A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder
- 31 October 2013
- journal article
- research article
- Published by Elsevier BV in Clinical Neurophysiology
- Vol. 124 (10), 1975-1985
- https://doi.org/10.1016/j.clinph.2013.04.010
Abstract
No abstract availableKeywords
This publication has 59 references indexed in Scilit:
- Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in DepressionPLOS ONE, 2012
- A machine learning approach for distinguishing age of infants using auditory evoked potentialsClinical Neurophysiology, 2011
- Current Source Density Measures of Electroencephalographic Alpha Predict Antidepressant Treatment ResponseBiological Psychiatry, 2011
- EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients-A case control studyBMC Neurology, 2011
- REST: A good idea but not the gold standardClinical Neurophysiology, 2010
- Regional brain metabolic correlates of self-reported depression severity contrasted with clinician ratingsJournal of Affective Disorders, 2010
- Distinct functional networks associated with improvement of affective symptoms and cognitive function during citalopram treatment in geriatric depressionHuman Brain Mapping, 2010
- Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depressionClinical Neurophysiology, 2009
- Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depressionPsychiatry Research: Neuroimaging, 2009
- Electroencephalographic Alpha Measures Predict Therapeutic Response to a Selective Serotonin Reuptake Inhibitor Antidepressant: Pre- and Post-Treatment FindingsBiological Psychiatry, 2008