Functional connectivity magnetic resonance imaging classification of autism
Functional connectivity magnetic resonance imaging classification of autism
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DOI：
10.1093/brain/awr263
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发表时间：
2011-12-01
期刊：
影响因子：
14.5
通讯作者：
Lainhart, Janet E.
中科院分区：
文献类型：
--
作者：
Anderson, Jeffrey S.;Nielsen, Jared A.;Lainhart, Janet E.
自闭症个体和正常发育对照组之间静息状态功能性磁共振成像连接的组间差异已被广泛复制，用于少数离散的脑区域，但自闭症连接异常的全脑分布尚未得到很好的表征。目前还不清楚功能连接是否足够强大，可以用作自闭症患者个体的诊断或预后指标。我们从一个由7266个感兴趣区域组成的网格中获得了成对的功能连接测量，该网格覆盖了40名患有自闭症的男性青少年和年轻人以及40名年龄，性别和智商匹配的典型发育受试者的整个灰质（2640万个连接）。每例受试者均采用单次静息状态血氧水平依赖性扫描8 min进行分类。一个留一分类器成功地区分了自闭症和对照组，灵敏度为83%，特异性为75%，总准确率为79%（P = 1.1 × 10 - 7）。在< 20岁的受试者中，分类器的准确率为89%（P = 5.4 x 10（-7））。在由来自六个家庭的21个个体组成的复制数据集中，这些个体具有受影响和未受影响的兄弟姐妹，分类器的准确率为71%（对于年龄< 20岁的受试者，准确率为91%）。孤独症患者的分类分数与社会反应量表（P = 0.05）、言语智商（P = 0.02）和孤独症诊断观察表-通用的综合社会和沟通分项分数（P = 0.05）显著相关。信息连接的分析表明，最强的相关值的感兴趣的区域对是最不正常的自闭症。负相关的感兴趣区域对在自闭症中显示出更高的相关性（较少的相关性），可能代表较弱的抑制性连接，特别是对于长连接（欧几里得距离> 10 cm）。表现出最大差异的大脑区域包括默认模式网络、上级顶叶、梭状回和前额叶。总体而言，年轻受试者的分类准确性更好，19岁后自闭症和对照受试者之间的差异逐渐缩小。自闭症患者未受影响的兄弟姐妹的分类分数与对照组比自闭症患者的分类分数更相似。这些发现表明自闭症的功能连接磁共振成像诊断分析的可行性。
Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 x 10(-7)). In subjects < 20 years of age, the classifier performed at 89% accuracy (P = 5.4 x 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects < 20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance > 10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.