Identifying vulnerable brain networks associated with Alzheimer's disease risk.
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2023-04
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The selective vulnerability of brain networks in individuals at risk for Alzheimer's disease (AD) may help differentiate pathological from normal aging at asymptomatic stages, allowing the implementation of more effective interventions. We used a sample of 72 people across the age span, enriched for the APOE4 genotype to reveal vulnerable networks associated with a composite AD risk factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed a high weight associated with genotype, and subgraphs involving the cuneus, temporal, cingulate cortices, and cerebellum. Adding cognitive metrics to the risk factor revealed the highest cumulative degree of connectivity for the pericalcarine cortex, insula, banks of the superior sulcus, and the cerebellum. To enable scaling up our approach, we extended tensor network principal component analysis, introducing CCA components. We developed sparse regression predictive models with errors of 17% for genotype, 24% for family risk factor for AD, and 5 years for age. Age prediction in groups including cognitively impaired subjects revealed regions not found using only normal subjects, i.e. middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. These modeling approaches represent stepping stones towards single subject prediction.
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Mahzarnia, Ali, Jacques A Stout, Robert J Anderson, Hae Sol Moon, Zay Yar Han, Kate Beck, Jeffrey N Browndyke, David B Dunson, et al. (2023). Identifying vulnerable brain networks associated with Alzheimer's disease risk. Cerebral cortex (New York, N.Y. : 1991), 33(9). pp. 5307–5322. 10.1093/cercor/bhac419 Retrieved from https://hdl.handle.net/10161/32034.
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Hae Sol Moon
Jeffrey Nicholas Browndyke
Dr. Browndyke is an Associate Professor of Behavioral Health & Neurosciences in the Department of Psychiatry & Behavioral Sciences. He has a secondary appointment as Assistant Professor of Cardiovascular & Thoracic Surgery.
Dr. Browndyke's research interests involve the use of advanced neurocognitive and neuroimaging techniques for perioperative contributions to delirium and later dementia risk, monitoring of late-life neuropathological disease progression, and intervention/treatment outcomes. His research also involves novel telehealth methods for remote neurocognitive evaluation and implementation of non-invasive neuromodulatory techniques to assist in postoperative recovery and dementia risk reduction.
Dr. Browndyke's clinical expertise is focused upon geriatric neuropsychology with an emphasis in the assessment, diagnosis, and treatment of dementia and related disorders in adults and US veteran patient populations.
David B. Dunson
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties of methods we develop.
Some highlight application areas:
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc. Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.
(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.
(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease. This includes an emphasis on infectious disease modeling, such as COVID-19.
Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).
Kim G Johnson
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