Multisite functional connectivity MRI classification of autism: ABIDE results.
Multisite functional connectivity MRI classification of autism: ABIDE results.
复制标题
DOI：
10.3389/fnhum.2013.00599
复制
发表时间：
2013
影响因子：
2.9
通讯作者：
Anderson JS
中科院分区：
文献类型：
--
作者：
Nielsen JA;Zielinski BA;Fletcher PT;Alexander AL;Lange N;Bigler ED;Lainhart JE;Anderson JS
背景：在自闭症患者中一直观察到功能连接 MRI 指标的系统性差异，主要表现为皮质-皮质连接性下降。之前使用全脑点对点功能连接对高功能自闭症进行单一受试者分类的尝试已经在广泛的年龄范围内对自闭症与对照受试者进行了约 80% 的准确分类。我们尝试使用自闭症脑成像数据交换 (ABIDE) 复制该方法和结果，包括从 964 名受试者和 16 个独立国际站点获得的静息态 fMRI 数据。方法：对于 964 名受试者中的每一位，我们在预处理后从覆盖灰质的 7266 个感兴趣区域（2640 万个“连接”）的网格中获得了成对的功能连接测量，其中包括运动和切片定时校正、解剖图像的配准、标准空间标准化以及通过运动参数、软组织、脑脊液和白质信号回归进行体素去除。连接被分组到多个容器中，并且在包含每组容器的连接上评估留一分类器。年龄、年龄平方、性别、惯用手和地点被纳入分类器的协变量中。结果：多站点预测的分类准确性显着优于机会，但比以前的单站点结果低得多。全脑分类的准确度高达 60%，其中涉及默认模式网络、海马旁和梭状回、岛叶、韦尼克区和顶内沟区域的连接的准确度最高。分类评分与症状严重程度、社交功能、日常生活技能和语言智商相关。 BOLD 成像时间较长的部位的分类准确度明显更高。结论：自闭症的多站点功能连接分类优于使用简单的留一分类器的概率，但其准确性比单站点结果差。尝试使用多位点分类器可能需要改进的分类算法、更长的 BOLD 成像时间以及标准化的采集参数，以实现未来可能的临床应用。
Background: Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites. Methods: For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million “connections”) after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier. Results: Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times. Conclusions: Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility.
登录
查看更多内容
影响因子：
5.7
作者：
Jiao, Yun;Chen, Rong;Ke, Xiaoyan;Chu, Kangkang;Lu, Zuhong;Herskovits, Edward H.
通讯作者：
Herskovits, Edward H.
影响因子：
4.2
作者：
Anderson, Jeffrey S.;Nielsen, Jared A.;Ferguson, Michael A.;Burback, Melissa C.;Cox, Elizabeth T.;Dai, Li;Gerig, Guido;Edgin, Jamie O.;Korenberg, Julie R.
通讯作者：
Korenberg, Julie R.
影响因子：
14.5
作者：
Just, MA;Cherkassky, VL;Minshew, NJ
通讯作者：
Minshew, NJ
影响因子：
14.5
作者：
Gotts, Stephen J.;Simmons, W. Kyle;Martin, Alex
通讯作者：
Martin, Alex
影响因子：
3.9
作者：
Constantino, JN;Davis, SA;Reich, W
通讯作者：
Reich, W