Resting-State Stereotactic Electroencephalography May Help Localize Epileptogenic Brain Regions

Abstract INTRODUCTION Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy, but requires days in the hospital with interventions to trigger several uncomfortable seizures. Our goal is to make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of connectivity with distance (P < .05, paired t-tests, corrected). Our connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve (AUC) of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

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Resting-State SEEG May Help Localize Epileptogenic Brain Regions

Neurosurgery ◽

10.1093/neuros/nyz351 ◽

2019 ◽

Vol 86 (6) ◽

pp. 792-801 ◽

Cited By ~ 5

Author(s):

Sarah E Goodale ◽

Hernán F J González ◽

Graham W Johnson ◽

Kanupriya Gupta ◽

William J Rodriguez ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Focal Epilepsy ◽

Area Under The Curve ◽

Brain Regions ◽

Clustering Coefficient ◽

Alpha Band ◽

Connectivity Model ◽

Connectivity Patterns ◽

Neurosurgical Treatment

Abstract BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

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EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity—A Machine Learning Approach

Journal of Clinical Medicine ◽

10.3390/jcm9123934 ◽

2020 ◽

Vol 9 (12) ◽

pp. 3934

Author(s):

Jeong-Youn Kim ◽

Hyun Seo Lee ◽

Seung-Hwan Lee

Keyword(s):

Resting State ◽

Symptom Severity ◽

Classification Accuracy ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Forward Selection ◽

Linear Discriminant ◽

Network Analyses ◽

Local Clustering Coefficient

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

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Aberrant Resting-State Regional Activity in Patients with Postpartum Depression

10.21203/rs.3.rs-942135/v1 ◽

2021 ◽

Author(s):

Shufen Zhang ◽

Bo Li ◽

Kai Liu ◽

xiaoming hou ◽

Ping Zhang

Keyword(s):

Postpartum Depression ◽

Resting State ◽

Depression Scale ◽

Area Under The Curve ◽

Brain Regions ◽

Clinical Severity ◽

Postpartum Women ◽

Roc Curve Analysis ◽

Dorsolateral Prefrontal ◽

Coefficient Of Concordance

Abstract The aim of this study is to investigate the alterations of individual local connectivity by regional homogeneity (ReHo) in patients with postpartum depression (PPD) during resting state, and their potential correlations with clinical severity. Thirty patients with PPD and twenty-nine matched healthy postpartum women within 4 weeks after delivery were recruited and performed resting-state functional magnetic resonance imaging (fMRI) scans. The ReHo value was computed as Kendall’s coefficient of concordance (KCC) in the present study, and intergroup differences in the voxel manner were analyzed. Correlations between ReHo values and clinical variables were also analyzed. Compared with healthy postpartum women, patients with PPD exhibited significantly higher ReHo in left precuneus and right hippocampus. ReHo was significantly lower in left dorsolateral prefrontal cortex (dlPFC) and right insula. Furthermore, ReHo values within the dlPFC negatively correlated with the Edinburgh postpartum depression scale (EPDS) score. The ROC curve analysis showed that the area under the curve (AUC) of the above four brain regions are all over 0.7. This study provided evidences of aberrant regional functional activity within brain regions involved in the maternal care network in PPD, and may contribute to the further understanding of neurobiology underlying PPD.

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Weighted Phase Lag Index and Graph Analysis: Preliminary Investigation of Functional Connectivity during Resting State in Children

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/186353 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 20

Author(s):

Erick Ortiz ◽

Krunoslav Stingl ◽

Jana Münßinger ◽

Christoph Braun ◽

Hubert Preissl ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Power Distribution ◽

Clustering Coefficient ◽

Graph Analysis ◽

Seed Region ◽

Phase Lag ◽

Alpha Band ◽

Occipital Area ◽

Weighted Phase Lag Index

Resting state functional connectivity of MEG data was studied in 29 children (9-10 years old). The weighted phase lag index (WPLI) was employed for estimating connectivity and compared to coherence. To further evaluate the network structure, a graph analysis based on WPLI was used to determine clustering coefficient (C) and betweenness centrality (BC) as local coefficients as well as the characteristic path length (L) as a parameter for global interconnectedness. The network’s modular structure was also calculated to estimate functional segregation. A seed region was identified in the central occipital area based on the power distribution at the sensor level in the alpha band. WPLI reveals a specific connectivity map different from power and coherence. BC and modularity show a strong level of connectedness in the occipital area between lateral and central sensors.Cshows different isolated areas of occipital sensors. Globally, a network with the shortestLis detected in the alpha band, consistently with the local results. Our results are in agreement with findings in adults, indicating a similar functional network in children at this age in the alpha band. The integrated use of WPLI and graph analysis can help to gain a better description of resting state networks.

