The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users.
The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users.
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DOI：
10.1007/s10162-021-00795-2
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Carlyon RP
中科院分区：
文献类型：
--
作者：
Garcia C;Goehring T;Cosentino S;Turner RE;Deeks JM;Brochier T;Rughooputh T;Bance M;Carlyon RP
人工耳蜗植入体（CI）患者特定神经兴奋模式的知识可以为优化疗效和改善语音感知结果提供重要信息。全景ECAP（“PECAP”）方法（Cosentino等人）使用正向掩蔽电诱发复合动作电位（ECAP）来估计CI刺激的神经激活模式。该算法需要测量探头和掩蔽电极的所有组合的ECAP，利用ECAP振幅反映探头和掩蔽电极的重叠兴奋区域的事实。在这里，我们提出了一个改进版本的PECAP算法，施加生物现实的约束的解决方案，这与以前的版本不同，产生详细的估计神经激活模式的建模电流传播和神经健康沿着脑内电极阵列，并能够识别多个区域的神经健康状况不佳。该算法的可靠性和准确性进行了评估，在三个方面：（1）计算机模拟的电流传播和神经健康的情况下，（2）比较人类CI用户的神经健康和电极蜗轴距离的心理物理相关性，（3）检测模拟的神经“死”区域（使用前向掩蔽）在人类CI用户。PECAP算法可靠地估计了计算机模拟的场景。集中阈值和算法的神经健康估计值之间存在中度但显著的负相关性，与以前的文献一致。它还正确地识别了所有七个CI用户的模拟“死亡”区域。修订后的PECAP算法提供了CI中神经兴奋模式的估计，可用于通知和优化临床环境中个体患者的CI刺激策略。
The knowledge of patient-specific neural excitation patterns from cochlear implants (CIs) can provide important information for optimizing efficacy and improving speech perception outcomes. The Panoramic ECAP (‘PECAP’) method (Cosentino et al.) uses forward-masked electrically evoked compound action-potentials (ECAPs) to estimate neural activation patterns of CI stimulation. The algorithm requires ECAPs be measured for all combinations of probe and masker electrodes, exploiting the fact that ECAP amplitudes reflect the overlapping excitatory areas of both probes and maskers. Here we present an improved version of the PECAP algorithm that imposes biologically realistic constraints on the solution, that, unlike the previous version, produces detailed estimates of neural activation patterns by modelling current spread and neural health along the intracochlear electrode array and is capable of identifying multiple regions of poor neural health. The algorithm was evaluated for reliability and accuracy in three ways: (1) computer-simulated current-spread and neural-health scenarios, (2) comparisons to psychophysical correlates of neural health and electrode-modiolus distances in human CI users, and (3) detection of simulated neural ‘dead’ regions (using forward masking) in human CI users. The PECAP algorithm reliably estimated the computer-simulated scenarios. A moderate but significant negative correlation between focused thresholds and the algorithm’s neural-health estimates was found, consistent with previous literature. It also correctly identified simulated ‘dead’ regions in all seven CI users evaluated. The revised PECAP algorithm provides an estimate of neural excitation patterns in CIs that could be used to inform and optimize CI stimulation strategies for individual patients in clinical settings.
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影响因子：
3.4
作者：
Iyer, Janani S.;Zhu, Ning;Stankovic, Konstantina M.
通讯作者：
Stankovic, Konstantina M.
影响因子：
2.4
作者：
DESAUVAGE, RC;CAZALS, Y;ARAN, JM
通讯作者：
ARAN, JM
影响因子：
--
作者：
BLAND, JM;ALTMAN, DG
通讯作者：
ALTMAN, DG
DOI：
10.1007/s10162-020-00773-0
发表时间：
2021-03
期刊：
Journal of the Association for Research in Otolaryngology : JARO
影响因子：
--
作者：
Brochier T;Guérit F;Deeks JM;Garcia C;Bance M;Carlyon RP
通讯作者：
Carlyon RP
影响因子：
3.7
作者：
He S;Shahsavarani BS;McFayden TC;Wang H;Gill KE;Xu L;Chao X;Luo J;Wang R;He N
通讯作者：
He N