Training Program

Trainee expectations

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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Mentored Research

Coursework

Journal Clubs/Seminars

Neurodegenerative Disease Grand Rounds

RCR/SRR

Annual Retreat

Professional Development

Scientific Meeting Attendance and Presentation

Summary of Requirements

Mentored Research

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.

Coursework

All trainees will complete 3 foundation courses, and have the option to participate in elective pathway-specific coursework.

Foundation courses (required):

Biomedical Image Analysis (CIS 5370)

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.

Current Alzheimer’s Disease and Related Dementia Concepts (NGG 5500)

This foundation course, co-taught by Drs. Corey McMillan and David Wolk, will provide an important foundation in the clinical and biological concepts and cutting-edge research topics on Alzheimer’s disease and related dementias (ADRD). The course will survey clinico-path correlations, biomarkers, genomics, basic science, and translational drug discovery. Each week there will be ~3 hour session that will include a one hour guest lecturer presentation on a designated topic (e.g., neuroimaging biomarkers) that will ideally cover some RCR/SRR content (e.g., biomarker disclosure, incidental findings). There will then be two student led journal article discussions with one reflecting foundational evidence (e.g. first PET amyloid paper) and another recent state-of-the-art evidence (e.g., amyloid PET evidence of anti-amyloid clearance).

Statistical Methods for Neuroimaging (BSTA 7510)

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.

Representative pathway-specific courses (elective):

Advanced Topics in Biostatistics (BSTA 7980)

This course provides an in-depth exploration of special topics within the field of biostatistics. The course covers a range of advanced statistical methods and their applications in various biostatistical domains, including clinical trials, causal inference, survival analysis, genetics and genomics, neuroimaging, and health informatics. The course emphasizes the unique aspects of these topics and their significance in biomedical research and public health. Throughout the course, ten faculty members will deliver presentations, each focusing on a special topic.

Statistical Methods for Risk Prediction and Precision Medicine (BSTA 7500)

This course will cover various topics for evaluating the performance of biomarkers to predict risk of clinical or disease outcomes, specifically including relative, absolute and competing risks for binary and time-to-disease outcomes; statistical inference for quantifying predictive accuracy with binary and time-to-event outcomes; statistical methods and inference for case-control study designs; Efficient study design issues for biomarker evaluation. This course is designed to help students 1) understand various concepts of risk in the medical literature; 2) understand various statistical methods for evaluating prediction performance of biomarkers and diagnostic tests and for designing efficient biomarker studies; 3) improve the ability to read critically papers published in statistical and medical journals on related topics; and 4) develop research ideas for risk prediction. Upon successfully completing this course, students will be able to: 1) Conduct statistical analysis for evaluating prediction performance of biomarkers and diagnostic tests; 2) Have a better ability to read and understand papers published in statistical and medical journals on related topics; and 3) Be well prepared to work on related topics for dissertation.

Introduction to Biomedical Data Science Research (STAT 9800)

This course offers a comprehensive introduction to biomedical data science research, tailored for graduate students from Statistics and various interdisciplinary domains. Aimed at facilitating end-to-end data science research capabilities, this course covers the development and application of computational methods and statistical techniques for analyzing voluminous datasets, particularly in biology, healthcare, and medicine. Students will gain insights into various data types prevalent in biomedical research, emerging large-scale data resources, and the art of formulating scientific questions. The course encompasses methodology research, scientific research, collaborative research, computing tools, software development, as well as scientific writing, including both research papers and grant proposals. By the end of the course, students will be equipped with the foundational skills and knowledge required to excel as statisticians and research scientists, whether they choose to pursue a career in industry or academia.

Disease Measurement (MTR 6030)

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 (EPID 6340)

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 in Epidemiologic Research (EPID 6210)

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 Models (EPID 6250)

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.

Networked Neuroscience (BE 5660)

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 (ESE 5460)

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 Machine Learning (CIS 6200)

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 (CIS 5450)

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 (EPID 5750)

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.

Physics of Medical/Molecular Imaging (BE 5830)

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 (NRSC 4266)

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.

Journal Clubs and Seminars

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 program faculty 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.

Neuroimaging of Neurodegenerative diseases Google group

Pathway-specific seminars (one required):

Penn FTDC/ADRC Human Studies Meeting

This monthly research meeting features presentations of ongoing research from members of the Penn FTD Center and ADRC. Attended by faculty, postdocs, and graduate students, this series focuses on research projects utilizing postmortem human tissue. The emphasis of this meeting is on works in progress rather than “finished” projects in order for trainees to gain valuable feedback to help guide the direction of their projects in real time.

Penn Center for Neurodegenerative Disease Research (CNDR) Journal Club

This weekly journal club series will support the Disease Mechanisms pathway. Organized by Dr. Edward Lee, CNDR Director, each meeting provides a journal club discussion centered on a review of a recent publication on basic (cellular and animal) or human studies research related to neurodegenerative disease. The multidisciplinary participants include trainees and faculty with expertise spanning molecular biology, genomics, neuropathology, and drug discovery.

Neurodegenerative Disease Grand Rounds

The Penn ADRC supports a biweekly Neurodegenerative Disease Grand Rounds series in which internal and external faculty present on their neurodegenerative disease research. This grand rounds series will provide an important biweekly forum to enrich the foundations and broad breadth of exposure for trainees to current issues and hot topics of neurodegenerative disease research. This series occurs every other Tuesday at 12pm.

Link to Neurodegenerative Disease Grand Rounds schedule

RCR/SRR

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.

Annual Retreat

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.

 Link to IOA retreat information page

Professional Development

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.

Scientific Meeting Attendance and Presentation

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:

Alzheimer’s Association International Conference (AAIC)

Organization of Human Brain Mapping (OHBM)

Alzheimer’s Imaging Consortium (AIC)

Summary of requirements