Upon entering the training program, both pre- and post-doctoral trainees will complete a structured training program that is comprised of a mentored research project, foundation and pathway coursework, seminars and journal clubs, participation in the ADRC didactics program, professional development activities including grant writing support, attendance and presentations at scientific meetings, and participation in an annual retreat. There will also be an emphasis on Responsible Conduct of Research and Rigor & Reproducibility, with particular attention to neuroimaging-related issues.
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A hallmark component of the training program will be a foundational research project led by each trainee with the mentorship support of at least two Training Team members, including one Core Preceptor and a Junior Preceptor. Each trainee will be expected to identify novel and feasible research questions that are not prescribed by their mentors but represent de novo evidence of critical thinking. If human subjects or animal research is involved in the proposed research, the trainees will also work with their mentorship team to learn the necessary procedures to obtain IRB and/or IACUC approvals.
All trainees will complete 3 foundation courses, and have the option to participate in elective pathway-specific coursework.
Foundation courses (required):
Biomedical Image Analysis (BE/CIS537)
This foundation course, taught by program faculty Dr. Paul Yushkevich, covers the fundamentals of advanced quantitative image analysis that apply to all of the major and emerging modalities in biological/biomaterials imaging and in vivo biomedical imaging. While traditional image processing techniques will be discussed to provide context, the emphasis will be on cutting edge aspects of all areas of image analysis (including registration, segmentation, and high-dimensional statistical analysis). Significant coverage of state-of-the-art biomedical research and clinical applications will be incorporated to reinforce the theoretical basis of the analysis methods.
Neuroimaging Statistical Methods (BSTA751)
This foundation course, co-taught by program faculty Drs. Taki Shinohara and Haochang Shou, is intended for students interested in both statistical methodology, and the process of developing this methodology, for the field of neuroimaging. This will include quantitative techniques that allow for inference and prediction from ultra-high dimensional and complex images. In this course, basics of imaging neuroscience and preprocessing will be covered to provide students with requisite knowledge to develop the next generation of statistical approaches for imaging studies. High-performance computational neuroscience tools and approaches for voxel- and region-level analyses will be studied. The multiple testing problem will be discussed, and the state-of-the art in the area will be examined.
Cellular and Molecular Mechanisms of Neurodegenerative Disease (BBB475)
This foundation course will familiarize students with advances in our understanding of the clinical features and pathogenesis of a wide range of ADRDs and disease processes. Students will analyze original research reports of proposed pathological processes that may represent steps in cell death pathways leading to the neuronal dysfunction and death seen in these diseases. Representative topics will include accumulation of misfolded and aberrant proteins, inflammatory response, synaptic failure, oxidative and nitrosative stress, along with several other biological pathways. Significant emphasis is placed on the fast-expanding field exploring genetic contributions to neurodegenerative disease. Identification of genetic mutations pathogenic for familial neurodegenerative diseases has been a major driving force in neurodegenerative research and pointed researchers towards essential molecular process that may underlie these disorders. Strategies for therapeutic intervention in the management, prevention, and cure of neurodegenerative disease will be addressed.
Representative pathway-specific courses (elective):
Disease Measurement (MTR603)
This lab and lecture-based course in the Neuroimaging Biomarkers & Trials pathway focuses on the effective incorporation of disease measurements into the design of clinical trials and translational research protocols, including understanding how "normal" values are determined, and how to interpret test results in the context of patients/research subjects.
Clinical Trials Outcomes: Measurement, Analysis, and Interpretation (EPID634)
This course in the Neuroimaging Biomarkers & Trials pathway focuses on gaining the skills necessary to select and/or design appropriate outcomes for a clinical trial. Students will be expected to learn about the problems inherent in the design of outcome measures of health and how to apply different epidemiologic and biostatistical concepts toward a solution. It is expected that at the conclusion of the course, students will be able to plan a clinical trial with a valid, responsive and interpretable outcome.
Longitudinal & Clustered Data (EPID621)
This course in the Neuroimaging Biomarkers & Trials pathway introduces the principles of and methods for longitudinal and clustered data analysis with special emphasis on clinical, epidemiologic, and public health applications; marginal and conditional methods for continuous and binary outcomes; mixed effects and hierarchical models; and simulations for power calculations.
Advanced Biostatistical Methods for Multivariable Prediction (EPID625)
This course in the Neuroimaging Biomarkers & Trials pathway is an introduction to statistical methods that can be used to evaluate biomarker prognostic studies and multivariate prediction models. Topics will include biostatistical evaluation of biomarkers, predictive models based on various regression modeling strategies and classification trees, assessing the predictive ability of a model; and internal and external validation of models. Students will learn about the statistical methods that are required by current reporting guidelines for biomarker prognostic studies.
Network Neuroscience (BE566)
This course in the Computational Approaches in Neuroimaging pathway covers the use of network science in understanding large-scale neuroimaging and neuronal-level brain circuitry. The human brain produces complex functions using a range of system components over varying temporal and spatial scales. These components are couples together by heterogeneous interactions, forming an intricate information-processing network.
Principles of Deep Learning (ESE546)
This course in the Computational Approaches in Neuroimaging pathway discusses general principles of deep learning that cut across the key principles in the design of networks for modern algorithms. These include the development of insight into popular empirical practices with a focus on the training of deep networks, builds the theoretical skills to develop new ideas in deep learning and to deploy deep networks in real world applications.
