The Specific Index Model of Resting-State fMRI Functional Connectivity in the Application to the Evaluation of Cognitive Score of Healthy Elderly
Guo Zhitong1,2, Ge Manling1,2*, Zhang Fuyi1,2, Song Zibo1,2, Xie Chong1,2, Yang Zekun1,2
1(State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China) 2(Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China)
Abstract The resting-state functional magnetic resonance imaging (rfMRI) has more advantages than the traditional scale test and task-state fMRI, especially in the cognitive detection on the elderly. However, functional bio-markers of healthy brain aging remain not totally clear. Herein, a specific index model derived from functional connectivity was proposed, aiming to study the possibility to identify the excellent or poor cognitive scores of the healthy elderly by the index model, and to seek the potential functional bio-markers to evaluate the cognitive scores by rfMRI instead of the traditional scale testing. A total of 98 healthy old people and a total of 90 healthy young people were volunteers. The former came from a cohort study of cognitive function of healthy elderly people in Portugal. According to the cognitive scores estimated by the scale tests before the rfMRI scan, 55 subjects with the excellent scores and 43 subjects with the poor scores were involved in the experiment group; the latter data came from GSP opened by the Harvard Hospital, the subjects aged between 18-35 years old with a medium level of cognition evaluation tested before rfMRI, were involved in the control group. After pre-processing the rfMRI data, the functional connectivity (FC) was computed on the whole brain one by one, then a FC-based specific index model was built up to estimate the FC deviation degree of old people relative to that of youth at a single brain area. Furthermore, the specific index values of the marked brain areas sensitive to the excellent scores and the poor scores could be estimated by statistics in a comparison study, by which the eigenvectors matrix were formed and input the machine learning model thereafter. Finally, the model of probability neural network (PNN) was utilized to classify the scores in the experimental groups and then the sorting rate was defined by N-fold validation. The specific index model could localize the functional bio-markers brain regions sensitive to the cognitive scores of healthy aging. There were 5 brain regions in the frontal lobe, temporal lobe and parietal lobe. By considering the indexes of 5 brain regions as inputs to the machine learning, the cognitive scores of healthy aging could be effectively classified with a sorting rate of 81.7%. This work was expected to provide an effective index and a new method for rfMRI to test the cognitive scores of the healthy elderly by combining the specific index modeling with a machine learning model.
Guo Zhitong,Ge Manling,Zhang Fuyi, et al. The Specific Index Model of Resting-State fMRI Functional Connectivity in the Application to the Evaluation of Cognitive Score of Healthy Elderly[J]. Chinese Journal of Biomedical Engineering, 2020, 39(6): 676-684.
[1] 吴琼,陈元园,赵欣,等. 基于MRI的认知相关的脑宏观-微观结构变化模式研究综述 [J].中国生物医学工程学报, 2019, 38(1): 94-101. [2] Santos NC, Costa PS, Cunha P, et al. Mood is a key determinant of cognitive performance in community-dwelling older adults: a cross-sectional analysis [J]. Age, 2013, 35(5): 1983-1993. [3] Santos NC, Costa PS, Cunha P, et al. Clinical, physical and lifestyle indicators and relationship with cognition and mood in aging: a cross-sectional analysis of distinct educational groups [J]. Frontiers in Aging Neuroscience, 2014, 6: 1-21. [4] An R, Liu GG. Cognitive impairment and mortality among the oldest-old Chinese [J]. International Journal of Geriatric Psychiatry, 2016,31(12): 1345-1353. [5] Costa PS, Santos NC, Cunha P, et al. The use of bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing [J]. PloS ONE, 2013, 8(8): e71940. [6] 尹宁,代扬杨,生晖,等. 基于复杂网络的磁刺激内关穴脑皮层功能连接分析 [J].