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

Abstract

2 The number of tables: 3 The number of figures: 2 The number of words in manuscript: 3809 The number of words in abstract: 248 Field: neuroimaging 3 Abstract Aim:Prior 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. Methods:We 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. Results:A 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. Conclusions:4 This 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)

Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction

“…Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. As an example, accelerated aging atrophy has been found in neurodegenerative diseases, such as Alzheimer’s Diseases [ 11 , 12 ]; other studies have investigated altered morphological patterns during neurodevelopment in neurological disorders, such as Autism Spectrum Disorder (ASD) [ 13 , 14 ]. Recently, a comprehensive index has been introduced to describe the brain age of a subject [ 15 ].…”

Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder

“…Frontiers in Neuroinformatics 08 frontiersin.org as between the intra-hemispheric R temporal fusiform cortex and the R hippocampus (Spera et al, 2019). Yamagata et al (2019) applied an ML method to determine resting-state functional connectivity modes based on MRI data and discovered that there was a distinct functional relationship between the right anterior cingulate cortex and right middle temporal gyrus in ASD, indicating this FC may provide a neurobiological marker for diagnosis.…”

Current development of biosensing technologies towards diagnosis of mental diseases

“…Abnormality of these biomarkers can be measured by biosensors to distinguish the presence and absence of disease states (Schmidt et al, 2011), which provides a more convenient and effective tool for rapid diagnosis. These bio-sensors utilize cutting-edge technology to not only collect and compare biological data, such as eyetracking data, electroencephalography (EEG), electrooculogram (EOG), and cognition and behavior in virtual reality (VR), but also employ machine learning algorithms to extract biometrics for the more objective results and higher accuracy (Burdea and Coiffet, 2003;Plitt et al, 2015;Ibrahim et al, 2018;Yaneva et al, 2018;Yamagata et al, 2019;Zhao et al, 2021). The multiple-signal sensors combined with machine learning were found to be a relatively novel trend, which offer a more comprehensive understanding of participant responses.…”