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Deydeep Kothapalli on Muck Rack

Deydeep Kothapalli

As seen in: Wiley Online Library, BioRxiv, medRxiv
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Articles

Evaluation of ComBat Harmonization for Reducing Across‐Tracer Differences in Regional Amyloid PET Analyses

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By Braden Yang, Tom Earnest, Sayantan Kumar, Deydeep Kothapalli
| Wiley Online Library Verified
ComBat is a data driven harmonization method which, unlike Centiloid, does not require a priori selection and stratification of training cohorts and is able to harmonize regional amyloid PET estimates. ComBat with no covariates performed the best in increasing the absolute agreement of regional amyloid PET measurements made within scan pairs of the same group of subjects using two different radiotracers.
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Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

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By Sayantan Kumar, Thomas W. Earnest, Braden Yang, Deydeep Kothapalli
| BioRxiv
Abstract INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS: We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET.
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Evaluation of ComBat harmonization for reducing across-tracer biases in regional amyloid PET analyses

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By Braden Yang, Tom Earnest, Sayantan Kumar, Deydeep Kothapalli
| medRxiv
AS reported receiving personal fees from BrightFocus for serving as a grant reviewer and stock from TheraPanacea outside the submitted work. All remaining authors have no conflicting interests to report. BY was supported by the Imaging Science Pathways NIH T32 EB014855. AS was supported by NIH award R01 AG067103 and BrightFocus Foundation grant ADR A2021042S.
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