[1]WU Xia,LI Rui,FENG Chunliang.Brain mechanism research based on intelligent computing[J].CAAI Transactions on Intelligent Systems,2021,16(5):850-856.[doi:10.11992/tis.202103029]
Copy

Brain mechanism research based on intelligent computing

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 5 Page number: 850-856 Column: 吴文俊人工智能自然科学奖一等奖 Public date: 2021-09-05
Title:
Brain mechanism research based on intelligent computing
Author(s):
WU Xia1; 2; LI Rui3; FENG Chunliang4
1. School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China; 2. Intelligence Technology and Education Engineering Research Center, Ministry of Education, Beijing 100816, China; 3. Health and Genetic Psychology Research Laboratory,Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; 4. School of Psychology, South China Normal University, Guangzhou 510631, China
Keywords:
brain mechanism;  neuroimaging;  fMRI;  intelligent computing;  brain connectivity;  brain disease;  brain andcognition;  brain netwo
CLC:
-
DOI:
10.11992/tis.202103029
Abstract:
-
References:
[1]WANG Q, MENG L, PANG J, et al. Characterization ofEEG data revealing relationships with cognitive and motorsymptoms in Parkinson’s disease: A systematic review[J].Frontiers in aging neuroscience, 2020, 12: 373.
[2]SADI M U, LANGELLA S, GIOVANELLO K S, et al.Accrual of functional redundancy along the lifespan and itseffects on cognition[J]. NeuroImage, 2021, 229: 117737.
[3]YANG T, DONG X, LEI X. Hard to initiate sleep: a newparadigm for resting-state fMRI[J]. Cognitiveneurodynamics, 2021: 15(1): 1–9.
[4]HAMA T, KOEDA M, IKEDA Y, et al. Bupropion in￾creases cerebral activation in auditory affective processing:A randomized controlled fMRI study[J]. Neuroscience letters, 2021, 749: 135716.[5]GORDON I, VOOS A C, BENNETT R H, et al. Brainmechanisms for processing affective touch[J]. Humanbrain mapping, 2013, 34(4): 914–922.
[6]蒲慕明. 脑科学研究的三大发展方向 [J]. 中国科学院院刊, 2019, 34(7): 807–813.
PU Muming. Three development directions of brain sci￾ence research[J]. Bulletin of Chinese academy of science,2019, 34(7): 807–813.
[7]APKARIAN A V, BUSHNELL M C, TREEDE R D, et al.Human brain mechanisms of pain perception and regulation in health and disease[J]. European journal of pain,2005, 9(4): 463–484.
[8]LIU Feng, WANG Shouyi, ROSENBERGER J, et al. Asparse dictionary learning framework to discover discriminative source activations in EEG brain mapping[C]//Proceedings of the Thirty-First AAAI Conference on Artifi￾cial Intelligence. San Francisco, California, USA, 2017.
[9]LI Kaiming, GUO Lei, NIE Jingxin, et al. Review of meth￾ods for functional brain connectivity detection usingfMRI[J]. Computerized medical imaging and graphics,2009, 33(2): 131–139.
[10]CHUNG T, NORONHA A, CARROLL K M, et al. Brainmechanisms of change in addiction treatment: models,methods, and emerging findings[J]. Current addiction re￾ports, 2016, 3(3): 332–342.[11]COMON P. Independent component analysis, A newconcept[J]. Signal processing, 1994, 36(3): 287–314.
[12]FRIEDMAN N, GEIGER D, GOLDSZMIDT M.Bayesian network classifiers[J]. Machine learning, 1997,29(2/3): 131–163.
[13]TO?I? I, FROSSARD P. Dictionary learning[J]. IEEEsignal processing magazine, 2011, 28(2): 27–38.
