Author

Andrew M. Michael

Rochester Institute of Technology

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:imaging· 3 Research Focus:differential expression· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

Angikar GhosaliD· 1 shared Aristeidis SotirasiD· 1 shared Bonnie J. NageliD· 1 shared David B. GoldstoniD· 1 shared Delin SuniD· 1 shared Duncan B. ClarkiD· 1 shared Ethan Campbell· 1 shared Fiona C. BakeriD· 1 shared

Activity by Year

2018-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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1 shared paperNeuroscience

Angikar GhosalORCID

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Author profile →

3 papers

Recent·Most cited

Alcohol Use Disrupts Age-Appropriate Cortical Thinning in Adolescence: A Data Driven Approach

Delin Sun, Viraj R. Adduru, Rachel Phillips, Heather C. Bouchard, Aristeidis Sotiras, Andrew M. Michael, Fiona C. Baker, Susan Frances Tapert, Sandra A. Brown, Duncan B. Clark, David B. Goldston, Kate Brody Nooner, Bonnie J. Nagel, Wesley Kurt Thompson, Michael D. De Bellis, Rajendra A. Morey

This study uses a data-driven method to show that alcohol use in adolescents affects cortical thinning differently across age groups.

bioRxiv · May 17, 2021 · PreprintNeuroscience

The effect of functionally-guided-connectivity-based rTMS on amygdala activation.

Lysianne Beynel, Ethan Campbell, Maria Naclerio, Jeffrey T. Galla, Angikar Ghosal, Andrew M. Michael, Nathan A. Kimbrel, Simon W. Davis, Lawrence G. Appelbaum

This study tests if functionally guided rTMS can modulate amygdala activation via its connectivity with the medial prefrontal cortex, finding increased activation post-rTMS.

Brain Sciences · Apr 13, 2021Peer-reviewedNeuroscience

Early Brain Imaging can Predict Autism: Application of Machine Learning to a Clinical Imaging Archive

Gajendra J. Katuwal, Stefi Alison Baum, Andrew M. Michael

This study uses machine learning to analyze brain imaging data from young children to predict autism spectrum disorder (ASD) with high accuracy.

bioRxiv · Nov 16, 2018 · PreprintBioinformatics