Environmental complexity on spatial brain volume and behavior across the Alzheimer’s spectrum
Poster No:
248
Submission Type:
Abstract Submission
Authors:
Naewoo Shin1, Karen Rodrigue1, May Yuan1, Kristen Kennedy1
Institutions:
1The University of Texas at Dallas, Dallas, TX
First Author:
Co-Author(s):
Introduction:
Spatial cognition is among the cognitive domains that exhibit decline with advanced normal aging [1]. Deficits in spatial navigation, however, are much more pronounced in Alzheimer's disease (AD) and it is considered one of the earliest signs of the disease [2,3]. While research on the role of aging on spatial navigation is growing, studies on local geospatial features in relation to AD risk are scarce. Greater opportunities for urban dwellers to utilize and strengthen cognitive maps via routinely navigating more complex spatial environments may exhibit neuroprotective properties. We recently introduced Environmental Complexity (EC), an index reflecting the frequency and density of street networks and landmark features/points of interest, computed by geo-locating participants from the National Alzheimer's Coordinating Center (NACC) for each zip-code zone across the USA. EC predicted cognitive status (cognitively normal, mild cognitive impairment (MCI), AD) with 95% classifier precision [4]. Here, we extend that work to explore the effects of EC on the maintenance of spatial navigation-related gray matter volume and spatial behavioral performance.
Methods:
This study utilized a sample of 660 participants (assigned as cognitively normal [n = 378], MCI [n = 114], and AD [n = 168]) from the NACC uniform data set (45-93 years old). The sample was limited to participants who stayed in the same 3-digit zip-code over the course of their visits. The AD spectrum was contrast coded as two orthogonal contrasts: DX1 (-.66, .33, .33) for healthy vs. non-healthy, and DX2 (0, -.5, .5) for MCI vs. AD. MRI estimation of gray matter volumes were processed following the ADNI four-tissue segmentation protocol. A priori regions of interest were selected for their association with egocentric and allocentric spatial navigation and were adjusted for ICV. We define the EC as the geometric average of diversity and abundance measures of spatial features in a 3-digit zip-code zone. Total of 20 network measures and landmark features in each 3-digit zip-code zone across the United States (154 total zones) were collected from Open Street Map and SafeGraph Core Places. EC is calculated as the square root of EntropyH* MaxRatio. EntropyH is the measure of the average diversity of all geospatial features. MaxRatio is the measure of abundance of spatial features within a 3-digit zip-code zone. Structural equation modeling was conducted to analyze the data, with EC and mean-centered age predicting latent allocentric and egocentric brain region volumes, AD spectrum status, and latent spatial behavioral performance.
Results:
Results indicate that greater EC was significantly positively associated with larger brain volumes in allocentric spatial regions, but not with egocentric regions. A significant indirect effect of EC on spatial cognition was identified through allocentric regions and DX1. Greater EC, related to greater brain volume was associated with less diagnosis of MCI or AD vs being cognitively normal, and having higher spatial behavioral scores. This mediation eliminated the direct association of EC on spatial behavior. Age was negatively associated with both brain volumes, as expected. There was also no significant direct relationship between age and spatial cognition, rather this association was mediated by brain volume and cognitive diagnosis status.
Conclusions:
These findings suggest that residing in spatially complex environments allows for the routine usage of cognitive neural mapping across time, which may help to stave off the brain atrophy that is associated with spatial navigation difficulties seen in Alzheimer's disease, and may be a target for future interventions. In sum, prevention of AD is of paramount concern and these findings suggest that residing in and routinely navigating spatially complex environments may be one mechanism to help stave off the brain atrophy associated with spatial navigation difficulties seen in aging and Alzheimer's disease.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Multivariate Approaches 2
Keywords:
Aging
Degenerative Disease
MRI
Multivariate
STRUCTURAL MRI
1|2Indicates the priority used for review
Provide references using author date format
[2] Lithfous, S., Dufour, A., & Després, O. (2013). Spatial navigation in normal aging and the prodromal stage of Alzheimer's disease: insights from imaging and behavioral studies. Ageing research reviews, 12(1), 201-213.
[3] Possin, K. L. (2010). Visual spatial cognition in neurodegenerative disease. Neurocase, 16(6), 466-487.
[4] Yuan, M., & Kennedy, K. M. (2023). Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach. The Journal of Prevention of Alzheimer's Disease, 10(2), 223-235.