scholarly journals Reduced Brain Connectivity in Clinical and Dimensional Autism Phenotypes Beyond Familial Confounding – A Twin Study

2021 ◽  
Author(s):  
Janina Neufeld ◽  
Simon Maier ◽  
Mirian Revers ◽  
Marco Reisert ◽  
Ralf Kuja-Halkola ◽  
...  
Keyword(s):  
Visual Processing ◽  
Diffusion Tensor ◽  
Twin Pair ◽  
Quantitative Estimation ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Medium Size ◽  
Genes And Environment

Abstract BackgroundPrevious studies on brain connectivity in clinical and dimensional autism have largely focused on selective connections and yielded inconsistent results. This study aimed to overcome these limitations. Global fiber tracking allowed a more unbiased assessment of white matter connectivity and utilizing a within-twin pair design introduced implicit control for genetic and environmental factors shared by twins and allowed conclusions regarding their impact. MethodsThe study examined the within-twin pair associations between structural brain connectivity of anatomically defined brain regions and both clinical autism spectrum diagnoses and dimensional autistic traits in 85 twin pairs (n=170; 56% monozygotic; 25 individuals with autism spectrum diagnosis). Structural connectivity was estimated using diffusion tensor imaging and linear regression models were fit, adjusted for IQ, other neurodevelopmental and psychiatric conditions and multiple testing. ResultsOverall, both clinical and dimensional autism phenotypes were associated with localized reductions in structural connectivity, despite comprehensively controlling for possible confounders, including all factors shared by twins. Twins fulfilling autism spectrum diagnostic criteria showed decreased brainstem-cuneus connectivity compared to their co-twins without the diagnosis. Further, twins with higher autistic traits showed decreased connectivity of the left hippocampus with the left fusiform and parahippocampal areas. These associations pointed into the same direction in mono- and dizygotic sub-cohorts, but were only significant in dizygotic twins.LimitationsThe recruitment approach of selecting primarily twin pairs discordant for autistic traits prevented a quantitative estimation of genetic and environmental contributions to brain correlates of clinical and dimensional autism. Further, assessing twins and excluding individuals with an IQ below 75 limited the generalizability of the findings. The statistical power allowed detecting medium-size or larger effects of dimensional autism. Finally, due the relatively small number of twin pairs discordant for a clinical autism, the results for clinical autism need to be interpreted with caution.ConclusionsReduced brainstem-cuneus connectivity might point towards alterations in low-level visual processing in clinical autism while reduced connectivity in networks crucial for visual and especially face processing seem to be more associated with dimensional aspects of autism. The results further suggest that the observed associations were potentially influenced by both genes and environment.

scholarly journals The Association Between Somatic Health, Autism Spectrum Disorder, and Autistic Traits

2019 ◽  
Vol 50 (4) ◽  
pp. 233-246 ◽  
Author(s):  
Pei-Yin Pan ◽  
Kristiina Tammimies ◽  
Sven Bölte
Keyword(s):  
Autism Spectrum Disorder ◽  
Twin Pair ◽  
Monozygotic Twins ◽  
Twin Studies ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Social Responsiveness ◽  
Somatic Health ◽  
Validated Questionnaire

AbstractThis study used a twin cohort to investigate the association of autism spectrum disorder (ASD) and autistic traits with somatic health. A total of 344 twins (172 pairs; mean age 15.56 ± 5.62 years) enriched for ASD and other neurodevelopmental conditions were examined. Medical history and current physical problems were collected with a validated questionnaire to determine twin’s somatic health. The Social Responsiveness Scale (SRS-2) was used to measure the participant’s severity of autistic traits. Identified somatic health issues with significant within-twin pair differences were tested in relation to both ASD diagnosis and autistic traits in a co-twin control model. Twins with ASD exhibited more neurological and immunological health problems compared to those without ASD (p = 0.005 and p = 0.004, respectively). The intra-pair differences of neurological conditions and SRS-2 score were significantly correlated in monozygotic twins differing for autism traits (r = 0.40, p = 0.001), while the correlation was not found for immunological problems. In addition, a conditional model for analysis of within-twin pair effects revealed an association between neurological problems and clinical ASD diagnosis (Odds ratio per neurological problem 3.15, p = 0.02), as well as autistic traits (β = 10.44, p = 0.006), after adjusting for possible effects of co-existing attention deficit hyperactivity disorder and general intellectual abilities. Our findings suggest that neurological problems are associated with autism, and that non-shared environmental factors contribute to the overlap for both clinical ASD and autistic traits. Further population-based twin studies are warranted to validate our results and examine in detailed the shared genetic and environmental contributions of neurological problems and ASD.

scholarly journals Structurally constrained effective brain connectivity

2021 ◽  
Author(s):  
Alessandro Crimi
Keyword(s):  
Community Detection ◽  
Autoregressive Model ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Autism Spectrum ◽  
Case Control ◽  
Functional Dependencies ◽  
The Brain

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal.The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.

