cBRAIN: Tracking all individuals and marking their brain activity for the study of collective brain research

Abstract

Understanding the neural mechanisms underlying the collective behaviors in a group is limited by the ability to track each individual and monitor its neural activity at the same time. Traditional neural recording systems send information over wires restraining the animals’ habitat or behavior, whilst recently introduced innovative approach using telemetry still has limited capacity in simultaneous measurement of multiple individuals in a group. Here, we describe a novel method, cBRAIN (collective brain research aided by instant neurodisplay), which was developed for the purpose of tracking the position and certain neural activity of all naturally behaving individuals in a group. Basically, the cBRAIN is composed of its hardware and algorithm. Firstly, the cBRAIN hardware includes a headstage, CCD camera, and data logger. The 3.5-g weighted headstage is a telemetry featuring recording and data transmission, and blue and red LED lights indicating the position and the occurrence of user-set neural activity, respectively. Also, the headstage contains an internal counter to sync between headstages and a programmable log array for LED trigger working in a recognition scheme to detect the moment when the neural activity of interests occurs. The data logger records raw data up to eight headstages with sampling rate which is tunable up to 20 kHz. The CCD camera records the optical signals with 10~170 Hz sampling rate and a resolution of 2048 x 1088. Secondly, the cBRAIN algorithm has three main purposes: (i) tracking the position of individual mouse, (ii) detecting the moment of red LED on and merging it into the position data, and (iii) statistical mapping such as space occupancy and location map of neural activities of interest, and presentation of inter-agent proximity for the study of collective motion of a group. Finally, we demonstrate a study of social hierarchy in a group using cBRAIN. A classification algorithm for identifying social behaviors such as fighting, chasing, sniffing, and huddling, built based on the tracking data, will be introduced. Lastly, the cBRAIN system therefore provides a promising ground for studying dynamic interplay between social and neural activities from naturally behaving individuals living in a group, and further associating the neural activities with collective behaviors of a group of mice.