Project ID NS-MH2023_05


Co Supervisor 1A IoPPN/Forensic and Neurodevelopmental ScienceWebsite

Co Supervisor 1B IoPPN/Forensic and Neurodevelopmental ScienceWebsite

Mapping the landscape of brain functional dynamics in autism spectrum disorder

Background: Autism Spectrum Disorders (ASD) is a highly diverse and heterogeneous condition. Histopathology and neuroimaging studies in children and adults with a diagnosis of ASD have shown subtle disruptions in the organisation of their neural systems. Understanding underlying mechanisms that lead to ASD and establishing who is most likely to benefit from treatment (stratification) is currently one of the most important neuroscientific challenges.

Dynamic functional connectivity (dFC) measures the constant neural adjustments needed to control different brain states, adapt to transient situations, and integrate information. Measures of dFC derived from resting-state functional MRI (rs-fMRI) are associated with cognitive flexibility and information processing capacity, whilst altered transitions between dFC brain states have been reported in ASD.

Aims: In this project we will map the functional dynamic landscape of brain states in a population of ASD participants and matched typically developing (TD) individuals, in order to identify and validate stratification biomarkers for ASD.

Techniques/skills: We will use data from the KCL-lead EU-AIMS LEAP project, the largest multi-centre, multi-disciplinary observational study worldwide. Data available includes 437 children and adults with ASD and 300 TD participants. We will also use data available from pharmacological studies taking place in our department (PI McAlonan; data available from Citalopram, Tianeptine, Arbaclofan, CDB, psylocibin,…) and assess the effect of drugs affecting different aspects of brain activity and excitatory/inhibitory balance in brain dynamics.
The student in this project will learn advanced data analysis techniques in order to characterise and assess brain dynamics (e.g. neuroimaging, graph theory, supervised/unsupervised machine learning, gradient projection analyses).

Yearly objectives:
Rotation: Anomaly networks to detect effect of pharmacological challenges in TD vs ASD
Y2: Characterisation of brain dynamic landscape in TD vs ASD (LEAP dataset)
Y3: Characterisation of brain dynamic landscape in pharmacological challenges
Y4: Integrating data and writing up

One representative publication from each co-supervisor:

Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, Gale-Grant O, Schuh A, Pietsch M, Chew A, Harper N, Falconer S, Poppe T, Hughes E, Hutter J, Price AN, Tournier J-D, Cordero-Grande L, Counsell SJ, Rueckert D, Arichi T, Hajnal JV, Edwards AD, Deprez M, Batalle D (2022); Predicting age and clinical risk from the neonatal connectome; Neuroimage 257, 119319

Wong, N. M., Dipasquale, O., Turkheimer, F., Findon, J. L., Wichers, R. H., Dimitrov, M., Murphy, C. M., Stoencheva, V., Robertson, D. M., Murphy, D. G., Daly, E., & McAlonan, G. M. (2022). Differences in social brain function in autism spectrum disorder are linked to the serotonin transporter: A randomised placebo-controlled single-dose crossover trial. Journal of psychopharmacology 36(6), 723-731.