A multimodal neural signature of face processing in autism within the fusiform gyrus
Authors: Floris Dorothea L. , Llera Alberto , Zabihi Mariam , Moessnang Carolin , Jones Emily J.H. , Mason Luke , Haartsen Rianne , Holz Nathalie E. , Mei Ting , Elleaume Camille , Vieira Bruno Hebling , Pretzsch Charlotte M. , Forde Natalie , Baumeister Sarah , Dell’Acqua Flavio , Durston Sarah , Banaschewski Tobias , Ecker Christine , Holt Rosemary J. , Baron-Cohen Simon , Bourgeron Thomas , Charman Tony , Loth Eva , Murphy Declan G. M. , Buitelaar Jan K. , Beckmann Christian F. ,   the EU-AIMS LEAP group, Langer Nicolas 
Published: January 2024

AbstractBackgroundDifferences in face processing are commonly reported in case/control studies of autism. Their neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). Nonetheless, it is poorly understood how different variation(s) in brain anatomy and functioncombineto impact face processing and social functioning. Extracting the shared information across different modalities is essential to better delineate the complex relationship between brain structure and function, leading to a more comprehensive understanding of the mechanisms underlying autism.MethodsHere, we leveraged data from the large multimodal EU-AIMS Longitudinal European Autism Project (LEAP) to study the cross-modal signature of face processing within the FFG across structural magnetic resonance imaging (MRI), resting-state fMRI (rs-fMRI), task-fMRI (based on the Hariri emotional faces task) and electroencephalography (EEG; recorded when observing facial stimuli) in a sample of 99 autistic and 105 non-autistic individuals (NAI) aged 6-30 years. We combined two methodological innovations: (i) normative modelling was employed on each imaging modality separately to derive individual-level deviations from a predicted developmental trajectory and (ii) unimodal deviations were fused through Linked Independent Component (IC) Analysis to simultaneously decompose the imaging data into underlying modes that characterise multi-modal signatures across the cohort. Next, we tested whether ICs significantly differed between autistic and NAI and whether multimodal ICs would outperform unimodal ICs in discriminating autistic individuals from NAI using a support vector machine under 10-fold cross-validation. Finally, we tested the association between multimodal ICs and cognitive, clinical measures of social or non-social functioning in autism using canonical correlation analysis (CCA).ResultsIn total, 50 independent components were derived. Among these one multimodal IC differed significantly between autistic and NAI (t=3.5,pFDR=0.03). This IC was mostly driven by bilateral rs-fMRI, bilateral structure, right task-fMRI, and left EEG loadings and implicated both face-selective and retinotopic regions of the FFG. Furthermore, multimodal ICs performed significantly better at differentiating autistic from NAI than unimodal ICs (p<0.001). Finally, there was a significant multivariate association between multimodal ICs and a set of cognitive and clinical features associated with social functioning (r=0.65,pFDR=0.008); but not with non-social features.DiscussionThe FFG appears to be a central region differentially implicated in autistic and NAI across a range of inter-related imaging modalities and category-selective regions in both the left and right hemispheres. Elucidating more integrated, individual-level neural associations of core social functioning in autism will pave the way for further work on identifying more fine-grained stratification, mechanistic and prognostic biomarkers, and the development of more personalised support.