英國一項(xiàng)最新研究說,，只要對大腦進(jìn)行約15分鐘的磁共振成像,，就可利用計(jì)算機(jī)分析出受試者是否患有孤獨(dú)癥,，這比傳統(tǒng)的心理分析診斷方式要快捷得多,，有利于及時(shí)對患者進(jìn)行治療,。
英國倫敦大學(xué)國王學(xué)院11日發(fā)布新聞公報(bào)說,，該校精神病學(xué)研究所研究人員首先對受試者大腦進(jìn)行約15分鐘的磁共振成像,，然后建立大腦灰質(zhì)結(jié)構(gòu)、形狀等方面的三維圖像,，最后利用計(jì)算機(jī)分析判斷是否存在孤獨(dú)癥癥狀,。利用這一技術(shù)對20名健康人和20名孤獨(dú)癥患者進(jìn)行測試的結(jié)果顯示，準(zhǔn)確度高達(dá)90％,。
參與研究的克里斯蒂娜·?？苏f，傳統(tǒng)診斷孤獨(dú)癥的方式是心理分析和走訪親朋好友,，既費(fèi)時(shí)又費(fèi)力,，因此這項(xiàng)快速檢測技術(shù)可為孤獨(dú)癥診療提供巨大幫助。目前,，研究人員只對成人進(jìn)行了測試,，她希望進(jìn)一步研究后也能應(yīng)用于兒童，因?yàn)樵皆绱_診越有利于治療和幫助患者,。
孤獨(dú)癥又稱自閉癥,，許多人認(rèn)為這種疾病是患者后天與外界交流不夠造成的，但實(shí)際上,，許多孤獨(dú)癥患者是因大腦結(jié)構(gòu)發(fā)育異常而導(dǎo)致出現(xiàn)社交障礙,。目前在英國,，孤獨(dú)癥發(fā)病率約為1%。（生物谷Bioon.com）
生物谷推薦原文出處：
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 patterns 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.
