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

scholarly journals Initial Evidence for Brain Plasticity Following a Digital Therapeutic Intervention for Depression

2019 ◽  
Vol 3 ◽  
pp. 247054701987788
Author(s):  
Megan M. Hoch ◽  
Gaelle E. Doucet ◽  
Dominik A. Moser ◽  
Won Hee Lee ◽  
Katherine A. Collins ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Resting State ◽  
Brain Plasticity ◽  
Memory Task ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Major Depressive ◽  
Emotional Faces ◽  
Task Training

Background Digital therapeutics such as cognitive–emotional training have begun to show promise for the treatment of major depressive disorder. Available clinical trial data suggest that monotherapy with cognitive–emotional training using the Emotional Faces Memory Task is beneficial in reducing depressive symptoms in patients with major depressive disorder. The aim of this study was to investigate whether Emotional Faces Memory Task training for major depressive disorder is associated with changes in brain connectivity and whether changes in connectivity parameters are related to symptomatic improvement. Methods Fourteen major depressive disorder patients received Emotional Faces Memory Task training as monotherapy over a six-week period. Patients were scanned at baseline and posttreatment to identify changes in resting-state functional connectivity and effective connectivity during emotional working memory processing. Results Compared to baseline, patients showed posttreatment reduced connectivity within resting-state networks involved in self-referential and salience processing and greater integration across the functional connectome at rest. Moreover, we observed a posttreatment increase in the Emotional Faces Memory Task-induced modulation of connectivity between cortical control and limbic brain regions, which was associated with clinical improvement. Discussion These findings provide initial evidence that cognitive–emotional training may be associated with changes in short-term plasticity of brain networks implicated in major depressive disorder. Conclusion Our findings pave the way for the principled design of large clinical and neuroimaging studies.

Sign in / Sign up

Export Citation Format

Share Document