TY - JOUR
T1 - Spatially informed Bayesian neural network for neurodegenerative diseases classification
AU - Payares-Garcia, David
AU - Mateu, Jorge
AU - Schick, Wiebke
N1 - Funding Information:
Data used in the preparation of this article were also obtained from the Parkinson's Progression Markers Initiative (PPMI) database ( https://www.ppmiinfo.org/data ). For up‐to‐date information on the study, visit https://www.ppmiinfo.org . PPMI—a public‐private partnership—is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including (list the full names of all of the PPMI funding partners found at https://www.ppmiinfo.org/fundingpartners ). Open Access funding enabled and organized by Projekt DEAL.
Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann‐La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( https://www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California.
Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
PY - 2023/1/30
Y1 - 2023/1/30
N2 - Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty. Also, the spatial heterogeneity of the brain alterations caused by neurodegenerative processes is not usually considered, despite the spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. In this article, we propose a classification technique that incorporates uncertainty and spatial information for distinguishing between healthy subjects and patients from four distinct neurodegenerative diseases: Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and Multiple Sclerosis. We introduce a spatially informed Bayesian neural network (SBNN) that combines a three-dimensional neural network to extract neurodegeneration features from MRI, Bayesian inference to account for uncertainty in diagnosis, and a spatially informed MRI image using hidden Markov random fields to encode cerebral spatial information. The SBNN model demonstrates that classification accuracy increases up to 25% by including a spatially informed MRI scan. Furthermore, the SBNN provides a robust probabilistic diagnosis that resembles clinical decision-making and can account for the heterogeneous medical presentations of neurodegenerative disorders.
AB - Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty. Also, the spatial heterogeneity of the brain alterations caused by neurodegenerative processes is not usually considered, despite the spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. In this article, we propose a classification technique that incorporates uncertainty and spatial information for distinguishing between healthy subjects and patients from four distinct neurodegenerative diseases: Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and Multiple Sclerosis. We introduce a spatially informed Bayesian neural network (SBNN) that combines a three-dimensional neural network to extract neurodegeneration features from MRI, Bayesian inference to account for uncertainty in diagnosis, and a spatially informed MRI image using hidden Markov random fields to encode cerebral spatial information. The SBNN model demonstrates that classification accuracy increases up to 25% by including a spatially informed MRI scan. Furthermore, the SBNN provides a robust probabilistic diagnosis that resembles clinical decision-making and can account for the heterogeneous medical presentations of neurodegenerative disorders.
KW - Bayesian inference
KW - Deep learning (DL)
KW - Neurodegenerative diseases
KW - Epidemiology
KW - Spatial information
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
KW - UT-Hybrid-D
U2 - 10.1002/sim.9604
DO - 10.1002/sim.9604
M3 - Article
SN - 0277-6715
VL - 42
SP - 105
EP - 121
JO - Statistics in medicine
JF - Statistics in medicine
IS - 2
ER -