Graph-Based Method for Anomaly Detection in Functional Brain Network using Variational Autoencoder

Event Abstract Back to Event Decreased Functional Connectivity in Schizophrenia: The Relationship Between Social Functioning and Graph Theoretical Network Measures Burak Erdeniz1*, Emin Serin1, Yelda İbadi2 and Cumhur Tas2 1 İzmir University of Economics, Psychology, Türkiye 2 Üsküdar University, Türkiye Background: Schizophrenia is a complex disorder where abnormalities in brain connectivity and social functioning play a central role. Currently, brain structures and mechanisms related to social functioning in schizophrenia are being intensively studied. However, the relationship between social functioning, and topological properties of brain functional networks remain still not well understood. The aim of this study is to explore the relationship between complex network characteristics such as small-world network properties and social functioning in schizophrenia. Methods: We tested this hypothesis by comparing differences in functional network topology and its correlation to clinical and behavioral measures in 23 patients diagnosed with Schizophrenia and 23 healthy controls during resting state. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia (14 male, 9 female) and 23 healthy volunteers (14 male, 9 female). Results: The results revealed that patients with schizophrenia show significantly decreased connectivity in a diversity of brain regions that include mainly the right orbito-frontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia characterized by reduced average path length compared to Healthy controls but no significant difference was found for clustering coefficient, local efficiency and global efficiency. However, when we analyzed nodal efficiency and nodal degree, we found significantly lower nodal efficiency and nodal –degree particularly in bilateral putamen and left amygdala in patients with Schizophrenia. Conclusion: These findings are consistent with previous studies that support disconnectivity hypothesis. The current results will help us to improve our understanding about the missing link between social cognition deficits and topological brain measures in Schizophrenia. Keywords: default-mode, graph theory, fMRI, small-world, Schizophrenia Keywords: Default-mode, graph theory, fMRI, Small-world, Schizophrenia Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016. Presentation Type: Oral Presentation in SAN 2016 Conference Topic: Oral Presentations Citation: Erdeniz B, Serin E, İbadi Y and Tas C (2016). Decreased Functional Connectivity in Schizophrenia: The Relationship Between Social Functioning and Graph Theoretical Network Measures. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00074 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 01 Aug 2016; Published Online: 01 Aug 2016. * Correspondence: Dr. Burak Erdeniz, İzmir University of Economics, Psychology, İzmir, Türkiye, burak.erdeniz@ieu.edu.tr Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas Google Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas Google Scholar Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas PubMed Burak Erdeniz Emin Serin Yelda İbadi Cumhur Tas Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

AimsAlcohol consumption could lead to a series of health problems and social issues. In the current study, we investigated the resting-state functional brain networks of healthy volunteers before and after drinking through graph-theory analysis, aiming to ascertain the effects of acute alcohol intake on topology and information processing mode of the functional brain networks.Materials and methodsThirty-three healthy volunteers were enrolled in this experiment. Each volunteer accepted alcohol breathalyzer tests followed by resting-state magnetic resonance imaging at three time points: before drinking, 0.5 h after drinking, and 1 h after drinking. The data obtained were grouped based on scanning time into control group, 0.5-h group and 1-h group, and post-drinking data were regrouped according to breath alcohol concentration (BrAC) into relative low BrAC group (A group; 0.5-h data, n = 17; 1-h data, n = 16) and relative high BrAC group (B group; 0.5-h data, n = 16; 1-h data, n = 17). The graph-theory approach was adopted to construct whole-brain functional networks and identify the differences of network topological properties among all the groups.ResultsThe network topology of most groups was altered after drinking, with the B group presenting the most alterations. For global network measures, B group exhibited increased global efficiency, Synchronization, and decreased local efficiency, clustering coefficient, normalized clustering coefficient, characteristic path length, normalized characteristic path length, as compared to control group. Regarding nodal network measures, nodal clustering coefficient and nodal local efficiency of some nodes were lower in B group than control group. These changes suggested that the network integration ability and synchrony improved, while the segregation ability diminished.ConclusionThis study revealed the effects of acute alcohol intake on the topology and information processing mode of resting-state functional brain networks, providing new perceptions and insights into the effects of alcohol on the brain.

