Abstract
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 3months 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.
Highlights
With no single marker yet available, combining different relevant data is one proposed way to increase diagnostic power for Alzheimer's disease (AD)
“Set 1” were patients with probable AD and cognitively normal (CN) controls from the Mayo Rochester Alzheimer's Disease Research Center (ADRC) and Mayo Alzheimer's Disease Patient Registry (ADPR) (Petersen et al, 1990) who had all three Mini-Mental State Exam (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) scores recorded within three months of their MRI scan
No improvement in accuracy occurred with spatial smoothing: MMSE: 0.66; DRS: 0.72; AVLT: 0.55
Summary
With no single marker yet available, combining different relevant data is one proposed way to increase diagnostic power for Alzheimer's disease (AD). After a diagnosis of mild cognitive impairment (MCI) or AD, the combination of cognitive tests and imaging can be used for both tracking progression of illness and treatment response. For all of these purposes, the ideal neuropsychological tests must closely reflect the atrophy patterns of the disease. VBM separately compares the volume of tissue around each point or voxel of the whole brain, has the advantage of not being biased to one particular region or structure (Ashburner and Friston, 2000), and is very useful for examining group differences. Though VBM can be applied to single subject data through comparisons of individual scans with those in a normal control group (Chetelat et al, 2008), the statistical assumptions underlying such a procedure are not without problems so that single subject analysis with VBM is limited in its scope in terms of translation to the clinic for individual patients
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