Yamagata, B. | bioRxiv Science

bioRxiv2019

Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

AimPrior structural MRI studies demonstrated atypical gray matter characteristics in siblings of individuals with autism spectrum disorder (ASD). However, they did not clarify which aspect of gray matter presents the endophenotype. Further, because they did not enroll siblings of TD people, they underestimated the difference between individuals with ASD and their unaffected siblings. The current study aimed to solve these questions.\n\nMethodsWe recruited 30 pairs of adult male siblings (15 of them have an ASD endophenotype, other 15 pairs not) and focused on four gray matter parameters: cortical volume and three surface-based parameters (cortical thickness, fractal dimension, and sulcal depth [SD]). First, we sought to identify a pattern of an ASD endophenotype, comparing the four parameters. Then, we compared individuals with ASD and their unaffected siblings in the cortical parameters to identify neural correlates for the clinical diagnosis accounting for the difference between TD siblings.\n\nResultsA sparse logistic regression with a leave-one-pair-out cross-validation showed the highest accuracy for the identification of an ASD endophenotype (73.3%) with the SD compared with the other three parameters. A bootstrapping analysis accounting for the difference in the SD between TD siblings showed a significantly large difference between individuals with ASD and their unaffected siblings in six out of 68 regions-of-interest accounting for multiple comparisons.\n\nConclusionsThis proof-of-concept study suggests that an ASD endophenotype emerges in SD and that neural correlates for the clinical diagnosis can be dissociated from the endophenotype when we accounted for the difference between TD siblings. (248/250 words)

neuroscience↗

bioRxiv2018

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

Endophenotype refers to a measurable and heritable component between genetics and diagnosis and exists in both individuals with a diagnosis and their unaffected siblings. We aimed to identify a pattern of endophenotype consisted of multiple connections. We enrolled adult male individuals with autism spectrum disorder (ASD) endophenotype (i.e., individuals with ASD and their unaffected siblings) and individuals without ASD endophenotype (i.e., pairs of typical development (TD) siblings) and utilized a machine learning approach to classify people with and without endophenotypes, based on resting-state functional connections (FCs). A sparse logistic regression successfully classified people as to the endophenotype (area under the curve=0.78, classification accuracy=75%), suggesting the existence of endophenotype pattern. A binomial test identified that nine FCs were consistently selected as inputs for the classifier. The least absolute shrinkage and selection operator with these nine FCs predicted severity of communication impairment among individuals with ASD (r=0.68, p=0.021). In addition, two of the nine FCs were statistically significantly correlated with the severity of communication impairment (r=0.81, p=0.0026 and r=-0.60, p=0.049). The current findings suggest that an ASD endophenotype pattern exists in FCs with a multivariate manner and is associated with clinical ASD phenotype.

neuroscience↗