Mild behavioral impairment brain phenotype relate to cognitive decline in dementia-free older adults
Poster No:
112
Submission Type:
Abstract Submission
Authors:
Kok Pin Ng1,2, Joanna Su Xian Chong3, Firoza Lussier4, Joseph Therriault5,6, Nesrine Rahmouni5,6, Melissa Savard5,6, Jenna Stevenson5, Tharick Pascoal4, Pedro Rosa-Neto5,6, Juan Helen Zhou3,7,8
Institutions:
1Department of Neurology, National Neuroscience Institute, Singapore, Singapore, 2Duke-NUS Medical School, Singapore, Singapore, 3Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 4Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 5McGill University Research Centre for Studies in Aging, McGill University, Montreal, Quebec, 6Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec, 7Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore, 8Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
First Author:
Kok Pin Ng
Department of Neurology, National Neuroscience Institute|Duke-NUS Medical School
Singapore, Singapore|Singapore, Singapore
Department of Neurology, National Neuroscience Institute|Duke-NUS Medical School
Singapore, Singapore|Singapore, Singapore
Co-Author(s):
Joanna Su Xian Chong
Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore
Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore
Firoza Lussier
Department of Psychiatry, School of Medicine, University of Pittsburgh
Pittsburgh, PA
Department of Psychiatry, School of Medicine, University of Pittsburgh
Pittsburgh, PA
Joseph Therriault
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
Nesrine Rahmouni
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
Melissa Savard
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
Jenna Stevenson
McGill University Research Centre for Studies in Aging, McGill University
Montreal, Quebec
McGill University Research Centre for Studies in Aging, McGill University
Montreal, Quebec
Tharick Pascoal
Department of Psychiatry, School of Medicine, University of Pittsburgh
Pittsburgh, PA
Department of Psychiatry, School of Medicine, University of Pittsburgh
Pittsburgh, PA
Pedro Rosa-Neto
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
McGill University Research Centre for Studies in Aging, McGill University|Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University
Montreal, Quebec|Montreal, Quebec
Juan Helen Zhou, Ph.D.
Yong Loo Lin School of Medicine, National University of Singapore|Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore|Department of Electrical and Computer Engineering, National University of Singapore
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Yong Loo Lin School of Medicine, National University of Singapore|Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore|Department of Electrical and Computer Engineering, National University of Singapore
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Introduction:
Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later-life emergent neuropsychiatric symptoms that represents an at-risk state for incident cognitive decline [1]. MBI is prevalent in non-demented individuals, and linked to worse cognitive performance at baseline and over time [2, 3]. MBI is thus increasingly recognised as an early non-cognitive manifestation of impending dementia.
The MBI checklist (MBI-C) is a validated screening test specifically developed to identify MBI in pre-dementia populations based on established diagnostic criteria [1, 4]. Emerging studies utilising MBI-C to identify MBI have demonstrated MBI-related brain functional connectivity (FC) disruptions in dementia-free older adults, particularly within control, salience and default networks [5-7]. However, no study has systematically examined whole brain within and between network FC disruptions related to multiple MBI subdomains and their longitudinal effects on cognition in dementia-free older adults.
Here, we addressed these gaps by examining associations of MBI and its subdomains with whole-brain FC and their links to global cognition and functional status in dementia-free older adults. We hypothesized that MBI-C scores would be associated with whole-brain FC disruptions particularly in control, default and salience networks, and that these MBI-related FC disruptions would relate to poorer cognitive performance and functional status at baseline and over time. Finally, we tested whether Alzheimer's disease (AD) markers (Aβ and tau burden) moderated the links between MBI-related FC dysfunction and cognition/functional status.
The MBI checklist (MBI-C) is a validated screening test specifically developed to identify MBI in pre-dementia populations based on established diagnostic criteria [1, 4]. Emerging studies utilising MBI-C to identify MBI have demonstrated MBI-related brain functional connectivity (FC) disruptions in dementia-free older adults, particularly within control, salience and default networks [5-7]. However, no study has systematically examined whole brain within and between network FC disruptions related to multiple MBI subdomains and their longitudinal effects on cognition in dementia-free older adults.
Here, we addressed these gaps by examining associations of MBI and its subdomains with whole-brain FC and their links to global cognition and functional status in dementia-free older adults. We hypothesized that MBI-C scores would be associated with whole-brain FC disruptions particularly in control, default and salience networks, and that these MBI-related FC disruptions would relate to poorer cognitive performance and functional status at baseline and over time. Finally, we tested whether Alzheimer's disease (AD) markers (Aβ and tau burden) moderated the links between MBI-related FC dysfunction and cognition/functional status.
Methods:
We studied 203 dementia-free older adults from the Translational Biomarkers in Aging and Dementia cohort with baseline MBI-C scores, fMRI, [18F]AZD4694 and [18F]MK6240 PET imaging as well as longitudinal Clinical Dementia Rating Sum of Boxes (CDR SOB) and Montreal Cognitive Assessment (MoCA) scores up to 32 months. Individual whole-brain FC matrices were first obtained from 144 regions-of-interest (ROIs) [8-10]. Multivariate associations between MBI-C subdomain scores and FC matrices were then examined using partial least squares correlation (PLSC). We next examined how baseline PLSC-derived connectome scores (denoting extent of MBI-related FC disruptions) independently and synergistically interacted with baseline global Aβ burden and temporal meta-ROI tau burden (via PET) to influence baseline and longitudinal change in CDR SOB and MoCA scores using linear regression models. All models controlled for age, sex, education, cognitive status and total intracranial volumes.
Results:
We identified a significant latent variable characterized by high scores in all MBI-C subdomains and whole-brain FC dysfunctions particularly between and within the default, control and salience networks in dementia-free older adults (Fig 1). Greater MBI-related FC disruptions were associated with lower MoCA scores and higher CDR SOB at baseline (Fig 2A & B) and faster decline in both measures over time (Fig 2C & D). Further, while both Aβ and tau burden were not associated with MBI-related FC disruptions at baseline, we found that baseline Aβ burden, but not tau burden, moderated the longitudinal effects of baseline MBI-related FC disruptions on MoCA scores (Fig 2E). Specifically, individuals with higher Aβ burden and greater MBI-related FC disruptions at baseline showed steeper MoCA score declines. In contrast, MBI-C total score had no independent or interactive effects with Aβ and tau burden on changes in MoCA score and CDR SOB over time.
Conclusions:
Our study identified a global MBI-related FC phenotype linked to global cognition and functional status in dementia-free older adults. Further, we showed a direct link between Aβ burden, MBI-related FC phenotype and future global cognition, suggesting that individuals with greater Aβ burden and MBI-related FC disruptions show faster rates of cognitive decline over time.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Higher Cognitive Functions:
Higher Cognitive Functions Other
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Keywords:
Aging
Cognition
Degenerative Disease
FUNCTIONAL MRI
Multivariate
Positron Emission Tomography (PET)
Other - Mild behavioral impairment
1|2Indicates the priority used for review
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