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Comparative Study
. 2014 Nov 30;224(2):81-8.
doi: 10.1016/j.pscychresns.2014.08.005. Epub 2014 Aug 17.

Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis

Affiliations
Comparative Study

Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis

Auriel A Willette et al. Psychiatry Res. .

Abstract

Identifying predictors of mild cognitive impairment (MCI) and Alzheimer's disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer's Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau(181p), and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau(181p). For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau(181p). ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.

Keywords: AD; Alzheimer׳s disease neuroimaging initiative; Data reduction; MCI.

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Figures

Fig. 1
Fig. 1. Stepwise-selected representative ICs
Stepwise discriminant analysis consistently selected 5 independent components (ICs) as the most significant GM covariance patterns among the different group classifications (e.g., CN vs. AD). The ICs were IC10 (“medial temporal lobe”), IC20 (“tempoparietal junction”), IC22 (“right anterior temporal lobe”), IC25 (“calcarine”), and IC27 (“posteromedial cortex”). Diagnostic labels for each group classifier are listed with a given IC for orientation purposes. AD = Alzheimer's Disease; CN = cognitively normal; IC = Independent Component; L = Left; MCI = Mild Cognitive Impairment. Brains are oriented in neurological space.

References

    1. Aksu Y, Miller DJ, Kesidis G, Bigler DC, Yang QX. An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients. PloS One. 2011;6:e25074. - PMC - PubMed
    1. Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease. Cerebral Cortex. 1991;1:103–116. - PubMed
    1. Ashburner J. A fast diffeomorphic image registration algorithm. Neurolmage. 2007;38:95–113. - PubMed
    1. Ashburner J, Friston KJ. Unified segmentation. Neurolmage. 2005;26:839–851. - PubMed
    1. Bakkour A, Morris JC, Dickerson BC. The cortical signature of prodromal AD: regional thinning predicts mild AD dementia. Neurology. 2009;72:1048–1055. - PMC - PubMed

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