

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain’s ‘functional connectivity’ is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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