Tract-Based Spatial Statistics Reveal Altered Relationship Between Non-verbal Reasoning Abilities and White Matter Integrity in Autism Spectrum Disorder

2013 ◽  
Vol 19 (6) ◽  
pp. 723-728 ◽  
Author(s):  
Timothy M. Ellmore ◽  
Hai Li ◽  
Zhong Xue ◽  
Stephen T.C. Wong ◽  
Richard E. Frye
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
White Matter Integrity ◽  
Typically Developing ◽  
Reasoning Abilities ◽  
Intellectual Abilities ◽  
And Gender ◽  
The Relationship

AbstractAltered brain connectivity accompanies autism spectrum disorders (ASD), but the relationship between connectivity and intellectual abilities, which often differs within ASD, and between ASD and typically developing (TD) children, is not understood. Here, diffusion tensor imaging (DTI) was used to explore the relationship between white matter integrity and non-verbal intelligence quotients (IQ) in children with ASD and in age- and gender-matched TD children. Tract-based spatial statistical analyses (TBSS) of DTI fractional anisotropy (FA) revealed altered relationships between white matter and IQ. Different relationships were found using within-group analyses, where regions of significant (p< .05, corrected) correlations in ASD overlapped minimally with regions of FA-IQ correlations in TD subjects. An additional between-groups analysis revealed significant correlation differences in widespread cortical and subcortical areas. These preliminary findings suggest altered brain connectivity may underlie some differences in intellectual abilities of ASD, and should be investigated further in larger samples as a function of development. (JINS, 2013,19, 1–6)

scholarly journals Sex differences along the Autism Continuum: A twin study of brain structure

2018 ◽  
Author(s):  
Élodie Cauvet ◽  
Annelies van’t Westeinde ◽  
Roberto Toro ◽  
Ralf Kuja-Halkola ◽  
Janina Neufeld ◽  
...  
Keyword(s):  
Sex Differences ◽  
Surface Area ◽  
Twin Pair ◽  
Autistic Traits ◽  
Monozygotic Twin ◽  
Autism Spectrum ◽  
Brain Morphology ◽  
Protective Mechanisms ◽  
Twin Design ◽  
Structural Brain

ABSTRACTFemales might possess protective mechanisms regarding autism spectrum disorder (ASD) and require a higher detrimental load, including structural brain alterations, before developing clinically relevant levels of autistic traits. This study examines sex differences in structural brain morphology in autism and autistic traits using a within-twin pair approach. Twin design inherently controls for shared confounders and enables the study of gene-independent neuroanatomical variation. N=148 twins (62 females) from 49 monozygotic and 25 dizygotic same-sex pairs were included. Participants were distributed along the whole continuum of autism including twin pairs discordant and concordant for clinical ASD. Regional brain volume, surface area and cortical thickness were computed. Within-twin pair increases in autistic traits were related to decreases in cortical volume and surface area of temporal and frontal regions specifically in female twin pairs, in particular regions involved in social communication, while only two regions were associated with autistic traits in males. The same pattern was detected in the monozygotic twin pairs only. Thus, non-shared environmental factors seem to impact female more than male cerebral architecture. Our results are in line with the hypothesis of a female protective effect in autism and highlights the need to study ASD in females separately from males.

scholarly journals The within-subject application of diffusion tensor MRI and CLARITY reveals brain structural changes in Nrxn2 deletion mice

2018 ◽  
Author(s):  
Eleftheria Pervolaraki ◽  
Adam L. Tyson ◽  
Francesca Pibiri ◽  
Steven L. Poulter ◽  
Amy C. Reichelt ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Orbitofrontal Cortex ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Anterior Cingulate ◽  
Diffusion Tensor Mri ◽  
Socially Relevant

AbstractBackgroundOf the many genetic mutations known to increase the risk of autism spectrum disorder, a large proportion cluster upon synaptic proteins. One such family of presynaptic proteins are the neurexins (NRXN), and recent genetic and mouse evidence has suggested a causative role for NRXN2 in generating altered social behaviours. Autism has been conceptualised as a disorder of atypical connectivity, yet how single-gene mutations affect such connectivity remains under-explored. To attempt to address this, we have developed a quantitative analysis of microstructure and structural connectivity leveraging diffusion tensor MRI (DTI) with high-resolution 3D imaging in optically cleared (CLARITY) brain tissue in the same mouse, applied here to the Nrxn2α knockout (KO) model.MethodsFixed brains of Nrxn2α KO mice underwent DTI using 9.4T MRI, and diffusion properties of socially-relevant brain regions were quantified. The same tissue was then subjected to CLARITY to immunolabel axons and cell bodies, which were also quantified.ResultsDTI revealed decreases in fractional anisotropy and increases in apparent diffusion coefficient in the amygdala (including the basolateral nuclei), the anterior cingulate cortex, the orbitofrontal cortex and the hippocampus. Radial diffusivity of the anterior cingulate cortex and orbitofrontal cortex was significantly increased in Nrxn2α KO mice, as were tracts between the amygdala and the orbitofrontal cortex. Using CLARITY, we find significantly altered axonal orientation in the amygdala, orbitofrontal cortex and the anterior cingulate cortex, which was unrelated to cell density.ConclusionsOur findings demonstrate that deleting a single neurexin gene (Nrxn2α) induces atypical structural connectivity within socially-relevant brain regions. More generally, our combined within-subject DTI and CLARITY approach presents a new, more sensitive method of revealing hitherto undetectable differences in the autistic brain.

