Individualized mapping of aberrant cortical thickness via stochastic cortical self-reconstruction
Year: 2025
Type: article
Abstract: Understanding individual differences in cortical structure is key to advancing diagnostics in neurology and psychiatry. Reference models aid in detecting aberrant cortical thickness, yet site-specific biases limit their direct application to unseen data, and region-wise averages prevent the detection of localized cortical changes. To address these limitations, we developed the Stochastic Cortical Self-Reconstruction (SCSR), a novel method that leverages deep learning to reconstruct cortical thickness maps at the vertex level without needing additional subject information. Trained on over 25,000 healthy individuals, SCSR generates highly individualized cortical reconstructions that can detect subtle thickness deviations. Our evaluations on independent test sets demonstrated that SCSR achieved significantly lower reconstruction errors and identified atrophy patterns that enabled better disease discrimination than established methods. It also hints at cortical thinning in preterm infants that went undetected by existing models, showcasing its versatility. Finally, SCSR excelled in mapping highly resolved cortical deviations of dementia patients from clinical data, highlighting its potential for supporting diagnosis in clinical practice.
Source: Medical Image Analysis
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Children's Hospital of Philadelphia, Medpace, Meso Scale Diagnostics, Synarc, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Children’s Hospital of Wisconsin Research Institute, Murdoch Children's Research Institute, Commonwealth Scientific and Industrial Research Organisation, Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Daiichi-Sankyo, Eisai, Astellas Pharma, Deutsches Zentrum für Neurodegenerative Erkrankungen, Shionogi, Servier, Mitsubishi Tanabe Pharma Corporation, IXICO, National Institutes of Health, Genentech, University of California, San Diego, Canadian Institutes of Health Research, Leibniz-Gemeinschaft, Japan Science and Technology Agency, Leibniz-Rechenzentrum
Awards: G1554142629, G1593117279, G1650819308, Alzheimer's Disease Neuroimaging Initiative, G2100670906, G2152643862, G2189010064, G2340207221, G2409989180, G2423387114, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G5207404921, G5209990037, G528601746, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G839208290, G8636843673, G8852128998, G8895907669, G8897063270, G8957659042, G9000039018, G9000122335
OpenAlex ID: W4414982876