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.

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 Signatures of Brain Network Alteration in Psychogenic Non-Epileptic Seizures: A Rest-EEG Study Based on Power Spectral Density and Phase Lag Index

2021 ◽  
Author(s):  
Giuseppe Varone ◽  
Wadii Boulila ◽  
Michele Lo Giudice ◽  
Bilel Benjdira ◽  
Nadia Mammone ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Brain Network ◽  
Oscillatory Activity ◽  
Alpha Power ◽  
Phase Lag ◽  
Distributed Information ◽  
Main Challenge ◽  
Large Populations

The main challenge in the clinical assessment of Psychogenic Non-Epileptic Seizures (PNES) is the lack of an electroencephalographic marker in the electroencephalography (EEG) readout. Although decades of EEG studies have focused on detecting cortical brain function underlying PNES, the principle of PNES remains poorly understood. To address this problem, electric potentials generated by large populations of neurons were collected during the resting state to be processed after that by Power Spectrum Density (PSD) for possible analysis of PNES signatures. Additionally, the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas has been observed using functional connectivity tools like Phase Lag Index (PLI) and graph-derived metrics. A cohort study of 20 PNES and 19 Healthy Control subjects (HC) were enrolled. The major finding is that PNES patients exhibited significant differences in alpha-power spectrum in brain regions related to cognitive operations, attention, working memory, and movement regulation. Noticeably, we observed that there exists an altered oscillatory activity and a widespread inter-regional phase desynchronization. This indicates changes in global efficiency, node betweenness, shortest path length, and small worldness in the delta, theta, alpha, and beta frequency bands. Finally, our findings look into new evidence of the intrinsic organization of functional brain networks that reflects a dysfunctional level of integration of local activity across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES.

scholarly journals Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis

Brain ◽  
2019 ◽  
Vol 143 (1) ◽  
pp. 150-160 ◽  
Author(s):  
Kim A Meijer ◽  
Martijn D Steenwijk ◽  
Linda Douw ◽  
Menno M Schoonheim ◽  
Jeroen J G Geurts
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Long Range ◽  
Short Range ◽  
Brain Regions ◽  
Healthy Controls ◽  
Network Efficiency ◽  
Structural Network ◽  
The Impact

Abstract An efficient network such as the human brain features a combination of global integration of information, driven by long-range connections, and local processing involving short-range connections. Whether these connections are equally damaged in multiple sclerosis is unknown, as is their relevance for cognitive impairment and brain function. Therefore, we cross-sectionally investigated the association between damage to short- and long-range connections with structural network efficiency, the functional connectome and cognition. From the Amsterdam multiple sclerosis cohort, 133 patients (age = 54.2 ± 9.6) with long-standing multiple sclerosis and 48 healthy controls (age = 50.8 ± 7.0) with neuropsychological testing and MRI were included. Structural connectivity was estimated from diffusion tensor images using probabilistic tractography (MRtrix 3.0) between pairs of brain regions. Structural connections were divided into short- (length < quartile 1) and long-range (length > quartile 3) connections, based on the mean distribution of tract lengths in healthy controls. To determine the severity of damage within these connections, (i) fractional anisotropy as a measure for integrity; (ii) total number of fibres; and (iii) percentage of tract affected by lesions were computed for each connecting tract and averaged for short- and long-range connections separately. To investigate the impact of damage in these connections for structural network efficiency, global efficiency was computed. Additionally, resting-state functional connectivity was computed between each pair of brain regions, after artefact removal with FMRIB’s ICA-based X-noiseifier. The functional connectivity similarity index was computed by correlating individual functional connectivity matrices with an average healthy control connectivity matrix. Our results showed that the structural network had a reduced efficiency and integrity in multiple sclerosis relative to healthy controls (both P < 0.05). The long-range connections showed the largest reduction in fractional anisotropy (z = −1.03, P < 0.001) and total number of fibres (z = −0.44, P < 0.01), whereas in the short-range connections only fractional anisotropy was affected (z = −0.34, P = 0.03). Long-range connections also demonstrated a higher percentage of tract affected by lesions than short-range connections, independent of tract length (P < 0.001). Damage to long-range connections was more strongly related to structural network efficiency and cognition (fractional anisotropy: r = 0.329 and r = 0.447. number of fibres r = 0.321 and r = 0.278. and percentage of lesions: r = −0.219; r = −0.426, respectively) than damage to short-range connections. Only damage to long-distance connections correlated with a more abnormal functional network (fractional anisotropy: r = 0.226). Our findings indicate that long-range connections are more severely affected by multiple sclerosis-specific damage than short-range connections. Moreover compared to short-range connections, damage to long-range connections better explains network efficiency and cognition.

