个体化连接组tACS可立即改善脑卒中后失语症的振荡网络和语言促进:一项基于功能障碍组靶向方法的可行性研究。

IF 2.3 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2025-09-04 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1635497
Chester Yee-Nok Cheung, Anthony Pak-Hin Kong, Mehdi Bakhtiar
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引用次数: 0

摘要

脑卒中后失语症(PSA)患者表现出显著的个体差异,这是由于个体之间独特的网络中断模式。这种复杂性限制了传统的一刀切脑刺激方法的有效性,但迄今为止还没有针对PSA功能网络的个体化tACS研究。本研究采用一种基于个体脑电图功能障碍的新型个体化靶向方法,旨在研究双位点同相tACS的即时网络调节和语言促进效应。方法:第一阶段采用基于网络的线性回归方法,从15例广东语失语症患者的言语产生脑电数据中识别失语症严重程度预测功能障碍组。采用两种靶向原则确定个体化刺激目标。基于恢复的目标定位旨在恢复集中在目标功能障碍组内但在个体中弱连接的目标边缘,而基于增强的目标定位则选择强连接的目标边缘。第二阶段涉及同一组的单期双盲假对照试验,以评估双位点7-Hz 1-mA tACS在四种情况下的即时效果:恢复期(RI),增强期(EI),增强反期(EA)和假手术(SH)。结果:在第一阶段,我们探索了一系列频带和EEG任务,并发现了发散命名任务下的左侧额颞叶θ网络对失语症严重程度的显著预测。第二阶段的单阶段临床试验表明,RI条件产生了目标节点强度、整体网络特性和发散命名性能的增加,这在假刺激和其他两种真实刺激条件下没有。讨论:这是第一个基于功能障碍组的数据驱动的个体化tACS在PSA中显示出即时的神经调节作用。研究结果表明,脑电图功能障碍组可以帮助确定tACS的有效个体化靶点,以促进临床有益的功能重组。尽管由于样本量小,可推广性有限,但该方法在长期治疗和其他基于网络的疾病方面具有重要的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Individualized connectomic tACS immediately improves oscillatory network with language facilitation in post-stroke aphasia: a feasibility study of a dysfunctome-based targeting approach.

Individualized connectomic tACS immediately improves oscillatory network with language facilitation in post-stroke aphasia: a feasibility study of a dysfunctome-based targeting approach.

Individualized connectomic tACS immediately improves oscillatory network with language facilitation in post-stroke aphasia: a feasibility study of a dysfunctome-based targeting approach.

Individualized connectomic tACS immediately improves oscillatory network with language facilitation in post-stroke aphasia: a feasibility study of a dysfunctome-based targeting approach.

Introduction: People with post-stroke aphasia (PSA) exhibit significant interindividual variability attributed to distinctive network disruption patterns across individuals. This complexity limits the effectiveness of conventional one-size-fits-all brain stimulation approaches, but to date no individualized tACS targeting on functional network was studied in PSA. This two-phase study aimed to investigate the immediate network-modulation and language-facilitation effects of dual-site in-phase tACS utilizing a novel individualized targeting method based on individual's EEG dysfunctome.

Methods: In the first phase, network-based linear regression was used to identify aphasia-severity-predictive dysfunctome from the speech-production EEG data of 15 Cantonese-speaking people with aphasia (PWA). Individualized stimulation targets were determined using two targeting principles. Restoration-based targeting aims to restore a target edge which is centralized within the target dysfunctome but weakly-connected in the individual, whereas enhancement-based targeting selects a strongly-connected target edge. The second phase involved a single-session double-blinded sham-controlled trial with the same group to evaluate the immediate effects of dual-site 7-Hz 1-mA tACS under four conditions: Restoration In-phase (RI), Enhancement In-phase (EI), Enhancement Anti-phase (EA), and Sham (SH).

Results: In the first phase, we explored a range of frequency bands and EEG tasks and identified a left frontal-temporal theta network under divergent naming task that significantly predicted aphasia severity. The single-session clinical trial in the second phase demonstrated that RI condition produced increases in the target node strength, global network properties, and divergent naming performance, which were absent in sham and the other two real stimulation conditions.

Discussion: This was the first-of-its-kind dysfunctome-based data-driven individualized tACS demonstrated immediate neuromodulatory effects in PSA. The findings suggest that EEG dysfunctome can help pinpointing effective individualized targets for tACS to promote clinically-beneficial functional reorganization. Despite limited generalizability due to the small sample, this methodology holds significant potential for application in longer-term treatment and other network-based disorders.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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