Neural transmission in the wired brain, new insights into an encoding-decoding-based neuronal communication model

大脑中的神经传递:基于编码-解码的神经元通讯模型的新见解

阅读:1

Abstract

Brain activity is known to be rife with oscillatory activity in different frequencies, which are suggested to be associated with intra-brain communication. However, the specific role of frequencies in neuronal information transfer is still an open question. To this end, we utilized EEG resting state recordings from 5 public datasets. Overall, data from 1668 participants, including people with MDD, ADHD, OCD, Parkinson's, Schizophrenia, and healthy controls aged 5-89, were part of the study. We conducted a running window of Spearman correlation between the two frontal hemispheres' Alpha envelopes. The results of this analysis revealed a unique pattern of correlation states alternating between fully synchronized and desynchronized several times per second, likely due to the interference pattern between two signals of slightly different frequencies, also named "Beating". Subsequent analysis showed this unique pattern in every pair of ipsilateral/contralateral, across frequencies, either in eyes closed or open, and across all ages, underscoring its inherent significance. Biomarker analysis revealed significantly lower synchronization and higher desynchronization for people older than 50 compared to younger ones and lower ADHD desynchronization compared to age-matched controls. Importantly, we propose a new brain communication model in which frequency modulation creates a binary message encoded and decoded by brain regions for information transfer. We suggest that the binary-like pattern allows the neural information to be coded according to certain physiological and biological rules known to both the sender and recipient. This digital-like scheme has the potential to be exploited in brain-computer interaction and applied technologies such as robotics.

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。