Detecting Sisease-Specific Patterns of Brain Structure Using Cortical Pattern Matching and a Population-Based Probabilistic Brain Atlas
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The rapid creation of comprehensive brain image databases mandates the development of mathematical algorithms to uncover disease specific patterns of brain structure and function in human populations. We describe our construction of probabilistic atlases that store detailed information on how the brain varies across age and gender, across time, in health and disease, and in large human populations. Specifically, we introduce a mathematical framework based on covariant partial differential equations (PDEs), pull-backs of mappings under harmonic flows, and high-dimensional random tensor fields to encode variations in cortical patterning, asymmetry and tissue distribution in a population-based brain image database (N =94 scans). We use this information to detect disease-specific abnormalities in Alzheimer's disease and schizophrenia, including dynamic changes over time. Illustrative examples are chosen to show how group patterns of cortical organization, asymmetry, and disease-specific trends can be resolved that are not apparent in individual brain images. Finally, we create four-dimensional (4D) maps 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 variation in diseased populations, and revealing how these features depend on demographic, genetic, clinical and therapeutic parameters.
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Cortical pattern matching was used to better localize cortical differences over time and increase the power to detect systematic changes.
“An image-analysis technique known as cortical pattern matching (25–27) was used to better localize cortical differences over time and increase the power to detect systematic changes (25).”
From Dynamic mapping of human cortical development during childhood through early adulthood (2004) · cites this paper as [26]
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The laboratory of neuro imaging: what it is, why it is, and how it came to be
Toga, A. W. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806432 · Cited by 6 · see citing works
Self-citationComputational biology for visualization of brain structure
Toga, A. W. (2005) · Anatomy and Embryology · doi:10.1007/s00429-005-0040-6 · Cited by 6 · see citing works
Self-citationIntroduction to Cartography of the Brain
Toga, A. W., & Mazziotta, J. C. (2002) · Elsevier eBooks · doi:10.1016/b978-012693019-1/50003-4 · Cited by 4 · see citing works
Self-citationDynamic Mapping of Alzheimer’s Disease
Thompson, P. M., Hayashi, K. M., de Zubicaray, G. I., Janke, A. L., Sowell, E. R., Rose, S., Semple, J. M., Herman, D. H., Hong, M., Dittmer, S. S., et al. (2004) · Research and perspectives in Alzheimer's disease · doi:10.1007/978-3-642-59300-0_8 · Cited by 4 · see citing works
Self-citationNeuroimaging Alzheimer's disease
Toga, A. W., Mega, M. S., & Thompson, P. M. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511526886.009 · Cited by 2 · see citing works
Self-citationMega, M. S., Dinov, I. I., Mazziotta, J. C., Manese, M., Thompson, P. M., Lindshield, C., Moussai, J., Tran, N., Olsen, K., Felix, J., et al. (2004) · Neurobiology of Aging · doi:10.1016/s0197-4580(04)80908-4 · Cited by 2 · see citing works
Self-citationStudy Of Neurobiological Images Based On Ontologies Used In Super-Resolution Algorithms
Pintilie, A.-M., & Boiangiu, C.-A. (2013) · RePEc: Research Papers in Economics · Cited by 1 · see citing works
Precise spatial normalization through novel neonatal brain MRI and CT atlas templates
Dastjerdi, O. M., Moghaddam, H. A., Wallois, F., & Grebe, R. (2014) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2014.6993454 · Cited by 1 · see citing works
Novel Multimodal Atlas Template for Spatial Normalization of Whole-Brain Images of Newborns
Dastjerdi, O. M., Moghaddam, H. A., Wallois, F., Grebe, R., & Ghadimi, S. (2016) · IRBM · doi:10.1016/j.irbm.2016.06.001 · Cited by 1 · see citing works
Simultaneous surface and volumetric registration using harmonic maps
Joshi, A. A., Shattuck, D., Thompson, P., & Leahy, R. (2007) · Electronic imaging · doi:10.1117/12.718137 · Cited by 2 · see citing works
Self-citationJoshi, A. A. (2018) · Neuromethods · doi:10.1007/978-1-4939-7647-8_1
- Authors
- Thompson, P. M., Mega, M. S., Vidal, C., Rapoport, J. L., & Toga, A. W.
- Affiliations
- University of California, Los Angeles (US)
- National Institute of Mental Health (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2001
- Volume
- 2082
- Pages
- 488-501
- ISSN
- 0302-9743 (ISSN-L)
- PMC
- PMC3016014
- OpenAlex
- W1479679132
- Cited by
- 67
- References
- 35
- Citations by year
- 2021: 1 · 2020: 1 · 2018: 1 · 2017: 1 · 2016: 3 · 2015: 1 · 2014: 1 · 2013: 4
- FWCI
- 11.56
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (P01MH052176)
- National Institute of Neurological Disorders and Stroke (R01NS038753)
- U.S. National Library of Medicine (R01LM005639)
- National Center for Research Resources (P41RR013642)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- probabilistic brain atlas, harmonic maps, cortical asymmetry, cortical organization, Alzheimer's disease, schizophrenia, brain development
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