Kevin Archer Pelphrey

The paper investigates altered aperiodic EEG spectral power during speech perception in youths with ASD, linking increased neural noise to lower verbal communication skills.

This study finds that adolescents with autism have higher biological aging rates in brain structures compared to neurotypical controls, as measured by DunedinPACNI.

This study identifies sex-specific morphological network alterations in adolescents with ASD using structural MRI, revealing distinct connectivity patterns between males and females.

This paper conducts a meta-analysis of fMRI data to compare neural activation during executive function tasks between preterm and full-term children and adults, finding age-specific differences in brain network engagement.

The paper introduces a novel metric, estimated axonal latency, to measure neuronal processing speed in autism spectrum disorder using MRI and EEG data.

This study identifies widespread associations between behavioral metrics and brain microstructure in ASD, suggesting age-related changes may drive different behavioral subtypes.

This study uses MRI metrics to identify microstructural differences in myelin and axons between ASD and neurotypical adolescents, finding increased extracellular water and decreased g-ratio and conduction velocity.

The paper constructs an enhancer-driven gene regulatory network from human forebrain organoids with ASD and neurotypical controls, identifying key regulators of excitatory neuron specification in macrocephalic ASD.

The paper replicates and compares intrinsic neural timescales in autism spectrum disorder and schizophrenia, finding reduced timescales in specific brain regions for schizophrenia compared to both typically developed individuals and autism spectrum disorder patients.

The paper models ASD in organoids and finds transcriptomic imbalances in excitatory cortical neuron subtypes during early neurogenesis in ASD probands.

The paper investigates how typical and autistic adolescents represent and update social knowledge during learning, finding that autistic adolescents rely only on their own preferences while typical adolescents use population preferences and prediction error updating.