Mapping attentive brain states in real time to support parent-child interaction in autism

Professor Emily Jones (Birkbeck), Dr Anna Gui (Birkbeck), Professor Rob Leech (King's College London, external colaborator) and Dr Silvia Dalvit (BabyBrains)

The proposed PhD aims to develop a novel brain-based approach for supporting families of children with autism with an interdisciplinary project at the intersection between developmental psychology, neuroimaging, machine learning. Autism affects social development in up to 1% of UK children, and is associated with significant reductions in quality of life in adulthood (Oakley et al., 2021). Although early interventions exist, the evidence-base remains poor (Sandbank et al., 2020). Augmenting early intervention is critical to improving outcomes, and must be done in partnership with families to ensure feasibility and acceptability.

Recently we have shown that autistic children show early differences in brain networks that support social engagement (Gui et al., 2021). Identifying the social activities that produce the strongest brain engagement for an individual child provides a route to identifying active ingredients of a strength-based intervention. This exciting PhD project will apply a novel artificial intelligence (AI) approach, called Neuroadaptive Optimisation (Lorenz et al., 2017), in young children (Gui et al., 2022). In this method, neuroimaging data are processed in real time and coupled with an AI algorithm to identify the social behaviours that maximise engagement during natural interactions between parents and their toddlers with and without autism.

The PhD student will be co-supervised by Dr Silvia Dalvit, the founder of the BabyBrains company. BabyBrains specialises in science-based parental communication, and has developed a training model that supports early science-informed parent-child interaction. Using the BabyBrains framework for parental engagement and communication, the PhD student will have the opportunity to learn how to ensure that parents and children are actively involved in an effective and fruitful way. By working with BabyBrains, the student will develop skills in participatory research and science communication.  

Thus, this PhD project is ideal for those who aim to gain strong quantitative skills in data analysis, AI and advanced neuroimaging technology and are highly motivated to communicate evidence-based findings and approaches to the non-scientific community in order to promote participation in science of families and children with autism.

References:

1. Oakley B, Loth E & Murphy DG (2021) Autism and mood disorders, International Review of Psychiatry, 33:3, 280-299, doi: 10.1080/09540261.2021.1872506
2. Sandbank M, Bottema-Beutel K, Crowley S, Cassidy M, Dunham K, Feldman JI, Crank J, Albarran SA, Raj S, Mahbub P, Woynaroski TG (2020) Project AIM: Autism intervention meta-analysis for studies of young children. Psychol Bull. 2020 Jan;146(1):1-29. doi: 10.1037/bul0000215
3. Gui A, Bussu G, Tye C, Elsabbagh M, Charman T, Johnson MH and Jones EJH (2021), Attentive brain states to faces in infants with and without later autism, Translational Psychiatry, 11: 196. doi: 10.1038/s41398-021-01315-9
4. Lorenz R, Hampshire A, Leech R (2017) Neuroadaptive Bayesian Optimization and Hypothesis Testing. Trends Cogn Sci, 21(3):155-167. doi: 10.1016/j.tics.2017.01.006.
5. Gui A, Throm EV, da Costa PF, Haartsen R, Leech R, Jones EJH (2022), Proving and improving the reliability of infant research with Neuroadaptive Bayesian Optimization, Inf. Child. Dev., e2296. Doi: 10.1002/icd.2296