SLEIC faculty and staff are deeply committed to education and training activities. Formal undergraduate and graduate courses that touch upon neuroimaging methods and related concepts are offered in several programs and departments including Psychology, Biomedical Engineering, and Human Development and Family Studies.
SLEIC also routinely offers training opportunities that provide an overview of software, hardware, and conceptual issues related to our neuroimaging methods.
Neuroimaging and Neuroimaging-Related Courses
Bioengineering 507: Biophysics and Neurophysiology of Functional Neuroimaging
Instructor: Nanyin Zhang, Ph.D.
Description: The objective of this course is to provide students with the background, theory and techniques of functional magnetic resonance imaging (fMRI). Topics will include the physics of MRI, the physiological basis of the fMRI signal, and then other neuroimaging techniques.
Psychology 511: Foundations of Cognitive and Affective Neuroscience
Instructor: Rick Gilmore, Ph.D.
Description: This course examines the basics of Human Behavioral Neuroscience including evolution of the nervous system, emotionality, fear and stress, face recognition and perception, social processes, cognitive process, and movement.
Psychology 511: Social, Cognitive, Affective Neuroscience (SCAN) methods
Instructor: Frank Hillary, Ph.D.
Description: The purpose of this course is to introduce you to BOLD fMRI methods. The first half of the course will emphasize basic MRI principles and theory and the second half with focus on “hands-on” data analysis. Students are encouraged to draw from their research areas of interest.
PSY 511.02: Transparent, Open, and Reproducible Research Practices in the Social and Behavioral Sciences
Instructor: Rick Gilmore, Ph.D.
Description: Is there a crisis of reproducibility in psychological science? What does it mean to ask the question? What are transparent, open, and reproducible research practices? Should one implement them? How? This course will seek answers to these questions.
Students will read about recent failures in research transparency and reproducibility and discuss the ethics of open practices in scientific research. Through a series of guided exercises students will learn how to use new research tools (e.g. RStudio, Jupyter/iPython, the Open Science Framework, GitHub, command line scripting) that make it easier to implement open and reproducible research practices. At the end of the course, students will be capable of implementing one or more new research practices into their own workflows. Evaluation will be based on in-class participation, short papers, and project assignments. No prior software development experience is required, but a willingness to learn new skills is essential.
Please email Rick Gilmore at (firstname.lastname@example.org) if you are interested in registering for the course.
Psychology 525: Cognitive Area Graduate Seminar
Advanced Neuroimaging Analyses
Instructor: Suzy Scherf, Ph.D.
Description: This course is designed to help you understand the range of data analysis options that are currently being used to analyze fMRI, DTI, and sMRI data. You should already have a basic understanding of sMRI and fMRI data and preprocessing strategies of such data before taking this class. The central goal of the course is that you learn how these varied analysis strategies allow you to address particular hypotheses about neuroimaging data.
Psychology 525: Cognitive Neuroscience of Learning and Memory: with a Focus on Aging
Instructor: Nancy Dennis, Ph.D., offered roughly every other year
Psychology 525.1 Cognitive Psychology Seminar
MATLAB For Behavioral Scientists
Instructor: Brad Wyble
Description: This seminar is for students who have little or no background in computer programming who wish to avail themselves of the power of computer programming. Once you learn how to program, you can analyze and view data however you want, you can build and run your own experiments, and you can build and run your own computational models. Learning how to program can make you a clearer thinker. Nothing focuses the mind like writing a computer program, and nothing is quite so humbling as generating code you're sure is right but which the computer tells you (in its own inimitable way) makes no sense. No less daunting is checking whether a program that runs perfectly well is actually producing sensible results.
The seminar will focus on MATLAB because it is a user-friendly language with a large, interactive community of users. All public computers at Penn State have MATLAB. MATLAB is an ideal language for your first foray into programming because it lets you create graphs and other visuals relatively easily. This in turn can help you learn or be reminded of mathematical concepts that may be useful to you.
The seminar will be run in a way that lets you progress as far as you can given your own needs and interests. Your final project aim will be a computational model of your own design or an experiment or data-analysis engine that you can use in your own ongoing research.
ESC 555 Neuroscience Data Analysis
Modern methods for the analysis of neural data
Instructor: Patrick Drew, Ph.D.
Prerequisite: BIOL 469 or equivalent
Modern neuroscience experimental methods can generate enormous amounts of complicated data, and a wealth of techniques has sprung up drawing from a wide variety of fields to analyze it. In this course, students will learn how to utilize a toolbox of mathematical and computational techniques to analyze electrophysiological, optical, and anatomical data. This course will cover the biophysical origin and measurement of brain signals, as well as the theoretical background of modern analysis methods and their practical implementation. Topics covered include: spectral methods, neural encoding and decoding, information theory, spike sorting, dimensionality reduction, bootstrap resampling methods and image processing.
Grading: 70% problem sets, 30% final project
fMRI Data Analysis
Instructor: Peter C.M. Molenaar, Ph.D.
Purpose of the Course: To build up a thorough understanding of statistical models used in connectivity mapping of multivariate fMRI time series and coherency analysis of EEG/MEG time series. To independently apply these models to real data.
