Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI.

OBJECTIVE: To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging. METHODS: We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification. RESULTS: The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance. CONCLUSION: The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

Microstructural abnormalities of the right hemisphere in preschool autism spectrum disorders.

BACKGROUND: This study aims to investigate microstructural abnormalities within and between hemispheres in preschool children with autism spectrum disorders (ASD) using diffusion basis spectrum imaging (DBSI). METHODS: A total of 35 ASD patients and 32 healthy controls (HC), matched for sex and age, underwent DBSI at 3T. We analyzed DBSI-derived indices of brain white matter using tract-based spatial statistics (TBSS) to compare ASD and HC groups. Support vector machine (SVM) classification was employed to evaluate the potential of positive DBSI parameters in distinguishing ASD patients. Additionally, correlation analyses were conducted to explore relationships between positive DBSI parameters and clinical scales. RESULTS: Patients in the ASD group exhibited significantly higher fiber ratios in the right brainstem tracts, increased radial diffusivity in the left superior longitudinal fasciculus, and reduced fractional anisotropy (FA) in various fiber tracts, including projection, commissural, and association fibers, compared to HC. Notably, the FA of the right cingulum correlated positively with the Gesell scale (r = 0.439, p = 0.008) and achieved a specificity of 90% in identifying ASD. CONCLUSION: The DBSI findings suggest asynchronous myelination in the right hemisphere and cerebellum in preschool ASD, with the FA value of the right cingulate gyrus appearing to be a reliable marker for ASD and may serve as a potential diagnostic parameter for preschool ASD.

Individual Brain Morphological Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation for Autism Spectrum Disorder Identification.

Background: Structural magnetic resonance imaging (sMRI) reveals abnormalities in patients with autism spectrum syndrome (ASD). Previous connectome studies of ASD have failed to identify the individual neuroanatomical details in preschool-age individuals. This paper aims to establish an individual morphological connectome method to characterize the connectivity patterns and topological alterations of the individual-level brain connectome and their diagnostic value in patients with ASD. Methods: Brain sMRI data from 24 patients with ASD and 17 normal controls (NCs) were collected; participants in both groups were aged 24-47 months. By using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method, all participants's morphological brain network were ascertained. Student's t-tests were used to extract the most significant features in morphological connection values, global graph measurement, and node graph measurement. Results: The results of global metrics' analysis showed no statistical significance in the difference between two groups. Brain regions with meaningful properties for consensus connections and nodal metric features are mostly distributed in are predominantly distributed in the basal ganglia, thalamus, and cortical regions spanning the frontal, temporal, and parietal lobes. Consensus connectivity results showed an increase in most of the consensus connections in the frontal, parietal, and thalamic regions of patients with ASD, while there was a decrease in consensus connectivity in the occipital, prefrontal lobe, temporal lobe, and pale regions. The model that combined morphological connectivity, global metrics, and node metric features had optimal performance in identifying patients with ASD, with an accuracy rate of 94.59%. Conclusion: The individual brain network indicator based on the JSSE method is an effective indicator for identifying individual-level brain network abnormalities in patients with ASD. The proposed classification method can contribute to the early clinical diagnosis of ASD.