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Original Article
Identification of Alzheimer's disease and mild cognitive impairment patients using individual-specific functional connectivity
WANG Xuetong  DONG Xiaoxi  LI Shuyu 

Cite this article as: Wang XT, Dong XX, Li SY. Identification of Alzheimer's disease and mild cognitive impairment patients using individual-specific functional connectivity[J]. Chin J Magn Reson Imaging, 2022, 13(4): 56-61, 68. DOI:10.12015/issn.1674-8034.2022.04.010.

[Abstract] Objective To explore the value of the individual-specific functional connectivity based on resting state functional magnetic resonance imaging (rs-fMRI) in the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI), and stable mild cognitive impairment (sMCI) and progress mild cognitive impairment (pMCI) patients.Materials and Methods We used ADNI dataset, which included 47 normal controls (NC), 66 sMCI, 24 pMCI, and 29 AD patients. The individual-specific functional connectivity was used as input to select features with least absolute shrinkage and selection operator (LASSO). And SVM was performed for AD/MCI/NC multiclassification and sMCI/pMCI classification. We extracted the most discriminative functional connectivities, the two-sample t-test (P<0.05) was used to compare the differences in the strength of the most discriminative functional connectivities between groups.Results Compared with the functional connectivity estimated by Pearson correlation (73.49%), the individual-specific functional connectivity estimated by multi-task learning-based sparse convex alternating structure optimization (MTL-sCASO) achieved 85.54% accuracy rate for AD/MCI/NC multiclassification. The individual-specific functional connectivity showed higher accuracy for identification sMCI and pMCI than the functional connectivity constructed by Pearson correlation (86.67% vs. 75.56%). The strength of the most discriminative connectivity were significantly different between groups.Conclusions The individual-specific connectivities is beneficial to the classification of AD and MCI, and the strength of functional connectivity could be used as a neuroimaging biomarker for the diagnosis of AD and MCI.
[Keywords] resting state functional magnetic resonance imaging;Alzheimer's disease;mild cognitive impairment;multi-task learning;individual-specific connectivity;early diagnosis;neuroimaging biomarker

WANG Xuetong   DONG Xiaoxi   LI Shuyu*  

School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China

Li SY, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81972160).
Received  2021-11-11
Accepted  2022-03-29
DOI: 10.12015/issn.1674-8034.2022.04.010
Cite this article as: Wang XT, Dong XX, Li SY. Identification of Alzheimer's disease and mild cognitive impairment patients using individual-specific functional connectivity[J]. Chin J Magn Reson Imaging, 2022, 13(4): 56-61, 68. DOI:10.12015/issn.1674-8034.2022.04.010.

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