Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
Open accessAbstract
Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses.
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Classifiers using multimodal biological, imaging, and neuropsychological markers can separate AD patients from cognitively normal individuals accurately.
“Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer’s disease (AD) patients from cognitively normal elders.”
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- Authors
- Fan, Y., Batmanghelich, N., Clark, C. M., & Davatzikos, C.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Davatzikos, Christos
- ORCID
- Davatzikos, Christos
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 39
- Issue
- 4
- Pages
- 1731-1743
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2861339
- OpenAlex
- W2079484785
- Cited by
- 477
- References
- 6
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 15 · 2023: 11 · 2022: 19 · 2021: 19 · 2020: 26 · 2019: 18 · 2018: 23 · 2017: 31 · 2016: 24 · 2015: 30 · 2014: 38 · 2013: 34 · 2012: 40
- FWCI
- 16.35
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health
- Northern California Institute for Research and Education
- National Institutes of Health (5u01ag024904-10)
- U.S. Food and Drug Administration
- National Institute on Aging (U01-AG-024904, R01 AG 14971, R01AG014971, U19AG010483)
- National Institute of Biomedical Imaging and Bioengineering
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- mild cognitive impairment, Alzheimer's disease, brain atrophy, cognitive decline prediction, hippocampal atrophy, medial temporal lobe, orbitofrontal cortex, posterior cingulate cortex, insula, uncus, computational neuroanatomy
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Atrophy, Brain, Cognition Disorders, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Pattern Recognition, Automated, Prognosis, ROC Curve, Multivariate Analysis, Nonlinear Dynamics, Infarction, Middle Cerebral Artery
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