Authors :
Presenting Author: Atsuro Daida, MD, PhD – Saitama Children's Medical Center, Saitama, Saitama, Japan
Yipeng Zhang, MS, PhD – Department of Electrical and Computer Engineering, University of California Los Angeles
Chenda Duan, BS, MS – University of California, Los Angeles
Sotaro Kanai, MD, PhD – Division of Pediatric Neurology, Department of Pediatrics, David Geffen School of Medicine at the University of California, Los Angeles, California, USA
Naoto Kuroda, MD, PhD – Wayne State University
Tonmoy Monsoor, MS, PhD – Department of Electrical and Computer Engineering, University of California Los Angeles
Shaun A. Hussain, MD, MS – Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine
Aria Fallah, MD, MSc, MBA – Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine
Noriko Salamon, MD, PhD – Department of Radiology, UCLA Medical Center, David Geffen School of Medicine
Raman Sankar, MD, PhD – Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine
Richard J. Staba, PhD – Department of Neurology, UCLA Medical Center, David Geffen School of Medicine
Jerome Engel Jr., MD, PhD – Department of Neurology, UCLA Medical Center, David Geffen School of Medicine
William Speier, PhD – Department of Radiological Sciences and Bioengineering, University of California Los Angeles
Eishi Asano, MD, PhD – Wayne State University
Vwani Roychowdhury, PhD – Department of Electrical and Computer Engineering, University of California Los Angeles
Hiroki Nariai, MD, PhD, MS – Department of Pediatrics, Division of Pediatric Neurology, David Geffen School of Medicine at the University of California, Los Angeles, California, USA
Rationale:
High-frequency oscillations (HFOs) are a promising biomarker for both physiological and pathological neuronal activity. While pediatric EEG shows dramatic changes during development, the evidence of the physiological HFOs remains sparse. Utilizing a multi-institutional large cohort, we created an atlas of non-spike HFOs, presumably physiological HFOs from non-epileptic channels.
Methods:
We included a cohort of 188 subjects from UCLA and Wayne State University, recorded with intracranial EEG, either grid or stereotactic EEG (SEEG). Channels were re-referenced to the averaged montage for grid electrodes and bipolar montages for the SEEG. Non-epileptic cortical channels at the frontal, parietal, temporal, and occipital lobes were analyzed to create an atlas. Thus, channels with spikes, channels within the lesion, or exhibiting frequent spike HFOs–utilizing the following method–were excluded from this analysis. We detected HFOs utilizing a short-term energy HFO detector followed by a deep-learning based artifact/spike HFOs classification under our published platform pyHFO (Zhang et al., J Neural Eng 2024). The HFO characteristics, such as HFO rate and power, were calculated at each anatomical area of interest based on the Desikan-Killiany-Touville Atlas. HFOs' characteristics were compared among the age groups defined as follows: young (0-6 years), middle (7-12 years), and old (13 years). Additionally, correlation between the HFO feature and age was analyzed.
Results:
The total number of channels was 13551, among them 8280 were non-epileptic cortical channels distributed as follows: 2934 in frontal lobe, 2004 in temporal lobe, 2095 in parietal lobe, 627 in occipital lobe, 620 in limbic regions. Topological assessment of HFOs rate demonstrated changes in distribution among ages (Figure1), showing frontal dominance in the young group, while it decreased and localized at the occipital lobe as the age gets older (Figure1). The HFO rate was significantly higher in the young group, compared to the middle and old group, with the middle group also exhibiting a higher rate than the older group in the frontal (mean: young vs. middle vs. old: 0.24 /min vs. 0.12 /min vs. 0.07 /min; Figure 2: left: blue = young, orange = middle, green=old), parietal (0.24 /min vs. 0.14 /min vs. 0.08 /min), and temporal lobes (0.16 /min vs. 0.08 /min vs. 0.05 /min; p< 0.001 for all analysis within frontal, parietal, and temporal ). In contrast, this trend was reversed in the occipital lobes, where the HFO rate increased with age (0.10/min vs. 0.17/min vs. 0.20/min; p < 0.01 between young vs old). These findings were further supported by a significant negative correlation between HFO rate and age in the frontal, parietal, and temporal lobes, as well as a significant positive correlation in the occipital lobes (p< 0.001 for all, occipital p = 0,02; Figure 2, right)