Biology subjects

Stern, Y.

Publications and source records attributed to Stern, Y..

25 records · Page 2Linked to original sources

bioRxiv2020

Global cognitive performance is not influenced by diurnal rhythm

ObjectiveThe human brain is influenced by different biological rhythms, including the daily 24-hour (diurnal) cycle. Past studies have reported evidence of variation in cognitive performance over the course of the day, and of differences in the peak time for cognition in older age. Here, we investigated these questions using two existing longitudinal studies of healthy adults. MethodsTime of neuropsychological battery testing was extracted from study records, and we analyzed cognitive performance measures from 4 domains (Vocabulary, Processing Speed, Fluid Reasoning, and Episodic Memory) in 543 healthy adults between the ages of 20 and 80. Time of day was dichotomized as morning (281 tested before noon), and afternoon (242 tested after noon). ResultsMultivariate analyses controlling for both gender and years of education revealed no significant effect of time of testing (or its interaction with participant age) on cognitive performance. These results suggest that diurnal effects during time periods typically used to test human subjects are unlikely to have a meaningful effect on performance on the neuropsychological tests that are used for standard cognitive assessment. ConclusionThis suggests that the effect of time of day on cognition in the context of aging may not be as ubiquitous as previously suggested, and thus is unlikely to represent a large confound in existing studies of cognition across the adult lifespan.

neuroscience↗

bioRxiv2019

Identifying a task-invariant cognitive reserve network using task potency

Cognitive reserve (CR) is thought to protect against the consequence of age- or disease-related structural brain changes across multiple cognitive domains. The neural basis of CR may therefore comprise a functional network that is actively involved in many different cognitive processes. To investigate the existence of such a \"task-invariant\" CR network, we measured functional connectivity in a cognitively normal sample between 20-80 years old (N=265), both at rest and during the performance of 11 separate tasks that aim to capture four latent cognitive abilities (i.e. vocabulary, episodic memory, processing speed, and fluid reasoning). For each individual, we determined the change in functional connectivity from the resting state to each task state, which is referred to as \"task potency\" (Chauvin et al., 2017; Chauvin et al., 2018). Task potency was calculated for each pair among 264 nodes (Power et al., 2011) and then summarized across tasks reflecting the same cognitive ability. Subsequently, we established the correlation between task potency and premorbid IQ or education (i.e. CR factors). We identified a set of 57 pairs in which task potency showed significant correlations with IQ, but not education, across all four cognitive abilities. These pairs were included in a principal component analysis, from which we extracted the first component to obtain a latent variable reflecting task potency in this task-invariant CR network. This task potency variable moderated the relationship between cortical thickness and episodic memory performance ({beta}=-.64, p=.01), and showed a direct effect on fluid reasoning ({beta}=.08, p<.01) after adjusting for the effects of cortical thickness. Our identification of this task-invariant network contributes to a better understanding of the mechanism underlying CR, which may facilitate the development of CR-enhancing treatments. Our work also offers a useful alternative operational measure of CR future studies.

neuroscience↗

bioRxiv2019

Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

Brain-predicted age difference scores are calculated by subtracting chronological age from brain age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylul University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

neuroscience↗

bioRxiv2019

Electronic Health Records Based Prediction of Future Incidence of Alzheimers Disease Using Machine Learning

Nationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimers disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: "definite AD" with diagnostic codes and dementia medication (n=614) and "probable AD" with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

bioinformatics↗

bioRxiv2019

White matter regions with low microstructure in young adults are associated with white matter hyperintensities in late life

BackgroundWhite matter hyperintensities (WMH) are areas of increased signal observed on T2-weighted magnetic resonance imaging (MRI) that reflect macrostructural white matter damage frequently observed in aging. The extent to which diminished microstructure precedes or results from white matter damage is unknown. The aim of this study was to evaluate the hypothesis that white matter areas that show normatively lower microstructure are most susceptible to develop WMH. MethodsFive hundred fifty-seven older adults (age: 73.9{+/-}5.7yrs) underwent diffusion weighted imaging (DWI) and T2-weighted magnetic resonance imaging (MRI). Diffusion weighted imaging scans were processed into parametric maps of fractional anisotropy (FA) and T2-weighted MRI scans were segmented into WMH. All images were spatially normalized to standard space. A FA template was created to represent normative values from a separate, independent sample of young, healthy adults (N=49, age: 25.8{+/-}2.8yrs) and a WMH frequency template was created from the segmented WMH in the older adults. We compared FA values between areas defined as WMH with those defined as normal appearing white matter (NAWM) in the older participants. White matter hyperintensity frequency was binned (0-5%, 5-10%, 10-15%, 15-20%, >20%) and we determined whether WMH frequency bins were different by normative FA values defined in the younger group. ResultsFractional anisotropy values were lower (p<0.001) in WMH regions compared with NAWM regions in the older sample. Areas with higher WMH frequency in older adults had lower FA values in younger adults (5-10%>10-15%>15-20%; p<0.001). DiscussionLow FA values are observed in frank WMH, but FA is also normatively low in regions with high WMH frequency prior to damage. Regions with normatively lower microstructure are more susceptible to future damage from factors such as chronic hypoperfusion or pathology.

neuroscience↗

bioRxiv2018

Diagnosis and Prognosis Using Machine Learning Trained on BrainMorphometry and White Matter Connectomes

Accurate, reliable prediction of risk for Alzheimers disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping--morphometry and structural connectomics--and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimers Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid {beta}, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.\n\nHighlightsO_LIWe showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.\nC_LIO_LIIn predicting the progression from MCI to AD, the morphometry model showed the best performance.\nC_LIO_LITwo independent clinical datasets were used in this study: one for model building, the other for generalizability testing.\nC_LI

neuroscience↗

bioRxiv2018

Accurate Prediction of Alzheimer’s Disease Using Multi-Modal MRI and High-Throughput Brain Phenotyping

Accurate, reliable prediction of risk for Alzheimers disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping--morphometry and connectomics--and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective memory complaints [SMC]) to test and validate the diagnostic models; and, secondly, Alzheimers Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the approach and the prognostic models with longitudinal follow up data. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study, outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a generalizability study using ADNI-2, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as CSF biomarker model (t-tau, p-tau, and Amyloid {beta}, and ratios). We also predicted MCI to AD progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model. The optimal classification accuracy in a single-site dataset and the reproduced results in multi-site dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and data-driven machine learning for predictive modeling in AD.

neuroscience↗