Interpreting Functional Connectivity in Alzheimer’s Disease as Cognitive Processes: Multinomial Processing Trees and the Common Model of Cognition
Year: 2026
Type: preprint
Abstract: Cognitive process models and brain connectivity analyses both provide a higher resolution into what cognitive functions are engaged during cognitive tasks than traditional methods. However, the two methods are typically applied in isolation. Our goal is to combine them, and ask which impaired cognitive functions are associated with a specific connectivity signature. Participants were 1,127 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of varying cognitive status at their last included time point: Cognitively Normal = 554, Mild Cognitive Impairment = 386, Alzheimer's Disease = 187. We combine Bayesian multinomial processing tree (MPT) cognitive modelling with resting-state functional connectivity analysis guided by the Common Model of Cognition. MPT modelling showed that Learned and Delayed Retrieval processes had the largest diagnostic differences (η² ≈ 0.40), while Short-term Retrieval was unaffected. MPT parameters were correlated with widespread brain connectivity changes. However, brain connectivity changes were collinear, and mostly attributable to the diagnostic group. The exception was the Delayed Retrieval process parameter, which was associated with functional connectivity between areas associated with Working Memory and areas associated with Long-Term Memory, even when corrected for all other parameters. Combining the two analyses allowed us to link a function defined at the neural level to a function defined at the behavioral level. This article describes the potential and current limits of joining the two classes of models.
Source: Research Square
Institutions: The Netherlands Cancer Institute, University of Groningen
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Meso Scale Diagnostics, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute on Aging
Awards: G1012222616, G1554142629, G1650819308, G1880935189, G2100670906, G2105457377, G2471646051, G2522063444, G3146424788, G3553156159, G3752451908, G3870774570, G3966393351, G4501223628, G4800177743, G5209990037, G528601746, G5365730499, G5654639750, G5740349702, G6173816828, G6371410416, G6447063303, G6719828067, G6792506723, G6899163935, G72415811, G764699806, G781725486, G8228018387, G8281378126, G839208290, G8895907669, G8957659042, G9000122335
OpenAlex ID: W7225378588