Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …

[HTML][HTML] Progress and challenges in research of the mechanisms of anhedonia in major depressive disorder

YA Su, T Si - General psychiatry, 2022 - ncbi.nlm.nih.gov
There is an increasing heavy disease burden of major depressive disorder (MDD) globally.
Both high diagnostic heterogeneity and complicated pathological mechanisms of MDD pose …

Multisite comparison of MRI defacing software across multiple cohorts

AE Theyers, M Zamyadi, M O'Reilly, R Bartha… - Frontiers in …, 2021 - frontiersin.org
With improvements to both scan quality and facial recognition software, there is an
increased risk of participants being identified by a 3D render of their structural neuroimaging …

Use of machine learning for predicting escitalopram treatment outcome from electroencephalography recordings in adult patients with depression

A Zhdanov, S Atluri, W Wong, Y Vaghei… - JAMA network …, 2020 - jamanetwork.com
Importance Social and economic costs of depression are exacerbated by prolonged periods
spent identifying treatments that would be effective for a particular patient. Thus, a tool that …

Effects of CYP2C19 and CYP2D6 gene variants on escitalopram and aripiprazole treatment outcome and serum levels: results from the CAN-BIND 1 study

F Islam, VS Marshe, L Magarbeh, BN Frey… - Translational …, 2022 - nature.com
Cytochrome P450 drug-metabolizing enzymes may contribute to interindividual differences
in antidepressant outcomes. We investigated the effects of CYP2C19 and CYP2D6 gene …

Genome-wide analysis suggests the importance of vascular processes and neuroinflammation in late-life antidepressant response

VS Marshe, M Maciukiewicz, AC Hauschild… - Translational …, 2021 - nature.com
Antidepressant outcomes in older adults with depression is poor, possibly because of
comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome …

The Canadian biomarker integration network in depression (CAN-BIND): magnetic resonance imaging protocols

GM MacQueen, S Hassel, SR Arnott, J Addington… - Journal of Psychiatry and …, 2019 - jpn.ca
Studies of clinical populations that combine MRI data generated at multiple sites are
increasingly common. The Canadian Biomarker Integration Network in Depression (CAN …

Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in …

K Dunlop, SJ Rizvi, SH Kennedy, S Hassel… - …, 2020 - nature.com
Anhedonia is thought to reflect deficits in reward processing that are associated with
abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in …

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

S Prompiengchai, K Dunlop - Neuropsychopharmacology, 2024 - nature.com
Abstract Treatment outcomes widely vary for individuals diagnosed with major depressive
disorder, implicating a need for deeper understanding of the biological mechanisms …

Metabolomic signatures associated with depression and predictors of antidepressant response in humans: A CAN-BIND-1 report

G Caspani, G Turecki, RW Lam, RV Milev… - Communications …, 2021 - nature.com
One of the biggest challenges in treating depression is the heterogeneous and qualitative
nature of its clinical presentations. This highlights the need to find quantitative molecular …