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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Structural connectomics in brain diseases.

Alessandra Griffa¹, Philipp S Baumann, Jean-Philippe Thiran

¹Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. alessandra.griffa@epfl.ch

|April 30, 2013

Summary

This summary is machine-generated.

This review explores in vivo connectome imaging using diffusion MRI and network theory. It assesses technical methods and reviews findings for diseases like dementia and schizophrenia.

Keywords:

Connectome DTI Diffusion MRI Graph theory Neurological disorders Psychiatric disorders Structural connectivity

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Area of Science:

Neuroimaging
Network Neuroscience
Computational Biology

Background:

In vivo connectome imaging integrates diffusion MRI, image processing, and network theory.
This approach bridges the gap between brain structure, behavior, and cognition.
Recent technical advancements enable exploring disease mechanisms.

Purpose of the Study:

To critically review technical solutions for clinical connectome studies.
To analyze the advantages and limitations of MRI acquisition, network building, and statistical analysis.
To review neuroimaging findings in dementia, schizophrenia, and multiple sclerosis.

Main Methods:

Review of technical literature for MRI acquisition, network construction, and statistical analysis.
Systematic literature review of connectome studies in selected neurological and psychiatric diseases.
Comparative analysis of findings across different disease cohorts.

Main Results:

Technical solutions for in vivo connectome imaging present both advantages and limitations.
Common and distinct neuroimaging biomarkers are identified across dementia, schizophrenia, and multiple sclerosis.
The review highlights the potential and challenges of applying connectome analysis to clinical populations.

Conclusions:

In vivo connectome imaging is a rapidly advancing field with significant clinical potential.
Understanding technical limitations is crucial for reliable disease mechanism exploration.
Connectome analysis offers insights into the neural underpinnings of various neurological and psychiatric disorders.