Deviant visual processing has been observed in autism spectrum disorder (ASD), manifesting as decreased P1 and P2 components of visual event-related potentials (ERPs). Alterations have been attributed to a failure of Bayesian inference, characterized by hypo-activation of top-down predictive abilities. To test this hypothesis, we measured the visual negativity (vN) as an ERP index of visual preparation hypothesized to mirror predictive brain activity. ERPs in a cued visual GO/NOGO task in 63 adolescents with ASD (IQ > 70, attention-deficit hyperactivity disorder excluded) were compared with ERPs in a sex- and age-matched group of 60 typically developing (TD) controls. The behavioral variables (omissions, commissions, reaction time, and reaction time variability), as well as ERP components reflecting, among other processes, cognitive control (contingent negative variation, P3 GO, P3 NOGO, N2 NOGO) did not differ between the groups. There were group differences in visually based ERPs. Besides P1 and P2 differences, the vN component differentiated the 2 groups with the highest effect size ( = 0.74). This ERP study lends support to the hypothesis suggesting that a Bayesian hypo-prediction could underlie unique perceptual experiences in individuals with ASD. This could lead to a predisposition to perceive the world with reduced influence and modulation from contextual cues, prior experiences, and pre-existing expectations.
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http://dx.doi.org/10.1177/15500594241227974 | DOI Listing |
J Dev Behav Pediatr
October 2024
College of Nursing, University of Kentucky, Lexington, KY.
Objective: (1) Evaluate differences in demographic distributions between those with autism alone and those with autism concomitant with anxiety and (2) assess demographic factors associated with receipt of psychological therapy in patients with autism.
Methods: Using deidentified health care claims data, we extracted all encounters among children with the International Classification of Diseases-10-CM for Autism Spectrum Disorder (F84) and flagged those with concomitant anxiety (F41.9).
Alzheimers Dement
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Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.
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December 2024
Faculty of Medicine, Arish University, Arish, North Sinai, Egypt.
Background: Lingual taste cells (LTCs) are taste buds' sensory cells that modulate gustation. This study's aim is to assess whether it can be successfully implanted in hippocampus, modulating learning and memory deficits observed in Alzheimer's Dementia (AD).
Methods: Retrospective trials on rodents i.
Alzheimers Dement
December 2024
Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.
View Article and Find Full Text PDFHum Brain Mapp
January 2025
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.
Insomnia disorder (ID) is a highly heterogeneous psychiatric disease, and the use of neuroanatomical data to objectively define biological subtypes is essential. We aimed to examine the neuroanatomical subtypes of ID by morphometric similarity network (MSN) and the association between MSN changes and specific transcriptional expression patterns. We recruited 144 IDs and 124 healthy controls (HC).
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