Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study

Al-Ezzi, Abdulhakim; Kamel, Nidal; Faye, Ibrahima; Gunaseli, Esther

doi:10.20944/preprints202102.0610.v1

Cited by 7 publications

(1 citation statement)

References 65 publications

(83 reference statements)

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“…To examine whether the SAD states are being represented appropriately, we developed a social performance task for SAD assessment to acquire EEG data that could generate substantial and original datasets. The social performance task was reported in our previous studies [50], [51]. In our studies, we have ensured proper consideration of the time factor and cooperative behaviors of the participants.…”

Section: B Eeg Data Acquisition and Experimental Designmentioning

confidence: 99%

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

et al. 2021

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Neuroimaging investigations have proven that social anxiety disorder (SAD) is associated with aberrations in the connectivity of human brain functions.The assessment of the effective connectivity (EC) of the brain and its impact on the detection and medication of neurodegenerative pathophysiology is hence a crucial concern that needs to be addressed. Nevertheless, there are no clinically certain diagnostic biomarkers that can be linked to SAD. Therefore, investigating neural connectivity biomarkers of SAD based on deep learning models (DL) has a promising approach with its recent underlined potential results. In this study, an electroencephalography (EEG)-based detection model for SAD is constructed through directed causal influences combined with a deep convolutional neural network (CNN) and the long shortterm memory (LSTM). The EEG data were classified by applying three different DL models, namely, CNN, LSTM, and CNN+LSTM to discriminate the severity of SAD (severe, moderate, mild) and healthy controls (HC) at different frequency bands (delta, theta, alpha, low beta, and high beta) in the default mode network (DMN) under resting-state condition. The DL model uses the EC features as input, which are derived from the cortical correlation within different EEG rhythms for certain cortical areas that are more susceptible to SAD. Experimental results revealed that the proposed model (CNN+LSTM) outperforms the other models in SAD recognition. For our dataset, the highest recognition accuracies of 92.86%, 92.86%, 96.43%, and 89.29%, specificities of 95.24%, 95.24%, 100%, and 90.91%, and sensitivities of 85.71%, 85.71%, 87.50%, and 83.33% were achieved by using CNN+LSTM model for severe, moderate, mild, and HC, respectively. The fundamental contribution of this analysis is the characterization of neural brain features using different DL models to categorize the severity of SAD, which can represent a potential biomarker for SAD.INDEX TERMS Effective connectivity network, convolutional neural networks (CNNs), social anxiety disorder (SAD), default mode network (DMN), deep learning models, partial directed coherence (PDC), Electroencephalogram (EEG), human brain mapping.

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Section: B Eeg Data Acquisition and Experimental Designmentioning

confidence: 99%

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

et al. 2021

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Rumination as a Transdiagnostic Phenomenon in the 21st Century: The Flow Model of Rumination

et al. 2023

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Rumination and its related mental phenomena share associated impairments in cognition, such as executive functions and attentional processes across different clinical conditions (e.g., in psychotic disorders). In recent decades, however, the notion of rumination has been increasingly narrowed to the “self-focused” type in depressive disorders. A closer review of the literature shows that rumination may be construed as a broader process characterized by repetitive thoughts about certain mental contents that interfere with one’s daily activities, not only limited to those related to “self”. A further examination of the construct of rumination beyond the narrowly focused depressive rumination would help expand intervention opportunities for mental disorders in today’s context. We first review the development of the clinical construct of rumination with regard to its historical roots and its roles in psychopathology. This builds the foundation for the introduction of the “Flow Model of Rumination (FMR)”, which conceptualizes rumination as a disruption of a smooth flow of mental contents in conscious experience that depends on the coordinated interactions between intention, memory, affect, and external events. The conceptual review concludes with a discussion of the impact of rapid technological advances (such as smartphones) on rumination. Particularly in contemporary societies today, a broader consideration of rumination not only from a cognition viewpoint, but also incorporating a human–device interaction perspective, is necessitated. The implications of the FMR in contemporary mental health practice are discussed.

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Acquisition and Processing of Brain Signals

Bizzego

Esposito

2021

Sensors

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Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study

Cited by 7 publications

References 65 publications

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

Rumination as a Transdiagnostic Phenomenon in the 21st Century: The Flow Model of Rumination

Acquisition and Processing of Brain Signals

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