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2307

Journal of Clinical and Translational Science ◽

10.1017/cts.2017.38 ◽

2017 ◽

Vol 1 (S1) ◽

pp. 6-6

Author(s):

Joey Annette Contreras ◽

Shannon L. Risacher ◽

Mario Dzemidzic ◽

John D. West ◽

Brenna C. McDonald ◽

...

Keyword(s):

Neurodegenerative Diseases ◽

Resting State ◽

Pearson Correlation ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Previous Literature ◽

Significant Difference ◽

Connectivity Patterns

OBJECTIVES/SPECIFIC AIMS: Recent evidence from resting-state fMRI studies have shown that brain network connectivity is altered in patients with neurodegenerative disorders. However, few studies have examined the complete connectivity patterns of these well-reported RSNs using a whole brain approach and how they compare between dementias. Here, we used advanced connectomic approaches to examine the connectivity of RSNs in Alzheimer disease (AD), Frontotemporal dementia (FTD), and age-matched control participants. METHODS/STUDY POPULATION: In total, 44 participants [27 controls (66.4±7.6 years), 13 AD (68.5.63±13.9 years), 4 FTD (59.575±12.2 years)] from an ongoing study at Indiana University School of Medicine were used. Resting-state fMRI data was processed using an in-house pipeline modeled after Power et al. (2014). Images were parcellated into 278 regions of interest (ROI) based on Shen et al. (2013). Connectivity between each ROI pair was described by Pearson correlation coefficient. Brain regions were grouped into 7 canonical RSNs as described by Yeo et al. (2015). Pearson correlation values were then averaged across pairs of ROIs in each network and averaged across individuals in each group. These values were used to determine relative expression of FC in each RSN (intranetwork) and create RSN profiles for each group. RESULTS/ANTICIPATED RESULTS: Our findings support previous literature which shows that limbic networks are disrupted in FTLD participants compared with AD and age-matched controls. In addition, interactions between different RSNs was also examined and a significant difference between controls and AD subjects was found between FP and DMN RSNs. Similarly, previous literature has reported a disruption between executive (frontoparietal) network and default mode network in AD compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach allows us to create profiles that could help compare intranetwork FC in different neurodegenerative diseases. Future work with expanded samples will help us to draw more substantial conclusions regarding differences, if any, in the connectivity patterns between RSNs in various neurodegenerative diseases.

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Signature of consciousness in the dynamics of resting-state brain activity

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1418031112 ◽

2015 ◽

Vol 112 (3) ◽

pp. 887-892 ◽

Cited By ~ 292

Author(s):

Pablo Barttfeld ◽

Lynn Uhrig ◽

Jacobo D. Sitt ◽

Mariano Sigman ◽

Béchir Jarraya ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Small World ◽

Brain Regions ◽

Connectivity Matrix ◽

Anatomical Connectivity ◽

Functional Dynamics ◽

Connectivity Patterns ◽

Continuous Stream

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients.

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Modeling Alpha-Band Functional Connectivity for MEG Resting State Data: Oscillations and Delays in a Spiking Neuron Model

BMC Neuroscience ◽

10.1186/1471-2202-14-s1-p99 ◽

2013 ◽

Vol 14 (S1) ◽

Author(s):

Tristan T Nakagawa ◽

Henry Luckhoo ◽

Mark Woolrich ◽

Morten Joensson ◽

Hamid Mohseni ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Neuron Model ◽

Alpha Band ◽

Spiking Neuron ◽

State Data ◽

Spiking Neuron Model

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Altered Brain Network Centrality in Patients With Orbital Fracture: A Resting-State fMRI Study. Running Head: DC Alterations in OF Patients

10.21203/rs.3.rs-1099816/v1 ◽

2021 ◽

Author(s):

Yi-Nuo Liu ◽

Yu-Xuan Gao ◽

Hui-Ye Shu ◽

Qiu-Yu Li ◽

Qian-Min Ge ◽

...

