Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Nov;8(11):1084-1093. doi: 10.1016/j.bpsc.2022.08.011. Epub 2022 Sep 6.

ABSTRACT

BACKGROUND: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group.

METHODS: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6-30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions-A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants’ GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles.

RESULTS: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group.

CONCLUSIONS: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

PMID:36075529 | DOI:10.1016/j.bpsc.2022.08.011

Mei T, Forde NJ, Floris DL, Dell’Acqua F, Stones R, Ilioska I, Durston S, Moessnang C, Banaschewski T, Holt RJ, Baron-Cohen S, Rausch A, Loth E, Oakley B, Charman T, Ecker C, Murphy DGM; EU-AIMS LEAP group, Beckmann CF, Llera A, Buitelaar JK.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2022 Sep 5:S2451-9022(22)00212-9. doi: 10.1016/j.bpsc.2022.08.011. Online ahead of print.

ABSTRACT

BACKGROUND: Although many studies have explored atypicalities in gray and white matter (GM, WM) morphology of autism, most of them rely on unimodal analyses that do not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between autism and controls and explore associations between these brain patterns and clinical measures.

METHODS: We studied 183 individuals with autism and 157 non-autistic individuals (6-30 years) in a large deeply phenotyped autism dataset (EU-AIMS LEAP). Linked Independent Component Analysis was utilized to link all participants’ GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relations between brain patterns and clinical profiles.

RESULTS: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula, frontal, pre- and post-central, cingulate, and caudate areas, and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM, and symptoms of restricted and repetitive behavior in the autism group.

CONCLUSIONS: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism, and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

PMID:36075529 | DOI:10.1016/j.bpsc.2022.08.011

Llera A, Brammer M, Oakley B, Tillmann J, Zabihi M, Amelink JS, Mei T, Charman T, Ecker C, Dell’Acqua F, Banaschewski T, Moessnang C, Baron-Cohen S, Holt R, Durston S, Murphy D, Loth E, Buitelaar JK, Floris DL, Beckmann CF.

BMC Med Res Methodol. 2022 Aug 16;22(1):229. doi: 10.1186/s12874-022-01656-z.

ABSTRACT

An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, e.g. Dev. Cogn. Neur. 32:43-54, 2018; PLoS Med. 12(3):e1001779, 2015; Elam and Van Essen, Enc. Comp. Neur., 2013, as well as in psychiatric cohorts, e.g. Trans. Psych. 10(1):100, 2020; Mol. Psych. 19:659-667, 2014; Mol. Aut. 8:24, 2017; Eur. Child and Adol. Psych. 24(3):265-281, 2015. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researchers aim to integrate relationships across multiple measures. Here we aim to evaluate different imputation strategies to fill in missing values in clinical data from a large (total N = 764) and deeply phenotyped (i.e. range of clinical and cognitive instruments administered) sample of N = 453 autistic individuals and N = 311 control individuals recruited as part of the EU-AIMS Longitudinal European Autism Project (LEAP) consortium. In particular, we consider a total of 160 clinical measures divided in 15 overlapping subsets of participants. We use two simple but common univariate strategies-mean and median imputation-as well as a Round Robin regression approach involving four independent multivariate regression models including Bayesian Ridge regression, as well as several non-linear models: Decision Trees (Extra Trees., and Nearest Neighbours regression. We evaluate the models using the traditional mean square error towards removed available data, and also consider the Kullback-Leibler divergence between the observed and the imputed distributions. We show that all of the multivariate approaches tested provide a substantial improvement compared to typical univariate approaches. Further, our analyses reveal that across all 15 data-subsets tested, an Extra Trees regression approach provided the best global results. This not only allows the selection of a unique model to impute missing data for the LEAP project and delivers a fixed set of imputed clinical data to be used by researchers working with the LEAP dataset in the future, but provides more general guidelines for data imputation in large scale epidemiological studies.

