Research Education Component
The Stanford ADRC Research Education Component (REC) will provide a formalized training program to prepare the next generation of researchers for careers in aging, Alzheimer’s disease, and Alzheimer’s disease-related disorders.
Stanford ADRC REC Leadership Team
Kathleen Poston, MD, MS
Dr. Poston is the Edward F. and Irene Thiele Pimley Professor of Neurology and the Neurological Sciences and Professor, by courtesy, of Neurosurgery in the Stanford University School of Medicine. She has subspecialty training in engineering, clinical movement disorders (formal fellowship) and dementia, as part of her NIH Career Development Award. She was the Principal Investigator for one of the Projects within the Stanford inaugural P50 ADRC and has been the Principal Investigator for multiple Michael J. Fox foundation awards, all studying cognition and dementia in patients with Lewy Body-spectrum diseases. She is also the Co-Director for the Stanford Lewy Body Dementia Research Center of Excellence.
Kaci Fairchild, PhD, ABPP
Associate Program Leader
Dr. Fairchild is the Associate Director of the Sierra Pacific MIRECC and the Fellowship Training Director for the Advanced Fellowship in Mental Illness Research and Treatment at Sierra Pacific MIRECC. She also holds an affiliate appointment as a Clinical Associate Professor of Psychiatry and Behavioral Sciences at Stanford University School of Medicine. She is a board-certified Geropsychologist with an active research program focused on the improving the lives of people affected by late life cognitive impairment through lifestyle interventions. Her work has been funded by the VA Office of Rehabilitation Research and Development and VA Cooperative Studies program, the Department of Defense, the National Institute on Aging, and the Alzheimer’s Association.
Kristen Wheeler, DPT, PT
Kristen graduated with a Bachelor of Science degree from Appalachian State University where she studied Exercise Science. Following her undergraduate training, she received a Doctor of Physical Therapy degree from Duke University in 2014. She worked as a physical therapist primarily with the geriatric population in home health and skilled nursing facilities. After working in healthcare for several years, Kristen realized that there is a need for major improvements in diagnosing and treating various conditions. This led to her desire to become involved in research. Kristen hopes to bring her clinical background into the research world to help discover ways of making a greater impact in the lives of the patients she has treated. She recently moved from Colorado to California and joined the Poston Lab in August 2020. Outside of work, she enjoys running, hiking and going on adventures with her husband and golden retriever.
Stanford ADRC REC Fellows
Alesha Heath, PhD (2022-2023)
Contribution of genetics to variable sleep disturbances in Alzheimer's disease
Those diagnosed with Alzheimer’s disease (AD) express a variable symptom base which may further contribute to cognitive decline or be protective against the neurodegeneration caused by this disorder. Sleep is one of these variables symptoms. As sleep is a critical regulator of both memory consolidation and the clearance of the toxic byproducts thought to be involved in AD pathology, disturbed sleep highly contributable to the progression of AD. Aspects of sleep architecture are known to be heritable and influenced by underlying genetics. Therefore, this project will expand on this past research, taking advantage of recently available large-scale whole genome sequencing and advances in tandem repeat genotyping to examine the association of multiple types of polymorphisms to sleep disturbances in a large AD database. By identifying genetic associations which could allow for early detection of sleep disturbances that accelerate the progression of this disorder, this will open up avenues to apply early interventions and monitor the progression of this symptom closely.
Jeff Nirschl, MD, PhD (2022-2023)
Jeff Nirschl, M.D., Ph.D. is a neuropathology fellow and post-doctoral researcher in the Medical AI and ComputeR Vision Lab (MARVL) led by Dr. Serena Yeung at Stanford University, Palo Alto, CA. As a physician-scientist and researcher he has built his career at the interface of bioinformatics, cell biology, computer vision, and healthcare with a focus on computational and digital pathology.
Deep phenotyping neuropathologic changes and transcriptomics in Alzheimer's disease with and without comorbid Lewy body disease
Alzheimer’s disease (AD) and Lewy body disease (LBD) pathologies often coexist, but the common molecular pathways underlying neurodegeneration in these disorders remain unclear. Neuropathologic evaluation remains essential to characterize the diverse spectrum of neuropathologic change and co-pathologies. Despite advances in computer vision and whole-slide imaging (WSI), standardized neuropathology reporting relies on manual, semi-quantitative measurements that require expert review and are subject to inter-observer variability. Further, traditional metrics ignore the complex, hierarchical interaction among neurons, glia, and co-pathologies (tangles, plaques, Lewy bodies) within microscopic brain regions. This proposal will develop novel deep learning algorithms to quantify detailed neuropathologic features from WSI and will also leverage spatial transcriptomics to characterize the unique and shared transcriptomic signatures in AD and AD+ LBD. This work will improve our understanding of the continuum of pathology in AD and LBD as well as reveal shared molecular pathways that underly neuron loss and degeneration in these devastating diseases.
Ramy Hussein, PhD (2021-2022)
Ramy is a former PostDoc Researcher at the Stanford Radiological Sciences Laboratory. He holds a PhD Degree in Electrical and Computer Engineering from the University of British Columbia, Canada. He works on problems at the intersection of Artificial Intelligence and Medicine, with a focus on medical imaging. Ramy is interested in developing and optimizing Machine Learning solutions for the early diagnosis and prediction of cerebrovascular and neurodegenrative diseases, with more focus on Ischemic Stroke and Alzheimer's Disease.
