Autism Is Associated With Interindividual Variations of Gray and White Matter Morphology

Mei, Ting; Forde, Natalie J.; Floris, Dorothea L.; Dell’Acqua, Flavio; Stones, Richard; Ilioska, Iva; Durston, Sarah; Moessnang, Carolin; Banaschewski, Tobias; Holt, Rosemary; Baron‐Cohen, Simon; Rausch, Annika; Loth, Eva; Oakley, Bethany; Charman, Tony; Ecker, Christine; Murphy, Declan; Beckmann, Christian F.; Llera, Alberto; Buitelaar, Jan K.

doi:10.1016/j.bpsc.2022.08.011

Cited by 7 publications

(14 citation statements)

References 61 publications

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“…In order to gain more comprehensive insights into cross-modal signatures of face processing, we merged the different individual-level deviations from all imaging modalities (grey matter volume, FFA-connectivity, T-maps contrasting the faces condition to the shapes condition, and the principal components of source reconstructed time series) using Linked Independent Component Analysis (LICA) 47,[49][50][51][52]56,86 (see SI). This is a Bayesian extension of the single modality ICA model which provides an automatic and simultaneous decomposition of the brain features into independent components (ICs) that characterize the inter-subject brain variability.…”

Section: Linked Independent Component Analysismentioning

confidence: 99%

“…Hemispheres were modelled separately given known brain asymmetric differences in autism 30,44,87 and to study the hemispheric contributions and model the different noise characteristics individually. We estimated 50 independent components based on our sample size and following recommendations described in earlier papers [50][51][52]56,86 (i.e., sample size ~N / 4). To evaluate the robustness of our selected model order (N=50), we re-ran LICA using different dimensional factorizations of subject loadings (N=40 and N=60) and computed correlations among them.…”

Section: Linked Independent Component Analysismentioning

confidence: 99%

“…Efficient modelling of this has been shown to increase robustness to noise 8,47,48 and sensitivity to detect potentially subtle effects in high-dimensional data that may otherwise be missed in one single modality 47–52 . Accordingly, prior multimodal efforts are promising as they show that combining information from brain structure and function significantly increases accuracy in predictive frameworks 52–56 . Also in autism, a recent study combining different neuroimaging measures of rs-fMRI, diffusion-weighted imaging and structural morphometry specifically showed that rs-connection topographies within the FFG were differentially implicated between autistic and NAI 57 .…”

Section: Introductionmentioning

confidence: 99%

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A multimodal neural signature of face processing in autism within the fusiform gyrus

Floris,

Llera,

Zabihi

et al. 2024

Preprint

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BackgroundDifferences 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.

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Section: Linked Independent Component Analysismentioning

confidence: 99%

Section: Linked Independent Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A multimodal neural signature of face processing in autism within the fusiform gyrus

Floris,

Llera,

Zabihi

et al. 2024

Preprint

Self Cite

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“…Adaptive Behavior Scale (50) with Communication, Daily Living, Socialization subscales), emotional face matching performance (i.e., Hariri faces task (52)), and social sensitivity to complex emotions (i.e., Reading the Mind in the Eyes test (51) [RMET]) and 2) non-social features comprising restricted, repetitive behaviours (RRBs) (i.e., ADOS-RRB, ADI-RRB, the Repetitive Behavior Scale (53) [RBS-R]), systemizing (i.e., the Systemizing Quotient (55)(56)(57) [SQ]), shape matching performance (i.e., Hariri shapes task, as the control condition to the Hariri emotional faces task) and sensory processing atypicalities (i.e., Short Sensory Pro le (54) [SSP]) (see SI and Table S8). To tackle missing clinical data and to not further reduce sample size, we used imputed clinical data (80), as in previous work with this dataset (41,81).…”

Section: Clinical and Cognitive Measuresmentioning

confidence: 99%

“…We can further penetrate across different biological spatial and temporal scales of variation leveraging the unique, complementary aspects covered by each individual imaging modality. Prior multimodal efforts are promising as they show that combining information from brain structure and function signi cantly increases accuracy in predictive frameworks (37)(38)(39)(40)(41). Also, a recent study combining different neuroimaging measures of rs-fMRI, diffusion-weighted imaging and structural morphometry speci cally showed that rs-connection topographies within the FFG were differentially implicated between autistic and NAI (42).…”

Section: Introductionmentioning

confidence: 99%

A multimodal neural signature of face processing in autism within the fusiform gyrus

Floris,

Llera,

Zabihi

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Differences 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 function combine to impact face processing and social functioning. Extracting the shared information across different modalities is essential to derive a more comprehensive understanding of the mechanisms underlying autism. Here, we leveraged a large multimodal sample to study the cross-modal signature of face processing within the FFG across four imaging modalities (structural MRI, resting-state fMRI [rs-fMRI], task-fMRI and EEG) in 204 individuals aged 7-30years comprising both autistic and non-autistic individuals (NAI). Combining two methodological innovations – normative modeling and linked independent component analysis – we integrated individual-level deviations across modalities to assess the efficacy of multimodal components in differentiating autistic from NAI and informing autism-associated social functioning. Autistic individuals differed significantly in a multimodal component, driven by bilateral rs-fMRI, bilateral structure, right task-fMRI, and left EEG loadings involving face-selective and retinotopic FFG regions. Multimodal components outperformed unimodal ones in differentiating autistic from NAI. Within the autism group, there was a significant multivariate association between multimodal components and a set of cognitive and clinical features associated with social functioning but not non-social features. These findings underscore the importance of elucidating individual-level, integrated neural associations of core social functioning in autism, offering potential for refined stratification and the identification of mechanistic and prognostic biomarkers.

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Neurobiological Concomitants of Autism as Informers of Clinical Practice: a Status Review

Bitsika

Sharpley

2023

Adv Neurodev Disord

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Objectives Although a great deal of research is conducted into the neurobiological correlates of autism, there has been no recent overview of the findings from this research in terms of how they translate to clinical practice. To provide an overview of recent literature in the fields of head size, brain volume, brain region, connectivity between brain regions, and synaptic function, as they apply to autism, with a view to informing clinical practice. Methods Major empirical and review papers on these topics from the last 30 years were identified via a search of PubMed, Google Scholar, and PsychInfo, using the descriptors ‘autism’, ‘head size’, ‘brain volume’, ‘brain region’, ‘connectivity between brain regions’, and ‘synaptic function’. To distil the key findings relevant to clinical practice, both authors conducted blind reviews, producing a set of ‘key’ papers for this purpose. Results Each field produced a number of major findings, which were then described and discussed, and translated into a table of ‘Findings’, ‘Diagnostic suggestions’ (for the clinician’s protocols), and ‘Clinical actions’ (suggestions for interactions with carers of autistic children and adults). Conclusions The stability of the research within each field varies, with some clear findings and others that are yet to be consistently verified. Despite this, some specific clinical implications for diagnosis and treatment (particularly clinician-carer interactions) are described.

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Autism Is Associated With Interindividual Variations of Gray and White Matter Morphology

Cited by 7 publications

References 61 publications

A multimodal neural signature of face processing in autism within the fusiform gyrus

A multimodal neural signature of face processing in autism within the fusiform gyrus

A multimodal neural signature of face processing in autism within the fusiform gyrus

Neurobiological Concomitants of Autism as Informers of Clinical Practice: a Status Review

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