Infra-Low Frequency Neurofeedback: A Systematic Mixed Studies Review

超低频神经反馈:系统性混合研究综述

阅读:1

Abstract

INTRODUCTION: Neurofeedback training is increasingly applied as a therapeutic tool in a variety of disorders, with growing scientific and clinical interest in the last two decades. Different Neurofeedback approaches have been developed over time, so it is now important to be able to distinguish between them and investigate the effectiveness and efficiency characteristics of each specific protocol. In this study we intend to examine the effects of Neurofeedback based on slow brain activity, the so-called Infra-Low Frequency (ILF) training a recently developed methodology that seems promising for the regulation of the central nervous system. AIMS: With this review we intend to summarize the currently existing literature on ILF-Neurofeedback, examine its quality and formulate indications about the clinical effectiveness of ILF-Neurofeedback. METHODS: Literature search was first conducted according to PRISMA principles, described, and then assessed using the MMAT appraisal tool. 18 well-documented studies of ILF-Neurofeedback training in human subjects were picked up and analyzed. Reports include group interventions as well as single case studies. RESULTS: Research data indicates good potential for ILF-Neurofeedback to influence brain activity and neurovegetative parameters. From the clinical profile, a salient common observation is a high level of individualization as a specific characteristic of ILF-Training: this feature seems to correlate with effectiveness of ILF-Neurofeedback, but also poses a challenge for researchers in terms of producing controlled and comparable findings; according to this point, some recommendation for future research on ILF-Neurofeedback are proposed. In conclusion, ILF-neurofeedback shows great potential for application for all those conditions in which the regulation of brain activity and neurophysiological processes are crucial. Further research will make it possible to complete the available data and to have a broader overview of its possible applications.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。