scholarly journals Attention bias modification for major depressive disorder: Effects on attention bias, resting state connectivity, and symptom change.

2015 ◽  
Vol 124 (3) ◽  
pp. 463-475 ◽  
Author(s):  
Christopher G. Beevers ◽  
Peter C. Clasen ◽  
Philip M. Enock ◽  
David M. Schnyer
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Resting State ◽  
Attention Bias ◽  
Symptom Change ◽  
Major Depressive ◽  
Attention Bias Modification ◽  
Disorder Effects ◽  
Resting State Connectivity ◽  
Bias Modification

scholarly journals Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder

2015 ◽  
Vol 40 (7) ◽  
pp. 1659-1673 ◽  
Author(s):  
Andrew Crowther ◽  
Moria J Smoski ◽  
Jared Minkel ◽  
Tyler Moore ◽  
Devin Gibbs ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Resting State ◽  
Major Depressive ◽  
Predictors Of Response ◽  
Resting State Connectivity

Thalamic hyperconnectivity as neurophysiological signature of major depressive disorder in two multicenter studies

2021 ◽  
Author(s):  
Selene Gallo ◽  
Ahmed ElGazzar ◽  
Paul Zhutovsky ◽  
Rajat Mani Thomas ◽  
Nooshin Javaheripour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Deep Learning ◽  
Depressive Disorder ◽  
Resting State ◽  
Support Vector ◽  
Major Depressive ◽  
Multicenter Studies ◽  
Disease Heterogeneity ◽  
Resting State Connectivity

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. Resting-state functional magnetic resonance imaging data were obtained from the REST-meta-MDD (N=2338) and PsyMRI (N=1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN) and performance was evaluated using 5-fold cross-validation. Results were visualized using GCN-Explainer, an ablation study and univariate t-testing.Mean classification accuracy was 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes.Whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Resting State Connectivity Differentiates Asymptomatic Patients With Bipolar Disorder and Major Depressive Disorder

2021 ◽  
Vol 89 (9) ◽  
pp. S362-S363
Author(s):  
Sabina Rai ◽  
Kristi Griffiths ◽  
Isabella Breukelaar ◽  
Ana Rita Barreiros ◽  
Wenting Chen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Resting State ◽  
Major Depressive ◽  
Asymptomatic Patients ◽  
Resting State Connectivity
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