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Keyvan Mahjoory

As seen in: PLOS, JAMA Psychiatry, BioRxiv
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Articles

Multimodal Machine Learning–Based Prediction of Relapse in Major Depressive Disorder

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By Lea Teutenberg, Nils Winter, Paula Usemann, Frederike Stein, Florian Thomas-Odenthal, Kira Flinkenflügel, Tiana Borgers, Susanne Meinert, Elisabeth Leehr, Dominik Grotegerd, Anne Sophie Hildebrand, Keyvan Mahjoory, Maximilian Konowski, Jennifer Spanagel, Leon Fehse, Dominik Heider, Till F. M. Andlauer, Friederike David, Andreas J. Forstner, Harald Renz, Corinna Bang, Andre Franke, Stephanie Witt, Marcella Rietschel, Fabian Streit, Nina Alexander, Andreas Jansen, Igor Nenadic, Benjamin Straube, Hamidreza Jamalabadi, Udo Dannlowski, Tim Hahn Verified, Tilo Kircher, Sara Papenbrock, Sabrina Teckentrup, Sarah Wellms, Philipp Schöppner
| JAMA Psychiatry Verified
Key Points Question Does machine learning–based integration of multiple data modalities, relative to single data modalities, improve relapse prediction in major depressive disorder? Findings In this prognostic study, depressive relapse within 2 years was predicted among 564 patients with major depressive disorder, with a highest classification accuracy of 64.43%.
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Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention

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By Keyvan Mahjoory, Andreas Bahmer, Molly J. Henry
| PLOS
Citation: Mahjoory K, Bahmer A, Henry MJ (2024) Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention. PLoS Comput Biol 20(8): e1012376. https://doi.org/10.1371/journal.pcbi.1012376 Editor: Frédéric E. Theunissen, University of California at Berkeley, UNITED STATES OF AMERICA Received: March 23, 2023; Accepted: July 30, 2024; Published: August 8, 2024 Copyright: © 2024 Mahjoory et al.
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Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention

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By Andreas Bahmer, Molly J. Henry, Keyvan Mahjoory
| BioRxiv
Abstract Human listeners have the ability to direct their attention to a single speaker in a multi- talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data.
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