Author
Dmitry Isaev
University of Southern CaliforniaORCID 0000-0001-9149-0479Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2017-2024 · peak 1/yrCo-Author Collaboration Network
10 top collaborators · 45 mutual co-author links6 papers
Recent·Most cited
- Genetic & Genomic MedicineAmerican Journal of PsychiatryPeer-reviewed
Subcortical brain alterations in carriers of genomic copy number variants
Kuldeep Kumar, Claudia Modenato, Clara A. Moreau…·Sébastien Jacquemont
This study examines subcortical brain alterations in CNV carriers across multiple neuropsychiatric disorders, finding shared and distinct patterns of structural changes.
26 citationstop 4% - NeuroscienceNeuropathology and Applied NeurobiologyPeer-reviewed
A systems-level analysis highlights microglial activation as a modifying factor in common forms of human epilepsy
André Altmann, Mina Ryten, Martina Di Nunzio…·Annamaria Vezzani
The paper identifies microglial activation as a factor contributing to cortical thinning in epilepsy by integrating neuroimaging and gene expression data.
61 citationstop 3% - NeuroscienceHuman Brain MappingPeer-reviewed
Subcortical Shape Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group
Tiffany Cheing Ho, Boris A. Gutman, Elena Pozzi…·Lianne Schmaal
This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.
- NeurosciencebioRxivPreprint
Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
Dmitry Petrov, Boris A. Gutman, Е.Е. Кузнецов…·Paul M. Thompson
The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.
2 citations - NeuroscienceLecture notes in computer sciencePeer-reviewed
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Dmitry Petrov, Boris A. Gutman, Shih-Hua…·Paul M. Thompson
This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.