scholarly journals Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Chronic Tinnitus ◽  
Healthy Controls ◽  
Tinnitus Distress ◽  
The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

scholarly journals Altered Topological Properties of Grey Matter Structural Covariance Networks in Complete Thoracic Spinal Cord Injury Patients: A Graph Theoretical Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wen-Li Wang ◽  
Yu-Lin Li ◽  
Mou-Xiong Zheng ◽  
Xu-Yun Hua ◽  
Jia-Jia Wu ◽  
...  
Keyword(s):  
Grey Matter ◽  
Brain Regions ◽  
Healthy Controls ◽  
Age And Gender ◽  
Structural Covariance ◽  
Clinical Variables ◽  
Cord Injury ◽  
Network Properties ◽  
And Gender ◽  
The Right

Purpose. This study is aimed at investigating brain structural changes and structural network properties in complete spinal cord injury (SCI) patients, as well as their relationship with clinical variables. Materials and Methods. Structural MRI of brain was acquired in 24 complete thoracic SCI patients ( 38.50 ± 11.19 years, 22 males) within the first postinjury year, while 26 age- and gender-matched healthy participants ( 38.38 ± 10.63 years, 24 males) were enrolled as control. The voxel-based morphometry (VBM) approach and graph theoretical network analysis based on cross-subject grey matter volume- (GMV-) based structural covariance networks (SCNs) were conducted to investigate the impact of SCI on brain structure. Partial correlation analysis was performed to explore the relationship between the GMV of structurally changed brain regions and SCI patients’ clinical variables, including injury duration, injury level, Visual Analog Scale (VAS), American Spinal Injury Association Impairment Scale (AIS), International Classification of Functioning, Disability and Health (ICF) scale, Self-rating Depression Scale (SDS), and Self-rating Anxiety Scale (SAS), after removing the effects of age and gender. Results. Compared with healthy controls, SCI patients showed higher SDS score ( t = 4.392 and p < 0.001 ). In the VBM analysis, significant GMV reduction was found in the left middle frontal cortex, right superior orbital frontal cortex (OFC), and left inferior OFC. No significant difference was found in global network properties between SCI patients and healthy controls. In the regional network properties, significantly higher betweenness centrality (BC) was noted in the right anterior cingulum cortex (ACC) and left inferior OFC and higher nodal degree and efficiency in bilateral middle OFCs, while decreased BC was noted in the right putamen in SCI patients. Only negative correlation was found between GMV of right middle OFC and SDS score in SCI patients ( r = − 0.503 and p = 0.017 ), while no significant correlation between other abnormal brain regions and any of the clinical variables (all p > 0.05 ). Conclusions. SCI patients would experience depressive and/or anxious feelings at the early stage. Their GMV reduction mainly involved psychology-cognition related rather than sensorimotor brain regions. The efficiency of regional information transmission in psychology-cognition regions increased. Greater GMV reduction in psychology region was related with more severe depressive feelings. Therefore, early neuropsychological intervention is suggested to prevent psychological and cognitive dysfunction as well as irreversible brain structure damage.

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 Neurophysiologic Correlates of Ketamine Sedation and Anesthesia

2017 ◽  
Vol 127 (1) ◽  
pp. 58-69 ◽  
Author(s):  
Phillip E. Vlisides ◽  
Tarik Bel-Bahar ◽  
UnCheol Lee ◽  
Duan Li ◽  
Hyoungkyu Kim ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Phase Locking ◽  
Brain Regions ◽  
Alpha Power ◽  
Phase Lag ◽  
Mean Power ◽  
Healthy Human ◽  
Ketamine Anesthesia ◽  
Gamma Power ◽  
Dose Dependent

