Author

Maurizio Fava M. D

Massachusetts General HospitalORCID 0000-0002-4635-0458Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 2 Assay:imaging· 2 Research Focus:batch correction / integration· 1 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Crystal M. CooperiD· 2 shared Madhukar H. TrivediiD· 2 shared Mary Louise PhillipsiD· 2 shared Myrna M. WeissmaniD· 2 shared Patrick J. McGrathiD· 2 shared Albert MontilloiD· 1 shared Alex TreacheriD· 1 shared Benji T. Kurian· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Crystal M. CooperORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Predictive Patterns of Antidepressant Response from Pre-Treatment Reward Processing using Functional MRI and Deep Learning: Key Results from the EMBARC Randomized Clinical Trial

Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher, Cooper Mellema, Crystal M. Cooper, Manish K. Jha, Benji T. Kurian, Maurizio Fava M. D, Patrick J. McGrath, Myrna M. Weissman, Mary Louise Phillips, Madhukar H. Trivedi, Albert Montillo

This paper uses deep learning on pre-treatment reward processing fMRI and clinical data to predict antidepressant outcomes, achieving up to 37% variance explained and remission prediction with NNT 2-4.

Biological Psychiatry · Mar 1, 2022Peer-reviewedBioinformatics

Harmonization of cortical thickness measurements across scanners and sites

Jean‐Philippe Fortin, Nicholas C. Cullen, Yvette I. Sheline, Warren D. Taylor, Irem Aselcioglu, Philip A. Cook, Phil Adams, Crystal M. Cooper, Maurizio Fava M. D, Patrick J. McGrath, Melvin G. McInnis, Mary Louise Phillips, Madhukar H. Trivedi, Myrna M. Weissman, Russell Taki Shinohara

ComBat, an empirical Bayes location-and-scale model from genomics, removes additive and multiplicative scanner effects from regional cortical thickness across 11 scanners while preserving age associations.

NeuroImage · Nov 20, 2017Peer-reviewedNeuroscience✦ In-depth digest