[14]LORD L D, STEVNER A B, DECO G, et al. Understanding principles of integration and segregation using whole￾brain computational connectomics: implications forneuropsychiatric disorders[J]. Philosophical transactions.series A, mathematical, physical, and engineering sciences, 2017, 375(2096): 1–21.
[15] TURK-BROWNE N B. Functional interactions as big data in the human brain[J]. Science, 2013, 342(6158):580–584.
[16]LYNN C W, BASSETT D S. The physics of brain network structure, function and control[J]. Nature reviewsphysics, 2019, 1(5): 318–332.
[17]CALHOUN V D, ADALI T, PEARLSON G D, et al. Amethod for making group inferences from functional MRIdata using independent component analysis[J]. Humanbrain mapping, 2001, 14(3): 140–151.
[18]LONDEI A, D’AUSILIO A, BASSO D, et al. Brain net￾work for passive word listening as evaluated with ICAand Granger causality[J]. Brain research bulletin, 2007,72(4/5/6): 284–292.
[19]WU Xia, LU Jie, CHEN Kewei, et al. Multiple neural net￾works supporting a semantic task: an fMRI study usingindependent component analysis[J]. NeuroImage, 2009,45(4): 1347–1358.
[20]LI Rui, WU Xia, FLEISHER A S, et al. Attention-relatednetworks in Alzheimer’s disease: a resting functional MRIstudy[J]. Human brain mapping, 2012, 33(5): 1076–1088.
[21]FERNANDEZ-DUQUE D, BLACK S E. Selective atten￾tion in early Dementia of Alzheimer Type[J]. Brain andcognition, 2008, 66(3): 221–231.
[22]BULLMORE E, SPORNS O. The economy of brain net￾work organization[J]. Nature reviews neuroscience, 2012,13(5): 336–349.
[23]ROEBROECK A, FORMISANO E, GOEBEL R. Map￾ping directed influence over the brain using Granger caus￾ality and FMRI[J]. NeuroImage, 2005, 25(1): 230–242.
[24]MUMFORD J A, RAMSEY J D. Bayesian networks forfMRI: a primer[J]. NeuroImage, 2014, 86: 573?582.
[25]LI Rui, CHEN Kewei, FLEISHER A S, et al. Large-scaledirectional connections among multi resting-state neuralnetworks in human brain: a functional MRI and Bayesiannetwork modeling study[J]. NeuroImage, 2011, 56(3):1035–1042.
[26]WU Xia, LI Rui, FLEISHER A S, et al. Altered defaultmode network connectivity in Alzheimer’s disease-a resting functional MRI and Bayesian network study[J]. Human brain mapping, 2011, 32(11): 1868–1881.
[27]LI R, WU X, CHEN K, et al. Alterations of directionalconnectivity among resting-state networks in Alzheimerdisease[J]. American journal of neuroradiology, 2013,34(2): 340–345.
[28]VIDAURRE D, SMITH S M, WOOLRICH M W. Brainnetwork dynamics are hierarchically organized in time[J].Proceedings of the national academy of sciences of theUnited States of America, 2017, 114(48): 12827–12832.
[29]LI Rui, ZHU Xinyi, YIN Shufei, et al. Multimodal inter vention in older adults improves resting-state functional connectivity between the medial prefrontal cortex and medial temporal lobe[J]. Frontiers in aging neuroscience,2014, 6: 39.
[30]LI Rui, ZHANG Jing, WU Xia, et al. Brain-wide resting state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence[J]. Brain structure and function, 2020, 225(5):1587–1600.
[31]CHAN R W, LEONG A T L, HO L C, et al. Low-frequency hippocampal-cortical activity drives brain-wideresting-state functional MRI connectivity[J]. Proceedingsof the national academy of sciences of the United Statesof America, 2017, 114(33): E6972–E6981.
[32]SOHEILI-NEZHAD S, JAHANSHAD N, GUELFI S, etal. Imaging genomics discovery of a new risk variant forAlzheimer’s disease in the postsynaptic SHARPINgene[J]. Human brain mapping, 2020, 41(13): 3737–3748.
