Detecting Primary Progressive Aphasia Atrophy Patterns: A Comparison of Visual Assessment and Quantitative Neuroimaging Techniques.

Background: There are now clinically available automated MRI analysis software programs that compare brain volumes of patients to a normative sample and provide z-score data for various brain regions. These programs have yet to be validated in primary progressive aphasia (PPA). Objective: To address this gap in the literature, we examined Neuroreader™ z-scores in PPA, relative to visual MRI assessment. We predicted that Neuroreader™ 1) would be more sensitive for detecting left > right atrophy in the cortical lobar regions in logopenic variant PPA clinical phenotype (lvPPA), and 2) would distinguish lvPPA (n = 11) from amnestic mild cognitive impairment (aMCI; n = 12). Methods: lvPPA or aMCI patients who underwent MRI with Neuroreader™ were included in this study. Two neuroradiologists rated 10 regions. Neuroreader™ lobar z-scores for those 10 regions, as well as a hippocampal asymmetry metric, were included in analyses. Results: Cohen's Kappa coefficients were significant in 10 of the 28 computations (k = 0.351 to 0.593, p≤0.029). Neuroradiologists agreed 0% of the time that left asymmetry was present across regions. No significant differences emerged between aMCI and lvPPA in Neuroreader™ z-scores across left or right frontal, temporal, or parietal regions (ps > 0.10). There were significantly lower z-scores in the left compared to right for the hippocampus, as well as parietal, occipital, and temporal cortices in lvPPA. Conclusion: Overall, our results indicated moderate to low interrater reliability, and raters never agreed that left asymmetry was present. While lower z-scores in the left hemisphere regions emerged in lvPPA, Neuroreader™ failed to differentiate lvPPA from aMCI.

Reversal learning in young and middle-age neurotypicals: Individual difference reaction time considerations.

Reversal learning is frequently used to assess components of executive function that contribute to understanding age-related cognitive differences. Reaction time (RT) is less characterized in the reversal learning literature, perhaps due to the daunting task of analyzing the entire RT distribution, but has been deemed a generally sensitive measure of cognitive aging. The current study extends our prior work to further characterize distributional properties of the reversal RT distribution and to distinguish groups of individuals with fractionated profiles of performance, which may be of clinical importance within the context of cognitive aging. Participant sample included young (n = 43) and community-dwelling, healthy, middle-aged (n = 139) adults. To explore individual differences, recursive partitioning analysis achieved a high classification rate by specifying decision tree rules that split participants into young and middle-aged groups. Mu (µ, efficient RT) was the most successful parameter in distinguishing age groups while sigma ( σ) and tau ( τ , ex-Gaussian indices of intra-individual variability) revealed more subtle individual differences. Accuracy measures did not contribute to separating the groups, suggesting that fractionated components of RT, as opposed to accuracy, can distinguish differences between young and middle-aged participants.