中国生物医学工程学报, 2019, 38(6): 695-701. [7] Binder JR, Desai RH. The neurobiology of semantic memory [J]. Trends in Cognitive Sciences, 2011, 15(11): 527-536. [8] Biswal B, Yetkin FZ, Haughton VM, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI [J]. Magnetic Resonance in Medicine, 1995, 34(4): 537-541. [9] Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI [J]. Nature Neuroscience, 2013,16(7): 832-837. [10] Zang Yufeng, He Yong, Zhu Chaozhe, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI [J]. Brain & Development, 2007,29(2): 83-91. [11] Zang Yufeng, Jiang Tianzi, Lu Yingli, et al. Regional homogeneity approach to fMRI data analysis [J]. NeuroImage, 2004, 22(1): 394-400. [12] Jiang Tianzi, He Yong, Zang Yufeng, et al. Modulation of functional connectivity during the resting state and the motor task [J]. Human Brain Mapping, 2004, 22(1): 63-71. [13] Damaraju E, Allen EA, Belger A, et al. Dynamic functional connectivity analysis reveals transient states ofdysconnectivity in schizophrenia [J]. NeuroImage Clinical, 2014, 45(7): 298-308. [14] Hutchison RM, Womelsdorf T, Allen EA, et al. Dynamic functional connectivity: promise, issues, and interpretations [J]. NeuroImage, 2013, 80(10): 360-378. [15] Jiang Tianzi. Brainnetome: A new-ome to understand the brain and its disorders [J]. NeuroImage, 2013, 80(8): 263-272. [16] Gallos LK, Makse HA, Sigman M. A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks [J]. Proceedings of The National Academy of Sciences of The United States of America, 2012, 109(8): 2825-2830. [17] Biswal B, Yetkin FZ, Haughton VM, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI [J]. Magnetic Resonance in Medicine, 1995, 34(4): 537-541. [18] Guerra-Carrillo B, Mackey AP, Bunge SA. Resting-state fMRI: a window into human brain plasticity [J]. Neuroscientist, 2014, 20(5): 522-533. [19] Uddin LQ, Kelly AM, Biswal BB, et al. Network homogeneity reveals decreased integrity of default-mode network in ADHD [J]. Journal of Neuroscience Methods, 2008, 169: 249-254. [20] Stufflebeam SM, Liu H, Sepulcre J, et al. Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging: Clinical article [J]. Journal of Neurosurgery, 2011, 114(6): 1693-1697. [21] Meunier D, Achard S, Morcom A, et al. Age-related changes in modular organization of human brain functional networks [J]. NeuroImage, 2009, 44(3): 715-723. [22] Shafto MA, Tyler LK.Language in the aging brain: The network dynamics of cognitive decline and preservation [J]. Science, 2014, 346(6209): 583-587. [23] Cabral J, Vidaurre D, Marques P, et al. Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest [J]. Scientific Reports, 2017, 7(1): 5135-5148. [24] Avram JH, Marisa OH, Timothy MO, et al. Brain genomics superstruct project initial data release with structural, functional, and behavioral measures [J].Scientific Data,2015,2: 150031-150047. [25] Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL [J]. NeuroImage, 2004, 23(S1): 208-219. [26] Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL [J]. NeuroImage, 2009, 45(S1): 173-186. [27] Jenkinson M, Beckmann CF, Behrens TE, et al. FSL [J]. NeuroImage, 2012, 62: 782-790. [28] Jenkinson M, Bannister P, Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images [J]. NeuroImage, 2002, 17(2): 825-841. [29] Smith SM. Fast robust automated brain extraction [J]. Human Brain Mapping, 2002, 17(3): 143-155. [30] Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain [J]. NeuroImage, 2002, 15(1): 273-289. [31] Hlinka J, Palus M, Vejmelka M, et al.Functional connectivity in resting-state fMRI: Is linear correlation sufficient? [J]. NeuroImage, 2011, 54(3): 2218-2225. [32] Specht DF.Probabilistic neural networks [J]. Networks, 1990, 3(1): 109-118. [33] Raichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function [J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(2): 676-682. [34] Greicius MD, Krasnow B, Reiss AL, et al. Functional connectivity in the resting brain [J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100: 253-258. [35] Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: Anatomy, function, and relevance to disease [J]. Annals of the New York Academy of Sciences, 2008, 1124: 1-38. [36] Wang Xiaoni,Zeng Yang,Chen Guanqun, et al.Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment [J]. Oncotarget, 2016, 7(31): 48953-48962. [37] Smart CM, Spulber G, Garcia-Barrera M. Structural brain changes evident in default mode network areas in older adults with subjective cognitive decline compared to healthy peers [J].Alzheimers & Dementia, 2014, 10(4): 608-608. [38] Benesty J,Chen Jingdong,Huang Yiteng, et al. On theimportance of the pearson correlation coefficient in noise reduction [J]. IEEE Transactions on Audio Speech and Language Processing, 2008, 16(4): 757-765. [39] Mohammed G, Qi Z, Warren B, et al. Inclusion of neuropsychological scores in atrophy models improves diagnostic classification of Alzheimer's disease and mild cognitive impairment [J].Computational Intelligence and Neuroscience, 2015, 2015(p1): 14-29. [40] 成妮娜,肖小华,胡火有,等.基于中心化自动加权多任务学习的早期轻度认知障碍诊断 [J].中国生物医学工程学报, 2019, 38(6): 653-661.
