Computational Brain Mapping and Neuroimaging

Neuroscience 172, 272 & Psychology 213
Auditorium: NRB #116

Tue & Thu 10:00 – 11:15 AM


This course will introduce the students to the modern methods of brain mapping and neuroimaging research. The course instructors have a wide range of expertise and will cover the basics of the state-of-the-art techniques for image acquisition, data pre-processing, analysis and visualization. A variety of applications in brain research and computational neuroscience will be discussed.

Course Director

Arthur W. Toga, Ph.D.
Professor of Neurology

Ivo D. Dinov, Ph.D.
Assistant Professor Statistics

David Shattuck, Ph.D.
Assistant Professor Neurology

Susan Bookheimer, Ph.D.
Professor Psychiatry & Biobehavioral Sciences

Elizabeth Sowell, Ph.D.
Assistant Professor Neurology

George Bartzokis, M.D.
Professor Neurology

Instructors’ Offices
Laboratory of Neuro Imaging
Neuroscience Research Building
635 Charles Young Dr., Suite 225
UCLA, Los Angeles, CA 90095

Office Hours: By arrangement

Tentative Schedule of Examinations
Grading policy and basis for Final Grade

  • Projects: 3rd, 6th and 9th week of classes:
  • Research Paper: week 10 (graduate students only!)
  • Final Exam: week 11
  • Final Exam Schedule: Final Examination Code: 13 – Monday, March 20, 2006, 11:30am-2:30pm, regular classroom.

Project Assignment Policy

Textbook & Syllabus

Brain Mapping: The Methods

Arthur W. Toga & John Mazziotta, University of California, Los Angeles, U.S.A.

Class Syllabus


  1. Introduction to Neuroimaging and Brain Mapping Terminology
  2. Review of Neuroanatomy
  3. The Physics of Neuroimaging (invasive and non-invasive, structural vs. functional, digital data representation)
  4. The Normal Brain: The Developing Brain, Matured Brain, Aging Brain
  5. Diseases: Depression, Schizophrenia, Autism, Bipolar disorder, Neurodegeneration and dementia (AD), Epilepsy, Multiple Sclerosis, Methamphetamine, Fetal Alcohol Syndrome, Head Trauma, Tumors.
  6. Challenges in computational neuroscience and brain imaging
  7. Preprocessing methods in Neuroimaging (intensity modulation, spatial normalization, filtering)
  8. Volumetric and Surface modeling, representation and analysis
  9. Statistical methods in structural and functional neuroimaging
  10. The LONI Grid-Compute Pipeline Environment
  11. Imaging in Psychology and Psychiatry
  12. Behavior, Memory, Language and Cognition