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Published on: August 7, 2017
Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM
Seyed Hani Hojjati1, Ata Ebrahimzadeh1, Ali Khazaee2
1Department of Electrical Engineering, Babol University of Technology, Babol, Iran.
Background:
We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI).
New Method:
Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features.
Results:
Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD.
Comparison With Existing Method(S):
To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC.
Conclusion:
Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.
Insights
This study uses resting-state fMRI and machine learning to accurately identify mild cognitive impairment (MCI) patients who will develop Alzheimer's disease (AD). This approach aids in early AD diagnosis by detecting brain changes linked to MCI conversion.
Area of Science:
- Neuroimaging
- Machine Learning
- Neurology
Background:
- Investigated the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD).
- Differentiated between MCI converters (MCI-C) and MCI non-converters (MCI-NC) using resting-state functional MRI (rs-fMRI).
Purpose of the Study:
- To develop a method for predicting AD progression in MCI patients.
- To utilize rs-fMRI data for early diagnosis of Alzheimer's disease.
Main Methods:
- Employed graph theory and machine learning, specifically a support vector machine (SVM).
- Utilized a novel feature selection algorithm to identify optimal features from local and global graph measures derived from rs-fMRI.
- Included 18 MCI-C and 62 MCI-NC participants.
Main Results:
- Achieved high classification accuracy (91.4%), sensitivity (83.24%), specificity (90.1%), and AUC (0.95) in distinguishing MCI-C from MCI-NC.
- Identified specific brain regions affected in Alzheimer's disease progression.
Conclusions:
- The proposed approach shows potential for early AD diagnosis.
- rs-fMRI data can predict MCI to AD conversion by revealing underlying affected brain regions.
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