Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach-Reference-Cited by-同舟云学术

Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach

Published:2024-01-17 Issue:3 Volume:25 Page:175-187
ISSN:1562-2975
Container-title:The World Journal of Biological Psychiatry
language:en
Short-container-title:The World Journal of Biological Psychiatry

Author:

Ramasubbu Rajamannar¹²,Brown Elliot C.³,Mouches Pauline⁴,Moore Jasmine A.⁴,Clark Darren L.¹²,Molnar Christine P.⁵,Kiss Zelma H. T.¹²,Forkert Nils D.⁴

Affiliation:

1. Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada

2. Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

3. School of Health and Care Management, Arden University, Berlin, Germany

4. Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, Canada

5. Department of Radiology, Cumming school of medicine, University of Calgary, Calgary, Alberta, Canada

Funder

Alberta Innovates Health Solutions

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15622975.2023.2300795

Reference58 articles.

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5. Frontal Theta Cordance Predicts 6-Month Antidepressant Response to Subcallosal Cingulate Deep Brain Stimulation for Treatment-Resistant Depression: A Pilot Study

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