Share this content in WeChat
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.

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

Chand GB, Wu J, Hajjar I, et al. Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment[J]. Brain Connect, 2017, 7(7): 401-412. DOI: 10.1089/brain.2017.0509.
Mao Y, Liao Z, Liu X, et al. Disrupted balance of long and short-range functional connectivity density in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients: a resting-state fMRI study[J]. Ann Transl Med, 2021, 9(1): 65. DOI: 10.21037/atm-20-7019.
Ibrahim B, Suppiah S, Ibrahim N, et al. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review[J]. Human brain mapping, 2021, 42(9): 2941-2968. DOI: 10.1002/hbm.25369.
Rathore S, Habes M, Iftikhar MA, et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages[J]. Neuroimage, 2017, 155: 530-548. DOI: 10.1016/j.neuroimage.2017.03.057.
Oh K, Chung YC, Kim KW, et al. Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning[J]. Sci Rep, 2019, 9(1): 18150. DOI: 10.1038/s41598-019-54548-6.
Hojjati SH, Ebrahimzadeh A, Khazaee A, et al. Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM[J]. J Neurosci Methods, 2017, 282: 69-80. DOI: 10.1016/j.jneumeth.2017.03.006.
Badhwar A, Tam A, Dansereau C, et al. Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis[J]. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2017, 8(1): 73-85. DOI: 10.1016/j.dadm.2017.03.007.
Yu R, Zhang H, An L, et al. Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification[J]. Hum Brain Mapp, 2017, 38(5): 2370-2383. DOI: 10.1002/hbm.23524.
Meszlényi RJ, Buza K, Vidnyánszky Z. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture[J]. Front Neuroinform, 2017, 11: 61. DOI: 10.3389/fninf.2017.00061.
Gratton C, Laumann TO, Gordon EM, et al. Evidence for two independent factors that modify brain networks to meet task goals[J]. Cell reports, 2016, 17(5): 1276-1288.
Gratton C, Laumann TO, Nielsen AN, et al. Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation[J]. Neuron, 2018, 98(2): 439-452. DOI: 10.1016/j.neuron.2018.03.035.
Wang X, Li Q, Zhao Y, et al. Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method[J]. Neuroimage, 2021, 238: 118252. DOI: 10.1016/j.neuroimage.2021.118252.
Lian C, Liu M, Wang L, et al. Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021: 1-13. DOI: 10.1109/TNNLS.2021.3055772.
Power JD, Cohen AL, Nelson SM, et al. Functional network organization of the human brain[J]. Neuron, 2011, 72(4): 665-678. DOI: 10.1016/j.neuron.2011.09.006.
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics[J]. PLoS One, 2013, 8(7): e68910. DOI: 10.1371/journal.pone.0068910.
Lee J, Ko W, Kang E, et al. A unified framework for personalized regions selection and functional relation modeling for early MCI identification[J]. NeuroImage, 2021, 236: 118048. DOI: 10.1016/j.neuroimage.2021.118048.
Anusha AS, Ranjan U, Sharma M, et al. Identification of Patterns of Cognitive Impairment for Early Detection of Dementia[C]. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020: 5498-5501.
Kong R, Yang Q, Gordon E, et al. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior[J]. Cerebral Cortex, 2021, 31(10): 4477-4500. DOI: 10.1093/cercor/bhab101.
Fountain-Zaragoza S, Heshung L, Jensen JH, et al. Predicting memory in preclinical AD from resting-state functional connectivity[J]. Alzheimer's & Dementia, 2021, 17(S4): e055627. DOI: 10.1002/alz.055627.
Cai S, Chong T, Peng Y, et al. Altered functional brain networks in amnestic mild cognitive impairment: a resting-state fMRI study[J]. Brain Imaging and Behavior, 2017, 11(3): 619-631. DOI: 10.1007/s11682-016-9539-0.
Liu L, Jiang H, Wang D, et al. A study of regional homogeneity of resting-state Functional Magnetic Resonance Imaging in mild cognitive impairment[J]. Behavioural Brain Research, 2021, 402: 113103. DOI: 10.1016/j.bbr.2020.113103.
Tang F, Zhu D, Ma W, et al. Differences Changes in Cerebellar Functional Connectivity Between Mild Cognitive Impairment and Alzheimer's Disease: A Seed-Based Approach[J]. Frontiers in Neurology, 2021, 12.
Delli Pizzi S, Punzi M, Sensi SL. Functional signature of conversion of patients with mild cognitive impairment[J]. Neurobiol Aging, 2019, 74: 21-37. DOI: 10.1016/j.neurobiolaging.2018.10.004.
Dillen KNH, Jacobs HIL, Kukolja J, et al. Functional Disintegration of the Default Mode Network in Prodromal Alzheimer's Disease[J]. J Alzheimers Dis, 2017, 59(1): 169-187. DOI: 10.3233/jad-161120.
Jin D, Wang P, Zalesky A, et al. Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease[J]. Human Brain Mapping, 2020, 41(12): 3379-3391. DOI: 10.1002/hbm.25023.
Li Y, Wang X, Li Y, et al. Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer's Disease[J]. Neural Plast, 2016, 2016: 4680972. DOI: 10.1155/2016/4680972.
Zhang T, Liao Q, Zhang D, et al. Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach[J]. Frontiers in Aging Neuroscience, 2021, 13.
Yamashita KI, Taniwaki Y, Utsunomiya H, et al. Cerebral blood flow reduction associated with orientation for time in amnesic mild cognitive impairment and Alzheimer disease patients[J]. J Neuroimaging, 2014, 24(6): 590-594. DOI: 10.1111/jon.12096.
Naël V, Pérès K, Dartigues JF, et al. Vision loss and 12-year risk of dementia in older adults: the 3C cohort study[J]. Eur J Epidemiol, 2019, 34(2): 141-152. DOI: 10.1007/s10654-018-00478-y.
Pistono A, Senoussi M, Guerrier L, et al. Language network connectivity increases in early Alzheimer's disease[J]. Journal of Alzheimer's Disease, 2021, 82(1): 447-460.

PREV The study of enhanced MR radiomics combining clinical factors in predicting early recurrence of hepatocellular carcinoma after resection
NEXT Alterations in amplitude of low frequency fluctuation and regional homogeneity in patients with neuromyelitis optica spectrum disorder and cognitive impairment: A resting-state functional magnetic resonance imaging study

Tel & Fax: +8610-67113815    E-mail: