Global MEG Resting State Functional Connectivity in Children with Autism and Sensory Processing Dysfunction.

Sensory processing dysfunction not only affects most individuals with autism spectrum disorder (ASD), but at least 5% of children without ASD also experience dysfunctional sensory processing. Our understanding of the relationship between sensory dysfunction and resting state brain activity is still emerging. This study compared long-range resting state functional connectivity of neural oscillatory behavior in children aged 8-12 years with autism spectrum disorder (ASD; N=18), those with sensory processing dysfunction (SPD; N=18) who do not meet ASD criteria, and typically developing control participants (TDC; N=24) using magnetoencephalography (MEG).

An Open-Label Study of Cranial Electrotherapy Stimulation on Behavioral Regulation in a Mixed Neurodevelopmental Clinical Cohort.

Objective: Individuals with neurodevelopmental disorders often report disturbances in the autonomic nervous system (ANS)-related behavioral regulation, such as sensory sensitivity, anxiety, and emotion dysregulation. Cranial electrotherapy stimulation (CES) is a method of non-invasive neuromodulation presumed to modify behavioral regulation abilities via ANS modulation. Here we examined the feasibility and preliminary effects of a 4-week CES intervention on behavioral regulation in a mixed neurodevelopmental cohort of children, adolescents, and young adults.

Altered Cerebellar White Matter in Sensory Processing Dysfunction Is Associated With Impaired Multisensory Integration and Attention.

Sensory processing dysfunction (SPD) is characterized by a behaviorally observed difference in the response to sensory information from the environment. While the cerebellum is involved in normal sensory processing, it has not yet been examined in SPD. Diffusion tensor imaging scans of children with SPD ( = 42) and typically developing controls (TDC; = 39) were compared for fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) across the following cerebellar tracts: the middle cerebellar peduncles (MCP), superior cerebellar peduncles (SCP), and cerebral peduncles (CP).

Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned.

White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers.

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8-12 years. In addition to conventional diffusion tensor imaging (DTI) maps - including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps.

Sensory over-responsivity: parent report, direct assessment measures, and neural architecture.

Background: Sensory processing difficulties are common across neurodevelopmental disorders. Thus, reliable measures are needed to understand the biological underpinnings of these differences. This study aimed to define a scoring methodology specific to auditory (AOR) and tactile (TOR) over-responsivity.

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

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.

Burden of de novo mutations and inherited rare single nucleotide variants in children with sensory processing dysfunction.

Background: In children with sensory processing dysfunction (SPD), who do not meet criteria for autism spectrum disorder (ASD) or intellectual disability, the contribution of de novo pathogenic mutation in neurodevelopmental genes is unknown and in need of investigation. We hypothesize that children with SPD may have pathogenic variants in genes that have been identified as causing other neurodevelopmental disorders including ASD. This genetic information may provide important insight into the etiology of sensory processing dysfunction and guide clinical evaluation and care.