Mapping Structural Brain Networks Linked to Mild Behavioral Impairment

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

Abstract

Project Abstract An estimated 6.7 million US adults live with Alzheimer’s disease (AD) and related dementias (ADRD); a figure that is expected to more than double by 2060 unless new prevention strategies or treatments emerge. Although later-life neuropsychiatric symptoms have been associated with a greater risk of dementia, they are all too often viewed and treated as independent of ADRD. This traditional approach overlooks the possibility that neuropsychiatric symptoms emerge due to AD/ADRD pathology build-up that drives structural changes in brain regions or networks that differ from those affected by the more common “memory first” phenotype. Thus, the proposed project leverages data from the National Alzheimer’s Coordinating Center (NACC) to evaluate a recently developed mild behavioral impairment (MBI) diagnostic framework that standardizes the assessment of neuropsychiatric symptoms in older adults during the pre-dementia phase(s). While MBI is typically linked with regional atrophy of the medial temporal lobes, this is likely an overly simplistic view. Thus, our central hypothesis is that MBI symptoms in early-stage clinical phenotypes (i.e., cognitively unimpaired and MCI) are associated with structural changes within brain networks implicated in the processing and integration of emotional experience--i.e., the salience and default-mode networks. To test this, we will define the network-level structural alterations linked to MBI in early clinical phenotypes (Aim 1) and quantify the predictive value of MBI-related structural network changes for dementia conversion (Aim 2). This is the first study to conduct a network-level analysis of the structural alterations associated with MBI in a nationally representative cohort and link longitudinal trajectories of network-level structural changes in those with MBI with the risk of conversion to dementia. Clarifying the neural correlates of MBI and their predictive value for dementia conversion may help with early detection of “at-risk” individuals and understanding of disease progression. These findings will pave the way for future studies integrating AD biomarkers and inform the development of targeted interventions for specific brain regions implicated in MBI. The current project aligns well with the NIA SCAN initiative to harmonize MRI data across AD Research Centers and will strengthen my plan to pursue a K award focusing on individualized treatments for MBI.