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

Rajamannar Ramasubbua Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada;b Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, CanadaCorrespondence[email protected]
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Elliot C. Brownc School of Health and Care Management, Arden University, Berlin, GermanyView further author information

Pauline Mouchesd Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, CanadaView further author information

Jasmine A. Moored Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, CanadaView further author information

Darren L. Clarka Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada;b Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, CanadaView further author information

Christine P. Molnare Department of Radiology, Cumming school of medicine, University of Calgary, Calgary, Alberta, CanadaView further author information

Zelma H. T. Kissa Department of Psychiatry, Clinical Neurosciences, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada;b Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, Calgary, Alberta, CanadaView further author information

Nils D. Forkertd Department of Radiology, Clinical Neurosciences, Hotchkiss Brain Institute, Cumming school of medicine, University of Calgary, Calgary, Alberta, CanadaView further author information

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

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

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Supplementary Table 1.docx

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