Home

Translated from Chinese by Kevin McCready from

http://www.wanfangdata.com.cn/qikan/periodical.Articles/zhsjk/zhsj2004/0404/040437.htm

CHINESE JOURNAL OF NEUROLOGY

2004 Vol.37 No.4 P.354-356

Digitized Periodical

--------

Executive Dysfunction and Alzheimer's Disease

Sun Houliang, Zhang Xinqing

Executive function is the cognitive skill used to plan, initiate, sequence and monitor goal directed behaviour in a flexible and optimum manner when implementing a specific task. Executive dysfunction is wide ranging in many neurological diseases including Alzheimer's. Although in 1994 the American Psychiatric Association supported the diagnosis of dementia as one of the clinical bases for executive dysfunction, executive dysfunction has still not received the emphasis it should in clinical settings. Moreover traditional methods of ascertaining dementia are not sensitive to executive dysfunction, causing the impaired executive function in dementia sufferers to be hugely underestimated or even ignored. Evidence increasingly demonstrates impairment of executive function in dementia sufferers is common. Executive function and the skills of daily living are very closely related to functional decline. Executive function can predict the transformation from mild cognitive impairment (MCI) to dementia and can independently decide the level of care required for dementia sufferers. Thus emphasising research into executive function and Alzheimer's disease is vitally important.



Author’s workplace: 100053, Capital Medical University Xuanwu Hospital Psychiatric Department



Article received: 24 December 2003. Published 23 August 2004

Please see PDF file for full article.



References：



［1］Royall DR, Lauterbach EC, Cummings JL, et al. Executive control function: a review of its promise and challenges for clinical research.A report from the Committee on Research of the American Neuropsychiatric Association. J Neuropsychiatry Clin Neurosci,2002, 14: 377-405.

［2］Smith EE, Jonides J. Storage and executive processes in the frontal lobes. Science, 1999, 283:1657-1661.

［3］Alexander GE, DeLong MR, Strick P. Parallel organization of functionally segregated circuits linking basal ganglia and cortex.Annu Rev Neurosci, 1986, 9:357-381.

［4］Graybiel AM. The basal ganglia and the initiation of movement. Rev Neurol (Paris), 1990, 146:540-544.

［5］Mega MS, Cummings JL. Frontal-subcortical circuits in neuropsychiatric disorders. J Neuropsychiatry Clin Neurosci, 1994,:358-370.

［6］Royall DR, Mahurin RK, Gray KF. Bedside assessment of executive cognitive impairment: the executive interview. J Am GeriatrSoc,1992,40: 1221-1226.

［7］Fan J, Fossella J, Sommer T, et al. Mapping the genetic variation of executive attention onto brain activity. Proc Natl Acad Sci USA,2003, 100: 7406-7411.

［8］Arnaiz E, Almkvist O. Neuropsychological features of mild cognitive impairment and preclinical Alzheimer's disease. Acta Neurol Scand Suppl, 2003, 179:34-41.

［9］Petersen RC, Smith GE, Waring SC, et al. Age associated memory impairment diagnosis: problem of reliability and concerns of terminology. Psychol Aging, 1991,6: 551-558.

［10］Greene JD, Hodges JR, Baddeley AD. Autobiographical memory and executive function in early dementia of Alzheimer type.Neuropsychologia, 1995, 33:1647-1670.

［11］Collette F, van der Linden M, Salmon E. Executive dysfunction in Alzheimer's disease. Cortex, 1999, 35: 57-72.

［12］Schillerstron JE. Executive control function in psychiatric and medical illness. J Psychiatr Practice, 2002, 8: 160-169.

［13］Hulette CM, Welsh-Bohmer KA, Murray MG, et al.Neuropathological and neuropsychological changes in "normal"aging: evidence for preclinical Alzheimer disease in cognitively normal individuals. J Neuropathol Exp Neurol, 1998, 57: 1168-1174.

［14］DeKosky ST, Scheff SW. Synapse loss in frontal cortex biopsies in Alzheimer's disease: correlation with cognitive severity. Ann Neruol,1990, 27:457-464.

