An Abnormal Cerebellar Network in Children with Autistic Spectrum Disorder: A Morphometric Study

Abstract

Autism is a neurodevelopmental disorder characterized by poor social abilities, communication deficiency and restricted behavioural patterns. Recently, scholars started to consider the possibility of detecting biological markers for better and faster diagnosing autism. This problem has been approached from different perspectives considering biochemical, neurophysiological, and neuroanatomical markers. Following this perspective, our intent was to investigate whether a structural brain signature of autism can be detected in children by using a whole brain morphological analysis. To this aim, we selected 43 male children with autistic spectrum disorder and 46 male controls, matched for age. Structural brain images (T1 image), intelligence scores (Full IQ, Verbal IQ, Performance IQ), and Autism Diagnostic Observation Schedule (ADOS) scores were considered for analyses. Source-Based Morphometry, a multivariate method based on Independent Component Analysis to detect maximally independent cortical networks of gray matter differences was applied to autistic and control brains. Results showed a statistically different network between ASD children and controls, including several cerebellar regions (Inferior Semi-lunar lobule, Tuble, Uvula, Pyramis, Declive, Cerebellar Tonsil) and the Fusiform Gyrus, confirming, but, also expanding previous results. In addition, separate temporal, frontal, and parietal networks were found to be significantly correlated with the Stereotyped Behaviour ADOS scores. These morphologic differences may be particularly useful in paving the way for future objective methods to diagnose autism.

Three shades of grey: detecting brain abnormalities in children with autism using source‐, voxel‐ and surface‐based morphometry

“…SoBM is an innovative approach to study the neural abnormalities in autism. As a multivariate data‐driven method, it exploits independent component analysis (ICA) to extract clusters of GM covarying between participants as structural networks not defined a priori (Xu et al ., ; Grecucci et al ., ; Pappaianni et al ., ). On the other hand, VBM is a univariate method that allows to find out relevant differences in GM concentration with a direct comparison in terms of clusters of voxels in the brain between groups.…”

“…They also found that the entity of GM abnormalities correlated with social and behavioural deficits as manifested by ASD. Another study tried to extend these results on younger children between 6 and 12 years old (Pappaianni et al ., ). They found a significant network including several cerebellar regions (as inferior semilunar lobule, tuble, uvula, pyramis, declive and cerebellar tonsil) and the fusiform gyrus.…”

“…To this aim, we applied source‐based morphometry (SoBM) on children with and without ASD. To avoid possible confounds due to different developmental trajectories (as possibly displayed by Pappaianni et al ., ), we restricted the age range of autistics and controls to a smaller range. We predict to find evidence of the ASN also in younger individuals with ASD, confirming and strengthening previous results (Grecucci et al ., ).…”

Testing the expanded continuum hypothesis of schizophrenia and bipolar disorder. Neural and psychological evidence for shared and distinct mechanisms

“…Despite the notable results, the conclusions of these studies are limited because most of them have compared SZ and BD only indirectly by analyzing each group of patients with respect to healthy controls (Mcintosh et al, 2004; Farrow et al, 2005; Arnone et al, 2009; Yu et al, 2010; Ellison-Wright and Bullmore, 2010). Moreover, an important methodological limitation is due to the use of univariate methods (such as VBM) to investigate large-scale networks that are involved in psychiatric disorders (Pappaianni et al, 2017; Pappaianni and Grecucci, 2016), since such methods do not consider the relation between different voxels in a whole brain fashion (Xu et al, 2009).…”

Diagnosis of Autism Spectrum Disorder using Cortical Thickness Measures from Structural Magnetic Resonance Brain Images

“…All these 1112 datasets collected MRI (functional and structural) and phenotypic information for each subject. The ABIDE website is http://fcon_1000.projects.nitrc.org/indi/abide/ which describes the scan procedures and parameters [3] [8]. The data available in the website is based on considerable variation SMRI acquisition protocols across sites and from the data of children and adolescents from three participating institutions.…”