Neural correlates and predictors of speech and language development in infants at elevated likelihood for autism: a systematic review - Morrel, Jessica; Singapuri, Kripi; Landa, Rebecca J.; Reetzke, Rachel (Frontiers in Human Neuroscience, 2023)
Autism spectrum disorder (ASD) is an increasingly prevalent and heterogeneous neurodevelopmental condition, characterized by social communicative differences, and a combination of repetitive behaviors, focused interests, and sensory sensitivities. Early speech and language delays are characteristic of young autistic children and are one of the first concerns reported by parents; often before their child's second birthday. Elucidating the neural mechanisms underlying these delays has the potential to improve early detection and intervention efforts. To fill this gap, this systematic review aimed to synthesize evidence on early neurobiological correlates and predictors of speech and language development across different neuroimaging modalities in infants with and without a family history of autism (at an elevated [EL infants] and low likelihood [LL infants] for developing autism, respectively). A comprehensive, systematic review identified 24 peer-reviewed articles published between 2012-2023, utilizing structural magnetic resonance imaging (MRI; n=2), functional MRI (fMRI; n=4), functional near-infrared spectroscopy (fNIRS; n=4), and electroencephalography (EEG; n=14). Three main themes in results emerged: compared to LL infants, EL infants exhibited (1) atypical language-related neural lateralization; (2) alterations in structural and functional connectivity; and (3) mixed profiles of neural sensitivity to speech and non-speech stimuli, with some differences detected as early as 6 weeks of age. These findings suggest that neuroimaging techniques may be sensitive to early indicators of speech and language delays well before overt behavioral delays emerge. Future research should aim to harmonize experimental paradigms both within and across neuroimaging modalities and additionally address the feasibility, acceptability, and scalability of implementing such methodologies in non-academic, community-based settings.