Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder.
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder.
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DOI：
10.1109/isbi45749.2020.9098555
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Montillo A
中科院分区：
文献类型：
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作者：
Mellema CJ;Treacher A;Nguyen KP;Montillo A
目前，自闭症谱系障碍(ASD)的诊断依赖于专家临床医生对行为测试的主观、耗时的评估。非侵入性功能磁共振成像(FMRI)表征了大脑的连通性，可用于提供诊断信息和医学民主化。然而，从功能磁共振成像成功构建预测模型，如深度学习模型，需要解决有关模型架构的关键选择，包括层的数量和每层的神经元数量。同时，从功能磁共振成像获得功能连接(FC)特征需要选择具有适当粒度级别的图谱。一旦建立了准确的诊断模型，确定哪些特征是ASD的预测性特征，以及是否在atlas粒度水平上学习了类似的特征是至关重要的。识别新的重要特征扩展了我们对ASD生物学基础的理解，同时识别证实了过去的发现并延伸到atlas水平的特征增强了模型的信心。为了确定合适的体系结构配置，比较了高性能模型和低性能模型的配置的概率分布。为了确定地图集粒度的影响，从3个粒度的地图集中提取连通性特征，并用排列特征重要性对重要特征进行排序。结果表明，性能最好的模型每层使用2-4个隐含层到16个神经元之间，这取决于粒度。在所有3个图谱粒度水平上被确定为重要的连接特征包括辅助运动回和语言联系皮质的Fc，这些区域的异常发育与ASD中常见的社会和感觉处理缺陷有关。重要的是，通常不包括在功能分析中的小脑，也被认为是一个连接异常的区域，对ASD具有很高的预测性。这项研究的结果确定了未来ASD研究中要包括的重要区域，帮助选择网络架构，并帮助确定适当的粒度级别，以促进ASD准确诊断模型的开发。
Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construction of predictive models, such as deep learning models, from fMRI requires addressing key choices about the model’s architecture, including the number of layers and number of neurons per layer. Meanwhile, deriving functional connectivity (FC) features from fMRI requires choosing an atlas with an appropriate level of granularity. Once an accurate diagnostic model has been built, it is vital to determine which features are predictive of ASD and if similar features are learned across atlas granularity levels. Identifying new important features extends our understanding of the biological underpinnings of ASD, while identifying features that corroborate past findings and extend across atlas levels instills model confidence. To identify aptly suited architectural configurations, probability distributions of the configurations of high versus low performing models are compared. To determine the effect of atlas granularity, connectivity features are derived from atlases with 3 levels of granularity and important features are ranked with permutation feature importance. Results show the highest performing models use between 2–4 hidden layers and 16–64 neurons per layer, granularity dependent. Connectivity features identified as important across all 3 atlas granularity levels include FC to the supplementary motor gyrus and language association cortex, regions whose abnormal development are associated with deficits in social and sensory processing common in ASD. Importantly, the cerebellum, often not included in functional analyses, is also identified as a region whose abnormal connectivity is highly predictive of ASD. Results of this study identify important regions to include in future studies of ASD, help assist in the selection of network architectures, and help identify appropriate levels of granularity to facilitate the development of accurate diagnostic models of ASD.