A whole-brain functional connectivity model of Alzheimer's disease pathology.

Autor: Prakash RS; Department of Psychology, The Ohio State University, Columbus, Ohio, USA.; Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA., McKenna MR; Department of Psychology, The Ohio State University, Columbus, Ohio, USA., Gbadeyan O; Department of Psychology, The Ohio State University, Columbus, Ohio, USA., Shankar AR; Department of Psychology, The Ohio State University, Columbus, Ohio, USA., Pugh EA; Department of Psychology, The Ohio State University, Columbus, Ohio, USA., Teng J; Department of Psychology, The Ohio State University, Columbus, Ohio, USA.; Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA., Andridge R; Division of Biostatistics, The Ohio State University, Columbus, Ohio, USA., Berry A; Department of Psychology, Brandeis University, Waltham, Massachusetts, USA., Scharre DW; Department of Neurology, Division of Cognitive Neurology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.
Jazyk: angličtina
Zdroj: Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2024 Dec 23. Date of Electronic Publication: 2024 Dec 23.
DOI: 10.1002/alz.14349
Abstrakt: Introduction: Alzheimer's disease (AD) is characterized by the presence of two proteinopathies, amyloid and tau, which have a cascading effect on the functional and structural organization of the brain.
Methods: In this study, we used a supervised machine learning technique to build a model of functional connections that predicts cerebrospinal fluid (CSF) p-tau/Aβ 42 (the PATH-fc model). Resting-state functional magnetic resonance imaging (fMRI) data from 289 older adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were utilized for this model.
Results: We successfully derived the PATH-fc model to predict the ratio of p-tau/Aβ 42 as well as cognitive functioning in older adults across the spectrum of healthy and pathological aging. However, the in-sample fit magnitude was low, indicating a need for further model development.
Discussion: Our pathology-based model of functional connectivity included representation from multiple canonical networks of the brain with intra-network connectivity associated with low pathology and inter-network connectivity associated with higher levels of pathology.
Highlights: Whole-brain functional connectivity model (PATH-fc) is linked to AD pathophysiology. The PATH-fc model predicts performance in multiple domains of cognitive functioning. The PATH-fc model is a distributed model including representation from all canonical networks.
(© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
Databáze: MEDLINE
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