Poster Presentations: P3
and also within ICN-specific cortical regions. Results: We found that even in the absence of any cognitive or structural changes, global cortical Ab burden was associated with functional connectivity in the DMN as well as cortical networks involved in executive control, motor and perceptual timing, and visual detection but not salience processing, attention, and working memory. We also found that there were significant effects of local network Ab burden (i.e., ICN Ab burden) on the network functional connectivity for all ICNs considered in this study; and the effects were associated more closely to local network Ab burden than global Ab burden. Conclusions: The relationship between local network Ab burden and disrupted intrinsic connectivity in various brain networks, in the absence of cognitive deficit and brain atrophy (and presumably absence of cortical tau tangles and neurodegeneration based on previous pathology reports), suggests that neuronal dysfunction may be due to local toxicity of Ab independent of the presence of tau. Furthermore, the results suggest that local toxicity of Ab may represent an early change in preclinical AD.
P3-166
METABOLIC RISK FACTOR BURDEN AND CORTICAL THICKNESS IN THE MESIAL TEMPORAL LOBE
Claire Murphy1,2,3, Elissa McIntosh2, Erin Green1, Aaron Jacobson2, Lori Haase1, Nobuko Kemmotsu4, 1SDSU/UCSD Joint Doctoral Program, San Diego, CA, USA; 2San Diego State University, San Diego, CA, USA; 3 University of California, San Diego, La Jolla, CA, USA; 4San Diego State University Research Foundation, San Diego, CA, USA. Contact e-mail:
[email protected] Background: Metabolic syndrome (MetS) involves a constellation of risk factors for cardiac and vascular disease that are significantly associated with cognitive decline and dementia in late life. The prevalence of MetS is rising and increases with age to more than 45% of adults > 60 years old. This study sought to identify neural correlates of metabolic risk factor burden in non-demented middleaged and older adults, specifically in mesial temporal lobe areas vulnerable to early Alzheimer’s disease. Methods: Participants were 26 middle aged and older adults who ranged in metabolic risk factor burden. The International Diabetes Federation guidelines include the following as contributors to metabolic risk factor burden: insulin resistance, dyslipidemia (elevated triglyceride and low high-density lipoprotein [HDL] cholesterol levels), central obesity, elevated blood pressure, and impaired glucose tolerance or diabetes mellitus. Metabolic risk factor burden was operationally defined by the sum of MetS criteria met by a participant. High-resolution T1-weighted structural MRI scans with prospective motion correction were acquired on a 3T scanner and processed using the Freesurfer image analysis suite. Age and gender were covariates in analyses. Results: Pearson correlations between cortical thickness estimates in mesial temporal lobe areas and metabolic risk factor burden were computed. Cortical thickness was negatively associated with metabolic risk factor burden in left entorhinal cortex, left parahippocampal gyrus, left temporal pole, and right temporal pole. Conclusions: The data revealed significant relationships between metabolic risk factor burden and cortical thickness in areas vulnerable to prodromal Alzheimer’s disease (notably, entorhinal cortex). We hypothesize that changes in mesial temporal lobe thickness associated with metabolic syndrome will contribute to cognitive decline in later life. The findings suggest the importance of further investigation of middle-aged and older adults who carry a heavy metabolic risk burden and thus may be at increased risk
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for cognitive decline and dementia. Supported by NIH grant AG004085-26 to CM. The authors have no conflicts of interest to declare. We thank the members of the Lifespan Human Senses Laboratory, and Dr. Thomas Liu and the staff of the UCSD Center for Functional MRI. P3-167
PREDICTING AND CHARACTERIZING DEVELOPMENT OF PRECLINICAL ALZHEIMER’S DISEASE
Matthew R. Brier1, John E. McCarthy2, Tammie L. S. Benzinger2, Yi Su2, Karl A. Friedrichsen2, John C. Morris1, Beau Ances2, Andrei Vlassenko2, 1 Washington University School of Medicine, Saint Louis, MO, USA; 2 Washington University in St. Louis, St. Louis, MO, USA. Contact e-mail:
[email protected] Background: Preclinical Alzheimer’s disease (AD) can be operationally defined as an abnormal amyloid-imaging scan. However, determination of abnormality is often made using a single scalar summary metric that exceeds some threshold. The selection of this threshold is arbitrary and some number of individuals who have normal scans will develop preclinical AD in the future. This work aims to identify those persons at high risk of future conversion and to characterize the longitudinal amyloid accumulation in those who develop preclinical AD. Methods: Regional PIB binding was measured within FreeSurfer defined anatomical regions of interest from 131 cognitively normal (clinical dementia rating ¼ 0) and had PIB scans defined as normal using mean cortical SUVR corrected for partial volume effects. At follow up (approx. 3 years later), 16 participants demonstrated positive PIB scans. Penalized linear regression was used to predict conversion status using only the first scan and to identify brain regions predictive of that conversion. Amyloid accumulation was investigated using the longitudinal scan and a canonical correlation approach to describe the topography of PIB accumulation. Results: Penalized regression was able to predict PIB conversion in the future more accurately than previously defined metrics in the independent validation cohort. The evolution of PIB topography longitudinally consisted of a local and distributed process. Some brain regions accumulated amyloid locally at the longitudinal time point. However, other brain regions were predictive of accumulation in distant brain regions at follow up. Conclusions: The proposed approach is well able to identify individuals who are amyloid negative at baseline but who become amyloid positive in the future. This suggests that there is a reliable pathological process that precedes overt detectable disease. Identification of these individuals is critical for the trial of disease modifying therapies. Further, these results support models of amyloid spread in the process of AD progression.
P3-168
PROGRESSION RATES FROM MILD COGNITIVE IMPAIRMENT TO DEMENTIA BY BIOMARKER AND MEMORY THRESHOLDS
Warren W. Barker1, Maria Greig-Custo1, Rosemarie Rodriquez1, David Loewenstein2, Malek Adjouadi3, Mohammed Goryawala3, Qi Zhou3, Ranjan Duara1,2,3, 1Mount Sinai Medical Center, Miami Beach, FL, USA; 2 University of Miami School of Medicine, Miami, FL, USA; 3Florida International University, Miami, FL, USA. Contact e-mail: Ranjan.Duara@ msmc.com Background: Amyloid load, hippocampal atrophy and memory impairment are associated with increased risk for progression from MCI to dementia. However, concrete estimates of the risk conferred by each of these factors, alone and in combination, are
