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Detection of preclinical neural dysfunction from functional connectivity graphs derived from task fMRI. An example
Yolanda Vives-Gilabert1, Ahmed Abdulkadir, Christoph P Kaller
1Freiburg Brain Imaging, University of Freiburg, Freiburg, Germany; Port d'Informació Científica (PIC), Campus UAB Edifici D, Bellaterra, Spain.
Psychiatry Research
|October 10, 2013
Summary
Functional brain changes detected by functional magnetic resonance imaging (fMRI) may precede neurodegeneration in Huntington
Area of Science:
- Neuroscience
- Medical Imaging
- Genetics
Background:
- Early identification of neurodegenerative diseases like Huntington's Disease (HD) is crucial for effective treatment before significant neuronal loss.
- Functional brain changes detected via functional magnetic resonance imaging (fMRI) are hypothesized to precede observable neurodegeneration.
Purpose of the Study:
- To investigate if functional connectivity patterns measured by fMRI can detect pre-symptomatic neurodegenerative changes in individuals with the Huntington's Disease genetic mutation.
- To evaluate the efficacy of pattern classification using graph-theory metrics on fMRI data for early HD detection.
Main Methods:
- Three independent cohorts of pre-symptomatic Huntington's Disease (HD) gene mutation carriers and matched controls underwent three fMRI tasks (motor, working memory, emotion induction).
- Functional connectivity was analyzed by correlating regional signals, and graph-theory measures (degree, clustering coefficient) were extracted.
- Pattern classifiers were used to discriminate between controls and HD gene carriers based on fMRI data.
Main Results:
- Classification accuracy using fMRI functional connectivity and graph-theory metrics did not outperform previous analyses based on general linear models or anatomical features.
- While within-subject fMRI data showed good stability, high between-subject variability resulted in chance-level classification accuracy.
- Standard graph metrics were insufficient for detecting subtle, disease-related changes in the presence of high inter-individual variability.
Conclusions:
- Functional magnetic resonance imaging (fMRI) and standard graph-theory metrics, in their current application, are insufficient for detecting pre-symptomatic neurodegenerative changes in Huntington's Disease due to high between-subject variability.
- Future research should focus on developing methods to reduce noise and variability in fMRI data to improve the detection of subtle, early disease-related brain changes.