Keyword(s):

Resting State ◽

Brain Activity ◽

Area Under The Curve ◽

Roc Curves ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Orbital Fracture ◽

Orbital Fractures ◽

Roc Curve Analysis

Abstract Objective: We aimed to identify potential functional network brain-activity abnormalities in patients with orbital fractures (OFs) by using the voxel-wise degree centrality (DC) method.Methods:We selected 20 patients with OFs (12 men and 8 women) and 20 healthy controls (HCs; 12 men and 8 women) matched by gender, age, and education level for this study. Resting-state functional magnetic resonance imaging (fMRI) has been widely used in various disciplines. We calculated receiver operating characteristic (ROC) curves to differentiate characteristics between patients with orbital fractures and HCs; in addition, we applied correlation analyses between behavioral performance and average DC values in different areas. The DC method served to evaluate spontaneous brain activity.Results:The DC values of patients with OFs were higher in the right cerebellum 9 area (Cerebelum_9_R) and left cerebellar peduncle 2 area (Cerebelum_Crus2_L) than those in HCs. The area under the curve (AUC) values for Cerebelum_9_R and Cerebelum_Crus2_L were 0.983 and 1, respectively. The accuracy of our ROC curve analysis result was reliable. Conclusion:Many brain regions seem to show abnormal brain network characteristics in patients with orbital fractures, suggesting potential neuropathic mechanisms.

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The effect of congenital blindness on resting-state functional connectivity revisited

Scientific Reports ◽

10.1038/s41598-021-91976-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maria J. S. Guerreiro ◽

Madita Linke ◽

Sunitha Lingareddy ◽

Ramesh Kekunnaya ◽

Brigitte Röder

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Visual Experience ◽

Brain Regions ◽

State Condition ◽

Resting State Functional Connectivity ◽

Congenital Blindness ◽

Eyes Closed ◽

Congenitally Blind ◽

Eyes Open

AbstractLower resting-state functional connectivity (RSFC) between ‘visual’ and non-‘visual’ neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition—as well as to evaluate the effect of resting state condition on group differences in RSFC—, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between ‘visual’ and non-‘visual’ circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.

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Visual predictions, neural oscillations and naïve physics

Scientific Reports ◽

10.1038/s41598-021-95295-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Blake W. Saurels ◽

Wiremu Hohaia ◽

Kielan Yarrow ◽

Alan Johnston ◽

Derek H. Arnold

Keyword(s):

Brain Activity ◽

Dynamic Environment ◽

Predictive Performance ◽

Brain Regions ◽

Internal Models ◽

Oscillatory Activity ◽

Alpha Band ◽

Core Property ◽

Ball Tracking ◽

Band Activity

AbstractPrediction is a core function of the human visual system. Contemporary research suggests the brain builds predictive internal models of the world to facilitate interactions with our dynamic environment. Here, we wanted to examine the behavioural and neurological consequences of disrupting a core property of peoples’ internal models, using naturalistic stimuli. We had people view videos of basketball and asked them to track the moving ball and predict jump shot outcomes, all while we recorded eye movements and brain activity. To disrupt people’s predictive internal models, we inverted footage on half the trials, so dynamics were inconsistent with how movements should be shaped by gravity. When viewing upright videos people were better at predicting shot outcomes, at tracking the ball position, and they had enhanced alpha-band oscillatory activity in occipital brain regions. The advantage for predicting upright shot outcomes scaled with improvements in ball tracking and occipital alpha-band activity. Occipital alpha-band activity has been linked to selective attention and spatially-mapped inhibitions of visual brain activity. We propose that when people have a more accurate predictive model of the environment, they can more easily parse what is relevant, allowing them to better target irrelevant positions for suppression—resulting in both better predictive performance and in neural markers of inhibited information processing.

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