Linda Larson‐Prior

High Impact

Professor

University of Arkansas for Medical Sciences

faculty

25 h-index 112 pubs 5,845 cited

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Biography and Research Information

OverviewAI-generated summary

Linda Larson‐Prior is a researcher at the University of Arkansas for Medical Sciences. Her work focuses on the application of advanced computational and analytical techniques to neuroscience research. She has published on topics including the Human Connectome Project, the standardization of brain network nomenclature, and the development of toolboxes for evaluating neuroimaging results. Her recent work also includes the application of machine learning to voice samples for the identification of Parkinson's disease and the development of EEG protocols for large-scale studies like the HEALthy Brain and Child Development (HBCD) Study.

Larson‐Prior leads a research group and has a significant publication record, with 112 total publications and 5,845 total citations, reflected in her h-index of 25. She is recognized as a highly cited researcher. Her collaborations include extensive work with colleagues at the University of Arkansas for Medical Sciences, such as Aaron S. Kemp and Tuhin Virmani, with whom she shares 17 publications, and Lakshmi Pillai, with whom she has 14 shared publications.

Metrics

  • h-index: 25
  • Publications: 112
  • Citations: 5,845

Selected Publications

  • Effects of Early Infant Nutrition on Aperiodic Exponent From 2 to 12 Months Using <scp>EEG</scp> Analysis (2026) DOI
  • Harnessing Native-Resolution 2D Embeddings for Lung Cancer Classification: A Feasibility Study with the RAD-DINO Self-supervised Foundation Model (2025) DOI
  • Body mass index, cortical thickness and executive function in late childhood (2025) DOI
  • Visual Working Memory in Pediatric Epilepsy using Magnetoencephalography: Pilot Study (2025) DOI
  • A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease (2025) DOI
  • Cost, Research, and Education: Providers’ Perspectives on Making Neurofeedback More Accessible (2025) DOI
  • An Activation Likelihood Estimation Meta-Analysis of Voxel-Based Morphometry Studies of Chemotherapy-Related Brain Volume Changes in Breast Cancer (2025) DOI
  • Cost, research, and education: Providers’ perspectives on making neurofeedback more accessible (2025) DOI
  • A network correspondence toolbox for quantitative evaluation of novel neuroimaging results (2025) DOI
  • Pre-trained convolutional neural networks identify Parkinson’s disease from spectrogram images of voice samples (2025) DOI
  • Pre-trained Convolutional Neural Networks Identify Parkinson’s Disease from Spectrogram Images of Voice Samples (2024) DOI
  • The development and structure of the HEALthy Brain and Child Development (HBCD) Study EEG protocol (2024) DOI
  • A network correspondence toolbox for quantitative evaluation of novel neuroimaging results (2024) DOI
  • A Systematic Review over the Effect of Early Infant Diet on Neurodevelopment: Insights from Neuroimaging (2024) DOI
  • Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson’s disease (2024) DOI

Collaborators

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