Predicting the Effectiveness of Music Therapy in Autism Using Resting-State fMRI

Abstract

Introduction

Autism spectrum disorder (ASD) is a neuro-developmental condition, in which symptomatic manifestations are manifolded and characterized by core impairments in social interaction, communication, and restricted or repetitive behaviours and interests. Music therapy (MT) is among the numerous therapeutic tools that are being used to address the core symptoms and mental health needs of individuals diagnosed with ASD. Currently, no reliable biomarkers exist to identify individuals with ASD or guide treatment selection.

Research Question

Resting-state functional connectivity (rsFC) is altered in the brain of individuals with ASD. Recent evidence has revealed different patterns of connectivity, with combined instances of both over- and under-connectivity [1]. Consequently, rsFC is currently being discussed to aid categorization of individuals with ASD and serve as a possible diagnostic tool [1]. Furthermore, results of randomized controlled trial (RCT) clinical studies such as [2] demonstrated that 8-12 weeks of MT is indeed efficacious in enhancing social communication and altering functional brain connectivity. The primary aim of Music4Autism (M4A) [3] is to replicate the findings of [2] and to further explore the hypothesis that changes in predictive abilities mediate changes in mental health outcomes and rsFC after MT. However, the objective of this research project is to investigate the inverse conclusion by using changes in rsFC not as an indication of treatment success, but their baseline measurements as a predictor that anticipates the effectiveness of MT.

Methodology

The assessment will be conducted utilizing psychometric measures and fMRI-neuroimaging data of 80 ASD diagnosed children aged 6-12 [3]. The rsFC analysis encompasses pre-MT baseline measurements and following [2] focusses on increased coupling between auditory and both striatal and motor regions, as well as decreased coupling between auditory and visual regions. To examine the relation between rsFC and MT effectiveness, connectivity strength scores for each participant will be used in a linear regression model to evaluate correlations. Among others, the effectiveness of MT will be assessed through symptom change on the Social Responsiveness Scale (SRS-2), in which parents rate items concerning the social functioning of their children.

Impact

This study aims to identify a neurobiological predictor (biomarker) of MT treatment success. It does so by examining connectivity of large-scale brain networks and its utilization to predict which individuals will efficiently benefit from MT. Thereby it supports further investigations of neurobiologically motivated models of music interventions in autism and explores the neurophysiological underpinnings of the disorder.

References

[1] J. V. Hull, L. B. Dokovna, Z. J. Jacokes, C. M. Torgerson, A. Irimia, and J. D. Van Horn, “Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review”, Front. Psychiatry, vol. 7, p. 205, Jan. 2017. doi: 10.3389/fpsyt.2016.00205.

[2] M. Sharda et al., “Music improves social communication and auditory–motor connectivity in children with autism”, Transl Psychiatry, vol. 8, no. 1, pp. 1–13, Oct. 2018, doi: 10.1038/s41398-018-0287-3.

[3] M. Ruiz et al., “Music for autism: a protocol for an international randomized crossover trial on music therapy for children with autism”, Front. Psychiatry, vol. 14, p. 1256771, Oct. 2023. doi: 10.3389/fpsyt.2023.1256771.