scholarly journals The Directionality of Fronto-Posterior Brain Connectivity Is Associated with the Degree of Individual Autistic Traits

2021 ◽  
Vol 11 (11) ◽  
pp. 1443
Author(s):  
Luca Tarasi ◽  
Elisa Magosso ◽  
Giulia Ricci ◽  
Mauro Ursino ◽  
Vincenzo Romei
Keyword(s):  
Brain Connectivity ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Clinical Population ◽  
Oscillatory Activity ◽  
Connectivity Pattern ◽  
Causality Analysis ◽  
Cortical Hierarchy ◽  
Neural Mapping ◽  
Specific Symptoms

Altered patterns of brain connectivity have been found in autism spectrum disorder (ASD) and associated with specific symptoms and behavioral features. Growing evidence suggests that the autistic peculiarities are not confined to the clinical population but extend along a continuum between healthy and maladaptive conditions. The aim of this study was to investigate whether a differentiated connectivity pattern could also be tracked along the continuum of autistic traits in a non-clinical population. A Granger causality analysis conducted on a resting-state EEG recording showed that connectivity along the posterior-frontal gradient is sensitive to the magnitude of individual autistic traits and mostly conveyed through fast oscillatory activity. Specifically, participants with higher autistic traits were characterized by a prevalence of ascending connections starting from posterior regions ramping the cortical hierarchy. These findings point to the presence of a tendency within the neural mapping of individuals with higher autistic features in conveying proportionally more bottom-up information. This pattern of findings mimics those found in clinical forms of autism, supporting the idea of a neurobiological continuum between autistic traits and ASD.

scholarly journals Impaired Structural Connectivity of Socio-Emotional Circuits in Autism Spectrum Disorders: A Diffusion Tensor Imaging Study

PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e28044 ◽  
Author(s):  
Stephanie H. Ameis ◽  
Jin Fan ◽  
Conrad Rockel ◽  
Aristotle N. Voineskos ◽  
Nancy J. Lobaugh ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Imaging Study ◽  
Autism Spectrum ◽  
Spectrum Disorders ◽  
Diffusion Tensor Imaging Study

scholarly journals Brain connectivity-informed regularization methods for regression

2017 ◽  
Author(s):  
Marta Karas ◽  
Damian Brzyski ◽  
Mario Dzemidzic ◽  
Joaquin Goni ◽  
David A. Kareken ◽  
...  
Keyword(s):  
Regularization Method ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Simulated Data ◽  
Penalized Regression ◽  
Laplacian Matrix ◽  
External Information ◽  
Penalty Term ◽  
Reduction Techniques

AbstractA challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to utilizing external information arising from the structural brain connectivity to inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. In the work presented, we address both theoretical and computational issues. The approach is illustrated using simulated data and compared with other penalized regression methods. Finally, we apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion tensor imaging (DTI) derived measure of structural connectivity.

scholarly journals Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study

2021 ◽  
Author(s):  
Varun Arunachalam Chandran ◽  
Christos Pliatsikas ◽  
Janina Neufeld ◽  
Garret O'Connell ◽  
Anthony Haffey ◽  
...  
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Diffusion Tensor ◽  
Autistic Traits ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Brain Volumes ◽  
White Matter Microstructure ◽  
The Social ◽  
Brain Correlates

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adults (N=91) with and without a clinical diagnosis of autism. We examined regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging). Our findings show widespread grey matter differences, including in the social brain regions, and some evidence for white matter microstructure differences related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.

scholarly journals Structurally Constrained Effective Brain Connectivity

2018 ◽  
Author(s):  
Alessandro Crimi ◽  
Luca Dodero ◽  
Fabio Sambataro ◽  
Vittorio Murino ◽  
Diego Sona
Keyword(s):  
Autoregressive Model ◽  
Brain Research ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Young Patients ◽  
Autism Spectrum ◽  
Case Control ◽  
Brain Targeting ◽  
The Brain

How function arises from structure is of interest in many fields from proteomics to neuroscience. In particular, among the brain research community the fusion of structure and function data can shed new lights on underlying operational network principles in the brain. Targeting this issue, the manuscript proposes a constrained autoregressive model generating “effective” connectivity given structural and functional information. In practice, an initial structural connectivity representation is altered according to functional data, by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The proposed model has been tested in a community detection framework, where the brain is partitioned using the effective networks across multiple subjects. The model is further validated in a case-control experiment, which aims at differentiating healthy subjects from young patients affected by autism spectrum disorder. Results showed that using effective connectivity resulted in clusters that better describe the functional interactions between different regions while maintaining the structural organization, and a better discrimination in the case-control classification task.

Sign in / Sign up

Export Citation Format

Share Document