Bioinformatics and Brain Imaging: Recent Advances and Neuroscience Applications

Paul Thompson
Assistant Professor of Neurology
UCLA School of Medicine and Laboratory of Neuro Imaging

[Tutorial Course Notes: .pdf (2.5MB) - with Figures]

Tutorial as part of a Short Course in Bioinformatics
(Organizer: Robert W. Williams, Center of Genomics and Bioinformatics, University of Tennessee, Memphis)

Society for Neuroscience Meeting, Orlando, FL, November 2-7, 2002.


Medical imaging and brain research are among the most challenging and fascinating topics of contemporary science. Exciting possibilities exist to improve both the state of the art in medicine and our understanding of the human brain. These opportunities have motivated many researchers to use powerful computational methods to analyze images of brain structure and function, applying them to key questions in medicine and neuroscience. Algorithms can now uncover disease-specific patterns of brain structure and function in whole populations. These tools now chart how the brain grows in childhood, detect abnormalities in disease, and visualize how genes, medication, and demographic factors affect the brain. Image analysis methods can also identify and monitor systematic patterns of altered anatomy in diseases such as Alzheimer's, tumor growth, epilepsy, and multiple sclerosis, and psychiatric disorders such as schizophrenia, autism, and dyslexia.

We briefly review recent developments in brain image analysis, focusing on (1) some of the main concepts and tools used when analyzing brain images, and (2) how to apply these tools to study key neuroscience questions relating to disease, development, and genetic influences on the brain. We describe a range of new tools that compare, contrast, and average imaging data in large human populations. We describe our construction of statistical brain atlases that store detailed information on how the brain varies across age and gender, across time, in health and disease, and over time. We also discuss some common mathematical methods that are currently being used to analyze variations in brain organization, cortical patterning, asymmetry and tissue distribution in several collaborative studies of brain development and disease (N=1000 scans). These studies typically use mathematics based on random field theory, partial differential equations (PDEs), differential geometry and image processing to encode anatomic variations in a population-based brain image database. This reference information to can be used to detect and visualize disease-specific abnormalities in Alzheimer's disease and schizophrenia, including dynamic changes and medication response over time. We use illustrative examples to show how population patterns of cortical organization, asymmetry, and disease-specific trends can be resolved that are not apparent in individual brain images. Specialized approaches are used to identify generic features of brain organization. These encode cortical pattern variations that complicate comparisons of brain data from one individual to another. We also discuss four-dimensional (4D) maps and atlases that store probabilistic information on the dynamics of brain change in development and disease. Digital atlases that generate these maps show considerable promise in identifying general patterns of structural and functional change in diseased populations, and linking them to cognitive, clinical and therapeutic parameters. Finally, we describe a framework to map how genes affect brain structure. The resulting genetic brain maps can be used in data mining applications, to help investigate inheritance patterns in diseases with known genetic risks.

For more information please visit:
where PDFs of tutorial papers and chapters are available.


[1]. Thompson PM, Giedd JN, Woods RP, MacDonald D, Evans AC, Toga AW (2000). Growth Patterns in the Developing Human Brain Detected Using Continuum-Mechanical Tensor Mapping, Nature 404(6774):190-193, March 9, 2000.
[2]. Thompson PM, Cannon TD, Narr KL, van Erp TGM, Poutanen VP, Huttunen M, Lönnqvist J, Standertskjöld-Nordenstam CG, Kaprio J, Khaledy M, Dail R, Zoumalan CI, Toga AW (2001). Genetic Influences on Brain Structure, Nature Neuroscience 4(12), November 5, 2001.
[3]. Thompson PM, Vidal CN, Giedd JN, Gochman P, Blumenthal J, Nicolson R, Toga AW, Rapoport JL (2001). Mapping Adolescent Brain Change Reveals Dynamic Wave of Accelerated Gray Matter Loss in Very Early-Onset Schizophrenia, Proceedings of the National Academy of Sciences of the USA 98(20):11650-11655, Sept. 25, 2001.
[4]. Thompson PM, Mega MS, Narr KL, Sowell ER, Blanton RE, Toga AW (2000). Brain Image Analysis and Atlas Construction, Tutorial Book Chapter in: SPIE Handbook of Medical Image Processing and Analysis, Fitzpatrick M, Sonka M [eds.], SPIE Press.

Fig. 1. Creating Brain Maps and Anatomical Models. An image analysis pipeline (Thompson et al., 2001) is shown here, which can reveal how brain structure varies in large populations, differs in disease, and is modulated by risk genes or medications. This approach aligns new 3D MRI scans from patients and controls (1) with an average brain template based on a population (here the ICBM template is used, developed by the International Consortium for Brain Mapping). Tissue classification algorithms then generate maps of gray matter, white matter and CSF (2). To help compare cortical features from subjects with different anatomy, individual gyral patterns are flattened (3) and warped to match a group average gyral pattern (4). If a color code indexing 3D cortical locations is flowed along with the same deformation field (5), a crisp group average model of the cortex can be made (6), relative to which individual gyral pattern differences (7), group variability (8) and cortical asymmetry (9) are computed. Once individual gyral patterns are aligned to the mean template, differences in gray matter distribution or thickness (10) are mapped, pooling data from homologous regions of cortex. Correlations can be mapped between disease-related deficits and genetic risk factors (11). Maps may also be generated visualizing linkages between deficits and clinical symptoms, cognitive scores, and medication effects, or risk genes.

Paul Thompson is an Assistant Professor of Neurology at the UCLA School of Medicine. His research focuses on the neuroscience, mathematics, software engineering and clinical aspects of neuroimaging and brain mapping. Dr. Thompson obtained his M.A. in Mathematics from Oxford University, England, and his Ph.D. in Neuroscience from UCLA. After research as a Fulbright Scholar and Howard Hughes Investigator at UCLA, Dr. Thompson has served on Technical Evaluation and Review Committees for the NIH, the NLM, and the Small Business Innovation Research (SBIR) Program, as well as grant review committees for the NICHD, NCRR and the Alzheimer's Disease Association. Recent awards include the SPIE Medical Imaging Award (1997), the Di Chiro Outstanding Scientific Paper Award (1998), and the 1998 Eiduson Neuroscience Award. Dr. Thompson serves on the scientific committees for a range of national IEEE and SPIE Medical Imaging meetings, and on the Editorial Board of the journal Medical Image Analysis. Working closely with his colleagues and students at UCLA for the last 8 years, Dr. Thompson's collaborative publications describe novel mathematical and computational strategies for mapping brain structure and function in health and disease, for mapping growth patterns in development, and for creating disease-specific atlases of the human brain. Some of his image analysis algorithms provide new methods to uncover disease-related patterns of brain structure and function, as well as isolating genetic effects on brain structure. His recent techniques for brain image analysis have been used in over 20 national and international collaborations, with research teams in pharmaceutical companies and other universities, using the image analysis methods to investigate a variety of diseases. Recent work has focused on mapping dynamic (4D) processes during brain development and in Alzheimer's Disease, mapping medication response and disease progression in schizophrenia and neuro-oncology, and creating population-based digital brain atlases that track therapeutic response in an individual or patient group. For more information, please see:


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    Paul Thompson, Ph.D.
    Assistant Professor of Neurology
    4238 Reed Neurology
    UCLA School of Medicine
    710 Westwood Plaza
    Westwood, Los Angeles CA 90095-1769, USA.

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