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使用贝叶斯统计方法重塑明适应视网膜电图。

Remodeling the light-adapted electroretinogram using a bayesian statistical approach.

作者信息

Brabec Marek, Marmolejo-Ramos Fernando, Loh Lynne, Lee Irene O, Kulyabin Mikhail, Zhdanov Aleksei, Posada-Quintero Hugo, Thompson Dorothy A, Constable Paul A

机构信息

Institute of Computer Science, Czech Academy of Sciences, Pod Vodarenskou Vezi 2, Prague 8, 182 00, Czech Republic.

National Institute of Public Health, Srobarova 48, Prague 10, 100 00, Czech Republic.

出版信息

BMC Res Notes. 2025 Jan 23;18(1):33. doi: 10.1186/s13104-025-07115-4.

DOI:10.1186/s13104-025-07115-4
PMID:39849598
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11760095/
Abstract

OBJECTIVE

To present a remodeling of the electroretinogram waveform using a covariance matrix to identify regions of interest and distinction between a control and attention deficit/hyperactivity disorder (ADHD) group. Electroretinograms were recorded in n = 25 ADHD (16 male; age 11.9 ± 2.7 years) and n = 38 (8 male; age 10.4 ± 2.8 years neurotypical control participants as part of a broad study into the determining if the electroretinogram could be a biomarker for ADHD. Flash strengths of 0.6 and 1.2 log cd.s.m on a white 40 cd.m background were used. Averaged waveforms from each eye and flash strength were analyzed with Bayesian regularization of the covariance matrices using 100 equal length time intervals. The eigenvalues of the covariance matrices were ranked for each group to indicate the degree of complexity within the regularized waveforms.

RESULTS

The correlation matrices indicated less correlation within the waveforms for the ADHD group in time intervals beyond 70 msec. The eigenvalue plots suggest more complexity within the ADHD group compared to the control group. Consideration of the correlation structure between ERG waveforms from different populations may reveal additional features for identifying group differences in clinical populations.

摘要

目的

利用协方差矩阵对视网膜电图波形进行重塑,以识别感兴趣区域,并区分对照组与注意力缺陷多动障碍(ADHD)组。作为一项关于视网膜电图能否作为ADHD生物标志物的广泛研究的一部分,对n = 25名ADHD患者(16名男性;年龄11.9±2.7岁)和n = 38名(8名男性;年龄10.4±2.8岁)神经典型对照参与者记录了视网膜电图。在白色40 cd.m背景下使用0.6和1.2 log cd.s.m的闪光强度。使用100个等长的时间间隔,通过协方差矩阵的贝叶斯正则化分析每只眼睛和闪光强度的平均波形。对每组协方差矩阵的特征值进行排序,以表明正则化波形内的复杂程度。

结果

相关矩阵表明,ADHD组在70毫秒以上的时间间隔内波形内的相关性较低。特征值图表明,与对照组相比,ADHD组内的复杂性更高。考虑不同人群视网膜电图波形之间的相关结构可能会揭示识别临床人群组间差异的其他特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/d147d1ffbb7e/13104_2025_7115_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/95fe5322feac/13104_2025_7115_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/866c572cdfa4/13104_2025_7115_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/d147d1ffbb7e/13104_2025_7115_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/95fe5322feac/13104_2025_7115_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/866c572cdfa4/13104_2025_7115_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2455/11760095/d147d1ffbb7e/13104_2025_7115_Fig2_HTML.jpg

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本文引用的文献

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Doc Ophthalmol. 2025 Feb;150(1):25-32. doi: 10.1007/s10633-024-10000-3. Epub 2024 Dec 15.
2
Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques.使用短时傅里叶变换和机器学习技术的视网膜电图分析
Bioengineering (Basel). 2024 Aug 26;11(9):866. doi: 10.3390/bioengineering11090866.
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Age-dependencies of the electroretinogram in healthy subjects.健康受试者的视网膜电图的年龄依赖性。
Doc Ophthalmol. 2024 Oct;149(2):99-113. doi: 10.1007/s10633-024-09991-w. Epub 2024 Sep 9.
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Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer.多小波分析和视觉Transformer 增强视网膜电图分类。
Sensors (Basel). 2023 Oct 26;23(21):8727. doi: 10.3390/s23218727.
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Group comparisons of the individual electroretinogram time trajectories for the ascending limb of the b-wave using a raw and registered time series.使用原始和注册时间序列对 b 波上升支的个体视网膜电图时间轨迹进行组间比较。
BMC Res Notes. 2023 Sep 29;16(1):238. doi: 10.1186/s13104-023-06535-4.
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