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Owen, J.

Publications and source records attributed to Owen, J..

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Sensory Over-Responsivity: Parent Report, Direct Assessment Measures, and Neural Architecture

BackgroundSensory processing differences are common across neurodevelopmental disorders. Thus, reliable measures are needed to understand biologic underpinnings of these differences. This study aims to define a scoring methodology specific to tactile (TOR) and auditory (AOR) over-responsivity. Second, using MRI Diffusion Tensor Imaging, we seek to determine whether children with AOR show measurable differences in their white matter integrity.\n\nMethodsThis study includes children with AOR and TOR from a mixed neurodevelopmental disorders cohort including autism and sensory processing dysfunction (n= 176) as well as neurotypical children (n= 128). We established cut-off scores for over-responsivity using the parent report: Short Sensory Profile (SSP), and the direct assessment: Sensory Processing-Three Dimensions:Assessment (SP-3D:A). Group comparisons, based on AOR phenotype, were then conducted comparing the white matter fractional anisotropy in 23 regions of interest.\n\nResultsUsing the direct assessment, 31% of the children with neurodevelopmental disorders had AOR and 27% had TOR. The Inter-test-agreement between SSP and SP-3D:A for AOR was 65% and TOR was 50%. Children with AOR had three white matter tracts showing decreased fractional anisotropy relative to children without AOR.\n\nConclusionsThis study identified cut scores for AOR and TOR using the SSP parent report and SP-3D:A observation. A combination of questionnaire and direct observation measures should be used in clinical and research settings. The SSP parent report and SP-3D:A direct observation ratings overlapped moderately for sensory related behaviors. Based on these initial structural neuroimaging results, we suggest a putative neural network may contribute to AOR.

neuroscience

Functional rerouting via the structural connectome is associated with better recovery after mild TBI

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4{+/-}8.0 years, 20 male) at 1 and 6 months post-injury. We quantified the relationship between functional and structural connectomes using network diffusion model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25=-2.15, p=0.04). None of the structural or functional global connectome metrics were significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6{+/-}8.8 years, 25 male). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = 0.27). We observe that increased network diffusion model propagation time, increased structural connectome segregation and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based post-injury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multi-modal measure of network diffusion model propagation time captures the interplay between these two mechanisms.\n\nAbbreviationsmild traumatic brain injury (mTBI), structural connectome (SC), functional connectome (FC), network diffusion (ND), functional MRI (fMRI), diffusion MRI (dMRI), principal component analysis (PCA), partial least squares regression (PLSR), confidence interval (CI), Attention Network Test (ANT), California Verbal Learning Test II (CVLT-II), Coma Recovery Scale - Revised (CRS-R)

neuroscience