［15］Cummings JL. Vascular subcortical dementias: clinical aspects.Dementia, 1994, 5:177-180.

［16］Roman GC, Royall DR. Executive control function: a rational basis for the diagnosis of vascular dementia. Alzheimer Dis Assoc Disord,1999,13: S69-S80.

［17］Pohjasvaara T, Leskela M, Vataja R, et al. Post-stroke depression,executive dysfunction and functional outcome. Eur J Neurol, 2002,9: 269-275.

［18］O'Sullivan M, Summers PE, Jones DK, et al. Normal-appearing white matter in ischemic leukoaraiosis: a diffusion tensor MRI study.Neurology, 2001, 57: 2307-2310.

［19］Cannata AP, Alberoni M, Franceschi M, et al. Frontal impairment in subcortical ischemic vascular dementia in comparison to Alzheimer's disease. Dement Geriatr Cogn Disord, 2002, 13:101-111.

［20］Yuspeh RL, Vanderploeg RD, Crowell TA, et al. Differences in executive functioning between Alzheimer's disease and subcortical ischemic vascular dementia. J Clin Exp Neuropsychol, 2002, 24:745 -754.

［21］Duck LM, Kaszniak AW. Executive control function in degenerative dementias: a comparative review. Neuropsychol Rev, 2000, 10: 75-99.

［22］Gregory CA, Orrel M, Sahakian B, et al. Can frontotemporal dementia and Alzheimer's disease be differentiated using a brief battery of tests? Int J Geriatr Psychiatry, 1997, 12:375-383.

［23］Dubos B, Pillon B. Cognitive deficits in Parkinson's disease. J Neurology, 1997, 244:2-8.

［24］Soliveri P, Monza D, Paridi D, et al. Neuropsychological follow up in patients with Parkinson's disease, striatonigral degeneration-type multisystem atrophy, and progressive supranuclear palsy. J Neurol Neurosurg Psychiatry, 2000, 69:313-318.

［25］Dymek MP, Atchison P, Harrell L, et al. Competency to consent to medical treatment in cognitively impaired patients with Parkinson's.Neurology, 2001, 56:17-24.

［26］Boyle PA, Paul R, Moser D, et al. Cognitive and neurologic predictors of functional impairment in vascular dementia. Am J Geriatr Psychiatry, 2003, 11:103-106.

［27］Griffith HR, Belue K, Sicola A, et al. Impaired financial abilities in mild cognitive impairment: a direct assessment approach. Neurology,2003, 60: 449-457.

［28］Albert MS, Moss MB, Tanzi R, et al. Preclinical prediction of AD using neuropsychological tests. J Int Neuropsychol Soc, 2001, 7:631-639.

［29］Chen P, Ratcliff G, Belle SH, et al. Patterns of cognitive decline in presymptomatic Alzheimer disease: a prospective community study.Arch Gen Psychiatry, 2001, 58:853-858.

［30］Royall DR, Cabello M, Polk M, et al. Executive dyscontrol: an important factor affecting the level of care received by elderly retirees. J Am Geriatr Soc, 1998, 46:1519-1524.



-----------

John Jonides earned his PhD from the University of Pennsylvania in 1975. He is currently Professor of Psychology and Neuroscience at the University of Michigan, as well as co-Director of the Functional MRI Laboratory there and editor of the journal, Cognitive, Affective, and Behavioral Neuroscience.

4428B East Hall 1109

(734)764-0192

jjonides@umich.edu



-----------

John A. Fossella, Ph.D.