Autism spectrum disorder (ASD) is increasingly conceptualized as a disorder of large-scale functional brain network organization rather than isolated regional abnormalities. Graph-theoretical analysis provides a principled framework for characterizing such distributed network reconfiguration. Here, we investigated global, nodal, and system-level functional network topology in ASD using a large, multi-site resting-state fMRI dataset. Resting-state fMRI data from 996 participants (428 ASD, 568 healthy controls) were obtained from the ABIDE I and II data repositories. Whole-brain weighted resting state functional networks were constructed using Pearson correlation. To improve robustness and reduce threshold-selection bias, graph-theoretical metrics were computed across a range of network sparsity thresholds and summarized using an area-under-the-curve (AUC) approach. At the global level, ASD was associated with reduced assortativity, and local efficiency, along with altered normalized characteristic path length (λ), indicating local information processing and subtle deviations in network integration relative to an optimal small-world topology. Nodal analyses revealed non-random, region-specific alterations predominantly affecting higher-order associative systems. Increased nodal centrality and hub-like properties were observed in frontal and parietal regions within the frontoparietal control and dorsal attention networks, whereas reduced nodal efficiency and centrality were primarily localized to limbic and anterior temporal regions, including the temporal pole. System-level analyses, controlling for age, sex, and acquisition site, further demonstrated network-specific topological reorganization across multiple functional systems. Clinical correlation analyses identified modest but significant associations between nodal topology and core ASD symptom severity, particularly within default mode, limbic, and attention networks. Together, these findings indicate that ASD is characterized by subtle yet reproducible multi-scale reorganization of functional brain network topology, supporting a systems-level account of ASD neurobiology and highlighting the clinical relevance of large-scale network architecture.

Aging is the fundamental of neurodegeneration and dementia, affecting every organ in the body. With the aggravation of global aging, more and more research is focusing on how brain changes in older adults. This study aimed to uncover the differences in brain functional networks from the perspective of graph theory between young individuals and older individuals. Here, 61 individuals in their 20s and 94 cognitively healthy old individuals in their 70s underwent a resting-state functional magnetic resonance imaging scan. Based on the graph theory method, a functional network was constructed for each participant. Our results revealed that brain functional networks in older adults maintained small-world properties. However other nodal parameters including degree centrality, betweenness centrality, shortest path lengths, local efficiency, nodal efficiency, and cluster coefficients showed significant differences in many nodes (brain regions) between the 2 groups. Moreover, we correlated these nodal parameters with age, exploring 8 brain regions significantly affected with age. 7 out of 8 brain regions including the bilateral superior parietal lobule, bilateral precuneus, right middle cingulate, right inferior parietal lobule and right transverse temporal gyri were distributed in the default mode network. Our findings, based on graph theory, provided evidence for the alteration of the default mode network in older adults from the perspective of the functional network.

Unprecedented 4D spatiotemporal infant regional cerebral blood flow framework and region-specific physiology–function coupling across infancy were elucidated, highlighting strong physiology–function coupling specifically at the default-mode network to meet extraneuronal metabolic demand for network emergence.

Cross-sectional data from a sample of older adults with obesity was used to determine how peripheral and neuronal insulin resistance (IR) relate to executive function and functional brain network topology. Older adults (n=71) with obesity but without type 2 diabetes were included. Peripheral IR was quantified by HOMA2-IR. Neuronal IR was quantified according to a proposed neuron-derived exosome-based method (NDE-IR). An executive function composite score, summed scores to the Auditory Verbal Learning Test (AVLT) trials 1-5, and functional brain networks generated from resting-state functional magnetic resonance imaging were outcomes in analyses. We used general linear models and a novel regression framework for brain network analysis to identify relationships between IR measures and brain-related outcomes. HOMA2-IR, but not NDE-IR, was negatively associated with executive function. Neither IR measure was associated with AVLT score. Peripheral IR was also related to hippocampal network topology in participants who had undergone functional neuroimaging. Neither peripheral nor neuronal IR were significantly related to network topology of the central executive network. Cognitive and functional imaging effects were observed from HOMA2-IR, but not NDE-IR. The hippocampus may be particularly vulnerable to effects of peripheral IR.

BackgroundAutoimmune diseases such as Systemic Lupus Erythematosus (SLE), Sjögren’s Syndrome (SS), and Hashimoto’s Thyroiditis (HT) frequently exhibit neuropsychiatric manifestations, including cognitive impairment, depression, anxiety, and so on, yet the exact pathogenesis underlying this association remain incompletely understood. Dysfunction of brain resting-state functional networks and cerebrospinal fluid (CSF) metabolite disturbances have been widely reported in psychiatric disorders. However, the application of resting-state functional magnetic resonance imaging (rsfMRI) and CSF metabolomics in the diagnosis and monitoring of autoimmune psychosis is still limited.MethodsA two-sample Mendelian randomization (MR) analysis was performed to investigate the causal relationships between three autoimmune diseases (SLE, SS, and HT, n = 14,267 to 402,090 individuals) and 191 rsfMRI phenotypes (n = 47,276 individuals), as well as 338 CSF metabolites. The genome-wide association study (GWAS) of three autoimmune diseases was used as the exposure, whereas rsfMRI phenotypes and 338 CSF metabolites were treated as the outcome. Inverse variance weighted (IVW) with P value < 0.05 was regarded as the primary approach for calculating causal estimates. Additionally, the false discovery rate (FDR)-adjusted P value (PFDR) < 0.05 was utilized to account for multiple testing. MR Egger method, weighted median method, simple mode method and weighted mode method were used for sensitive analysis.ResultsOur analyses identified 5 causal relationships between SLE and the 191 rsfMRI phenotypes, 48 between SS and the 191 rsfMRI phenotypes, and 4 between HT and the 191 rsfMRI phenotypes. Additionally, we found 8 causal relationships between HT and CSF metabolites. Furthermore, all three diseases were significantly associated with the temporal lobe and triple networks (default mode network (DMN), salience network (SN), and central executive network (CEN)), which are the core brain regions and functional networks for cognition. Following FDR correction, 6 causal relationships between SS and the 191 rsfMRI phenotypes were further validated.ConclusionsOur study pinpoints important brain functional networks and CSF metabolites potentially implicated in the pathogenesis of psychiatric disorders associated with autoimmune diseases and highlights critical brain regions for the development of novel therapeutics.

