Cargando…
Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns
BACKGROUND: Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODS: Using...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349933/ https://www.ncbi.nlm.nih.gov/pubmed/37461451 http://dx.doi.org/10.1101/2023.07.02.547427 |
_version_ | 1785074028216582144 |
---|---|
author | Zhao, Kanhao Xie, Hua Fonzo, Gregory A. Carlisle, Nancy Osorio, Ricardo S. Zhang, Yu |
author_facet | Zhao, Kanhao Xie, Hua Fonzo, Gregory A. Carlisle, Nancy Osorio, Ricardo S. Zhang, Yu |
author_sort | Zhao, Kanhao |
collection | PubMed |
description | BACKGROUND: Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODS: Using large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes. RESULTS: We identified two reliable neuropsychiatric subsyndromes – behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction. CONCLUSIONS: Our findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients. |
format | Online Article Text |
id | pubmed-10349933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-103499332023-07-17 Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns Zhao, Kanhao Xie, Hua Fonzo, Gregory A. Carlisle, Nancy Osorio, Ricardo S. Zhang, Yu bioRxiv Article BACKGROUND: Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODS: Using large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes. RESULTS: We identified two reliable neuropsychiatric subsyndromes – behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction. CONCLUSIONS: Our findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients. Cold Spring Harbor Laboratory 2023-07-03 /pmc/articles/PMC10349933/ /pubmed/37461451 http://dx.doi.org/10.1101/2023.07.02.547427 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Zhao, Kanhao Xie, Hua Fonzo, Gregory A. Carlisle, Nancy Osorio, Ricardo S. Zhang, Yu Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns |
title | Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns |
title_full | Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns |
title_fullStr | Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns |
title_full_unstemmed | Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns |
title_short | Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns |
title_sort | defining dementia subtypes through neuropsychiatric symptom-linked brain connectivity patterns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349933/ https://www.ncbi.nlm.nih.gov/pubmed/37461451 http://dx.doi.org/10.1101/2023.07.02.547427 |
work_keys_str_mv | AT zhaokanhao definingdementiasubtypesthroughneuropsychiatricsymptomlinkedbrainconnectivitypatterns AT xiehua definingdementiasubtypesthroughneuropsychiatricsymptomlinkedbrainconnectivitypatterns AT fonzogregorya definingdementiasubtypesthroughneuropsychiatricsymptomlinkedbrainconnectivitypatterns AT carlislenancy definingdementiasubtypesthroughneuropsychiatricsymptomlinkedbrainconnectivitypatterns AT osorioricardos definingdementiasubtypesthroughneuropsychiatricsymptomlinkedbrainconnectivitypatterns AT zhangyu definingdementiasubtypesthroughneuropsychiatricsymptomlinkedbrainconnectivitypatterns |