Course Outline: fMRI time series analysis and connectivity mapping:
- Concise overview physical principles of MRI and EEG/MEG;
- Introduction multivariate time series analysis;
- Overview general linear model (GLM);
- Structural equation modeling of functional connectivity maps (SEM);
- Vector autoregressive modeling of effective connectivity maps (VAR) and Granger causality testing;
- Unified SEM modeling of effective connectivity maps (u-SEM, i.e., a combination of SEM and VAR) with automatic search based on Lagrange multiplier testing;
- Extension of u-SEM for event-related designs with external input (eu-SEM) with automatic search based on Lagrange multiplier testing;
- State space modeling (SSM), a combination of dimension reduction and eu-SEM;
- Alternative ways of pooling across heterogeneous subjects;
- Modeling time-varying maps by means of sliding window techniques and time-varying SSMs;
- Introduction Fourier analysis;
- Introduction EEG/MEG coherency analysis.
In each session of the course, take home exercises will be given, involving data to which new techniques discussed in class have to be applied. The solutions have to be submitted before the next class. My solutions then will be sent around and will be discussed at the beginning of the next class.
BIOE 597: Computational Modeling and Statistics for Bioengineering
Instructor: Xiao Liu, Ph.D.
Pre-requisite: Senior undergraduate, graduate, or instructor’s permission
Description: An essential mission of biomedical engineering is to distill biological data into meaningful mathematical and statistical representations using computational tools. This course covers statistical methods and computational models needed for biomedical engineering research, particularly those involving multivariate data. Students are expected to acquire a variety of skills for modeling and analyzing data arising from typical biomedical engineering research at the molecular, cellular, and system levels, and then to apply the knowledge toward discovery of bioengineering principles from experimental observations and big data.
HDFS 597 Developmental Neuroscience of Adolescence
Instructor: Chuck Geier, Ph.D.
Description: The study of adolescence necessitates cross-disciplinary inquiry using multiple methodologies as change occurs across numerous domains and timescales. Notably, considerable change occurs in an individual’s brain (neural) function, their behavior, and their social environments. Importantly, adolescence is also a time of heightened vulnerability to psychosocial disorders (e.g., risky behaviors, substance abuse, etc.). In this course, students will evaluate a mix of foundational and cutting-edge research investigating various changes of adolescence, principally from a developmental cognitive neuroscience perspective. Particular emphasis will be placed on understanding non-invasive neuroimaging techniques (e.g., functional magnetic resonance imaging) and the critical role these tools have played in our understanding of human development.
Psychology 452: Learning and Memory
Instructor: Nancy Dennis, Ph.D.
This course will provide an in-depth study into the area of Learning and Memory. Discussion topics will include focus on such topics as: conditioning, implicit learning, working memory; episodic memory; eyewitness testimony; false memory; autobiographical memory and semantic memory (see specific course content below). The course will conclude with an examination of individual differences in memory including memory in childhood and advanced aging, Alzheimer’s disease and amnesia. Reading will mainly come from the course text, “Memory” by Baddeley, Eysenck, & Anderson and will be supplemented, when appropriate, by readings from both behavioral and neuroimaging literature, as well as the study of animal models and patient populations. As a cognitive neuroscientist, I plan to provide you with a solid background into the cognitive processes underlying the topics outlines below while also providing you with an introduction to the neural substrates mediating these processes.
BBH 203: Neurological Bases of Human Behavior: An introduction to biopsychology, emphasizing the structure and function of the human brain
Instructor: Potter, Lindsey Nichole Casher
Description: The nervous system provides the biological underpinning of behavior, and several scientific fields are concerned with the relationship between the nervous system and behavior. The goal of this course is to introduce the principle methods, findings, and theories of these scientific fields. Topics include (a) the anatomy and physiology of the nervous system, (b) how the nervous system gives rise to perception, action, language, memory, emotion and reproductive behavior, and (c) how drugs and mental illnesses affect the nervous system and alter normalperceptual, cognitive, and emotional behavior. The course prepares students for a number of more advanced courses in Psychology and Biobehavioral Health that address specialized topics in neuroscience, and may satisfy a requirement of these majors.
Psychology 497: Cognitive Neuroscience
Instructor: Michele Diaz, Ph.D.
Description: Cognitive Neuroscience strives to understand the brain bases of cognition. In this course we will explore the cognitive and neural processes that support major components of cognition such as attention, vision, language, motor control, navigation, and memory. The course will introduce students to basic neuroanatomy, functional imaging techniques including electrophysiology and functional Magnetic Resonance Imaging (fMRI), as well as behavioral measures of cognition. We will discuss the experimental techniques and the ways in which inferences about the brain bases of cognition are made. We will consider evidence from healthy adults, as well as from patients with neurological diseases such as Alzheimer's disease, Parkinson's disease, Huntington's disease, amnesia, and stroke. Course materials will include a textbook and some primary source documents, such as journal articles. Methods of evaluation will include exams (60%), thought papers (25%), quizzes (7.5%), and class participation (7.5%).