Sackler Institute for Developmental Psychobiology

Department of Psychiatry, Box 140

Weill Medical College of Cornell University

1300 York Ave.

New York, NY 10021

jaf2014@med.cornell.edu

(212) 746-3781

FAX (212) 746-5755



---------------

万方数据资源系统


生物物理学报
ACTA BIOPHYSICA SINICA
2005 Vol.21 No.1 P.19-25
数字化期刊

基于微阵列数据的基因网络预测方法研究进展

PROGRESS ON METHODS FOR INFERRING THE GENE NETWORKS FROM MICROARRAY DATA

王明怡　 夏顺仁　陈作舟　

摘　要：DNA 微阵列技术可同时定量测定成千上万个基因在生物样本中的表达水平,从这一技术获得的全基因组范围表达数据为揭示基因间复杂调控关系提供了可能.研究人员试图通过数学和计算方法来构建遗传互作的模型,这些基因调控网络模型有聚类法、布尔网络、贝叶斯网络、微分方程等.文章对网络重建计算方法的研究现状进行了较为全面的综述,比较了不同模型的优缺点,并对该领域进一步的研究趋势进行了展望.
关键词：基因网络;微阵列;聚类;布尔网络;微分方程;贝叶斯网络
分类号：Q617

基金项目：国家自然科学基金资助课题(60272029)和浙江省自然科学基金资助课题(M603227)
作者简介：通讯作者:夏顺仁,电话:(0571)87951703E-mail:srxia@mai1.bme.zju edu.cn
作者单位：王明怡（浙江大学生命科学学院,杭州,310029;中国计量学院计算机科学与工程学系,杭州,310018）　
　　　　　夏顺仁（浙江大学CAD&CG国家重点实验室,杭州,310027;浙江大学生物医学工程教育部重点实验室,杭州,310027）　
　　　　　陈作舟（浙江大学生命科学学院,杭州,310029）　

