https://doi.org/10.1140/epjs/s11734-024-01323-y
Regular Article
Poincaré maps and CCM: EEG insights of cognitive impairment
1
Academic Area of Mathematics and Physics, Autonomous University of the State of Hidalgo, 42184, Mineral de la Reforma, Hidalgo, Mexico
2
Consejo Nacional de Humanidades, Ciencias y Tecnologías, Av. Insurgentes sur 1582, 03940, Mexico City, Mexico
3
Instituto Nacional de Psiquiatría “Ramón de la Fuente Muñiz”, 14370, Mexico City, Mexico
4
Unidad de Investigación en Neurodesarrollo, National Autonomous University of Mexico, 76230, Juriquilla, Querétaro, Mexico
Received:
3
May
2024
Accepted:
29
August
2024
Published online:
29
October
2024
Older adults suffering from cognitive impairment no dementia (CIND) are at higher risk of developing a severe neurodegenerative disorder, such as Alzheimer’s disease. The diagnosis of CIND is commonly carried out by neuropsychological methods; however, physiological markers may not only corroborate but are posing significant challenges for an early, objective, and automatized identification of CIND. A novel approach using Poincaré maps, a mathematical tool commonly employed in dynamical systems analysis is presented. Based on Electroencephalographic (EEG) recordings during the eyes-closed resting state condition, Poincaré maps were constructed, transforming these data into phase-space representations. By examining the structure and characteristics of these maps, CIND was identified by subtle alterations that may demonstrate the ability of Poincaré maps to capture underlying cognitive patterns and reveal deviations from normal cognitive aging. These deviations are observed as distinct clusters or irregularities in the map, serving as potential biomarkers for CIND detection. Moreover, the complex correlation measure (CCM) was incorporated to precisely quantify the temporal dynamics within the Poincaré maps, it was expected to visualize such differences in the temporal dynamics plots as well as in the reported CCM values from the two experimental groups, using specialized visualization software developed for this purpose. It was hypothesized, and verified, that Poincaré maps for the CIND group will exhibit smaller SD1 (short-term variability) and SD2 (long-term variability) values in the EEG regions associated with decision-making and memory compared to the control group. In addition, the temporal dynamics illustrated using CCM were expected to exhibit greater complexity and larger scale in the CU compared to the CIND group. This is particularly novel as it introduces a unique approach to differentiating between CU and CIND groups using Poincaré maps and CCM, a method not previously documented in EEG recordings during resting state.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.