Data-driven imaging marker for cerebral consequences of Post-Acute Sequelae of COVID19 - prospective validation and [18F]FEPPA PET correlation

Motivation: Recognizing neurological symptoms in Post-Acute Sequelae of COVID-19 (PASC), this study investigates neuroinflammatory markers to develop reliable neuroimaging diagnostics. Goal(s): To validate CoreFA, a data-driven imaging marker, for detecting white matter changes and to explore its correlation with [18F]FEPPA PET neuroinflammatory markers. Approach: Diffusion MRI-derived fractional anisotropy (FA) was assessed in white matter tracts to derive CoreFA. CoreFA was validated in a prospective cohort and examined for association with neuroinflammation via FEPPA PET. Results: CoreFA demonstrated high diagnostic accuracy (AUC = 0.81 for initial group; AUC = 0.77 for prospective cohort). [18F]FEPPA PET scans confirmed neuroinflammation in regions with reduced FA. Impact: The strong correlation between CoreFA and neuroinflammatory markers from PET imaging provides a potential biomarker for assessing PASC-related brain changes.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/3929
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data-driven imaging marker for cerebral consequences of Post-Acute Sequelae of COVID19 - prospective validation and [18F]FEPPA PET correlation

Christine Delorenzo, P. Vaska, Ramin V. Parsey, Jia Ying et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Medical Imaging Techniques and Applications
article

Data-driven imaging marker for cerebral consequences of Post-Acute Sequelae of COVID19 - prospective validation and [18F]FEPPA PET correlation

Christine Delorenzo, P. Vaska, Ramin V. Parsey, Jia Ying, Zennur Sekendiz, Sean Clouston, Minos Kritikos, Chuan Huang, Benjamin J. Luft, Ashley Fontana
article en

Abstract

Motivation: Recognizing neurological symptoms in Post-Acute Sequelae of COVID-19 (PASC), this study investigates neuroinflammatory markers to develop reliable neuroimaging diagnostics. Goal(s): To validate CoreFA, a data-driven imaging marker, for detecting white matter changes and to explore its correlation with [18F]FEPPA PET neuroinflammatory markers. Approach: Diffusion MRI-derived fractional anisotropy (FA) was assessed in white matter tracts to derive CoreFA. CoreFA was validated in a prospective cohort and examined for association with neuroinflammation via FEPPA PET. Results: CoreFA demonstrated high diagnostic accuracy (AUC = 0.81 for initial group; AUC = 0.77 for prospective cohort). [18F]FEPPA PET scans confirmed neuroinflammation in regions with reduced FA. Impact: The strong correlation between CoreFA and neuroinflammatory markers from PET imaging provides a potential biomarker for assessing PASC-related brain changes.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Medical Imaging Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.