Altered resting-state functional connectivity in patients with obsessive–compulsive disorder: A magnetoencephalography study

Koh, Min Jung; Seol, Jae Hong; Kang, Jee In; Kim, Bong Soo; Namkoong, Kee; Chang, Jin Woo; Kim, Se Joo

doi:10.1016/j.ijpsycho.2017.10.012

Cited by 14 publications

(13 citation statements)

References 52 publications

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“…Another study of OCD also found decreased interhemispheric coherence and lagged non-linear coherence between frontal brain areas during rest, including the anterior cingulate cortex, the superior frontal gyrus, and the left medial frontal gyrus [19]. Although these results were inconsistent regarding the abnormalities of specific EEG frequency bands and showed both decreases and increases of low-frequency (delta and theta bands) as well as fast-frequency (alpha and beta bands) activities [19] [20], multiple frequency bands of brain activity were indeed closely associated with the brain abnormalities of OCD patients, which was also supported by the studies of resting-state MEG based on inter-regional phase synchrony among the OFC, insula, and cortical regions of the limbic lobe [21]. And then, the study of whole-brain functional networks in different frequency bands would promote our understanding of the alterations of brain dynamics and topological properties of OCD patients from a global perspective based on resting-state EEG.…”

Section: Introductionmentioning

confidence: 55%

“…After eliminating the influence of various artifacts, the 30s artifact-free EEG data (15000 sample points) were selected from each participant and divided into five segments. Each segment of the EEGs was decomposed into the conventional EEG frequency bands, including theta (4-7 Hz), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31-48 Hz) bands for calculating PLVs. To further improve the reliability of the current results, we took the average PLV of the five segments in each frequency band and applied them in the subsequent analysis of brain functional networks.…”

Section: B Eeg Acquisition and Data Preprocessingmentioning

confidence: 99%

“…Based on the previous findings of neuroimaging and neuroelectrophysiology, the aim of the current study is to evaluate whether whole-brain functional networks of OCD patients are aberrant in different frequency bands during different resting states. Here, the PLV was used for measuring the phase synchrony between channel pairs to construct FCs, which was particularly suitable for connectivity analysis by providing measurements of neural signal temporal relationships and regardless of their signal amplitude [21] [22]. Furthermore, resting-state EEGs were acquired during EC and EO states, which were fundamental behaviors for directing attention to the external and the internal world [23] [24].…”

Section: Introductionmentioning

confidence: 99%

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Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

Tan

Jin

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions), and few studies have assessed the whole-brain functional connectivity architecture of OCD with electroencephalogram (EEG) during different resting states. Graph theory and network-based statistics (NBS) were employed to examine the neural synchronization and the whole-brain functional connectivity (FC) based on the phase-locking value (PLV) of OCD patients and healthy controls (HCs) during eyes-closed (EC) and eyes-open (EO) states. Compared with HCs, OCD patients exhibited not only decreased global synchronization in terms of phase synchrony but also aberrant global topological properties (decreased average shortest path lengths and normalized shortest path lengths together with increased global efficiencies and normalized clustering coefficients) together with inhibited intra-hemispheric and interhemispheric FCs during rest, which suggested an imbalance between functional integration and segregation of brain networks for OCD patients. Meanwhile, OCD patients had increased global efficiencies and normalized clustering coefficients, but decreased average clustering coefficients and normalized shortest path lengths together with significantly decreased FCs in the alpha band from EC to EO states, which suggested a dynamic switch between highly integrated (EC state) and highly specialized (EO state) modes of information processing. Moreover, the decreased FCs of OCD patients showed obvious hemispheric asymmetry within or between groups during EC and EO states, which might serve as a potential biomarker to classify OCD patients from HCs.

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Section: Introductionmentioning

confidence: 55%

Section: B Eeg Acquisition and Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

Tan

Jin

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…These elaborations of the neuroanatomical OCD model stemmed from functional brain imaging studies revealing a difference between patients with OCD and controls during cognitive and affective tasks in these regions (e.g., Mataix-Cols et al, 2004;Simon et al, 2010Simon et al, , 2014Via et al, 2014;Norman et al, 2016). Moreover, even at rest, statistically significant structural and functional regional differences were also found (e.g., Rotge et al, 2009;de Wit et al, 2014;Göttlich et al, 2014;Piras et al, 2015;Reess et al, 2016;Tian et al, 2016;Boedhoe et al, 2017Boedhoe et al, , 2018Fouche et al, 2017;Gürsel et al, 2018;Koh et al, 2018;Brennan et al, 2019;Hao et al, 2019;Yang et al, 2019).…”

Section: Probing the Neuroanatomical Location Of The Neurobiological Fault In Obsessive-compulsive Disordermentioning

confidence: 99%

The Psychopharmacology of Obsessive-Compulsive Disorder: A Preclinical Roadmap

et al. 2019

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Servier and has received research funding from Servier and Lundbeck. Except for income from the primary employer and research funding to B.H.H. from the above-mentioned organizations and agencies, no financial support or compensation has been received from any individual or corporate entity over the past 3 years for research or professional services, and there are no personal financial holdings that could be perceived as constituting a potential conflict of interest.

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“…Functional and structural connectivity studies have revealed differences in a wide range of neurological conditions. For example, using resting-state magnetoencephalography (MEG) data, differences in functional connectivity were found in obsessive compulsive disorder patients as compared to healthy controls (Koh et al, 2018). Similarly, changes detected in white matter structural connectivity of epilepsy patients have been used to differentiate epilepsy patients with and without cognitive degeneration (Vaessen et al, 2011) and resting-state functional network analysis has been used to evaluate seizure networks in presurgical evaluation of medial temporal lobe epilepsy (Bettus et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

Carr

Gershon

Shafiabadi

et al. 2021

Front. Integr. Neurosci.

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A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.

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Altered resting-state functional connectivity in patients with obsessive–compulsive disorder: A magnetoencephalography study

Cited by 14 publications

References 52 publications

Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

The Psychopharmacology of Obsessive-Compulsive Disorder: A Preclinical Roadmap

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

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