Computer-assisted imaging to assess brain structure in healthy and diseased brains
Abstract
Neuroanatomical structures may be profoundly or subtly affected by the interplay of genetic and environmental factors, age, and disease. Such effects are particularly true in healthy ageing individuals and in those who have neurodegenerative diseases. The ability to use imaging to identify structural brain changes associated with different neurodegenerative disease states would be useful for diagnosis and treatment. However, early in the progression of such diseases, neuroanatomical changes may be too mild, diffuse, or topologically complex to be detected by simple visual inspection or manually traced measurements of regions of interest. Computerised methods are being developed that can capture the extraordinary morphological variability of the human brain. These methods use mathematical models sensitive to subtle changes in the size, position, shape, and tissue characteristics of brain structures affected by neurodegenerative diseases. Neuroanatomical features can be compared within and between groups of individuals, taking into account age, sex, genetic background, and disease state, to assess the structural basis of normality and disease. In this review, we describe the strengths and limitations of algorithms of existing computer-assisted tools at the most advanced stage of development, together with available and foreseeable evidence of their usefulness at the clinical and research level.
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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 [27]
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References 76 · Cited by 371
- Authors
- Ashburner, J., Csernansk, J. G., Davatzikos, C., Fox, N. C., Frisoni, G. B., & Thompson, P. M.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Washington University in St. Louis (US)
- University of Pennsylvania (US)
- Centro San Giovanni di Dio Fatebenefratelli (IT)
- University of California, Los Angeles (US)
- Corresponding author
- Frisoni, Giovanni B
- Venue
- The Lancet Neurology
- Publisher
- Elsevier BV
- Published
- 2003-02
- Volume
- 2
- Issue
- 2
- Pages
- 79-88
- ISSN
- 1474-4422 (ISSN-L)
- OpenAlex
- W2152839913
- Cited by
- 371
- References
- 76
- Citations by year
- 2026: 3 · 2024: 6 · 2023: 7 · 2022: 9 · 2021: 11 · 2020: 11 · 2019: 9 · 2018: 3 · 2017: 6 · 2016: 13 · 2015: 12 · 2014: 16 · 2013: 20 · 2012: 20
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Neurology (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute on Aging (AG-93-07, R01 AG 14971, R01AG014971)
- National Institute of Mental Health (P20MH062130, MH62130, MH/AG 60883, P20 MH/DA52176)
- National Institute of Neurological Disorders and Stroke (NS38753, R01NS038753)
- U.S. National Library of Medicine (LM/MH05639)
- National Center for Research Resources (P41RR013642, P41 RR13642)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- neurodegenerative diseases, healthy aging, brain morphometry, automated brain segmentation, brain shape analysis, brain structural changes, disease diagnosis, genetic and environmental factors
- MeSH terms
- Algorithms, Brain, Brain Diseases, Brain Mapping, Cerebral Cortex, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Disease Progression, Neurodegenerative Diseases
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