Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) have provided promising results in the diagnosis of Alzheimer's disease (AD), though the utility of integrating sMRI with rs-fMRI has not been explored thoroughly. We investigated the performances of rs-fMRI and sMRI in single modality and multi-modality approaches for classifying patients with mild cognitive impairment (MCI) who progress to probable AD-MCI converter (MCI-C) from those with MCI who do not progress to probable AD-MCI non-converter (MCI-NC). The cortical and subcortical measurements, e.g. cortical thickness, extracted from sMRI and graph measures extracted from rs-fMRI functional connectivity were used as features in our algorithm. We trained and tested a support vector machine to classify MCI-C from MCI-NC using rs-fMRI and sMRI features. Our algorithm for classifying MCI-C and MCI-NC utilized a small number of optimal features and achieved accuracies of 89% for sMRI, 93% for rs-fMRI, and 97% for the combination of sMRI with rs-fMRI. To our knowledge, this is the first study that investigated integration of rs-fMRI and sMRI for identification of the early stage of AD. Our findings shed light on integration of sMRI with rs-fMRI for identification of the early stages of AD.

Original languageEnglish (US)
Pages (from-to)30-39
Number of pages10
JournalComputers in Biology and Medicine
Volume102
DOIs
StatePublished - Nov 1 2018

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Magnetic resonance imaging
Alzheimer Disease
Magnetic Resonance Imaging
Cognitive Dysfunction
Support vector machines

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

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Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI. / Alzheimer's Disease Neuroimaging Initiative.

In: Computers in Biology and Medicine, Vol. 102, 01.11.2018, p. 30-39.

Research output: Contribution to journalArticle

Alzheimer's Disease Neuroimaging Initiative. / Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI. In: Computers in Biology and Medicine. 2018 ; Vol. 102. pp. 30-39.
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abstract = "Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) have provided promising results in the diagnosis of Alzheimer's disease (AD), though the utility of integrating sMRI with rs-fMRI has not been explored thoroughly. We investigated the performances of rs-fMRI and sMRI in single modality and multi-modality approaches for classifying patients with mild cognitive impairment (MCI) who progress to probable AD-MCI converter (MCI-C) from those with MCI who do not progress to probable AD-MCI non-converter (MCI-NC). The cortical and subcortical measurements, e.g. cortical thickness, extracted from sMRI and graph measures extracted from rs-fMRI functional connectivity were used as features in our algorithm. We trained and tested a support vector machine to classify MCI-C from MCI-NC using rs-fMRI and sMRI features. Our algorithm for classifying MCI-C and MCI-NC utilized a small number of optimal features and achieved accuracies of 89{\%} for sMRI, 93{\%} for rs-fMRI, and 97{\%} for the combination of sMRI with rs-fMRI. To our knowledge, this is the first study that investigated integration of rs-fMRI and sMRI for identification of the early stage of AD. Our findings shed light on integration of sMRI with rs-fMRI for identification of the early stages of AD.",
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