Advanced Topics in Artificial Intelligence (CIS620)
This seminar course in the Computational Approaches in Neuroimaging pathway reviews how the foundations of artificial intelligence (AI) have shifted dramatically in the last decade, with probabilistic and statistical frameworks for classical problems resulting in new algorithms, analyses and applications. The course examines a sampling of models and methods in modern AI, including probabilistic reasoning (Bayesian networks and graphical models), machine learning and neural networks, and computational neuroscience.
Big Data Analytics (CIS545)
This course in the Computational Approaches in Neuroimaging pathway reviews the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks including basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification.
Introduction to Genetic Epidemiology (EPID575)
This course in the Disease Mechanisms pathway recognizes the increasing need for epidemiologists to understand the genetic basis of disease, read, and interpret genetic studies, and incorporate the collection and analysis of genetic information into studies of disease etiology. The objectives of this course are to provide an understanding of: 1) basic genetics, 2) the tools used by geneticists and genetic epidemiologists, and 3) the integration of genetic data into study designs.
Molecular Imaging (BE583)
This course in the Disease Mechanisms pathway provides a survey of modern imaging modalities, including PET, and covers concepts related to contrast media and targeted molecular imaging. Topics focus on the chemistry and mechanisms of contrast agents, approaches for identifying molecular markers of disease mechanisms, ligand screening strategies and toxicology/pharmacology related to development of imaging agents.
Molecular Genetics of Neurological Disease (BIBB466)
This course in the Disease Mechanisms pathway focuses on the molecular basis of neurological diseases including ADRDs, exploring in detail key papers that cover topics including defining the disease genes, development of animal models that provide mechanistic insight, and seminal findings that reveal molecular understanding. The course will provide a perspective from initial molecular determination through current status. Students will gain an understanding of how the molecular basis of a disease is discovered (from classical genetics to modern genomics) and how such diseases can be modeled in simple genetic systems for mechanistic insight.
Quantitative Imaging & Analysis for Biologists (CAMB709)
This course in the Disease Mechanisms pathway reviews how accurate and quantitative analysis of image data can reveal important details invisible to other techniques; these details can, in turn, provide mechanistic insights into previously inscrutable phenomena. This course will provide an introduction to the fundamentals of modern light microscopy and image analysis. Topics include image acquisition methods, image data handling, object identification and tracking, simple modeling and macro programming, and single-molecule techniques.
Trainees will also participate in a biweekly foundation journal club, as well as an additional pathway-specific seminar.
Foundation journal club: Neuroimaging & Neurodegeneration (required)
This biweekly meeting will provide a foundation journal club for all trainees. Established in 2019 and led by a junior preceptor, Dr. Sandhitsu Das, this meeting includes a presentation and critical discussion of a current paper related to neuroimaging of ADRD or the potential for application of methods from other disease approaches to improve our understanding of ADRD. The meeting is frequently attended by many of the program faculty and their trainees. Trainees of this program will be required to present a paper at least once per year.
Pathway-specific seminars (one required):
Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Seminar
This monthly seminar and journal club series will support the Neuroimaging Biomarkers & Trials pathway. It is comprised by a group of statisticians studying etiology and clinical practice through medical imaging including a focus on longitudinal analytic approaches and multivariate definitions of neuroimaging outcomes.
Center for Biomedical Image Computing & Analytics (CBICA) Seminar
This biweekly biomedical imaging seminar series will support the Computational Approaches in Neuroimaging pathway. It will feature internal and external scientists who are invited to present state-of-the-art methodologies and trends in computational image analysis, with emphasis on big data and precision medicine.
Penn Neurodegeneration Genomics Center (PNGC) Journal Club
The PNGC is an institutionally supported interdisciplinary research center that integrates faculty, including many of the program faculty and directed by Li-San Wang, and trainees with a broad interest in ADRD genomics research. The focus of this monthly journal club will support the Disease Mechanisms pathway. It will include discussions and critiques of recent papers that are relevant to translational applications including integration of genomics with neuroimaging and the use of genomics to improve understanding of ADRD mechanisms.
The Penn ADRC supports a biweekly “Data & Didactics” series in which lectures from ADRC Core leaders are presented to describe current ADRC research activities, as well as emerging topics and presentations on works in progress and recently completed research. With the support of the ADRC this didactics forum will provide an important biweekly forum to enrich the foundations and broad breadth of exposure for trainees to current issues and hot topics of ADRD research.
Trainees will participate in bimonthly RCR sessions in topics such as robust pipelines, spatiotemporal statistics, overfitting in ML/AI, navigating incidental findings, and many more. Schedule is forthcoming.
Trainees will be required to participate in the annual Penn Institute on Aging Retreat. Each year the retreat will include a special poster session dedicated to ADRD neuroimaging.
All participating predoctoral trainee candidates will be required to develop and submit a NRSA F31 proposal with their mentoring team. Similarly, all postdoctoral trainees will be required to produce a F32 or K-series career development proposal with their mentoring team to enhance interdisciplinary work. All trainees will develop a scientific and training plan with the support of their mentorship team near the end of their first training year. As part of the proposal development, trainees will also be required to participate in a mock study section of their proposal comprised of the other trainees in the program, several training faculty, and all of the MPI Leadership Team.
The training program will support the travel costs and registration for each trainee to attend one scientific meeting in person per year. Trainees will be encouraged to attend both large scientific meetings to provide exposure to the many cross-disciplinary research activities that are essential to ADRD translational neuroimaging and smaller scientific workshops to provide a highly focused small-group environment with increased one-on-one exposure to peers and scientists. Representative scientific meetings and workshops include: