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Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

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Abstract

Endophenotype refers to a measurable and heritable component between genetics and diagnosis, and the same endophenotype is present in both individuals with a diagnosis and their unaffected siblings. Determination of the neural correlates of an endophenotype and diagnosis is important in autism spectrum disorder (ASD). However, prior studies enrolling individuals with ASD and their unaffected siblings have generally included only one group of typically developing (TD) subjects; they have not accounted for differences between TD siblings. Thus, they could not differentiate the neural correlates for endophenotype from the clinical diagnosis. In this context, we enrolled pairs of siblings with an ASD endophenotype (individuals with ASD and their unaffected siblings) and pairs of siblings without this endophenotype (pairs of TD siblings). Using resting-state functional MRI, we first aimed to identify an endophenotype pattern consisting of multiple functional connections (FCs) then examined the neural correlates of FCs for ASD diagnosis, controlling for differences between TD siblings. Sparse logistic regression successfully classified subjects as to the endophenotype (area under the curve = 0.78, classification accuracy = 75%). Then, a bootstrapping approach controlling for differences between TD siblings revealed that an FC between the right middle temporal gyrus and right anterior cingulate cortex was substantially different between individuals with ASD and their unaffected siblings, suggesting that this FC may be a neural correlate for the diagnosis, while the other FCs represent the endophenotype. The current findings suggest that an ASD endophenotype pattern exists in FCs, and a neural correlate for ASD diagnosis is dissociable from this endophenotype. (250 words).

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Acknowledgements

This study is the result of “Development of BMI Technologies for Clinical Application” carried out under the Strategic Research Program for Brain Sciences by the Japan Agency for Medical Research and Development (AMED). This work is partly supported by a grant from The Japan Foundation for Pediatric Research (to YA).

Funding

This study is the result of “Development of BMI Technologies for Clinical Application” carried out under the Strategic Research Program for Brain Sciences by the Japan Agency for Medical Research and Development (AMED). This work is partly supported by a grant from The Japan Foundation for Pediatric Research (to YA).

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Correspondence to Yuta Aoki.

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The study was prepared in accordance with the ethical standards of the Declaration of Helsinki.

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Written informed consent was obtained from all the participants, after they had received a complete explanation of the study.

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Yamagata, B., Itahashi, T., Fujino, J. et al. Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder. Brain Imaging and Behavior 13, 1689–1698 (2019). https://doi.org/10.1007/s11682-018-9973-2

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