{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T06:27:48Z","timestamp":1786429668758,"version":"build-2736575974"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed a cross-sectional convenience sample of 1,117 adults (age 18\u201378 years; mean 31.4 \u00b1 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\u2013Wallis 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Three connected branch profiles were identified: Cluster 0 (\n                      <jats:italic>n<\/jats:italic>\n                      = 781, 69.9%), a high-accuracy reference profile; Cluster 1 (\n                      <jats:italic>n<\/jats:italic>\n                      = 98, 8.8%), a rapid-processing profile with domain-specific visuospatial variability; and Cluster 2 (\n                      <jats:italic>n<\/jats:italic>\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 (\u03b7\n                      <jats:sup>2<\/jats:sup>\n                      = 0.195), Stroop-4 accuracy (\u03b7\n                      <jats:sup>2<\/jats:sup>\n                      = 0.194), figure-shape accuracy (\u03b7\n                      <jats:sup>2<\/jats:sup>\n                      = 0.240), figure-shape-color accuracy (\u03b7\n                      <jats:sup>2<\/jats:sup>\n                      = 0.251), and figure-shape-position accuracy (\u03b7\n                      <jats:sup>2<\/jats:sup>\n                      = 0.253; all\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.0001). Fixed-tree projections reproduced assignments with mean adjusted Rand index, normalized mutual information, and raw agreement of 1.000 \u00b1 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1898458","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T05:35:23Z","timestamp":1786426523000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Trajectory-based identification of cognitive-performance phenotypes across adulthood from psychophysiological testing"],"prefix":"10.3389","volume":"18","author":[{"given":"Alexander E.","family":"Zapryalov","sequence":"first","affiliation":[{"name":"Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod","place":["Nizhny Novgorod, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey V.","family":"Stasenko","sequence":"additional","affiliation":[{"name":"Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod","place":["Nizhny Novgorod, Russia"]},{"name":"Moscow Center for Advanced Studies","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadezhda A.","family":"Chumankina","sequence":"additional","affiliation":[{"name":"Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod","place":["Nizhny Novgorod, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danila D.","family":"Shashnin","sequence":"additional","affiliation":[{"name":"Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod","place":["Nizhny Novgorod, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria V.","family":"Vedunova","sequence":"additional","affiliation":[{"name":"Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod","place":["Nizhny Novgorod, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"296","DOI":"10.3390\/e22030296","article-title":"Robust and scalable learning of complex intrinsic dataset geometry via ElPiGraph","volume":"22","author":"Albergante","year":"2020","journal-title":"Entropy"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194878","article-title":"Working memory, age and education: a lifespan fMRI study","author":"Archer","year":"2018","journal-title":"PLoS ONE"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1146\/annurev-psych-120710-100422","article-title":"Working memory: theories, models, and controversies","volume":"63","author":"Baddeley","year":"2012","journal-title":"Annu. 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