Working memory describes our capacity to temporarily hold information and adaptively translate it later into a behavioral response, and it is critical for the cognitive functions of human beings.
Previous studies have proved the persistent neuron activity in the hippocampus during working memory. The hippocampus is composed of two subregions-- anterior hippocampus and posterior hippocampus. How the two subregions interact during working memory remains unknown.
Recently, a research team led by Prof. JIANG Tianzi from the Institute of Automation of the Chinese Academy of Sciences revealed the neural oscillations and the dynamic process within the anterior and the posterior hippocampus during working memory processes by combining the Brainnetome Atlas with the high spatiotemporal resolution of human intracranial EEG signals.
Their findings were published in Journal of Neuroscience on Nov. 24.
The researchers recorded intracranial EEG from the anterior and the posterior hippocampus in pre-surgical epilepsy patients while they performed a working memory task. The elevated low-frequency neural oscillations in both the anterior and posterior hippocampus indicated that both regions engaged in the maintenance of the working memory information.
Moreover, they found increased theta/alpha band (3-12 Hz) phase synchronization between anterior and posterior subregions. Importantly, the theta/alpha band-coordinated unidirectional influence from the posterior to the anterior hippocampus supported the successful working memory information, while the working memory errors were associated with bidirectional interactions between the anterior and posterior hippocampus.
This research verifies the neural mechanism of the hippocampal fine structure during working memory by the Brainnetome Atlas for the first time, which is beneficial for the development of new biomarkers for related neurological diseases.
The dynamics of the anterior and the posterior hippocampus during working memory processing.
Contact:
ZHANG Xiaohan, PIO, Institute of Automation, Chinese Academy of Sciences
Email: xiaohan.zhang@ia.ac.cn