15 mai 2021

Les sous-types de comportement social-communicatif déséquilibré et de comportement répétitif restreint du "trouble du spectre de l'autisme" présentent des circuits neuronaux différents

Aperçu: G.M.

La communication sociale (SC) et les comportements répétitifs restreints (CRR) sont des domaines de symptômes diagnostiques de l'autisme. La gravité de la SC et du CRRpeut différer considérablement au sein et entre les individus et peut être étayée par différents circuits neuronaux et mécanismes génétiques.
La modélisation de l'équilibre SC-RRB pourrait aider à identifier comment les circuits neuronaux et les mécanismes génétiques correspondent à une telle hétérogénéité phénotypique. 

Ici, nous avons développé un modèle de stratification phénotypique qui permet des prédictions de sous-types SC = CRR, SC> CRR et CRR> SC très précises (97-99%) hors échantillon. En appliquant ce modèle aux données IRMf à l'état de repos de l'ensemble de données EU-AIMS LEAP (n = 509), nous constatons que si les sous-types phénotypiques partagent de nombreux points communs en termes de connectivité fonctionnelle intrinsèque, ils montrent également des différences réplicables au sein de certains réseaux par rapport à un groupe au développement typique (DT).
Plus précisément, le réseau somatomoteur est hypoconnecté avec les circuits périsylviens en SC> CRR et les circuits d'association visuelle en SC = CRR. Le sous-type SC = CRR montre une hyperconnectivité entre le moteur médial et les circuits de saillance antérieure.
Les gènes qui sont fortement exprimés dans ces réseaux montrent un modèle d'enrichissement différentiel avec des gènes connus associés à l'autisme, indiquant que ces circuits sont affectés par différents mécanismes génomiques associés à l'autisme. 

Ces résultats suggèrent que les sous-types de déséquilibre SC-CRR partagent de nombreux points communs, mais expriment également des différences subtiles dans les circuits neuronaux fonctionnels et les fondements génomiques derrière ces circuits. 

. 2021 May 14;4(1):574. doi: 10.1038/s42003-021-02015-2.

