http://www.frontiersin.org/neuroscience/agingneuroscience/paper/10.3389/fnagi.2010.00024/

Vollständiger Abstract

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<h4>Background</h4>Multidimensional psychophysiological batteries reveal substantial inter-individual variation in processing speed, accuracy, memory, executive control, and visuospatial performance. Because the present sample is predominantly young to middle-aged, the analysis is framed as adult cognitive-performance phenotyping rather than identification of clinical cognitive-aging stages.<h4>Methods</h4>We analyzed a cross-sectional convenience sample of 1,117 adults (age 18-78 years; mean 31.4 ± 12.0 years; 65.8% women) assessed with a web-based psychophysiological battery. Forty-two cognitive, psychomotor, demographic, anthropometric, lifestyle, and self-reported health variables were standardized after singular-value-decomposition imputation of one missing BMI value. DANCo and local PCA estimated intrinsic dimensionalities of 5.83 and 3.82, respectively. Six principal components (39.24% cumulative variance; seventh-component increment 3.67%) were used to fit an elastic principal tree. Between-branch differences were evaluated by Kruskal-Wallis tests with Holm-adjusted Dunn comparisons or chi-square tests, with effect sizes. Assignment reproducibility was evaluated in 100 random 80% subsamples projected onto the fixed reference tree. The workflow is an unsupervised statistical/geometric structure-learning analysis rather than supervised prediction; no train/test predictive model or learned longitudinal dynamics are claimed.<h4>Results</h4>Three connected branch profiles were identified: Cluster 0 (<i>n</i> = 781, 69.9%), a high-accuracy reference profile; Cluster 1 (<i>n</i> = 98, 8.8%), a rapid-processing profile with domain-specific visuospatial variability; and Cluster 2 (<i>n</i> = 238, 21.3%), a slower, lower-accuracy profile enriched for older age and positive insomnia/disease indicators. Moderate-to-large effects were observed for Stroop-4 speed (η 2 = 0.195), Stroop-4 accuracy (η 2 = 0.194), figure-shape accuracy (η 2 = 0.240), figure-shape-color accuracy (η 2 = 0.251), and figure-shape-position accuracy (η 2 = 0.253; all <i>p</i> < 0.0001). Fixed-tree projections reproduced assignments with mean adjusted Rand index, normalized mutual information, and raw agreement of 1.000 ± 0.000. In the additional sensitivity analyses, exclusion of the binary variables changed the solution from three to nine clusters (ARI = 0.1203), and reduction from six to four PCs changed it from three to seven clusters (ARI = 0.1736). All binary features in the dataset are directly or indirectly related to cognitive functions or to the interpretation of psychophysiological test results. Removing the complete binary-variable block therefore changed not only the numerical feature space but also the substantive interpretation of the analysis, particularly because this block included age-associated lifestyle and health factors such as smoking, alcohol use, insomnia, and disease status.<h4>Conclusion</h4>Elastic principal trees represent cognitive-performance profiles as connected branches and provide a graph-ordering coordinate unavailable from ordinary discrete clustering. The results are exploratory, cross-sectional, and non-diagnostic and should not be generalized to neurodegenerative aging without older, clinically characterized, longitudinal cohorts. Among the examined alternatives, the original full-feature six-PC solution was retained for interpretation because the alternative solutions produced additional very small clusters that could not be characterized reliably.

Abstract: PubMed · Datensatz