Ashley D. Harris

The paper describes the development of a harmonized multisite MRI protocol for pediatric brain imaging and validates its cross-site consistency.

This paper develops a deep learning framework to improve GABA-edited MRS quality by optimizing transient averaging based on quality scores.

The paper introduces a Python toolbox, SMART_MRS, to simulate common artifacts in GABA-edited MRS data, enhancing the diversity and quality of simulated data.

This study compares complex-valued and real-valued inputs and convolutions in CNNs for frequency and phase correction of GABA-edited MRS data, finding complex inputs with real convolutions most effective.

This study identifies altered brain metabolites in children with prenatal alcohol exposure linked to executive functioning and excitability.

This paper presents a benchmark challenge to compare machine learning models for GABA-edited MRS reconstruction using reduced transients, finding that deep learning models achieve comparable metrics to conventional methods.

This study investigates how linewidth and SNR affect GABA+ concentration estimates from MRS data using six modelling algorithms, finding significant associations with Lorentzian linewidth.

This paper applies ComBat to harmonize multi-site MRS data, revealing biological effects obscured by site and vendor differences.

The paper examines age-related changes in brain metabolite levels in children aged 2-11 years using MRS, finding age-related increases and decreases in several key metabolites in specific brain regions.

This study measures GABA and glutamate levels in children with migraine using MRS, finding lower glutamate in the visual cortex and associations between metabolite levels and migraine characteristics.

This study examines the effects of tDCS on GABA and glutamate levels in children's brains, finding increased glutamate in the HD-tDCS group but no changes in GABA.