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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Connectomics: a new paradigm for understanding brain disease.
Alex Fornito1, Edward T Bullmore2
1Monash Clinical and Imaging Neuroscience, School of Psychology and Psychiatry & Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton 3168, Victoria, Australia.
Brain disorder research now focuses on interconnected neural systems, not just specific regions. Imaging connectomics, using magnetic resonance imaging (MRI), offers new insights into diseases like Alzheimer's and schizophrenia.
Area of Science:
- Neuroscience
- Systems Neuroscience
- Neuroimaging
Background:
- Pathophysiological models of brain disorders are shifting focus from localized pathology to interconnected neural systems.
- Advances in connectomics enable comprehensive mapping of neural connections.
- Magnetic resonance imaging (MRI) is crucial for in vivo assessment of brain network connectivity.
Purpose of the Study:
- To introduce basic concepts of connectomics.
- To review how imaging connectomics provides new insights into brain diseases.
- To focus on Alzheimer's disease and schizophrenia.
Main Methods:
- Review of recent developments in imaging connectomics.
- Analysis of circuit-level, connectome-wide, and topological changes.
- Utilizing magnetic resonance imaging (MRI) for in vivo assessment.
Main Results:
- Imaging connectomics reveals new insights into brain diseases.
- Research highlights the role of network disturbances in aetiopathology.
- Development of new biomarkers for clinical translation is stimulated.
Conclusions:
- Conceptualizing brain disease as a complex system disturbance is advantageous.
- Connectomics offers a paradigm shift in understanding brain disorders.
- This approach moves beyond discrete pathology in isolated brain regions.
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