Differences in Functional Level and Central Symptom of Network Structures in the Patients Seeking Treatment for Panic Disorder Before and During the COVID-19 Pandemic

Hyun-Ju Kim(Hyun-Ju Kim),Minji Bang(Minji Bang),Chun Il Park(Chun Il Park),Chongwon Pae(Chongwon Pae),Sang-Hyuk Lee(Sang-Hyuk Lee) 대한신경정신의학회 2023 PSYCHIATRY INVESTIGATION Vol.20 No.3

Objective Mental health problems such as anxiety, panic, and depression have been exacerbated by the coronavirus disease-2019 (COVID-19). This study aimed to compare the symptom severities and overall function before and during the COVID-19 pandemic among patients with panic disorder (PD) seeking treatment compared to healthy controls (HCs). Methods Baseline data were collected from the two groups (patients with PD and HCs) in two separate periods: before COVID-19 (Jan 2016-Dec 2019) and during COVID-19 (Mar 2020-Jul 2022). A total 453 participants (before COVID-19: 246 [139 patients with PD and 107 HCs], during COVID-19: 207 [86 patients with PD and 121 HCs]) was included. Scales for panic and depressive symptoms and overall function were administered. Additionally, network analyses were performed to compare the two groups within the patients with PD. Results The results of two-way analysis of variance analyses showed that patients with PD enrolled during COVID-19 showed higher levels of interoceptive fear and lower overall functioning. In addition, a network comparison test revealed that a significantly high strength and expected influence for agoraphobia and avoidance in patients with PD during COVID-19. Conclusion This study suggested that the overall function could have worsened, and the importance of agoraphobia and avoidance as a central symptom may have increased in patients with PD seeking treatment during COVID-19.

Energy landscape analysis of the subcortical brain network unravels system properties beneath resting state dynamics

Kang, Jiyoung,Pae, Chongwon,Park, Hae-Jeong ACADEMIC PRESS 2017 NEUROIMAGE Vol.149 No.-

<P><B>Abstract</B></P> <P>The configuration of the human brain system at rest, which is in a transitory phase among multistable states, remains unknown. To investigate the dynamic systems properties of the human brain at rest, we constructed an energy landscape for the state dynamics of the subcortical brain network, a critical center that modulates whole brain states, using resting state fMRI. We evaluated alterations in energy landscapes following perturbation in network parameters, which revealed characteristics of the state dynamics in the subcortical brain system, such as maximal number of attractors, unequal temporal occupations, and readiness for reconfiguration of the system. Perturbation in the network parameters, even those as small as the ones in individual nodes or edges, caused a significant shift in the energy landscape of brain systems. The effect of the perturbation on the energy landscape depended on the network properties of the perturbed nodes and edges, with greater effects on hub nodes and hubs-connecting edges in the subcortical brain system. Two simultaneously perturbed nodes produced perturbation effects showing low sensitivity in the interhemispheric homologous nodes and strong dependency on the more primary node among the two. This study demonstrated that energy landscape analysis could be an important tool to investigate alterations in brain networks that may underlie certain brain diseases, or diverse brain functions that may emerge due to the reconfiguration of the default brain network at rest.</P>

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

Seong, Si-Baek,Pae, Chongwon,Park, Hae-Jeong Frontiers Media S.A. 2018 Frontiers in neuroinformatics Vol.12 No.-

<P>In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications.</P>

Effective connectivity during working memory and resting states: A DCM study

Jung, Kyesam,Friston, Karl J.,Pae, Chongwon,Choi, Hanseul H.,Tak, Sungho,Choi, Yoon Kyoung,Park, Bumhee,Park, Chan-A,Cheong, Chaejoon,Park, Hae-Jeong Elsevier 2018 NeuroImage Vol.169 No.-

