Chris Rordens Neuropsychology Lab Documentation

Image to Inference (PSYC589/888)

Image to Inference (PSYC589/888)

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Details:

  • Course Title: “Neuroimaging: from image to inference”.

  • Instructor: Chris Rorden : Office 227 Discovery I (guest lecturers from John Absher, Roger Newman-Norlund, Alex Teghipco and Natalie Hetherington)

  • Course Code: (Undergrad, CRN 19184) PSYC 589(Grad, CRN 12488) PSYC 888,3 credits. In addition, scientists are free to audit this course. Suitable for faculty, post-docs, PhD students and advanced undergraduate students.

  • When: Fall 2022 1:15-2:30 T/Th, Aug 18-Dec 1

  • Where:Hamilton 238

  • Formal Syllabus

  • Course slides: Google Slides format

  • License: the slides and material for this course are distributed under the Creative Commons license . Further details are in the notes section of the PowerPoint file.

  • Textbook: Functional Magnetic Resonance Imaging by Huettel, Song, and McCarthy .

  • Supplemental textbook: Poldrack et al..

  • Description: Functional magnetic resonance imaging is a recent and powerful tool for inferring brain function. This technique identifies brain regions that are activated by different tasks – for example we can find the brain regions that activate when someone sees a familiar face. This course is designed to give students an understanding of the potential and limitations of this technique, and the ability to critically evaluate the inferences that can be drawn from fMRI. The course describes all stages of an fMRI study – from the design of the behavioral task (e.g. asking the participant to view faces), to the image processing (e.g. correcting images for head movements that occurred during scanning), through to statistical analysis (identifying brain regions that are activated by a task).

Lectures

  • Brain stimulation: Transcranial Magnetic Stimulation (TMS), Transcranial Direct Current Stimulation (tDCS). Roger Newman-Norlund and Chris Rorden

  • Graduate student presentations: Resting state analysis, effective and functional connectivity, independent components analysis, neural current MRI?

Assessment and Assignments

The final grade is weighted 30% quizzes, 40% on homework assignments and 30% on the essay. Letter grades assigned as follows A = 90-100%, B = 80-90%, C = 70-80%, D = 60-70%, F = <60%. Graduate students (PSYC888) must also present a research article as a class presentation. This presentation is scored as pass or fail that modifies the grade on the essay by x1.0 (pass) or x0.5 (fail), so that a perfect essay (100%) with a failed presentation (x0.5) yields a weighted score of 50%. Material from this article will be included in the quiz, so underdraduates will want to pay careful attention to this presentation. Homework description: Students will submit regular homework assignments, which are due at noon on their due date. Assignments are due in the students’ dropbox folder unless otherwise specified. Essay description: Students will write an essay that describes the merits, limitations and potential of a current or potential technique used to infer brain function. Essays should extend beyond the information in the course. Examples include: ERP vs fMRI, MEG, functional connectivity, Independent Component Analysis, Adaptation Designs.

Learning Outcomes

  • Understand the basic elements of neuroimaging.

  • Understand strengths and limitations of complementary tools used in cognitive neuroscience.

  • Ability to evaluate how contemporary methods can be used to understand cognitive functions.

  • Practice software for viewing, preprocessing and statistically analyzing brain imaging data.

  • Practice writing in the form of scientific report that relates behavioral and biomedical constructs.

Attendance

Attendance throughout class is required. By registering for this class you are confirming your availability during class. If you must miss a class, you should talk to the instructor ahead of time. For emergencies (flu, car trouble) it is strongly preferred that you send a text message to the instructor at the time of the class. Failure to meet the 10% rule described in the academic regulations will have homework assignment scores diminished by the proportion of the absences across the term (e.g. missing 15% of classes will mean your final score reflects 85% of your homework score).

Plagiarism

University policy regarding plagiarism, cheating and other forms of academic dishonesty is followed explicitly [See Carolina Community: Student Handbook and Policy Guide, Academic Responsibility]. Any case will be reported to the Dean of the College of Arts and Sciences. A “0” score will be given on a plagiarized assignment, and may result in an “F” for the course in extreme cases.

Disabilities

Students who have disabilities must have certification from the Office of Disability Services and must make clear during the first week of class what accommodations they expect. Students with disabilities must complete the same exams and assignments as other students in order to get course credit.

Calendar

This course follows the Fall 2022 academic calendar .

Schedule

The final grade is weighted 30% quizzes, 40% on homework assignments and 30% on the essay. Letter grades assigned as follows A = 90-100%, B = 80-90%, C = 70-80%, D = 60-70%, F = <60%. Graduate students (PSYC888) must also present a research article as a class presentation. This presentation is scored as pass or fail that modifies the grade on the essay by x1.0 (pass) or x0.5 (fail), so that a perfect essay (100%) with a failed presentation (x0.5) yields a weighted score of 50%. Material from this article will be included in the quiz, so underdraduates will want to pay careful attention to this presentation. Homework description: Students will submit regular homework assignments, which are due at noon on their due date. Assignments are due in the students’ dropbox folder unless otherwise specified. Essay description: Students will write an essay that describes the merits, limitations and potential of a current or potential technique used to infer brain function. Essays should extend beyond the information in the course. Examples include: ERP vs fMRI, MEG, functional connectivity, Independent Component Analysis, Adaptation Designs.