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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease
Samaneh Kazemifar1,2, Kathryn Y Manning1,2, Nagalingam Rajakumar3
1Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
Abstract:
Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from 18F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from 18F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β1-42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD.
Insights
Researchers developed a new resting-state functional MRI (RS-fMRI) metric to measure neuronal activity. This novel indicator effectively differentiates Alzheimer's disease (AD) patients from healthy controls, showing potential for early diagnosis.
Area of Science:
- Neuroimaging
- Biomedical Engineering
- Neurology
Background:
- Alzheimer's disease (AD) diagnosis relies on identifying altered brain activity, often assessed via functional MRI (fMRI) and PET scans.
- Existing methods like task-based fMRI, resting-state fMRI (RS-fMRI), and FDG-PET reveal abnormalities in AD patients.
- A need exists for novel, accessible indicators of neuronal function in AD research.
Purpose of the Study:
- To introduce and validate a new neuronal activity indicator derived from RS-fMRI textural features.
- To investigate the relationship between this novel RS-fMRI metric and glucose metabolism measured by FDG-PET.
- To assess the potential of the RS-fMRI metric in distinguishing between individuals with AD and healthy elderly controls (NEC).
Main Methods:
- Utilized resting-state functional MRI (RS-fMRI) data from 15 probable AD patients and 15 NEC from the AD Neuroimaging Initiative.
- Applied independent component analysis (ICA) to RS-fMRI data to identify neuronal components (NC).
- Quantified regional brain activity using a novel metric based on the variation of the RS-fMRI signal within identified NCs.
- Measured standardized uptake values relative to the cerebellum (SUVR) from partial volume corrected 18F fluorodeoxyglucose-PET (FDG-PET) images.
Main Results:
- The novel RS-fMRI brain activity metric was significantly lower in the accumbens of AD patients compared to NEC.
- Glucose metabolism (SUVR) was significantly reduced in the amygdala and hippocampus of AD patients.
- A significant positive linear correlation was observed between the RS-fMRI brain activity metric and glucose SUVR measurements.
- The RS-fMRI activity metric showed preliminary positive correlations with cognitive measures and CSF amyloid β1-42 levels, though not significant after correction.
Conclusions:
- The novel RS-fMRI derived brain activity metric is a viable indicator of neuronal function.
- This metric demonstrates the ability to differentiate between individuals with Alzheimer's disease and healthy elderly controls.
- The findings support the potential of this easy-to-implement RS-fMRI metric as a diagnostic tool in AD research.
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