scholarly journals Association of ventricular size on executive function and attention is modified by Apolipoprotein E and moderated by pulse pressure in Alzheimer's disease

2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Shraddha Sapkota ◽  
G. Peggy McFall ◽  
Roger A. Dixon ◽  
Mario Masellis ◽  
Sandra E. Black
2021 ◽  
pp. 1-16
Author(s):  
Shraddha Sapkota ◽  
G. Peggy McFall ◽  
Mario Masellis ◽  
Roger A. Dixon ◽  
Sandra E. Black

Background: Differential cognitive trajectories in Alzheimer’s disease (AD) may be predicted by biomarkers from multiple domains. Objective: In a longitudinal sample of AD and AD-related dementias patients (n = 312), we tested whether 1) change in brain morphometry (ventricular enlargement) predicts differential cognitive trajectories, 2) further risk is contributed by genetic (Apolipoprotein E [APOE] ɛ4+) and vascular (pulse pressure [PP]) factors separately, and 3) the genetic + vascular risk moderates this pattern. Methods: We applied a dynamic computational approach (parallel process models) to test both concurrent and change-related associations between predictor (ventricular size) and cognition (executive function [EF]/attention). We then tested these associations as stratified by APOE (ɛ4–/ɛ4+), PP (low/high), and APOE+ PP (low/intermediate/high) risk. Results: First, concurrently, higher ventricular size predicted lower EF/attention performance and, longitudinally, increasing ventricular size predicted steeper EF/attention decline. Second, concurrently, higher ventricular size predicted lower EF/attention performance selectively in APOE ɛ4+ carriers, and longitudinally, increasing ventricular size predicted steeper EF/attention decline selectively in the low PP group. Third, ventricular size and EF/attention associations were absent in the high APOE+ PP risk group both concurrently and longitudinally. Conclusion: As AD progresses, a threshold effect may be present in which ventricular enlargement in the context of exacerbated APOE+ PP risk does not produce further cognitive decline.


2014 ◽  
Vol 10 ◽  
pp. P808-P808
Author(s):  
Femke Soetewey ◽  
Hanne Struyfs ◽  
Erik Stoops ◽  
Christine Van Broeckhoven ◽  
Hugo Vanderstichele ◽  
...  

2006 ◽  
Vol 22 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Timothy Kleiman ◽  
Kristina Zdanys ◽  
Benjamin Black ◽  
Tracy Rightmer ◽  
Monique Grey ◽  
...  

1998 ◽  
Vol 18 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Pilar Quiroga ◽  
Carlos Calvo ◽  
Cecilia Albala ◽  
Julio Urquidi ◽  
JoséL. Santos ◽  
...  

2016 ◽  
Vol 113 (42) ◽  
pp. E6535-E6544 ◽  
Author(s):  
Xiuming Zhang ◽  
Elizabeth C. Mormino ◽  
Nanbo Sun ◽  
Reisa A. Sperling ◽  
Mert R. Sabuncu ◽  
...  

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.


2006 ◽  
Vol 2 ◽  
pp. S179-S179
Author(s):  
Jonathan M. Schott ◽  
Basil H. Ridha ◽  
Sebastian J. Crutch ◽  
Elizabeth K. Warrington ◽  
Martin N. Rossor ◽  
...  

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