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Cited by 552Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsFunctional Brain Connectivity Studies

Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI☆

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MISRA, C, FAN, Y, DAVATZIKOS, C·2009·NeuroImage
DOI 10.1016/j.neuroimage.2008.10.031PMC PMC2648825
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Abstract

High-dimensional pattern classification was applied to baseline and multiple follow-up MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI), in order to investigate the potential of predicting short-term conversion to Alzheimer's Disease (AD) on an individual basis. MCI participants that converted to AD (average follow-up 15 months) displayed significantly lower volumes in a number of grey matter (GM) regions, as well as in the white matter (WM). They also displayed more pronounced periventricular small-vessel pathology, as well as an increased rate of increase of such pathology. Individual person analysis was performed using a pattern classifier previously constructed from AD patients and cognitively normal (CN) individuals to yield an abnormality score that is positive for AD-like brains and negative otherwise. The abnormality scores measured from MCI non-converters (MCI-NC) followed a bimodal distribution, reflecting the heterogeneity of this group, whereas they were positive in almost all MCI converters (MCI-C), indicating extensive patterns of AD-like brain atrophy in almost all MCI-C. Both MCI subgroups had similar MMSE scores at baseline. A more specialized classifier constructed to differentiate converters from non-converters based on their baseline scans provided good classification accuracy reaching 81.5%, evaluated via cross-validation. These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis.

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  • Brain atrophy as revealed by structural MRI is able to discriminate preclinical stages and time evolution of Alzheimer's disease at 1-year follow-up.

    “it has been shown that brain atrophy as revealed by structural MRI is able to discriminate preclinical stages and time evolution of AD at 1‐year follow‐up. 34 , 35 , 36”

    From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [36]

    The Discussion reports that baseline structural-MRI atrophy distinguished MCI patients who converted to AD from those who did not, with conversion occurring after about 15 months on average. It does not establish broad discrimination of AD’s time evolution at a one-year follow-up: the Discussion says longitudinal rates differed significantly only for periventricular white-matter abnormalities and temporal-horn CSF volume, and that baseline measures were much better discriminators. Page numbers were not available in our copy. (LLM, conf=0.86)

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Authors
MISRA, C., FAN, Y., & DAVATZIKOS, C.
Affiliations
University of Pennsylvania (US)
Corresponding author
DAVATZIKOS, C
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2009-02-15
Volume
44
Issue
4
Pages
1415-1422
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2008.10.031
PMC
PMC2648825
OpenAlex
W2155164847
Cited by
552
References
4
Citations by year
2026: 6 · 2025: 16 · 2024: 25 · 2023: 22 · 2022: 34 · 2021: 25 · 2020: 29 · 2019: 35 · 2018: 37 · 2017: 38 · 2016: 29 · 2015: 50 · 2014: 43 · 2013: 34 · 2012: 43
FWCI
19.91
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Northern California Institute for Research and Education
  • National Institutes of Health (5u01ag024904-10)
  • National Institute on Aging (U01-AG-024904, R01 AG 14971, R01AG014971, U19AG010483)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
mild cognitive impairment, Alzheimer's disease, conversion prediction, brain atrophy, gray matter atrophy, white matter atrophy, biomarkers, longitudinal MRI, ADNI
MeSH terms
Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Atrophy, Brain, Cognition Disorders, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Pattern Recognition, Automated, Prognosis, Sensitivity and Specificity, Reproducibility of Results, Disease Progression

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