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

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

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

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

Emotion-based brain mechanisms and predictors for SSRI and CBT treatment of anxiety and depression: a randomized trial.

Mechanisms and predictors for the successful treatment of anxiety and depression have been elusive, limiting the effectiveness of existing treatments and curtailing the development of new interventions. In this study, we evaluated the utility of three widely used neural probes of emotion (experience, regulation, and perception) in their ability to predict symptom improvement and correlate with symptom change following two first-line treatments-selective serotonin reuptake inhibitors (SSRIs) and cognitive-behavioral therapy (CBT). Fifty-five treatment-seeking adults with anxiety and/or depression were randomized to 12 weeks of SSRI or CBT treatment (ClinicalTrials.gov identifier: NCT01903447). Functional magnetic resonance imaging (fMRI) was used to examine frontolimbic brain function during emotion experience, regulation, and perception, as probed by the Emotion Regulation Task (ERT; emotion experience and regulation) and emotional face assessment task (EFAT; emotion perception). Brain function was then related to anxiety and depression symptom change. Results showed that both SSRI and CBT treatments similarly attenuated insula and amygdala activity during emotion perception, and greater treatment-related decrease in insula and amygdala activity was correlated with greater reduction in anxiety symptoms. Both treatments also reduced amygdala activity during emotion experience but brain change did not correlate with symptom change. Lastly, greater pre-treatment insula and amygdala activity during emotion perception predicted greater anxiety and depression symptom improvement. Thus, limbic activity during emotion perception is reduced by both SSRI and CBT treatments, and predicts anxiety and depression symptom improvement. Critically, neural reactivity during emotion perception may be a non-treatment-specific mechanism for symptom improvement

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