Trajectory-based identification of cognitive-performance phenotypes across adulthood from psychophysiological testing.
Background: 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...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 20 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196444175&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196444175 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2026 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 196444175 196444175 196444175 10.3389/fnagi.2026.1898458 196444175 ppf: 1 ppct: 19 formats: tig: atl: Trajectory-based identification of cognitive-performance phenotypes across adulthood from psychophysiological testing. aug: au: Zapryalov, Alexander E. Stasenko, Sergey V. Chumankina, Nadezhda A. Shashnin, Danila D. Vedunova, Maria V. affil: Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod, Nizhny Novgorod, Russia sug: subj: Cognition In Adulthood Psychomotor Performance Psychophysiology Machine Learning Reproducibility of Results Human Cross Sectional Studies Convenience Sample Adult Middle Age Processing Speed Male Female Anthropometry Life Style Self Report Health Status Body Mass Index Age Factors Reaction Time Executive Function Memory, Short Term Task Performance and Analysis Descriptive Statistics Kruskal-Wallis Test Chi Square Test Post Hoc Analysis Data Analysis Software Random Sample Neuropsychological Tests Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: 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. Methods: 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. Results: Three connected branch profiles were identified: Cluster 0 (n = 781, 69.9%), a high-accuracy reference profile; Cluster 1 (n = 98, 8.8%), a rapid-processing profile with domain-specific visuospatial variability; and Cluster 2 (n = 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 (η2 = 0.195), Stroop-4 accuracy (η2 = 0.194), figure-shape accuracy (η2 = 0.240), figure-shape-color accuracy (η2 = 0.251), and figure-shape-position accuracy (η2 = 0.253; all p < 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. Conclusion: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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