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Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI.

Rajpoot K, Riaz A, Majeed W, Rajpoot N - PLoS ONE (2015)

Bottom Line: The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data.Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers.The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science & Information Technology, King Faisal University, Al Ahsa, Kingdom of Saudi Arabia; School of Electrical Engineering and Computer Science, National University of Sciences & Technology, Islamabad, Pakistan.

ABSTRACT
The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.

No MeSH data available.


Related in: MedlinePlus

Brain region functional network: visualization of the correlation matrix [8] and community matrix obtained using (5).The difference between healthy and epileptic subjects is not prominent in the correlation matrix while it is prominent in community matrix (highlighted by boxes). This figure is suitable for visualization in color display.
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pone.0134944.g002: Brain region functional network: visualization of the correlation matrix [8] and community matrix obtained using (5).The difference between healthy and epileptic subjects is not prominent in the correlation matrix while it is prominent in community matrix (highlighted by boxes). This figure is suitable for visualization in color display.

Mentions: The community matrix K represents functional brain network in a sparse manner which may not be possible with the typical correlation matrix. We can observe from Fig 2 that the connectivity differences between normal and epileptic subjects may be difficult to identify from dense data in the correlation matrix while the differences may be identified relatively easily from the sparse data in the community matrix.


Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI.

Rajpoot K, Riaz A, Majeed W, Rajpoot N - PLoS ONE (2015)

Brain region functional network: visualization of the correlation matrix [8] and community matrix obtained using (5).The difference between healthy and epileptic subjects is not prominent in the correlation matrix while it is prominent in community matrix (highlighted by boxes). This figure is suitable for visualization in color display.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4529140&req=5

pone.0134944.g002: Brain region functional network: visualization of the correlation matrix [8] and community matrix obtained using (5).The difference between healthy and epileptic subjects is not prominent in the correlation matrix while it is prominent in community matrix (highlighted by boxes). This figure is suitable for visualization in color display.
Mentions: The community matrix K represents functional brain network in a sparse manner which may not be possible with the typical correlation matrix. We can observe from Fig 2 that the connectivity differences between normal and epileptic subjects may be difficult to identify from dense data in the correlation matrix while the differences may be identified relatively easily from the sparse data in the community matrix.

Bottom Line: The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data.Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers.The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science & Information Technology, King Faisal University, Al Ahsa, Kingdom of Saudi Arabia; School of Electrical Engineering and Computer Science, National University of Sciences & Technology, Islamabad, Pakistan.

ABSTRACT
The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.

No MeSH data available.


Related in: MedlinePlus