参考文献：

[1]DeRisi JL, lyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale.Science, 1997,278:680～686
[2]Chee M, Yang R, Hubbell E, Bemo A, Huang XC, Stem D,Winkler J, Lockhart DJ, Morris MS, Fodor SP. Accessing genetic information with high-density DNA arrays. Science,1996,274:610～614
[3]Carr DB, Somogyi R, Michaels G. Templates for looking at gene expression clustering. Statistical Computing & Statistical Graphics Newsletter, 1997,8:20～29
[4]Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns.Proc Natl Acad Sci USA, 1998,95:14863～14868
[5]Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D,Lockhart DJ, Davis RW. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell, 1998,2:65～73
[6]Hughes TR, Marton M J, Jones AR, Roberts C J, Stoughton R,Armour CD, Bennett MA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepa- niants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH. Functional discovery via a compendium of expression profiles. Cell, 2000, 102:109～126
[7]Michaels GS, Carr DB, Askenazi M, Fuhrman S, Wen X,Somogyi R. Cluster analysis and data visualization of large-scale gene expression data. Pac Symp Biocomput, 1998,3:42～53
[8]Butte AJ, Kohane IS. Mutual information relevance networks:functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput, 2000,4:418～429
[9]Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA, 1999,96:6745～6750
[10]Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM.Systematic determination of genetic network architecture.Nat Genet, 1999,22:281～285
[11]Herzel H, Beule D, Kielbasa S, Korbel J, Sers C, Malik A,Eickhoff H, Lehrach H, Schuchhardt J. Extracting information from eDNA arrays. Chaos, 2001,11:98～107
[12]Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S,Dmitrovsky E, Lander ES, Golub TR. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA, 1999,96:2907～2912
[13]Ben-hur A, Elisseeff A, Guyon, I. A stability based method for discovering structure in clustered data. Pac Symp Biocomput, 2002,7:6～17
[14]Ben Dor A, Shamir R, Yakhini Z. Clustering gene expression pattems. J Comput Biol, 1999,6:281～297
[15]Lukashin AV, Fuchs R. Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters. Bioinformatics, 2001,17:405～414
[16]Heyer LJ, Kruglyak S, Yooseph S. Exploring expression data:identification and analysis of coexpressed genes. Genome Res,1999,9:1106～1115
[17]Sharan R, Shamir R. CLICK: a clustering algorithm with applications to gene expression analysis. Proc Int Conf Intell Syst Mol Biol, 2000,8:307～316
[18]Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R,Staudt L, Chan WC, Botstein D, Brown P. ‘Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol, 2000,1:research0003.1～0003.21
[19]D'haeseleer P, Liang S, Somogyi R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 2000,16:707～726
[20]Akutsu T, Miyano S, Kuhara S. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Par Symp Biocomput, 1999,4:17～28
[21]Akutsu T, Miyano S, Kuhara S. Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J Comput Biol, 2000,7:331～343
[22]Liang S, Fuhrman S, Somogyi R. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput, 1998,3:18～29
[23]Wahde M, Hertz J. Coarse-grained reverse engineering of genetic regulatory networks. Biosystems, 2000,55:129～136
[24]Chen T, He HL, Church GM. Modeling gene expression with differential equations. Par Symp Biocomput, 1999,4:29～40
[25]D'haeseleer P, Wen X, Fuhrman S, Somogyi R. Linear modeling of mRNA expression levels during CNS development and injury. Pac Symp Biocomput, 1999,4:41～52
[26]Akutsu T, Miyano S, Kuhara S. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics,2000,16:727～734
[27]Heckerman D. A tutorial on learning with Bayesian networks.In: Jordan MI, ed. Learning in graphical models. USA: MIT Press, 1998,301～354
[28]Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. J Comput Biol, 2000,7:601～620
[29]Imoto S, Goto T, Miyano S. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Par Symp Biocomput,2002,7:175～186
[30]Hartemink AJ, Gifford DK, Jaakkola TS, Young RA. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Par Symp Biocomput, 2001,6:422～433
[31]Pe'er D, Regev A, Elidan G, Friedman N. Inferring subnetworks from perturbed expression profiles. Bioinformatics,2001,17:S215～S224
[32]Yoo C, Thorsson V, Cooper GF. Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data. Pac Symp Biocomput, 2002,7:498～509
[33]Smith VA, Jarvis ED, Hartemink AJ. Evaluating functional network inference using simulations of complex biological systems. Bioinformatics, 2002,18:S216～S224
[34]Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 2003,19:2271～2282
[35]Ong IM, Glasner JD, Page D. Modelling regulatory pathways in E. coli from time series expression profiles. Bioinformatics,2002,18:S241～S248
[36]Weaver DC, Workman CT, Stormo GD. Modeling regulatory networks with weight matrices. Pac Symp Biocomput, 1999,4:112～123
[37]徐肖江,王连水,丁达夫.从酵母表达时间序列估计基因调控网络.生物化学与生物物理学报,2003,35(8):707～716
[38]Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D,Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics, 2003,34:166～176
[39]Segal E, Friedman N, Koller D, Regev A. A module map showing conditional activity of expression modules in cancer.Nature Genetics, 2004,36:1090～1098
[40]Sohlert F, Hanisch D, Zimmer R. New methods for joint analysis of biological networks and expression data. Bioinformatics, 2004,20:1517～1521
[41]Creighton C, Hanash S. Mining gene expression databases for association rules. Bioinformatics, 2003,19:79～86
[42]Thomas R, Mehrotra S, Papoutsakis ET, Hatzimanikatis V. Amodel-based optimization framework for the inference on gene regulatory networks from DNA array data. Bioinformatics, 2004,20:3221～3235
[43]Nachman I, Regev A, Friedman I. Inferring quantitative models of regulatory networks from expression data. Bioinformatics, 2004,20:i248～i256
[44]Zak DE, Gonye GE, Schwaber JS, Doyle FJ. Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.Genome Research, 2003,13:2396～2405
[45]Hartemink AJ, Gifford DK, Jaakkola TS, Young RA. Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput,2002,7:437～449
[46]Tamada Y, Kim S, Bannai H, Imoto S, Tashiro K, Kuhara S, Miyano S. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics, 2003,19: Suppl 2, Ⅱ227～11236
[47]Friedman N. Inferring cellular networks using probabilistic graphical models. Science, 2004,303:799～80

收稿日期：2004年10月21日

出版日期：2005年2月1日