Functional connectivity of cortical-cerebellar networks in relation to sensorimotor behavior and clinical features in autism spectrum disorder

Unravelling the Cerebellar Involvement in Autism Spectrum Disorders: Insights into Genetic Mechanisms and Developmental Pathways

Autism spectrum disorders (ASDs) are complex neurodevelopmental conditions characterized by deficits in social interaction and communication, as well as repetitive behaviors. Although the etiology of ASD is multifactorial, with both genetic and environmental factors contributing to its development, a strong genetic basis is widely recognized. Recent research has identified numerous genetic mutations and genomic rearrangements associated with ASD-characterizing genes involved in brain development. Alterations in developmental programs are particularly harmful during critical periods of brain development. Notably, studies have indicated that genetic disruptions occurring during the second trimester of pregnancy affect cortical development, while disturbances in the perinatal and early postnatal period affect cerebellar development. The developmental defects must be viewed in the context of the role of the cerebellum in cognitive processes, which is now well established. The present review emphasizes the genetic complexity and neuropathological mechanisms underlying ASD and aims to provide insights into the cerebellar involvement in the disorder, focusing on recent advances in the molecular landscape governing its development in humans. Furthermore, we highlight when and in which cerebellar neurons the ASD-associated genes may play a role in the development of cortico-cerebellar circuits. Finally, we discuss improvements in protocols for generating cerebellar organoids to recapitulate the long period of development and maturation of this organ. These models, if generated from patient-induced pluripotent stem cells (iPSC), could provide a valuable approach to elucidate the contribution of defective genes to ASD pathology and inform diagnostic and therapeutic strategies.

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.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).