<P><B>Abstract</B></P> <P>Although the relationship between resting-state <I>functional</I> connectivity and task-related activity has been addressed, the relationship between task and resting-state directed or <I>effective</I> connectivity – and its behavioral concomitants – remains elusive. We evaluated effective connectivity under an N-back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting-state data, from the same subjects, to model connectivity among the same brain regions engaged by the N-back task. This allowed us to: (i) examine the relationship between intrinsic (task-independent) effective connectivity during resting (A<SUB>rest</SUB>) and task states (A<SUB>task</SUB>), (ii) cluster phenotypes of task-related changes in effective connectivity (B<SUB>task</SUB>) across participants, (iii) identify edges (B<SUB>task</SUB>) showing high inter-individual effective connectivity differences and (iv) associate reaction times with the similarity between B<SUB>task</SUB> and A<SUB>rest</SUB> in these edges. We found a strong correlation between A<SUB>rest</SUB> and A<SUB>task</SUB> over subjects but a marked difference between B<SUB>task</SUB> and A<SUB>rest</SUB>. We further observed a strong clustering of individuals in terms of B<SUB>task</SUB>, which was not apparent in A<SUB>rest</SUB>. The task-related effective connectivity B<SUB>task</SUB> varied highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between B<SUB>task</SUB> and A<SUB>rest</SUB> at the edges from the parietal to the frontal lobes was positively correlated with 2-back reaction times. This result implies that a greater change in context-sensitive coupling – from resting-state connectivity – is associated with faster reaction times. In summary, task-dependent connectivity endows resting-state connectivity with a context sensitivity, which predicts the speed of information processing during the N-back task.</P>

Dynamic effective connectivity in resting state fMRI

Park, Hae-Jeong,Friston, Karl J.,Pae, Chongwon,Park, Bumhee,Razi, Adeel ACADEMIC PRESS 2018 NEUROIMAGE Vol.180 No.2

<▼1><P>Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between-window) level given connectivity estimates from the first (within-window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group-level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between-session and between-subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity – and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions – and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity.</P></▼1><▼2><P><B>Highlights</B></P><P>•<P>We describe efficient estimation of dynamics in resting state effective connectivity.</P>•<P>Spectral DCM and PEB are used to model fluctuations in neuronal coupling over time.</P>•<P>Dynamics in responses are explained in terms of its causes (effective connectivity).</P>•<P>Baseline and dynamic components of the default mode connectivity are identified.</P></P></▼2>

Differences in Functional Level and Central Symptom of Network Structures in the Patients Seeking Treatment for Panic Disorder Before and During the COVID-19 Pandemic

Kim Hyun-Ju,Bang Minji,Park Chun Il,Pae Chongwon,Lee Sang-Hyuk 대한신경정신의학회 2023 PSYCHIATRY INVESTIGATION Vol.20 No.4

Objective Mental health problems such as anxiety, panic, and depression have been exacerbated by the coronavirus disease-2019 (COVID-19). This study aimed to compare the symptom severities and overall function before and during the COVID-19 pandemic among patients with panic disorder (PD) seeking treatment compared to healthy controls (HCs).Methods Baseline data were collected from the two groups (patients with PD and HCs) in two separate periods: before COVID-19 (Jan 2016–Dec 2019) and during COVID-19 (Mar 2020–Jul 2022). A total 453 participants (before COVID-19: 246 [139 patients with PD and 107 HCs], during COVID-19: 207 [86 patients with PD and 121 HCs]) was included. Scales for panic and depressive symptoms and overall function were administered. Additionally, network analyses were performed to compare the two groups within the patients with PD.Results The results of two-way analysis of variance analyses showed that patients with PD enrolled during COVID-19 showed higher levels of interoceptive fear and lower overall functioning. In addition, a network comparison test revealed that a significantly high strength and expected influence for agoraphobia and avoidance in patients with PD during COVID-19.Conclusion This study suggested that the overall function could have worsened, and the importance of agoraphobia and avoidance as a central symptom may have increased in patients with PD seeking treatment during COVID-19.