Corpus callosum in cognitive and sensory processing: insights into autism

Demopoulos, Carly; Yu, Nina; Paul, Lynn K.; Sherr, Elliott H.; Marco, Elysa J.

doi:10.2217/fnl.14.70

Cited by 10 publications

(6 citation statements)

References 85 publications

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“…Lower ED in the splenium of corpus callosum in children with ASD may be related to deficits in visual processing (Di et al, 2018). Notably, callosal ab-normalities, such as the diminished ED noted herein, are one of the most well-replicated findings for individuals with ASD, with implications for slower transmission of information leading to deleterious consequences for processing of nuanced and socially rich visual information (Demopoulos et al, 2015;Marco et al, 2012).…”

Section: Discussionmentioning

confidence: 60%

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

et al. 2019

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Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling (• 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.

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Section: Discussionmentioning

confidence: 60%

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

et al. 2019

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“…This regional predilection is thought to be related to vulnerability of oligodendrocyte precursors—thus “at-risk” territory [ 4 ]. Furthermore, children with agenesis of the corpus callosum, a syndrome of hemispheric disconnection, also show differences in sensory processing [ 5 , 6 ]. However, in the extant literature, sensory dysfunction in children with genetic and injury-based conditions is approached with a broad sensory framework that does not answer the question of whether SOR results from a regional disruption and/or plasticity of dedicated neuronal networks.…”

Section: Discussionmentioning

confidence: 99%

“…Sensory processing dysfunction (SPD), broadly defined, refers to a clinical deficit in the ability to modulate, discriminate, or create an organized response to sensory information and affects up to 16% of children [ 1 ]. Children born prematurely and those with congenital malformations affecting white matter tracts such as agenesis of the corpus callosum are particularly vulnerable to SPD [ 2 – 6 ]. Due to the disruptions in sensory processing, children with SPD may demonstrate atypical or delayed intellectual, language, or motor milestones [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

White matter microstructure of children with sensory over-responsivity is associated with affective behavior

Wren-Jarvis,

Powers,

Lazerwitz

et al. 2024

J Neurodevelop Disord

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Background Sensory processing dysfunction (SPD) is linked to altered white matter (WM) microstructure in school-age children. Sensory over-responsivity (SOR), a form of SPD, affects at least 2.5% of all children and has substantial deleterious impact on learning and mental health. However, SOR has not been well studied using microstructural imaging such as diffusion MRI (dMRI). Since SOR involves hypersensitivity to external stimuli, we test the hypothesis that children with SOR require compensatory neuroplasticity in the form of superior WM microstructural integrity to protect against internalizing behavior, leaving those with impaired WM microstructure vulnerable to somatization and depression. Methods Children ages 8–12 years old with neurodevelopmental concerns were assessed for SOR using a comprehensive structured clinical evaluation, the Sensory Processing 3 Dimensions Assessment, and underwent 3 Tesla MRI with multishell multiband dMRI. Tract-based spatial statistics was used to measure diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics from global WM and nineteen selected WM tracts. Correlations of DTI and NODDI measures with measures of somatization and emotional disturbance from the Behavioral Assessment System for Children, 3rd edition (BASC-3), were computed in the SOR group and in matched children with neurodevelopmental concerns but not SOR. Results Global WM fractional anisotropy (FA) is negatively correlated with somatization and with emotional disturbance in the SOR group but not the non-SOR group. Also observed in children with SOR are positive correlations of radial diffusivity (RD) and free water fraction (FISO) with somatization and, in most cases, emotional disturbance. These effects are significant in boys with SOR, whereas the study is underpowered for girls. The most affected white matter are medial lemniscus and internal capsule sensory tracts, although effects of SOR are observed in many cerebral, cerebellar, and brainstem tracts. Conclusion White matter microstructure is related to affective behavior in children with SOR.

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“…This cognitive and sensory impairments are reported to correlate (Demopoulos et al, 2015). To examine whether sensory trajectories were explained by cognitive abilities, cognitive level was added to the model as a static predictor.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal analysis of sensory responsivity from infancy to school age in children at high and low familial likelihood for autism

Gunderson,

Symons,

Kohli

et al. 2024

JCPP Advances

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BackgroundEmpirical evidence regarding the development of sensory responsivity in young children at high likelihood to develop autism spectrum disorder (ASD) remains relatively limited. It is unclear how sensory responsivity behaviors may change over time and impact later developmental outcomes. The goals of this study were to (a) characterize developmental trajectories of sensory responsivity across infancy (∼12 months), toddlerhood (∼24 months), and school age (6–11 years) in children at high and low familial likelihood for ASD; and (b) determine if sensory responsivity in infancy predicts adaptive and cognitive functioning at school age among children with ASD.MethodsGeneralized linear mixed effects models were used to examine scores from the Sensory Experiences Questionnaire in three groups of children including high‐likelihood children later diagnosed with ASD (HL‐ASD; n = 30), high‐likelihood children without ASD (HL‐Neg; n = 150), and low‐likelihood control children not meeting ASD diagnostic criteria (LL‐Neg; n = 94). Hierarchical linear regression was then used to examine the association between sensory responsivity scores in infancy and functional adaptive and cognitive outcomes at school age for children at high likelihood of ASD.ResultsDevelopment of sensory responsivity from infancy to later childhood is best estimated by the effects of chronological age and Group for Sensory Seeking and Hypo responsivity and the additional effect of the interaction of Group and chronological age for Total and Hyper responsivity. Early elevated Hypo responsivity and Sensory Seeking scores are negatively associated with later adaptive behavior but not cognitive level.ConclusionOverall, higher degrees of Sensory Seeking and Hypo sensory responsivity are detectable in autistic children's behavioral repertoires by 12 months of age and associate with reduced adaptive functioning in middle childhood. These results point to the potential importance of early detection and treatment implications of early sensory behaviors.

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Corpus callosum in cognitive and sensory processing: insights into autism

Cited by 10 publications

References 85 publications

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

White matter microstructure of children with sensory over-responsivity is associated with affective behavior

Longitudinal analysis of sensory responsivity from infancy to school age in children at high and low familial likelihood for autism

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