J.NeuroSci.：快速检测自闭症

英国伦敦大学国王学院11日发布新闻公报说，该校精神病学研究所研究人员首先对受试者大脑进行约15分钟的磁共振成像，然后建立大脑灰质结构、形状等方面的三维图像，最后利用计算机分析判断是否存在孤独症症状。利用这一技术对20名健康人和20名孤独症患者进行测试的结果显示，准确度高达90％。

参与研究的克里斯蒂娜·埃克说，传统诊断孤独症的方式是心理分析和走访亲朋好友，既费时又费力，因此这项快速检测技术可为孤独症诊疗提供巨大帮助。目前，研究人员只对成人进行了测试，她希望进一步研究后也能应用于儿童，因为越早确诊越有利于治疗和帮助患者。

孤独症又称自闭症，许多人认为这种疾病是患者后天与外界交流不够造成的，但实际上，许多孤独症患者是因大脑结构发育异常而导致出现社交障碍。目前在英国，孤独症发病率约为1%。（生物谷Bioon.com）

生物谷推荐原文出处：

The Journal of Neuroscience  doi:10.1523/JNEUROSCI.5413-09.2010

Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
Christine Ecker,1 Andre Marquand,2 Janaina Mour?o-Miranda,3,4 Patrick Johnston,1 Eileen M. Daly,1 Michael J. Brammer,2 Stefanos Maltezos,1 Clodagh M. Murphy,1 Dene Robertson,1 Steven C. Williams,3 and Declan G. M. Murphy1

1Section of Brain Maturation, Department of Psychological Medicine, Institute of Psychiatry, 2Brain Image Analysis Unit, Department of Biostatistics, Institute of Psychiatry, and 3Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College, London SE5 8AF, United Kingdom, and 4Centre for Computational Statistics and Machine Learning, Department of Computer Science, University College London, London WC1E 6BT, United Kingdom

Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. Here, we demonstrate how a multiparameter classification approach can be used to characterize the complex and subtle structural pattern of gray matter anatomy implicated in adults with ASD, and to reveal spatially distributed patterns of discriminating regions for a variety of parameters describing brain anatomy. A set of five morphological parameters including volumetric and geometric features at each spatial location on the cortical surface was used to discriminate between people with ASD and controls using a support vector machine (SVM) analytic approach, and to find a spatially distributed pattern of regions with maximal classification weights. On the basis of these patterns, SVM was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80%, respectively. However, the ability of individual cortical features to discriminate between groups was highly variable, and the discriminating p#p#分页标题#e#atterns of regions varied across parameters. The classification was specific to ASD rather than neurodevelopmental conditions in general (e.g., attention deficit hyperactivity disorder). Our results confirm the hypothesis that the neuroanatomy of autism is truly multidimensional, and affects multiple and most likely independent cortical features. The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition.