TY - JOUR
T1 - Predicting clinical scores from magnetic resonance scans in Alzheimer's disease
AU - Stonnington, Cynthia M.
AU - Chu, Carlton
AU - Klöppel, Stefan
AU - Jack, Clifford R.
AU - Ashburner, John
AU - Frackowiak, Richard S.J.
N1 - Funding Information:
This work was supported by the Wellcome Trust (grant 075696 2/04/2 to R.S.J.F.), Mayo Clinic (grant to C.M.S.), the National Institute on Aging (grants P50 AG16574 , U01 AG06786 , and AG11378 to Mayo Clinic Rochester, MN), the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer's Disease Research Program, and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Foundation (to Mayo Clinic Rochester, MN), Alzheimer's disease Neuroimaging Initiative (ADNI; Principal Investigator: Michael Weiner; National Institutes of Health grant U01 AG024904); ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering. The authors thank Kewei Chen, PhD, Justin Venditti, and Eric Reiman, MD for help with the ADNI data set and thoughtful suggestions.
PY - 2010/7
Y1 - 2010/7
N2 - Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3. months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale-Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P< 0.0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.
AB - Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3. months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale-Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P< 0.0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.
KW - ADAS-Cog
KW - AVLT
KW - Alzheimer's disease
KW - DRS
KW - MMSE
KW - Machine learning
KW - Multivariate
KW - Relevance vector regression
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U2 - 10.1016/j.neuroimage.2010.03.051
DO - 10.1016/j.neuroimage.2010.03.051
M3 - Article
C2 - 20347044
AN - SCOPUS:77952888499
SN - 1053-8119
VL - 51
SP - 1405
EP - 1413
JO - NeuroImage
JF - NeuroImage
IS - 4
ER -