孤独症儿童和青少年结构磁共振成像的多变量搜索灯分类
Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism.
机构信息
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
出版信息
Biol Psychiatry. 2011 Nov 1;70(9):833-41. doi: 10.1016/j.biopsych.2011.07.014. Epub 2011 Sep 3.
BACKGROUND
Autism spectrum disorders (ASD) are neurodevelopmental disorders with a prevalence of nearly 1:100. Structural imaging studies point to disruptions in multiple brain areas, yet the precise neuroanatomical nature of these disruptions remains unclear. Characterization of brain structural differences in children with ASD is critical for development of biomarkers that may eventually be used to improve diagnosis and monitor response to treatment.
METHODS
We use voxel-based morphometry along with a novel multivariate pattern analysis approach and searchlight algorithm to classify structural magnetic resonance imaging data acquired from 24 children and adolescents with autism and 24 age-, gender-, and IQ-matched neurotypical participants.
RESULTS
Despite modest voxel-based morphometry differences, multivariate pattern analysis revealed that the groups could be distinguished with accuracies of approximately 90% based on gray matter in the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes-regions within the default mode network. Abnormalities in the posterior cingulate cortex were associated with impaired Autism Diagnostic Interview communication scores. Gray matter in additional prefrontal, lateral temporal, and subcortical structures also discriminated between groups with accuracies between 81% and 90%. White matter in the inferior fronto-occipital and superior longitudinal fasciculi, and the genu and splenium of the corpus callosum, achieved up to 85% classification accuracy.
CONCLUSIONS
Multiple brain regions, including those belonging to the default mode network, exhibit aberrant structural organization in children with autism. Brain-based biomarkers derived from structural magnetic resonance imaging data may contribute to identification of the neuroanatomical basis of symptom heterogeneity and to the development of targeted early interventions.
背景
自闭症谱系障碍（ASD）是一种神经发育障碍，患病率接近 1:100。结构成像研究指出，多个大脑区域存在中断，但这些中断的确切神经解剖学性质尚不清楚。对 ASD 儿童大脑结构差异的特征描述对于开发生物标志物至关重要，这些标志物最终可能用于改善诊断并监测治疗反应。
方法
我们使用体素形态计量学以及一种新的多变量模式分析方法和搜索灯算法，对 24 名自闭症儿童和青少年以及 24 名年龄、性别和智商匹配的神经典型参与者的结构磁共振成像数据进行分类。
结果
尽管基于体素的形态计量学差异较小，但多变量模式分析显示，这两个组可以根据后扣带回皮质、内侧前额叶皮质和双侧内侧颞叶（默认模式网络内的区域）的灰质以约 90%的准确率区分开来。后扣带回皮质的异常与自闭症诊断访谈沟通评分受损有关。额外的前额叶、外侧颞叶和皮质下结构中的灰质也以 81%至 90%的准确率区分了两组。下额枕额束和上纵束以及胼胝体的膝部和干部的白质可达到 85%的分类准确率。
结论
包括默认模式网络在内的多个大脑区域在自闭症儿童中表现出异常的结构组织。源自结构磁共振成像数据的基于大脑的生物标志物可能有助于确定症状异质性的神经解剖学基础，并开发针对早期干预的靶向干预措施。
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