Design of an embedded fluorescence imaging system for implantable optical neural recording
The brain is the most complex and least understood biological system known to man. New imaging techniques are providing scientists with an entirely new perspective on the study of the functional brain at a neural circuit level, enabling in-depth understanding of both physiological processes and animal models of neurological and psychiatric diseases which currently lack effective treatments. These new tools come at the cost of meeting the challenges associated with the miniaturization of the hardware for in vivo recordings. Here we propose a miniaturized wearable device which enables to record neuronal activations with single cell resolution in rodents for in vivo, long term studies of neural activity in virtually any region of the brain. Additionally, we introduce new techniques for processing a new set of data and mining the relevant information from the recorded neural activity. The proposed image preprocessing techniques include image registration, automatic cell detection and calcium transient extraction algorithms designed for real-time hardware implementation, anticipating the application of single cell neural recordings jointly with optogenetic stimulation in a feedback control loop. The new developed tools were applied to the study of the neural activity in the di- rect and indirect pathways on the dorsal striatum and their role in locomotor activity, a controversial topic due to the lack of techniques for selectively and independently study these neural circuits with sucient detail. Our findings challenge the long standing classical model for D1 and D2 neurons, showing how neural activity in the indirect pathway cannot be explained as inhibitory for locomotor activity. Through the application of a k-means based clustering algorithm we propose a new model for the direct and indirect pathway role in locomotion, and demonstrate the remarkable heterogeneity in striatal D1 and D2 cell populations. The study of acute cocaine effect as a mean for pharmacologically increase locomotor activity further proved the diversity in the response of D1 and D2 neurons within the same cell population. Finally, through the application of machine learning algorithms, we show how neural activity in the dorsal striatum (particularly D2 neurons) can be used as a good predictor for behavior in open field tests.
Chiu, Purdue University.
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