The identification of mild cognitive impairments (MCI) via either structural magnetic resonance imaging (sMRI) or functional MRI (fMRI) has great potential due to the non-invasiveness of the techniques. Furthermore, these techniques allow longitudinal follow-ups of single subjects via repeated measurements. sMRI- or fMRI-based biomarkers have been adopted separately to diagnose MCI; however, there has not been a systematic effort to integrate sMRI- and fMRI-based features to increase MCI detection accuracy. This study investigated whether the detection of MCI can be improved via the integration of biomarkers identified from both sMRI and fMRI modalities. Regional volume sizes and neuronal activity levels of brains from MCI subjects were compared with those from healthy controls and used to identify biomarkers from sMRI and fMRI data, respectively. In the subsequent classification phase, MCI was automatically detected using a support vector machine algorithm that employed the identified sMRI- and fMRI-based biomarkers as an input feature vector. The results indicate that the fMRI-based biomarkers provided more information for detecting MCI than the sMRI-based biomarkers. Moreover, the integrated feature sets using the sMRI- and fMRI-based biomarkers consistently showed greater detection accuracy than the feature sets based only on the fMRI-based biomarkers. The results demonstrate that integration of sMRI and fMRI modalities can provide supplemental information to improve the diagnosis of MCI relative to either the sMRI or fMRI modalities alone.
Bibliographical noteFunding Information:
Sources of Support: This work was supported by the World Class University (WCU) program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education, Science and Technology (R31-10008) and by the Basic Science Research Program, NRF grant of Korea (2012-0002342). These funding sources had no involvement in the study design, analysis, interpretation, writing of the report, or the decision to submit the article for publication.
- Functional MRI
- Mild cognitive impairment
- Pattern classification
- Structural MRI
- Support vector machine
- Volumetric analysis
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging