Introduction: Patients with transient ischemic attack (TIA) and minor stroke demonstrate cognitive impairment, and a four-fold risk of late-life dementia. Aim: To study the extent to which the rates of brain volume loss in TIA patients differ from healthy controls and how they are correlated with cognitive impairment. Methods: TIA or minor stroke patients were tested with a neuropsychological battery and underwent T1 weighted volumetric magnetic resonance imaging scans at fixed intervals over a 3 years period. Linear mixed effects regression models were used to compare brain atrophy rates between groups, and to determine the relationship between atrophy rates and cognitive function in TIA and minor stroke patients. Results: Whole brain atrophy rates were calculated for the TIA and minor stroke patients; n = 38 between 24 h and 18 months, and n = 68 participants between 18 and 36 months, and were compared to healthy controls. TIA and minor stroke patients demonstrated a significantly higher whole brain atrophy rate than healthy controls over a 3 years interval ( p = 0.043). Diabetes ( p = 0.012) independently predicted higher atrophy rate across groups. There was a relationship between higher rates of brain atrophy and processing speed (composite P = 0.047 and digit symbol coding P = 0.02), but there was no relationship with brain atrophy rates and memory or executive composite scores or individual cognitive tests for language (Boston naming, memory recall, verbal fluency or Trails A or B score). Conclusion: TIA and minor stroke patients experience a significantly higher rate of whole brain atrophy. In this cohort of TIA and minor stroke patients changes in brain volume over time precede cognitive decline.

We compare reliable change scores and recently published anchor-based cutoffs for minimum clinically important difference (MCID) for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) in a sample of patients diagnosed with various forms of dementia. For memory clinic patients with dementia evaluated twice over a one-year interval (N = 53), observed retest RBANS index scores were compared with predicted retest index scores based on regression formulae developed from cognitively healthy older adults. Patient RBANS change scores were also compared to suggested MCID anchors. Patients with dementia demonstrated a reliable decline on most RBANS indices, with evidence that the Visuospatial/Constructional and Language Indices might be less sensitive to decline over time. Although there was consistency between MCID and reliable changes in this sample, there was a substantial proportion where the MCID was exceeded, with no reliable change. We attempted to create MCIDs from the Clinical Dementia Rating Sum of Box scores for RBANS reliable change scores, but failed to find significant associations. Overall, the findings support use of the regression based reliable change approach, but we caution use of the MCID approach for the RBANS.

Computerized cognitive screening tools, such as the self-administered Computerized Assessment of Memory Cognitive Impairment (CAMCI), require little training and ensure standardized administration and could be an ideal test for primary care settings. We conducted a secondary analysis of a data set including 887 older adults (M age ϭ 72.7 years, SD ϭ 7.1 years; 32.1% male; M years education ϭ 13.4, SD ϭ 2.7 years) with CAMCI scores and independent diagnoses of mild cognitive impairment (MCI). A study by the CAMCI developers used a portion of this data set with a machine learning decision tree model and suggested that the CAMCI had high classification accuracy for MCI (sensitivity ϭ 0.86, specificity ϭ 0.94). We found similar support for accuracy (sensitivity ϭ 0.94, specificity ϭ 0.94) by overfitting a decision tree model, but we found evidence of lower accuracy in a cross-validation sample (sensitivity ϭ 0.62, specificity ϭ 0.66). A logistic regression model, however, discriminated modestly in both training (sensitivity ϭ 0.72, specificity ϭ 0.80) and cross-validation data sets (sensitivity ϭ 0.69, specificity ϭ 0.74). Evidence for strong accuracy when overfitting a decision tree model and substantially reduced accuracy in cross-validation samples was replicated across 500 bootstrapped samples. In contrast, the evidence for accuracy of the logistic regression model was similar in the training and cross-validation samples. The logistic regression model produced accuracy estimates consistent with other published CAMCI studies, suggesting evidence for classification accuracy of the CAMCI for MCI is likely modest. This case study illustrates the general need for cross-validation and careful evaluation of the generalizability of machine learning models. Public Significance StatementUsing an archival database, a decision tree machine learning method demonstrated overfitting and had substantially reduced evidence for classification accuracy measured in cross-validation, but the statistical method results in similar evidence of classification in training and cross-validation samples. The evidence for classification accuracy of the Computerized Assessment of Mild Cognitive Impairment for cognitive impairment is modest, and this has clinical implications.

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