Abstract
Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer's disease (AD) mouse model. Epochs capturing specific behaviors were automatically identified and extracted prior to further analysis. This was achieved by the novel design of an ultra-miniature wearable EEG recorder, the NAT-1 device, and its in-situ IR recording module. The device is described in detail, and its contribution to enabling new neuroscience is demonstrated.
Highlights
T HE major healthcare challenges facing developed countries result from diseases of middle and old age
Infrared (IR) event-stamping, with video motion tracking, enables rapid, automated and sensitive EEG phenotyping of Alzheimers Disease (AD) mouse models [1]
A broad range of experimental studies have already been undertaken with the utilization of NAT-1 devices [1], [2], and several are described in more detail, from a circuit/sensor perspective, in this paper
Summary
T HE major healthcare challenges facing developed countries result from diseases of middle and old age. Considering the small body mass of mice (20-30g), improved welfare can be best accomplished through reduction of device weight Meeting this demand preserves the ability to undertake such research, but as will be highlighted in this paper, it can enable new methodologies and support new discoveries in neuroscience through innovation at the level of sensors and ultra-miniature systems. Infrared (IR) event-stamping (a notable feature of the NAT-1 device), with video motion tracking, enables rapid, automated and sensitive EEG phenotyping of Alzheimers Disease (AD) mouse models [1] This approach has proven valuable in achieving the precise synchrony between behavioral and electrophysiological data records required to make use of novel time resolved quantitative EEG analyses.
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