Age-related Dynamics of High-gamma Power and Connectivity Modulations During Visually-prompted Expressive Language Processing
Abstract number :
3.286
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2024
Submission ID :
236
Source :
www.aesnet.org
Presentation date :
12/9/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Brian Ervin, PhD – Cincinnati Children's Hospital Medical Center
Jason Buroker, BS – Cincinnati Children's Hospital Medical Center
Craig Scholle, BS – Cincinnati Children's Hospital Medical Center
Paul Horn, PhD – Cincinnati Children's Hospital Medical Center
Leonid Rozhkov, BS – Cincinnati Children's Hospital Medical Center
James Leach, MD – Cincinnati Children's Hospital Medical Center
Francesco Mangano, DO, FACS, FAAP, FACOS – Cincinnati Children's Hospital Medical Center
Jesse Skoch, MD, FAANS, FACS – Cincinnati Children's Hospital Medical Center
Hansel Greiner, MD – Cincinnati Children's Hospital Medical Center
Katherine Holland, MD, PhD – Cincinnati Children's Hospital Medical Center
Ravindra Arya, MD, DM – Cincinnati Children's Hospital Medical Center
Rationale: In patients with intractable epilepsy, invasive language mapping is routinely performed to minimize the risks of post-surgical deficits. These maps include cortical locations of language processing but remain agnostic of developmental stages and neuronal circuitry. Herein, we explore age-related dynamics of neuronal circuits supporting development of expressive language processing as a step towards incorporating age-informed network dynamics into epilepsy surgery plans.
Methods: We analyzed high-gamma (50-150 Hz) power and connectivity modulations to define cortical regions and neuronal circuits supporting visual naming in 126 patients (age range: 3.9 to 28.9 years), divided evenly into three groups (childhood: 3-11, adolescence: 11-16, early adulthood: 16-29) undergoing standard-of-care stereo-EEG monitoring. Trials were aligned on picture display, bandpass filtered, and Hilbert transformed. The log-transformed power was averaged across trials, and the power during test was compared to rest via t-test. Corrected imaginary phase locking value was calculated for test and rest conditions using the mne_connectivity package in Python, and compared via t-test. Generalized linear models were used to identify age-related linear trends in power and connectivity, with age as a continuous variable. Cortical regions were defined using the Brainnetome whole-brain atlas.
Results: We found distinct functional networks at each age group, with consistent bilateral occipital activation. Linguistic processing strengthened and consolidated with age in the left middle and inferior frontal gyri, left superior parietal lobule, right sensorimotor cortex, and bilateral insula. Left superior temporal gyrus and right postcentral gyrus were highly connected in younger age groups, but right middle temporal and left supramarginal gyrus became important hubs in early adulthood, especially in interhemispheric communication.
Conclusions: Our results demonstrate three stages of development of visually-cued expressive language, wherein cortical substrates supporting visual decoding (bilateral occipital gyrus, fusiform gyrus, hippocampus, and precuneus) feature prominently throughout, but those known to support visual perception (bilateral superior parietal lobule, left inferior temporal gyrus), lexical-semantic retrieval (left inferior parietal lobule, left middle and inferior frontal gyri, right middle temporal gyrus), and language articulation (bilateral sensorimotor cortex and insula) in adults tend to strengthen with age. Unsurprisingly, connections involving these same regions tend to undergo significant reorganization throughout development, while those supporting visual decoding do not. This study is an important step toward expanded models for neurobiology of language development, integrating age-specific network dynamics into neurosurgical decision-making, and improved patient outcomes.
Funding: Funded by NIH (NINDS) R01 NS115929
Neurophysiology