Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Abstract

Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD). Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation, connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and large-scale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.

A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective

“…A systematic search was conducted for relevant studies in three databases (PubMed, Web of Science and Scopus) on 30/05/2018, using the following search terms which were refined from previous reviews (Brambilla et al, 2003; Li et al, 2017; Stanfield et al, 2008): (“Autism” OR “Asperger” OR “Autism spectrum disorder” OR “ASD”) AND (“structural” OR “classification” OR (“sulcal mapping” OR “gyrification index” OR “sulcal pits”)) AND (“magnetic resonance imaging” OR “MRI”). Additionally, manual searches were conducted among the reference sections of the retrieved studies and reviews.…”

“…However the exact etiopathogenesis of ASD remains unclear, with predominant theories proposing genetic factors affecting cortical migration (Nickl‐Jockschat and Michel, 2011) and synaptic regulation (Takahashi et al, 2012), and altered developmental processes leading to both hypo‐ and hyper‐connectivity in different brain regions (Conti et al, 2017; Kana et al, 2014; Muller et al, 2011), specifically local over‐connectivity, long distance under connectivity (Wass, 2011), and excessive growth in several brain regions (Polšek et al, 2011). Consequently, there have been a wide range of structural brain regions implicated with ASD, most commonly that of early brain overgrowth and head circumference (Mosconi et al, 2009; Sacco et al, 2015), as well as more localised brain regions that may be associated with the social and motor impairments characteristic of ASD, including the frontal lobes, amygdala, cerebellum (Amaral et al, 2008; Li et al, 2017; Sivapalan and Aitchison, 2014), corpus callosum (Bellani et al, 2013; Hrdlicka, 2008; Stigler et al, 2011) and basal ganglia (Calderoni et al, 2014; Dougherty et al, 2016a). However, there has not yet been an agreement on structural changes in the brain that reflect these underlying mechanisms of ASD, limiting the utility of machine learning to provide accurate diagnoses of ASD and patient prognoses (Kassraian‐Fard et al, 2016).…”

Altered structural brain asymmetry in autism spectrum disorder: large-scale analysis via the ENIGMA Consortium

“…While much of the neurobiology of ASD remains unknown, subtle alterations of brain structure appear to be involved (reviewed by (4,5)). These include differences in total brain volume (children with ASD have shown a larger average volume), as well as alterations in the inferior frontal, superior temporal, and orbitofrontal cortices, and the caudate nucleus.…”

Características Neuroanatómicas del Síndrome de Asperger

“…Para dilucidar estas controversias, las investigaciones futuras del TEA deberían dirigirse hacia el suministro de información sensible y específica de biomarcadores que, en combinación con las pruebas de comportamiento, podría conducir más fácilmente a un diagnóstico preciso y temprano del trastorno (Pina-Camacho et al). En general, estudios multi-céntricos y multi-modales basados en grandes grupos de pacientes es una de las estrategias clave para aumentar la probabilidad de descubrir nuevos biomarcadores efectivos que caractericen de manera integral las características clínicas y síntomas cognitivos del Síndrome de Asperger (Ruggeri et al, 2014;Li et al, 2017). …”