Abstract Background Previous studies have demonstrated inconsistent neurophysiologic effects of ketamine, although discrepant findings might relate to differences in doses studied, brain regions analyzed, coadministration of other anesthetic medications, and resolution of the electroencephalograph. The objective of this study was to characterize the dose-dependent effects of ketamine on cortical oscillations and functional connectivity. Methods Ten healthy human volunteers were recruited for study participation. The data were recorded using a 128-channel electroencephalograph during baseline consciousness, subanesthetic dosing (0.5 mg/kg over 40 min), anesthetic dosing (1.5 mg/kg bolus), and recovery. No other sedative or anesthetic medications were administered. Spectrograms, topomaps, and functional connectivity (weighted and directed phase lag index) were computed and analyzed. Results Frontal theta bandwidth power increased most dramatically during ketamine anesthesia (mean power ± SD, 4.25 ± 1.90 dB) compared to the baseline (0.64 ± 0.28 dB), subanesthetic (0.60 ± 0.30 dB), and recovery (0.68 ± 0.41 dB) states; P &lt; 0.001. Gamma power also increased during ketamine anesthesia. Weighted phase lag index demonstrated theta phase locking within anterior regions (0.2349 ± 0.1170, P &lt; 0.001) and between anterior and posterior regions (0.2159 ± 0.1538, P &lt; 0.01) during ketamine anesthesia. Alpha power gradually decreased with subanesthetic ketamine, and anterior-to-posterior directed connectivity was maximally reduced (0.0282 ± 0.0772) during ketamine anesthesia compared to all other states (P &lt; 0.05). Conclusions Ketamine anesthesia correlates most clearly with distinct changes in the theta bandwidth, including increased power and functional connectivity. Anterior-to-posterior connectivity in the alpha bandwidth becomes maximally depressed with anesthetic ketamine administration, suggesting a dose-dependent effect.

scholarly journals Functional connectome differences in individuals with hallucinations across the psychosis continuum

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maya J. L. Schutte ◽  
Marc M. Bohlken ◽  
Guusje Collin ◽  
Lucija Abramovic ◽  
Marco P. M. Boks ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Healthy Controls ◽  
Bipolar I Disorder ◽  
Functional Connectome ◽  
Psychosis Continuum ◽  
Mri Scans

AbstractHallucinations 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.

scholarly journals Functional connectivity alterations in Parkinson’s disease during the stop-signal task

2018 ◽  
Author(s):  
Chris Vriend ◽  
Douglas T. Leffa ◽  
James P. Trujillo ◽  
Niels J.H.M. Gerrits ◽  
Femke E. de Boer ◽  
...  
Keyword(s):  
Reaction Time ◽  
Functional Connectivity ◽  
Response Inhibition ◽  
Functional Activity ◽  
Brain Regions ◽  
Anterior Insula ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Healthy Controls ◽  
Dopaminergic Medication

AbstractAlthough deficits in response inhibition are common in Parkinson’s disease (PD), studies on their neural correlates are relatively scarce. In our previous study, medication-naïve PD patients showed, among others, lower activation of the inhibition-related bilateral inferior frontal gyrus (IFG) compared with healthy controls while performing the stop-signal task. Here we report on a follow-up study in the same cohort.Fourteen medicated PD patients and 16 healthy controls performed the same stop-signal task during MRI; 3.1±1.0 years after our previous study. During that time, all PD patients had started to use dopaminergic medication. We analyzed task-related functional activity and connectivity in the bilateral IFG and anterior insula, brain regions that are important response inhibition. Task-related functional connectivity was analyzed with generalized psychophysiological interaction.PD patients were significantly slower on response initiation (GO reaction time) and response inhibition (stop-signal reaction time) than healthy controls. There were no between-group differences in functional activity. On the other hand, functional connectivity of the IFG and anterior insula was significantly lower in PD compared with healthy controls. Mainly the inferior parietal lobule and dorsolateral prefrontal cortex were less connected with these seed regions.These results show that early-stage medicated PD patients show lower task-related functional connectivity but not activity of brain regions that are important for response inhibition; the IFG and anterior insula. We tentatively speculate that the use of dopaminergic medication upheld task-related activity but not connectivity.

scholarly journals Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Satoru Hiwa ◽  
Shogo Obuchi ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Support Vector ◽  
Neural Basis ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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