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry

Collaborators, Affiliations

Abstract

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

References

    1. Lai, M.-C. & Lombardo, M. V. Baron-Cohen S. Autism. Lancet 383, 896–910 (2014). - PubMed - DOI - PMC
    1. Lord, C. et al. Autism spectrum disorder. Nat. Rev. Dis. Prim. 6, 5 (2020). - PubMed - DOI - PMC
    1. Lombardo, M. V., Lai, M.-C. & Baron-Cohen, S. Big data approaches to decomposing heterogeneity across the autism spectrum. Mol. Psychiatry 24, 1435–1450 (2019). - PubMed - PMC - DOI
    1. Happé, F. & Ronald, A. The ‘fractionable autism triad’: a review of evidence from behavioural, genetic, cognitive and neural research. Neuropsychol. Rev. 18, 287–304 (2008). - PubMed - DOI - PMC
    1. Graybiel, A. M. Habits, rituals, and the evaluative brain. Annu Rev. Neurosci. 31, 359–387 (2008). - PubMed - DOI - PMC
    1. Langen, M., Durston, S., Kas, M. J. H., van Engeland, H. & Staal, W. G. The neurobiology of repetitive behavior: …and men. Neurosci. Biobehav. Rev. 35, 356–365 (2011). - PubMed - DOI - PMC
    1. Kennedy, D. P. & Adolphs, R. The social brain in psychiatric and neurological disorders. Trends Cogn. Sci. (Regul. Ed.) 16, 559–572 (2012). - DOI
    1. Ronald, A., Happe, F. & Plomin, R. The genetic relationship between individual differences in social and nonsocial behaviours characteristic of autism. Developmental. Sci. 8, 444–458 (2005). - DOI
    1. Ronald, A., Happé, F., Price, T. S., Baron-Cohen, S. & Plomin, R. Phenotypic and genetic overlap between autistic traits at the extremes of the general population. J. Am. Acad. Child Adolesc. Psychiatry 45, 1206–1214 (2006). - PubMed - DOI - PMC
    1. Ronald, A. et al. Genetic heterogeneity between the three components of the autism spectrum: a twin study. J. Am. Acad. Child Adolesc. Psychiatry 45, 691–699 (2006). - PubMed - DOI
    1. Warrier, V. et al. Social and non-social autism symptoms and trait domains are genetically dissociable. Commun. Biol. 2, 328 (2019). - PubMed - PMC - DOI
    1. Georgiades, S. et al. Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach. J. Child Psychol. Psychiatry 54, 206–215 (2013). - PubMed - DOI - PMC
    1. Hu, V. W. & Steinberg, M. E. Novel clustering of items from the Autism Diagnostic Interview-Revised to define phenotypes within autism spectrum disorders. Autism Res. 2, 67–77 (2009). - PubMed - PMC - DOI
    1. Cholemkery, H., Medda, J., Lempp, T. & Freitag, C. M. Classifying autism spectrum disorders by ADI-R: subtypes or severity gradient? J. Autism Dev. Disord. 46, 2327–2339 (2016). - PubMed - DOI - PMC
    1. Happé, F. & Frith, U. Annual research review: looking back to look forward—changes in the concept of autism and implications for future research. J. Child Psychol. Psychiatry 61, 218–232 (2020). - PubMed - DOI - PMC
    1. Richiardi, J. et al. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015). - PubMed - PMC - DOI
    1. Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015). - PubMed - PMC - DOI
    1. Fornito, A., Arnatkevičiūtė, A. & Fulcher, B. D. Bridging the gap between connectome and transcriptome. Trends Cogn. Sci. (Regul. Ed.) 23, 34–50 (2019). - DOI
    1. Charman, T. et al. The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation. Mol. Autism 8, 27 (2017). - PubMed - PMC - DOI
    1. Loth, E. et al. The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Mol. Autism 8, 24 (2017). - PubMed - PMC - DOI
    1. Oldehinkel, M. et al. Altered connectivity between cerebellum, visual, and sensory-motor networks in autism spectrum disorder: results from the EU-AIMS Longitudinal European Autism Project. Biol. Psychiatry.: Cogn. Neurosci. Neuroimaging 4, 260–270 (2019).
    1. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). - PubMed - PMC - DOI
    1. Gorgolewski, K. J. et al. Tight fitting genes: finding relations between statistical maps and gene expression patterns. F1000 Posters 5, 1607 (2014).
    1. Romero-Garcia, R., Warrier, V., Bullmore, E. T., Baron-Cohen, S. & Bethlehem, R. A. I. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol. Psychiatry 24, 1053–1064 (2019). - PubMed - DOI
    1. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008). - PubMed - DOI
    1. Lombardo, M. V. et al. Unsupervised data-driven stratification of mentalizing heterogeneity in autism. Sci. Rep. 6, 35333 (2016). - PubMed - PMC - DOI
    1. Chen, C. P. et al. Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clin. 8, 238–245 (2015). - DOI
    1. Holiga, Š. et al. Patients with autism spectrum disorders display reproducible functional connectivity alterations. Sci. Transl. Med. 11, eaat9223 (2019). - PubMed - DOI
    1. Lombardo, M. V. et al. Different functional neural substrates for good and poor language outcome in autism. Neuron 86, 567–577 (2015). - PubMed - PMC - DOI
    1. Lombardo, M. V. et al. Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat. Neurosci. 21, 1680–1688 (2018). - PubMed - PMC - DOI
    1. Redcay, E. & Courchesne, E. Deviant functional magnetic resonance imaging patterns of brain activity to speech in 2–3-year-old children with autism spectrum disorder. Biol. Psychiatry 64, 589–598 (2008). - PubMed - PMC - DOI
    1. Eyler, L. T., Pierce, K. & Courchesne, E. A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain 135, 949–960 (2012). - PubMed - PMC - DOI
    1. Dinstein, I. et al. Disrupted neural synchronization in toddlers with autism. Neuron 70, 1218–1225 (2011). - PubMed - PMC - DOI
    1. Adolphs, R., Damasio, H., Tranel, D., Cooper, G. & Damasio, A. R. A role for somatosensory cortices in the visual recognition of emotion as revealed by three-dimensional lesion mapping. J. Neurosci. 20, 2683–2690 (2000). - PubMed - PMC - DOI
    1. Keysers, C., Kaas, J. H. & Gazzola, V. Somatosensation in social perception. Nat. Rev. Neurosci. 11, 417–428 (2010). - PubMed - DOI - PMC
    1. Stevenson, R. A. et al. Multisensory temporal integration in autism spectrum disorders. J. Neurosci. 34, 691–697 (2014). - PubMed - PMC - DOI
