您正在浏览:主页 > 游戏新闻 > Front Hum Neurosci雷霆之怒网页游戏私服：大脑扫描或可帮助自闭症诊断
作者：雷霆之怒公益服 来源：http://www.edmi.com.cn 时间：2020-09-07 22:14
研究揭示，孤独症谱系障碍的成年个体处理社会事务相比正常个体差异很大，这也就表明损伤的大脑连接性对这一现象负有主要责任。研究者通过对患者进行大脑扫描就可以看到，由于损伤的大脑连接性导致患者大脑中出现一系列损伤的大脑区域。
Gopikrishna Deshpande1,2, Lauren E. Libero3, Karthik R. Sreenivasan1, Hrishikesh D. Deshpande3 and Rajesh K. Kana3*
Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism.
PMC:doi:10.3389/fnhum.2013.00670
Rajesh Kana博士表示，由于大脑的连接性可以作为自闭症的神经标志，因此其也将用于临床上对自闭症的检测；我们发现大脑不同区域间的信息转移在自闭症患者大脑中表现地较差一些。研究者对大脑扫描的19个路径中发现了大脑连接性的相关数据，这就可以帮助预测是否个体患有自闭症，其准确率高达95.9%。
(Credit: University of Alabama at Birmingham, Department of Psychology)
对个体进行大脑扫描或者有助于帮助诊断自闭症。
Identification of neural connectivity signatures of autism using machine learning
2013年10月19日 讯 /生物谷BIOON/ --近日，来自阿拉巴马大学等处的研究者通过研究表示，对个体进行大脑扫描或者有助于帮助诊断自闭症，相关研究刊登于国际杂志Frontiers in Human Neuroscience上。
改变自闭症患者的大脑连接性或许对于其理解社会事务会有帮助，而且患者大脑较弱的连接性会阻碍大脑不同区域间的交叉交流。在未来5-10年，研究者将会继续深入研究来改善当前的自闭症诊断手段，变态雷霆之怒页游，以及通过研究改善患者的大脑连接性，他们希望深入的研究为理解自闭症发病的根源以及开发相应的治疗手段可以提供更好的思路和建议。（生物谷Bioon.com）
<<上一篇：繁星戏剧村芭蕾舞 阿拉波拉卡形象 >>
<<下一篇：盘点适合改编成《神庙逃亡》的10部电影 >>