EEG Classification based on Image Configuration in Social Anxiety Disorder

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs

Aberrant Amygdala–Frontal Cortex Connectivity During Perception Of Fearful Faces And At Rest In Generalized Social Anxiety Disorder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97272/1/da22014.pd

Altered Activation Of The Rostral Anterior Cingulate Cortex In The Context Of Emotional Face Distractors In Children And Adolescents With Anxiety Disorders

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109274/1/da22289.pd

Neural correlates of explicit and implicit emotion processing in relation to treatment response in pediatric anxiety

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136676/1/jcpp12658_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136676/2/jcpp12658.pd

EEG Classification based on Image Configuration in Social Anxiety Disorder

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy 6– 7% higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs. Index Terms— EEG, deep learning, classification

Comorbid anxiety increases cognitive control activation in Major Depressive Disorder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134086/1/da22541.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134086/2/da22541_am.pd

A supramolecular assembly formed by influenza A virus genomic RNA segments

The influenza A virus genome consists of eight viral RNAs (vRNAs) that form viral ribonucleoproteins (vRNPs). Even though evidence supporting segment-specific packaging of vRNAs is accumulating, the mechanism ensuring selective packaging of one copy of each vRNA into the viral particles remains largely unknown. We used electron tomography to show that the eight vRNPs emerge from a common ‘transition zone’ located underneath the matrix layer at the budding tip of the virions, where they appear to be interconnected and often form a star-like structure. This zone appears as a platform in 3D surface rendering and is thick enough to contain all known packaging signals. In vitro, all vRNA segments are involved in a single network of intermolecular interactions. The regions involved in the strongest interactions were identified and correspond to known packaging signals. A limited set of nucleotides in the 5′ region of vRNA 7 was shown to interact with vRNA 6 and to be crucial for packaging of the former vRNA. Collectively, our findings support a model in which the eight genomic RNA segments are selected and packaged as an organized supramolecular complex held together by direct base pairing of the packaging signals

Self-Reported Sleep Quality Modulates Amygdala Resting-State Functional Connectivity in Anxiety and Depression

Sufficient sleep plays an important role in neurocognitive function, yet, problematic sleep is ubiquitous in the general population. It is also frequently predictive of, and concurrent with, internalizing psychopathologies (IPs) such as anxiety and depression suggesting sleep quality is dimensional and transdiagnostic. Along with problematic sleep, IPs are characterized by negative affectivity, therefore, prominent neurobiological models of internalizing conditions involve the amygdala, a region central to emotion. In resting-state studies (independent of sleep considerations), abnormalities in amygdala-frontal functional connectivity are commonly observed suggesting emotion dysregulation may contribute to clinically-relevant phenotypes. In a separate line of research, studies of sleep deprivation, and insomnia disorder suggest sleep loss may alter amygdala-frontal connectivity. Taken together, findings point to shared neurobiology between sleep and emotion systems, however, the impact of sleep quality on the amygdala circuit in anxiety or depression is unclear. Therefore, we evaluated variance in naturalistic sleep quality on amygdala-based circuity in individuals with and without psychiatric illness. Resting-state fMRI data was collected in 87 un-medicated, treatment-seeking adults diagnosed with a primary anxiety disorder (n = 68) or primary depressive disorder (n = 19) in addition to healthy individuals (n = 40). Regression analysis was conducted with bilateral anatomical amygdala as seed regions and self-reported sleep quality was indexed with a validated self-report measure, the Pittsburgh Sleep Quality Index (PSQI). Post-hoc analysis was performed to evaluate whether diagnostic status (primary anxiety, primary depression, healthy) significantly explained functional connectivity results. Whole-brain regression analysis, controlling for anxiety and depression symptoms, revealed worse sleep quality (i.e., higher PSQI total scores) predicted increased left amygdala-subgenual anterior cingulate functional connectivity and reduced connectivity with posterior cerebellar lobe and superior temporal gyrus. For right amygdala, increased coupling with postcentral gyrus corresponded with worse sleep. Post-hoc analysis did not detect a significant relationship between diagnostic status and whole-brain findings. Results expand on previous studies and indicate variance in sleep quality tracks brain pathways involved in cognitive-emotion functions implicated in the neurobiology of IPs that may extend to individuals at risk for clinical anxiety or depression. Altogether, the clinical relevance of identifying phenotypes to improve our understanding of psychopathology may be improved by incorporating sleep quality

Increased Activation Of The Anterior Cingulate Cortex During Processing Of Disgust Faces In Individuals With Social Phobia

Background: Researchers have examined the role of differential activation of various brain regions involved in processing emotional information in subjects with social phobia. These studies have focused mostly on the activation of the amygdala. The anterior cingulate cortex (ACC) also has been implicated in processing emotional information, but its role in social phobia has not been examined. Methods: We recruited subjects with social phobia and matched them with non-anxious control subjects. Participants viewed facial expressions of disgust ( disgust faces ) and neutral facial expressions ( neutral faces ). We measured brain activation, focusing on the ACC, using functional magnetic resonance imaging. We also recorded participants\u27 ratings of emotional valence of faces, as well as response latencies to make these valence judgments. We repeated this procedure using three different sets of facial expressions. Results: Individuals with social phobia exhibited a significant increase in ACC activity compared with non-anxious control subjects when processing disgust versus neutral faces. Additionally, compared with control subjects, subjects with social phobia were faster in their ratings of disgust faces and rated the neutral faces more negatively. Conclusions: Our findings demonstrate that the ACC might be involved in affective processing of negative information in socially phobic subjects. © 2005 Society of Biological Psychiatry