[33]LEVY J A, CHELUNE G J. Cognitive-behavioral pro￾files of neurodegenerative dementias: beyond Alzheimer’sdisease[J]. Journal of geriatric psychiatry and neurology,2007, 20(4): 227–238.
[34]PAN Yongsheng, LIU Mingxia, LIAN Chunfeng, et al.Spatially-constrained fisher representation for brain dis￾ease identification with incomplete multi-modal neuroIm￾ages[J]. IEEE transactions on medical imaging, 2020,39(9): 2965–2975.
[35]HAO Xiaoke, BAO Yongjin, GUO Yingchun, et al.Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer’sdisease[J]. Medical image analysis, 2020, 60: 101625.
[36]LIU Cirong, YE F Q, YEN C C C, et al. A digital 3D atlas of the marmoset brain based on multi-modal MRI[J].NeuroImage, 2018, 169: 106–116.
[37]CERCIGNANI M, BOUYAGOUB S. Brain microstructure by multi-modal MRI: is the whole greater than thesum of its parts [J]. NeuroImage, 2018, 182: 117–127.
[38]GROVES A R, SMITH S M, FJELL A M, et al. Benefitsof multi-modal fusion analysis on a large-scale dataset:life-span patterns of inter-subject variability in corticalmorphometry and white matter microstructure[J].NeuroImage, 2012, 63(1): 365–380.
[39]SHAW L M, VANDERSTICHELE H, KNAPIK‐CZA JKA M, et al. Cerebrospinal fluid biomarker signature inAlzheimer’s disease neuroimaging initiative subjects[J].Annals of neurology, 2009, 65(4): 403–413.
[40]XU Lele, WU Xia, LI Rui, et al. Prediction of progress ive mild cognitive impairment by multi-modal neuroimaging biomarkers[J]. Journal of Alzheimer’s disease, 2016,51(4): 1045–1056.
[41] DAMASIO A R. How the brain creates the mind[J]. entific American, 1999, 281(6): 112–117.[42]BROWN J W. Time, will, and mental process[M]. NewYork: Plenum Press, 1996.
[43]VAN OVERWALLE F. Social cognition and the brain: ameta‐analysis[J]. Human brain mapping, 2009, 30(3):829–858.
[44]SETH A K, BARRETT A B, BARNETT L. Grangercausality analysis in neuroscience and neuroimaging[J].Journal of neuroscience, 2015, 35(8): 3293–3297.
[45]DESHPANDE G, LACONTE S, JAMES G A, et al. Multivariate granger causality analysis of fMRI data[J]. Human brain mapping, 2009, 30(4): 1361–1373.
[46]KóNYA L. Exports and growth: granger causality analysis on OECD countries with a panel data approach[J]. Economic modelling, 2006, 23(6): 978–992.
[47]STOKES P A, PURDON P L. A study of problems encountered in Granger causality analysis from a neuroscience perspective[J]. Proceedings of the nationalacademy of sciences of the United States of America,2017, 114(34): E7063–E7072.
[48]FENG Chunliang, LUO Yuejia, KRUEGER F. Neuralsignatures of fairness-related normative decision makingin the ultimatum game: a coordinate-based metaanalysis[J]. Human brain mapping, 2015, 36(2): 591–602.
[49]FENG Chunliang, DESHPANDE G, LIU Chao, et al. Diffusion of responsibility attenuates altruistic punishment: afunctional magnetic resonance imaging effective connectivity study[J]. Human brain mapping, 2016, 37(2):[50]663–677.
[50]KAELBLING L P, LITTMAN M L, MOORE A W. Reinforcement learning: a survey[J]. Journal of artificial intelligence research, 1996, 4: 237–285.
[51]SUTTON R S, BARTO A G. Reinforcement learning: anintroduction[M]. 2nd ed. Cambridge: MIT Press, 2018.
Similar References:

Memo

-

Last Update: 2022-08-25

Copyright © CAAI Transactions on Intelligent Systems