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Ariza M, Béjar J, Barrué C, Cano N, Segura B, Cortés CU, Junqué C, Garolera M. Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-023-01748-x. [PMID: 38285245 DOI: 10.1007/s00406-023-01748-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 12/18/2023] [Indexed: 01/30/2024]
Abstract
The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.
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Affiliation(s)
- Mar Ariza
- Grup de Recerca en Cervell, Cognició i Conducta, Consorci Sanitari de Terrassa (CST), Terrassa, Spain
- Unitat de Psicologia Mèdica, Departament de Medicina, Universitat de Barcelona (UB), Barcelona, Spain
| | - Javier Béjar
- Departament de Ciències de la Computació, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain.
| | - Cristian Barrué
- Departament de Ciències de la Computació, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain
| | - Neus Cano
- Grup de Recerca en Cervell, Cognició i Conducta, Consorci Sanitari de Terrassa (CST), Terrassa, Spain
- Departament de Ciències Bàsiques, Universitat Internacional de Catalunya (UIC), Sant Cugat del Vallès, Spain
| | - Bàrbara Segura
- Unitat de Psicologia Mèdica, Departament de Medicina, Universitat de Barcelona (UB), Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain
| | - Claudio Ulises Cortés
- Departament de Ciències de la Computació, Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona, Spain
| | - Carme Junqué
- Unitat de Psicologia Mèdica, Departament de Medicina, Universitat de Barcelona (UB), Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain
| | - Maite Garolera
- Grup de Recerca en Cervell, Cognició i Conducta, Consorci Sanitari de Terrassa (CST), Terrassa, Spain.
- Neuropsychology Unit, Consorci Sanitari de Terrassa (CST), Terrassa, Spain.
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Bohn L, Drouin SM, McFall GP, Rolfson DB, Andrew MK, Dixon RA. Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study. BMC Geriatr 2023; 23:837. [PMID: 38082372 PMCID: PMC10714519 DOI: 10.1186/s12877-023-04546-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults. METHODS The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects. RESULTS We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast. CONCLUSIONS Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.
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Affiliation(s)
- Linzy Bohn
- Department of Psychology, University of Alberta, P217 Biological Sciences Building, Edmonton, AB, T6G 2E9, Canada.
- Neuroscience and Mental Health Institute, University of Alberta, 2-132 Li Ka Shing Center for Health Research Innovation, Edmonton, AB, T6G 2E1, Canada.
| | - Shannon M Drouin
- Department of Psychology, University of Alberta, P217 Biological Sciences Building, Edmonton, AB, T6G 2E9, Canada
- Neuroscience and Mental Health Institute, University of Alberta, 2-132 Li Ka Shing Center for Health Research Innovation, Edmonton, AB, T6G 2E1, Canada
| | - G Peggy McFall
- Department of Psychology, University of Alberta, P217 Biological Sciences Building, Edmonton, AB, T6G 2E9, Canada
- Neuroscience and Mental Health Institute, University of Alberta, 2-132 Li Ka Shing Center for Health Research Innovation, Edmonton, AB, T6G 2E1, Canada
| | - Darryl B Rolfson
- Department of Medicine, Division of Geriatric Medicine, University of Alberta, 13-135 Clinical Sciences Building, Edmonton, AB, T6G 2G3, Canada
| | - Melissa K Andrew
- Department of Medicine, Division of Geriatric Medicine, Dalhousie University, 5955 Veterans' Memorial Lane, Halifax, NS, B3H 2E1, Canada
| | - Roger A Dixon
- Department of Psychology, University of Alberta, P217 Biological Sciences Building, Edmonton, AB, T6G 2E9, Canada
- Neuroscience and Mental Health Institute, University of Alberta, 2-132 Li Ka Shing Center for Health Research Innovation, Edmonton, AB, T6G 2E1, Canada
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Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer's Disease: A Diffusion Tensor Probabilistic Tractography Study. J Clin Med 2023; 12:jcm12030967. [PMID: 36769615 PMCID: PMC9917574 DOI: 10.3390/jcm12030967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/17/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
The incidence of Alzheimer's disease (AD) has been increasing each year, and a defective hippocampus has been primarily associated with an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. Thus, in the current study, we evaluated the hippocampal white matter (WM) connectivity in patients with early-stage AD before and after donepezil treatment using probabilistic tractography, and we further determined the WM integrity and changes in brain volume. Ten patients with early-stage AD (mean age = 72.4 ± 7.9 years; seven females and three males) and nine healthy controls (HC; mean age = 70.7 ± 3.5 years; six females and three males) underwent a magnetic resonance (MR) examination. After performing the first MR examination, the patients received donepezil treatment for 6 months. The brain volumes and diffusion tensor imaging scalars of 11 regions of interest (the superior/middle/inferior frontal gyrus, the superior/middle/inferior temporal gyrus, the amygdala, the caudate nucleus, the hippocampus, the putamen, and the thalamus) were measured using MR imaging and DTI, respectively. Seed-based structural connectivity analyses were focused on the hippocampus. The patients with early AD had a lower hippocampal volume and WM connectivity with the superior frontal gyrus and higher mean diffusivity (MD) and radial diffusivity (RD) in the amygdala than HC (p < 0.05, Bonferroni-corrected). However, brain areas with a higher (or lower) brain volume and WM connectivity were not observed in the HC compared with the patients with early AD. After six months of donepezil treatment, the patients with early AD showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected).
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Renn BN, Schurr M, Zaslavsky O, Pratap A. Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care. Front Psychiatry 2021; 12:734909. [PMID: 34867524 PMCID: PMC8634654 DOI: 10.3389/fpsyt.2021.734909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/07/2021] [Indexed: 11/26/2022] Open
Abstract
Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust.
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Affiliation(s)
- Brenna N Renn
- Department of Psychology, University of Nevada, Las Vegas, NV, United States
| | - Matthew Schurr
- Department of Psychology, University of Nevada, Las Vegas, NV, United States
| | - Oleg Zaslavsky
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, United States
| | - Abhishek Pratap
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON, Canada.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.,Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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