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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
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Differences in topological progression profile among neurodegenerative diseases from imaging data
Sara Garbarino1,2, Marco Lorenzi2, Neil P Oxtoby1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Elife
|December 4, 2019
Summary
This study introduces "topological profiles" to understand how neurodegenerative diseases like Alzheimer's spread through the brain. These profiles, combining brain connectivity features, better explain disease progression than single measures.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Imaging
Background:
- Brain connectivity influences the spread of neurodegeneration.
- Existing methods analyze connectivity descriptors in isolation and focus on late-stage disease.
- Understanding disease propagation mechanisms is crucial for developing targeted treatments.
Purpose of the Study:
- To introduce the concept of a "topological profile" for characterizing disease propagation in the brain.
- To develop a method for estimating topological profiles from the full course of pathology accumulation.
- To compare the explanatory power of topological profiles against single connectivity descriptors for neurodegenerative diseases.
Main Methods:
- Utilized disease progression modeling to estimate topological profiles.
- Analyzed pathology accumulation data across the full disease course at cohort and individual levels.
- Compared topological profiles with single graph descriptors for Alzheimer's disease, multiple sclerosis, and normal aging.
Main Results:
- Topological profiles explained observed atrophy patterns better than individual connectivity descriptors.
- Individual topological profiles generally clustered around cohort-level profiles.
- Individuals with profiles closer to cohort-level profiles exhibited disease-specific features.
Conclusions:
- Topological profiles offer a more comprehensive approach to understanding brain connectivity's role in disease propagation.
- The findings provide new insights into the biological mechanisms driving pathology spread in Alzheimer's disease and multiple sclerosis.
- This framework can be applied to study other neurodegenerative conditions and normal brain aging.
Keywords:
ageingalzheimer's diseasecomputational biologyconnectivitydisease progression modellinghumanmultiple sclerosisneurodegenerationneurosciencesystems biologyMore Related Videos
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