Multimodal Deep Learning for Medical Imaging and Clinical Data Fusion: Paving the Way for Better Prediction and Prognosis of Alzheimer’s Disease
Dementia is the loss of cognitive functioning (thinking, remembering, and reasoning) and behavioral abilities to such an extent that it interferes with a person’s daily life and activities. Worldwide, around 50 million people have dementia, and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common cause of dementia among older adults. Mild cognitive impairment (MCI) is the intermediary stage condition between the expected cognitive decline of normal aging and the more serious decline of Alzheimer’s disease and other dementias. Around one third of MCI individuals progress to dementia within 3 years following the initial diagnosis. Identifying the MCI individuals at high risk of developing Alzheimer’s disease is crucial for fighting against this disease. Thus, we are developing a variety of multimodal Artificial Intelligence algorithms that can effectively integrate heterogeneous sources of medical data to achieve more reliable and accurate predictions of Alzheimer’s disease, and also increase our understanding of which source(s) of medical data have the greatest impact on the prediction performance. This will help achieve better prognosis of Alzheimer’s disease and also reduce burdens on data collection and patient burnout in clinical practice.
Joe Winer, PhD (2021-2022)
Joe Winer completed his PhD in Psychology at UC Berkeley working with Matthew Walker and William Jagust. During his PhD, Joe combined objective and subjective sleep assessment with PET imaging to investigate connections between sleep disruption and Alzheimer's disease in the context of healthy aging. In his REC Fellowship at the Stanford ADRC, Joe plans to explore how tracking sleep and other factors in everyday life can provide information about brain health and cognitive trajectories in aging and neurodegenerative diseases.
Characterizing relationships between sleep-wake rhythms, neuroinflammation, and cognition in neurodegenerative disease
Sleep and daytime activity patterns are known to change across the lifespan, and both are affected in neurodegenerative disease. New research suggests that these changes are not only symptoms of disease, but may affect cognition and disease progression. Actigraphy watches, which use technology similar to the accelerometers in our phones, can be used to collect sleep-wake activity data outside the laboratory. This project will collect actigraphy data from healthy older adults, patients with Alzheimer's disease, and patients with Lewy body disease enrolled at the Stanford ADRC in order to characterize patterns of sleep-wake activity across disease severity. These data will be combined with cognitive assessments and disease biomarkers in order to investigate the contribution of sleep-wake dysfunction to cognitive decline and inflammatory dysregulation in the progression of both Alzheimer's and Lewy body disease.
Ehsan Adeli, PhD (2020-2021)
Ehsan is a Clinical Assistant Professor at the Department of Psychiatry and Behavioral Sciences at Stanford School of Medicine. He is affiliated with the Computational Neuroscience Lab (CNS Lab) is Psychiatry and the Stanford Vision and Learning (SVL) Lab, Stanford AI Lab (SAIL) in the Department of Computer Science. His research interest lies at the intersection of computational neuroscience and computer vision applied to healthcare applications. Learn more
Data-Driven Stratification of Neurodegenerative Disorders Using Video-MRI Analysis
Video recordings of patient movements are commonly used to assess the physical impact of disease by performing the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or the Short Physical Performance Battery (SPPB). Traditionally, the videos are reviewed by medical experts, which coarsely categorize the movement. In this project, we propose to automatically quantify movements from videos for extracting video-based digital biomarkers of neurodegenerative diseases. These digital biomarkers will then be related to neural systems obtained from MRIs (imaging phenotypes). This procedure can then automatically relate movement patterns with brain circuitry.
Tammy Tran, PhD (2020-2021)
Dr. Tammy Tran is a Postdoctoral Fellow in the Department of Psychology working with Dr. Anthony Wagner and Dr. Elizabeth Mormino on the Stanford Aging and Memory Study. She received her PhD at Johns Hopkins University in 2019 and received training in neuropsychology and high-resolution functional and structural neuroimaging, studying cognitively normal older adults and patients with Alzheimer’s disease. She was a fellow on a T31 Aging and Age-Related Disorders Training Program at the Johns Hopkins School of Public health and awarded a National Defense Science and Engineering Graduate Fellowship.
Molecular and imaging biomarkers underlying neurodegeneration in aging
Neuropathological changes emerge decades prior to clinical manifestation in Alzheimer’s disease. Extant data suggests that early cognitive decline may be predicted by several pathophysiological abnormalities, detectable by in vivo biomarkers early in the disease trajectory including the presence of molecular and imaging biomarkers for tau and amyloid. There is emerging evidence that cognitive decline is also predicted by cortical thinning across medial temporal lobe (MTL) regions, particularly in entorhinal cortex and CA1-SRLM, a sublayer that serves as an interface between entorhinal cortex and other hippocampal subfields. Using high-precision metrics (high resolution 3T and ultra-high resolution 7T MRI), I will examine neurodegeneration (including subregion-specific cortical thickness and hippocampal volume) in relation to examine imaging biomarkers (Tau PET(F-PI2620)) and molecular biomarkers (e.g., Ab42/Ab40 ratio, pTau181, t-tau) and investigate how these promising biomarkers correspond to cognitive function in putatively healthy older adults.
Videos y presentaciónes de ADRC
Núcleo de Bioestadísticas, Bioinformática, y Manejo de Datos
Núcleo de Neuropatología y Especímenes biológicos
Núcleo de Divulgación, Inscripción, y Educación
El Componente de Educación en Investigación (REC)
El Precioso Regalo de Donación