Graph convolutional autoencoders with co-learning of graph structure and node attributes

Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.

Dysfunction between dorsal caudate and salience network associated with impaired cognitive flexibility in obsessive-compulsive disorder: A resting-state fMRI study

Functional brain networks in clinical high-risk for bipolar disorder and psychosis

Many studies have demonstrated that the pathophysiology and clinical symptoms of Parkinson's disease (PD) are inhomogeneous. However, the symptom-specific intrinsic neural activities underlying the PD subtypes are still not well understood. Here, 15 tremor-dominant PD patients, 10 non-tremor-dominant PD patients, and 20 matched normal controls (NCs) were recruited and underwent resting-state functional magnetic resonance imaging (fMRI). Functional brain networks were constructed based on randomly generated anatomical templates with and without the cerebellum. The regional network efficiencies (i.e., the local and global efficiencies) were further measured and used to distinguish subgroups of PD patients (i.e., with tremor-dominant PD and non-tremor-dominant PD) from the NCs using linear discriminant analysis. The results demonstrate that the subtype-specific functional networks were small-world-organized and that the network regional efficiency could discriminate among the individual PD subgroups and the NCs. Brain regions involved in distinguishing between the study groups included the basal ganglia (i.e., the caudate and putamen), limbic regions (i.e., the hippocampus and thalamus), the cerebellum, and other cerebral regions (e.g., the insula, cingulum, and calcarine sulcus). In particular, the performances of the regional local efficiency in the functional network were better than those of the global efficiency, and the performances of global efficiency were dependent on the inclusion of the cerebellum in the analysis. These findings provide new evidence for the neurological basis of differences between PD subtypes and suggest that the cerebellum may play different roles in the pathologies of different PD subtypes. The present study demonstrated the power of the combination of graph-based network analysis and discrimination analysis in elucidating the neural basis of different PD subtypes.

Previous studies have shown that the brain network topology correlates with the cognitive function. However, few studies have investigated the relationship between functional brain networks that process sensory inputs and outputs. In this study, we focus on steady-state paradigms using a periodic visual stimulus, which are increasingly being used in both brain-computer interface (BCI) and cognitive neuroscience researches. Using the graph theoretical analysis, we investigated the relationship between the topology of functional networks entrained by periodic stimuli and steady state visually evoked potentials (SSVEP) using two frequencies and eleven subjects. First, the entire functional network (Network 0) of each frequency for each subject was constructed according to the coherence between electrode pairs. Next, Network 0 was divided into three sub-networks, in which the connection strengths were either significantly (positively for Network 1, negatively for Network 3) or non-significantly (Network 2) correlated with the SSVEP responses. Our results revealed that the SSVEP responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while these responses were negatively correlated with the characteristic path length of Networks 0, 1 and 2. Furthermore, the strengths of these connections that significantly correlated with the SSVEP (both positively and negatively) were mainly found to be long-range connections between the parietal-occipital and frontal regions. These results indicate that larger SSVEP responses correspond with better functional network topology structures. This study may provide new insights for understanding brain mechanisms when using SSVEPs as frequency tags.

Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning

Brain disorder diagnosis via resting-state functional magnetic resonance imaging (rs-fMRI) is usually limited due to the complex imaging features and sample size. For brain disorder diagnosis, the graph convolutional network (GCN) has achieved remarkable success by capturing interactions between individuals and the population. However, there are mainly three limitations: 1) The previous GCN approaches consider the non-imaging information in edge construction but ignore the sensitivity differences of features to non-imaging information. 2) The previous GCN approaches solely focus on establishing interactions between subjects (i.e., individuals and the population), disregarding the essential relationship between features. 3) Multisite data increase the sample size to help classifier training, but the inter-site heterogeneity limits the performance to some extent. This paper proposes a knowledge-aware multisite adaptive graph Transformer to address the above problems. First, we evaluate the sensitivity of features to each piece of non-imaging information, and then construct feature-sensitive and feature-insensitive subgraphs. Second, after fusing the above subgraphs, we integrate a Transformer module to capture the intrinsic relationship between features. Third, we design a domain adaptive GCN using multiple loss function terms to relieve data heterogeneity and to produce the final classification results. Last, the proposed framework is validated on two brain disorder diagnostic tasks. Experimental results show that the proposed framework can achieve state-of-the-art performance.