Browsing by Author "Sanjay, Apoorva Bharthur"

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    APOE ε4 carrier status and sex differentiate rates of cognitive decline in early- and late-onset Alzheimer's disease
    (Wiley, 2023) Polsinelli, Angelina J.; Logan, Paige E.; Lane, Kathleen A.; Manchella, Mohit K.; Nemes, Sára; Sanjay, Apoorva Bharthur; Gao, Sujuan; Apostolova, Liana G.; Neurology, School of Medicine
    Background: We studied the effect of apolipoprotein E (APOE) ε4 status and sex on rates of cognitive decline in early- (EO) and late- (LO) onset Alzheimer's disease (AD). Method: We ran mixed-effects models with longitudinal cognitive measures as dependent variables, and sex, APOE ε4 carrier status, and interaction terms as predictor variables in 998 EOAD and 2562 LOAD participants from the National Alzheimer's Coordinating Center. Results: APOE ε4 carriers showed accelerated cognitive decline relative to non-carriers in both EOAD and LOAD, although the patterns of specific cognitive domains that were affected differed. Female participants showed accelerated cognitive decline relative to male participants in EOAD only. The effect of APOE ε4 was greater in EOAD for executive functioning (p < 0.0001) and greater in LOAD for language (p < 0.0001). Conclusion: We found APOE ε4 effects on cognitive decline in both EOAD and LOAD and female sex in EOAD only. The specific patterns and magnitude of decline are distinct between the two disease variants. Highlights: Apolipoprotein E (APOE) ε4 carrier status and sex differentiate rates of cognitive decline in early-onset (EO) and late-onset (LO) Alzheimer's disease (AD). APOE ε4 in EOAD accelerated decline in memory, executive, and processing speed domains. Female sex in EOAD accelerated decline in language, memory, and global cognition. The effect of APOE ε4 was stronger for language in LOAD and for executive function in EOAD. Sex effects on language and executive function decline differed between EOAD and LOAD.
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    Predicting the Emergence of Major Neurocognitive Disorder Within Three Months After a Stroke
    (Frontiers Media, 2021-08-16) Aamodt, Eva Birgitte; Schellhorn, Till; Stage, Edwin; Sanjay, Apoorva Bharthur; Logan, Paige E.; Otero Svaldi, Diana; Apostolova, Liana G.; Saltvedt, Ingvild; Kristiansen Beyer, Mona; Neurology, School of Medicine
    Background: Neurocognitive disorder (NCD) is common after stroke, with major NCD appearing in about 10% of survivors of a first-ever stroke. We aimed to classify clinical- and imaging factors related to rapid development of major NCD 3 months after a stroke, so as to examine the optimal composition of factors for predicting rapid development of the disorder. We hypothesized that the prediction would mainly be driven by neurodegenerative as opposed to vascular brain changes. Methods: Stroke survivors from five Norwegian hospitals were included from the “Norwegian COgnitive Impairment After STroke” (Nor-COAST) study. A support vector machine (SVM) classifier was trained to distinguish between patients who developed major NCD 3 months after the stroke and those who did not. Potential predictor factors were based on previous literature and included both vascular and neurodegenerative factors from clinical and structural magnetic resonance imaging findings. Cortical thickness was obtained via FreeSurfer segmentations, and volumes of white matter hyperintensities (WMH) and stroke lesions were semi-automatically gathered using FSL BIANCA and ITK-SNAP, respectively. The predictive value of the classifier was measured, compared between classifier models and cross-validated. Results: Findings from 227 stroke survivors [age = 71.7 (11.3), males = (56.4%), stroke severity NIHSS = 3.8 (4.8)] were included. The best predictive accuracy (AUC = 0.876) was achieved by an SVM classifier with 19 features. The model with the fewest number of features that achieved statistically comparable accuracy (AUC = 0.850) was the 8-feature model. These features ranked by their weighting were; stroke lesion volume, WMH volume, left occipital and temporal cortical thickness, right cingulate cortical thickness, stroke severity (NIHSS), antiplatelet medication intake, and education. Conclusion: The rapid (<3 months) development of major NCD after stroke is possible to predict with an 87.6% accuracy and seems dependent on both neurodegenerative and vascular factors, as well as aspects of the stroke itself. In contrast to previous literature, we also found that vascular changes are more important than neurodegenerative ones. Although possible to predict with relatively high accuracy, our findings indicate that the development of rapid onset post-stroke NCD may be more complex than earlier suggested.
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    Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes
    (Springer, 2018-01) Svaldi, Diana O.; Goñi, Joaquín; Sanjay, Apoorva Bharthur; Amico, Enrico; Risacher, Shannon L.; West, John D.; Dzemidzic, Mario; Saykin, Andrew; Apostolova, Liana; Neurology, School of Medicine
    Alzheimer’s disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer’s spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.