    1. Foss-Feig, J. H. et al. An extended multisensory temporal binding window in autism spectrum disorders. Exp. Brain Res. 203, 381–389 (2010). - PubMed - PMC - DOI
    1. Russo, N. et al. Multisensory processing in children with autism: high-density electrical mapping of auditory-somatosensory integration. Autism Res. 3, 253–267 (2010). - PubMed - DOI - PMC
    1. Crippa, A., Forti, S., Perego, P. & Molteni, M. Eye-hand coordination in children with high functioning autism and Asperger’s disorder using a gap-overlap paradigm. J. Autism Dev. Disord. 43, 841–850 (2013). - PubMed - DOI
    1. Dowd, A. M., McGinley, J. L., Taffe, J. R. & Rinehart, N. J. Do planning and visual integration difficulties underpin motor dysfunction in autism? A kinematic study of young children with autism. J. Autism Dev. Disord. 42, 1539–1548 (2012). - PubMed - DOI - PMC
    1. Glazebrook, C., Gonzalez, D., Hansen, S. & Elliott, D. The role of vision for online control of manual aiming movements in persons with autism spectrum disorders. Autism 13, 411–433 (2009). - PubMed - DOI - PMC
    1. Marko, M. K. et al. Behavioural and neural basis of anomalous motor learning in children with autism. Brain 138, 784–797 (2015). - PubMed - PMC - DOI
    1. Nebel, M. B. et al. Intrinsic visual-motor synchrony correlates with social deficits in Autism. Biol. Psychiatry 79, 633–641 (2016). - PubMed - DOI - PMC
    1. Bhat, A. N., Landa, R. J. & Galloway, J. C. Current perspectives on motor functioning in infants, children, and adults with autism spectrum disorders. Phys. Ther. 91, 1116–1129 (2011). - PubMed - DOI - PMC
    1. Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N. & Cauraugh, J. H. Motor coordination in autism spectrum disorders: a synthesis and meta-analysis. J. Autism Dev. Disord. 40, 1227–1240 (2010). - PubMed - DOI - PMC
    1. Green, D. et al. Impairment in movement skills of children with autistic spectrum disorders. Dev. Med. Child Neurol. 51, 311–316 (2009). - PubMed - DOI - PMC
    1. Uddin, L. Q. et al. Salience network–based classification and prediction of symptom severity in children with Autism. JAMA Psychiatry 70, 869 (2013). - PubMed - PMC - DOI
    1. Green, S. A., Hernandez, L., Bookheimer, S. Y. & Dapretto, M. Salience network connectivity in Autism is related to brain and behavioral markers of sensory overresponsivity. J. Am. Acad. Child Adolesc. Psychiatry 55, 618–626.e1 (2016). - PubMed - PMC - DOI
    1. Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014). - PubMed - DOI - PMC
    1. Rubenstein, J. L. R. & Merzenich, M. M. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2, 255–267 (2003). - PubMed - PMC - DOI
    1. Sohal, V. S. & Rubenstein, J. L. R. Excitation-inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders. Mol. Psychiatry 24, 1248–1257 (2019). - PubMed - PMC - DOI
    1. Velmeshev, D. et al. Single-cell genomics identifies cell type–specific molecular changes in autism. Science 364, 685–689 (2019). - PubMed - PMC - DOI
    1. Tang, S. et al. Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics and behavioral study. Biol. Psychiatry 87, 1071–1082 (2020). - PubMed - DOI
    1. Zerbi V. et al. Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes. Neuroscience https://doi.org/10.1101/2020.10.15.340588 (2020).
    1. Lord, C., Bishop, S. & Anderson, D. Developmental trajectories as autism phenotypes. Am. J. Med. Genet. C. Semin. Med. Genet. 169, 198–208 (2015). - PubMed - PMC - DOI
    1. Georgiades, S., Bishop, S. L. & Frazier, T. Editorial perspective: longitudinal research in autism—introducing the concept of ‘chronogeneity’. J. Child Psychol. Psychiatry 58, 634–636 (2017). - PubMed - DOI
    1. Kim, S. H. et al. Variability in Autism symptom trajectories using repeated observations from 14 to 36 months of age. J. Am. Acad. Child Adolesc. Psychiatry 57, 837–848.e2 (2018). - PubMed - PMC - DOI
    1. Lord, C., Rutter, M. & Le Couteur, A. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685 (1994). - PubMed - DOI - PMC
    1. Huerta, M., Bishop, S. L., Duncan, A., Hus, V. & Lord, C. Application of DSM-5 criteria for autism spectrum disorder to three samples of children with DSM-IV diagnoses of pervasive developmental disorders. Am. J. Psychiatry 169, 1056–1064 (2012). - PubMed - PMC - DOI
    1. Charrad M., Ghazzali N., Boiteau V., Niknafs A. NbClust: An R package for determining the relevant number of clusters in a data set. J. Stat. Soft. 61, https://doi.org/10.18637/jss.v061.i06 (2014).
    1. Kundu, P., Inati, S. J., Evans, J. W., Luh, W.-M. & Bandettini, P. A. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60, 1759–1770 (2012). - PubMed - DOI
    1. Kundu, P. et al. Multi-echo fMRI: a review of applications in fMRI denoising and analysis of BOLD signals. Neuroimage 154, 59–80 (2017). - PubMed - DOI
    1. Posse, S. et al. Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging. Magn. Reson. Med. 42, 87–97 (1999). - PubMed - DOI
    1. Kundu, P. et al. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc. Natl Acad. Sci. U.S.A 110, 16187–16192 (2013). - PubMed - PMC - DOI
    1. Lombardo, M. V. et al. Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing. Neuroimage 142, 55–66 (2016). - PubMed - PMC - DOI
    1. Griffanti, L. et al. Hand classification of fMRI ICA noise components. Neuroimage 154, 188–205 (2017). - PubMed - PMC - DOI
    1. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. (Regul. Ed.) 17, 666–682 (2013). - DOI
    1. Smith, S. M. et al. Network modelling methods for FMRI. Neuroimage 54, 875–891 (2011). - PubMed - DOI
    1. Marrelec, G. et al. Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage 32, 228–237 (2006). - PubMed - DOI
    1. Verhagen, J. & Wagenmakers, E.-J. Bayesian tests to quantify the result of a replication attempt. J. Exp. Psychol. Gen. 143, 1457–1475 (2014). - PubMed - DOI - PMC
    1. Satterstrom F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell https://doi.org/10.1016/j.cell.2019.12.036 (2020).
    1. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018). - PubMed - PMC - DOI
    1. Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016). - PubMed - PMC - DOI

Aucun commentaire: