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Novel ThickNet features for the discrimination of amnestic MCI subtypes.

Raamana PR, Wen W, Kochan NA, Brodaty H, Sachdev PS, Wang L, Beg MF - Neuroimage Clin (2014)

Bottom Line: Individuals with md-aMCI are found to exhibit higher risk of conversion to AD.The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively.Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, Canada.

ABSTRACT

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between healthy aging and Alzheimer's disease (AD), and consists of two subtypes: single-domain aMCI (sd-aMCI) and multi-domain aMCI (md-aMCI). Individuals with md-aMCI are found to exhibit higher risk of conversion to AD. Accurate discrimination among aMCI subtypes (sd- or md-aMCI) and controls could assist in predicting future decline.

Methods: We apply our novel thickness network (ThickNet) features to discriminate md-aMCI from healthy controls (NC). ThickNet features are extracted from the properties of a graph constructed from inter-regional co-variation of cortical thickness. We fuse these ThickNet features using multiple kernel learning to form a composite classifier. We apply the proposed ThickNet classifier to discriminate between md-aMCI and NC, sd-aMCI and NC and; and also between sd-aMCI and md-aMCI, using baseline T1 MR scans from the Sydney Memory and Ageing Study.

Results: ThickNet classifier achieved an area under curve (AUC) of 0.74, with 70% sensitivity and 69% specificity in discriminating md-aMCI from healthy controls. The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively.

Conclusions: The proposed ThickNet classifier demonstrated potential for discriminating md-aMCI from controls, and in discriminating sd-aMCI from md-aMCI, using cortical features from baseline MRI scan alone. Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone. This result may offer the possibility of early detection of Alzheimer's disease via improved discrimination of aMCI subtypes.

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Related in: MedlinePlus

Comparison of sensitivity, for different values of TNP and α, obtained from RHsT method. The combination with the best performance (highest AUC) in each experiment is highlighted with a black oval.
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f0035: Comparison of sensitivity, for different values of TNP and α, obtained from RHsT method. The combination with the best performance (highest AUC) in each experiment is highlighted with a black oval.


Novel ThickNet features for the discrimination of amnestic MCI subtypes.

Raamana PR, Wen W, Kochan NA, Brodaty H, Sachdev PS, Wang L, Beg MF - Neuroimage Clin (2014)

Comparison of sensitivity, for different values of TNP and α, obtained from RHsT method. The combination with the best performance (highest AUC) in each experiment is highlighted with a black oval.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215394&req=5

f0035: Comparison of sensitivity, for different values of TNP and α, obtained from RHsT method. The combination with the best performance (highest AUC) in each experiment is highlighted with a black oval.
Bottom Line: Individuals with md-aMCI are found to exhibit higher risk of conversion to AD.The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively.Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, Canada.

ABSTRACT

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between healthy aging and Alzheimer's disease (AD), and consists of two subtypes: single-domain aMCI (sd-aMCI) and multi-domain aMCI (md-aMCI). Individuals with md-aMCI are found to exhibit higher risk of conversion to AD. Accurate discrimination among aMCI subtypes (sd- or md-aMCI) and controls could assist in predicting future decline.

Methods: We apply our novel thickness network (ThickNet) features to discriminate md-aMCI from healthy controls (NC). ThickNet features are extracted from the properties of a graph constructed from inter-regional co-variation of cortical thickness. We fuse these ThickNet features using multiple kernel learning to form a composite classifier. We apply the proposed ThickNet classifier to discriminate between md-aMCI and NC, sd-aMCI and NC and; and also between sd-aMCI and md-aMCI, using baseline T1 MR scans from the Sydney Memory and Ageing Study.

Results: ThickNet classifier achieved an area under curve (AUC) of 0.74, with 70% sensitivity and 69% specificity in discriminating md-aMCI from healthy controls. The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively.

Conclusions: The proposed ThickNet classifier demonstrated potential for discriminating md-aMCI from controls, and in discriminating sd-aMCI from md-aMCI, using cortical features from baseline MRI scan alone. Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone. This result may offer the possibility of early detection of Alzheimer's disease via improved discrimination of aMCI subtypes.

Show MeSH
Related in: MedlinePlus