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Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

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.

SUBMITTER: Stonnington CM

PROVIDER: S-EPMC2871976 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

Stonnington Cynthia M CM Chu Carlton C Klöppel Stefan S Jack Clifford R CR Ashburner John J Frackowiak Richard S J RS

NeuroImage 20100325 4

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 c ...[more]

PMID: 20347044

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Project description:BackgroundMissing data are a notable problem in Alzheimer's disease clinical trials. One cause of missing data is participant dropout. The Research Attitudes Questionnaire is a 7-item instrument that measures an individual's attitudes toward biomedical research, with higher scores indicating more favorable attitudes. The objective of this study was to describe the performance of the Research Attitudes Questionnaire over time and to examine whether Research Attitudes Questionnaire scores predict study dropout and other participant behaviors that affect trial integrity.MethodsThe Research Attitudes Questionnaire was collected at baseline and weeks 26 and 52 from each member of 119 participant/study partner dyads enrolled in a Phase 2, randomized, double-blind, placebo-controlled mild-to-moderate Alzheimer's disease clinical trial. Within-subject longitudinal analyses examined change in Research Attitudes Questionnaire scores over time in each population. Logistic regression analyses that controlled for trial arm and clustering in trial sites were used to assess whether baseline Research Attitudes Questionnaire scores predicted trial completion, study medication compliance, and enrollment in optional substudies.ResultsParticipants and study partners endorsed statistically similar ratings on the Research Attitudes Questionnaire that were stable over time. Participants with baseline Research Attitudes Questionnaire scores above 28.5 were 4.7 (95% confidence interval = 1.01 to 21.95) times as likely to complete the trial compared to those with lower scores. Applying the same cutoff, baseline study partner Research Attitudes Questionnaire scores were similarly able to predict study completion (odds ratio = 4.2, 95% confidence interval = 1.71 to 10.32). Using a score cutoff of 27.5, higher participant Research Attitudes Questionnaire scores predicted study medication compliance (odds ratio = 5.85, 95% confidence interval = 1.34 to 25.54). No relationship was observed between Research Attitudes Questionnaire score and participation in optional substudies.ConclusionThis brief instrument that measures research attitudes may identify participants at risk for behaviors that cause missing data.

Project description:Background and aimsCross-sectional studies have shown that magnetic resonance elastography (MRE) is accurate in the noninvasive detection of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD). However, there are limited data on the longitudinal association between an increase in liver stiffness on MRE and fibrosis progression in NAFLD. Therefore, using a well-characterized prospective cohort of patients with biopsy-proven NAFLD, we aimed to examine the longitudinal association between a 15% increase in liver stiffness on MRE and fibrosis progression in NAFLD.Approach and resultsThis prospective cohort study included 102 patients (62.7% women) with biopsy-proven NAFLD who underwent contemporaneous MRE and liver biopsy at baseline followed by a repeat paired liver biopsy and MRE assessment. The primary outcome was odds of fibrosis progression by one or more stage as assessed by the Nonalcoholic Steatohepatitis Clinical Research Network histologic scoring system. The mean (±SD) of age and body mass index (BMI) were 52 (±14) years and 32.6 (±5.3) kg/m2 , respectively. The median time interval between the two paired assessments was 1.4 years (interquartile range 2.15 years). The number of patients with fibrosis stages 0, 1, 2, 3, and 4 was 27, 36, 12, 17, and 10, respectively. In unadjusted analysis, a 15% increase in MRE was associated with increased odds of histologic fibrosis progression (odds ratio [OR], 3.56; 95% confidence interval [CI], 1.17-10.76; P = 0.0248). These findings remained clinically and statistically significant even after multivariable adjustment for age, sex, and BMI (adjusted OR, 3.36; 95% CI, 1.10-10.31; P = 0.0339). A 15% increase in MRE was the strongest predictor of progression to advanced fibrosis (OR, 4.90; 95% CI, 1.35-17.84; P = 0.0159).ConclusionsA 15% increase in liver stiffness on MRE may be associated with histologic fibrosis progression and progression from early fibrosis to advanced fibrosis.

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Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

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Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

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