Autism in Fragile X Syndrome; A Functional MRI Study of Facial Emotion-Processing

Abstract

Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and autism spectrum disorder, and among those with fragile X syndrome, approximately 1/3rd meet a threshold for an autism spectrum disorder (ASD) diagnosis. Previous functional imaging studies of fragile X syndrome have typically focused on those with fragile X syndrome compared to either neurotypical or autism spectrum disorder control groups. Further, the majority of previous studies have tended to focus on those who are more intellectually able than is typical for fragile X syndrome. In this study, we examine the impact of autistic traits in individuals with fragile X syndrome on a paradigm looking at facial emotion processing. The study included 17 individuals with fragile X syndrome, of whom 10 met criteria for autism as measured by the Autism Diagnostic Observation Schedule (ADOS). Prior to the scan, participants rehearsed on a mock scanner to help acclimatize to the scanner environment and thus allow more severely affected individuals to participate. The task examined the blood-oxygen-level-dependent (BOLD) response to fearful and neutral faces taken from the Ekman faces series. Individuals in the autism group had a region of significantly reduced activity centered on the left superior temporal gyrus, compared to those with FXS alone, in response to the fearful faces. We suggest that autism in individuals with fragile X syndrome is associated with similar changes in the neurobiology of facial emotion processing as seen in idiopathic autism.

Calcium channelopathies and intellectual disability: a systematic review

“…Thus, there is a possibility that other cases were missed due to the limitation of this modality. Advanced imaging methods including the fMRI and H-MRS revealed brain abnormalities (including metabolic changes) that could not be detected with conventional MRI for the cases diagnosed with temporal lobe epilepsy and autism [ 31 , 32 ], NGD [ 33 ], and minimal hepatic encephalopathy with liver cirrhosis [ 34 ]. Besides, there was a correlation between choline/creatine ratio and cognitive deficits and genotypes for the cases diagnosed with NGD [ 33 ].…”

“…Cases with normal conventional MRI can have concealed malformations, which can be detected by advanced brain imaging methods. For instance, functional MRI (fMRI) can detect brain abnormalities that conventional MRI cannot spot in temporal lobe epilepsy and autism [ 31 , 32 ]. Similarly, proton magnetic resonance spectroscopy (H-MRS) can detect abnormalities in cases diagnosed with neuronopathic Gaucher’s disease (NGD) [ 33 ].…”

Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis

“…Scrubbing follows a model-driven strategy, whereby the volumes affected by extreme motion are identified and additional scan nulling regressors (with 1 s at the volumes where motion spikes are detected and 0 s elsewhere) are regressed out from the fMRI either directly in the GLM as covariates or nuisance regressors, or via multiple regression where the output residuals constitute the signal free of noise ( Siegel et al, 2014 ). Alternatively, volumes associated with motion outliers can be interpolated based on non-corrupted volumes ( Mazaika et al, 2009 ; Tierney et al, 2016 ; Caballero-Gaudes and Reynolds, 2017 ; Mckechanie et al, 2019 ; Rudas et al, 2020 ).…”

Task-based fMRI in early Multiple Sclerosis: what is the best head motion correction approach?

“…These can be either regressed out via GLM where the regressors are added directly in the GLM and accounted as covariates or nuisance regressors, or via multiple regression where the output residuals constitute the signal free of noise. Alternatively, volumes associated with motion outliers can be interpolated based on non-corrupted volumes (Caballero-Gaudes and Reynolds, 2017;Mazaika et al, 2009;Mckechanie et al, 2019;Rudas et al, 2020). Despite all the worthy efforts, there is still no consensus regarding the optimal number of MP-related regressors to consider for tackling head shifts, nor the most appropriate additional approach to mitigate motion outliers (Zaitsev et al, 2015).…”

“…These can be either regressed out via GLM where the regressors are added directly in the GLM and accounted as covariates or nuisance regressors, or via multiple regression where the output residuals constitute the signal free of noise. Alternatively, volumes associated with motion outliers can be interpolated based on non-corrupted volumes (Caballero-Gaudes and Reynolds, 2017; Mazaika et al, 2009; Mckechanie et al, 2019; Rudas et al, 2020).…”