Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study
Abstract Dementia due to Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder characterized by various cognitive and behavioral decline factors. In this work, we propose an extension of the traditional k-means clustering for multivariate time series data to cluster joint trajectorie...
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BMC
2025-03-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-025-00441-0 |
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| author | Patrizia Ribino Claudia Di Napoli Giovanni Paragliola Davide Chicco Francesca Gasparini |
| author_facet | Patrizia Ribino Claudia Di Napoli Giovanni Paragliola Davide Chicco Francesca Gasparini |
| author_sort | Patrizia Ribino |
| collection | DOAJ |
| description | Abstract Dementia due to Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder characterized by various cognitive and behavioral decline factors. In this work, we propose an extension of the traditional k-means clustering for multivariate time series data to cluster joint trajectories of different features describing progression over time. The algorithm we propose here enables the joint analysis of various longitudinal features to explore co-occurring trajectory factors among markers indicative of cognitive decline in individuals participating in an AD progression study. By examining how multiple variables co-vary and evolve together, we identify distinct subgroups within the cohort based on their longitudinal trajectories. Our clustering method enhances the understanding of individual development across multiple dimensions and provides deeper medical insights into the trajectories of cognitive decline. In addition, the proposed algorithm is also able to make a selection of the most significant features in separating clusters by considering trajectories over time. This process, together with a preliminary pre-processing on the OASIS-3 dataset, reveals an important role of some neuropsychological factors. In particular, the proposed method has identified a significant profile compatible with a syndrome known as Mild Behavioral Impairment (MBI), displaying behavioral manifestations of individuals that may precede the cognitive symptoms typically observed in AD patients. The findings underscore the importance of considering multiple longitudinal features in clinical modeling, ultimately supporting more effective and individualized patient management strategies. |
| format | Article |
| id | doaj-art-7da99b25776745fbb0a431ace150eebd |
| institution | DOAJ |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-7da99b25776745fbb0a431ace150eebd2025-08-20T02:49:31ZengBMCBioData Mining1756-03812025-03-0118113110.1186/s13040-025-00441-0Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression studyPatrizia Ribino0Claudia Di Napoli1Giovanni Paragliola2Davide Chicco3Francesca Gasparini4Consiglio Nazionale delle Ricerche (CNR), Istituto di Calcolo e Reti ad Alte PrestazioniConsiglio Nazionale delle Ricerche (CNR), Istituto di Calcolo e Reti ad Alte PrestazioniConsiglio Nazionale delle Ricerche (CNR), Istituto di Calcolo e Reti ad Alte PrestazioniDipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-BicoccaDipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-BicoccaAbstract Dementia due to Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder characterized by various cognitive and behavioral decline factors. In this work, we propose an extension of the traditional k-means clustering for multivariate time series data to cluster joint trajectories of different features describing progression over time. The algorithm we propose here enables the joint analysis of various longitudinal features to explore co-occurring trajectory factors among markers indicative of cognitive decline in individuals participating in an AD progression study. By examining how multiple variables co-vary and evolve together, we identify distinct subgroups within the cohort based on their longitudinal trajectories. Our clustering method enhances the understanding of individual development across multiple dimensions and provides deeper medical insights into the trajectories of cognitive decline. In addition, the proposed algorithm is also able to make a selection of the most significant features in separating clusters by considering trajectories over time. This process, together with a preliminary pre-processing on the OASIS-3 dataset, reveals an important role of some neuropsychological factors. In particular, the proposed method has identified a significant profile compatible with a syndrome known as Mild Behavioral Impairment (MBI), displaying behavioral manifestations of individuals that may precede the cognitive symptoms typically observed in AD patients. The findings underscore the importance of considering multiple longitudinal features in clinical modeling, ultimately supporting more effective and individualized patient management strategies.https://doi.org/10.1186/s13040-025-00441-0Alzheimer’s diseaseDementiaNeuropsychological symptomsClusteringMultivariate longitudinal study |
| spellingShingle | Patrizia Ribino Claudia Di Napoli Giovanni Paragliola Davide Chicco Francesca Gasparini Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study BioData Mining Alzheimer’s disease Dementia Neuropsychological symptoms Clustering Multivariate longitudinal study |
| title | Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study |
| title_full | Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study |
| title_fullStr | Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study |
| title_full_unstemmed | Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study |
| title_short | Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study |
| title_sort | multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an alzheimer s disease progression study |
| topic | Alzheimer’s disease Dementia Neuropsychological symptoms Clustering Multivariate longitudinal study |
| url | https://doi.org/10.1186/s13040-025-00441-0 |
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