Differential Response to SSRI versus Placebo and Distinct Neural Signatures Among Data-Driven Subgroups of Patients with Major Depressive Disorder

2020 ◽  
Author(s):  
Cherise R. Chin Fatt ◽  
Crystal M. Cooper ◽  
Manish K. Jha ◽  
Abu Minhajuddin ◽  
A. John Rush ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Differential Response ◽  
Data Driven ◽  
Major Depressive ◽  
Neural Signatures

DATA-DRIVEN COURSE TRAJECTORIES IN PRIMARY CARE PATIENTS WITH MAJOR DEPRESSIVE DISORDER

2014 ◽  
Vol 31 (9) ◽  
pp. 778-786 ◽  
Author(s):  
Klaas J. Wardenaar ◽  
Henk-Jan Conradi ◽  
Peter de Jonge
Keyword(s):  
Primary Care ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Data Driven ◽  
Major Depressive ◽  
Primary Care Patients

scholarly journals Data-driven subtypes of major depressive disorder: a systematic review

BMC Medicine ◽  
2012 ◽  
Vol 10 (1) ◽  
Author(s):  
Hanna M van Loo ◽  
Peter de Jonge ◽  
Jan-Willem Romeijn ◽  
Ronald C Kessler ◽  
Robert A Schoevers
Keyword(s):  
Systematic Review ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Data Driven ◽  
Major Depressive

Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder

2009 ◽  
Vol 169 (2) ◽  
pp. 132-138 ◽  
Author(s):  
Andrew F. Leuchter ◽  
Ian A. Cook ◽  
William S. Gilmer ◽  
Lauren B. Marangell ◽  
Karl S. Burgoyne ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Differential Response ◽  
Major Depressive

scholarly journals Theory of Lehmer Transform and Its Applications in Identifying the Electroencephalographic Signature of Major Depressive Disorder

2021 ◽  
Author(s):  
Masoud Ataei ◽  
Xiaogang Wang
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Time Series Data ◽  
Data Driven ◽  
Series Data ◽  
Eeg Signals ◽  
Major Depressive ◽  
Data Driven Approach ◽  
Highly Oscillatory ◽  
Theoretical Results

Abstract We propose a novel transform called Lehmer transform and establish theoretical results which are used to compress and characterize large volumes of highly volatile time series data. It will be shown that our proposed method could be used as a practical data-driven approach for analyzing extreme events in nonstationary and highly oscillatory stochastic processes such as biological signals. We demonstrate the advantage of the proposed transform in comparison with traditional methods such as Fourier and Wavelets transforms through an example of devising a classifier to discern the patients with major depressive disorder from the healthy subjects using their recorded EEG signals and provide the computational results. We show that the proposed transform can be used for building better and more robust classifiers with significant accuracy.

Sign in / Sign up

Export Citation Format

Share Document