PMID:35971088 | PMC:PMC9380301 | DOI:10.1186/s12874-022-01656-z

Hansen JY, Shafiei G, Vogel JW, Smart K, Bearden CE, Hoogman M, Franke B, van Rooij D, Buitelaar J, McDonald CR, Sisodiya SM, Schmaal L, Veltman DJ, van den Heuvel OA, Stein DJ, van Erp TGM, Ching CRK, Andreassen OA, Hajek T, Opel N, Modinos G, Aleman A, van der Werf Y, Jahanshad N, Thomopoulos SI, Thompson PM, Carson RE, Dagher A, Misic B.

Nat Commun. 2022 Aug 10;13(1):4682. doi: 10.1038/s41467-022-32420-y.

ABSTRACT

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

PMID:35948562 | PMC:PMC9365855 | DOI:10.1038/s41467-022-32420-y

Shi H, Schweren LJS, Ter Horst R, Bloemendaal M, van Rooij D, Arias Vasquez A, Hartman CA, Buitelaar JK.

Brain Behav Immun. 2022 Aug 6;106:100-110. doi: 10.1016/j.bbi.2022.07.165. Online ahead of print.

ABSTRACT

BACKGROUND: Dietary patterns have been associated with variations in behavior. However, evidence has been limited and mixed, and the underlying mechanism remains unclear.

OBJECTIVE: Extend a previous study reporting significant associations between food patterns and behavioral disinhibition and explore whether low-grade inflammation is linked to behaviors and mediates the association between diet and behavioral disinhibition.

DESIGN: Among participants of the UK Biobank (UKB) we extracted a single behavioral disinhibition principal component using the UKB touchscreen questionnaire, Mental Health Questionnaire (MHQ), and registered diagnoses. We identified four dietary patterns (prudent diet, elimination of wheat/dairy/eggs, meat-based diet, full-cream dairy consumption) by using the Food Frequency Questionnaire (FFQ). Immune biomarkers and an aggregated inflammation score (INFLA-score) were used to characterize low-grade inflammation. Associations between dietary patterns and immune biomarkers, between immune biomarkers and disinhibition were assessed, with adjustment for demographics, lifestyle factors, and somatic health conditions. Next, mediation analyses were run to examine whether the association between dietary patterns and disinhibition was partially explained by inflammatory levels. We also conducted subgroup analyses to explore whether associations and the mediation effect differed by sex, age, ethnicity/race, body-mass-index (BMI), and socioeconomic status (SES).

RESULTS: The prudent diet was negatively, and the meat-based diet was positively associated with several pro-inflammatory biomarkers. Most immune biomarkers were positively associated with disinhibition (numbers of lymphocytes (β_standardized = 0.082, p < 0.001), monocytes (β_standardized = 0.043, p < 0.001), neutrophils (β_standardized = 0.071, p < 0.001), platelets (β_standardized = 0.022, p < 0.001), leukocytes (β_standardized = 0.093, p < 0.001), C-reactive protein (β_standardized = 0.051, p < 0.001), and for INFLA-score (β_standardized = 0.074, p < 0.001). In the mediation model, the INFLA-score mediated the association between prudent diet and meat-based diet and disinhibition score, with a significant indirect effect of low-grade inflammation for the prudent diet-disinhibition association (β_standardized = -0.007, p < 0.001) and for meat-disinhibition association (β_standardized = 0.001, p < 0.001)). Although all effects were small, covariates and interaction term adjustments did not attenuate the effects, and neither did most subgroup-only analyses.

CONCLUSIONS: The prudent diet was associated with a lower disinhibition score and this effect was partially mediated by the lower inflammation. Reversely, the meat-based diet was linked to more inflammation, which was associated with more disinhibition. Our findings suggest mediating effects of immune function in the relationship between diet and behavioral disinhibition. However further alternative designs such as interventional trials are needed to establish causal effects.

PMID:35944739 | DOI:10.1016/j.bbi.2022.07.165