Remote optogenetic control of the enteric nervous system and brain-gut axis in freely-behaving mice enabled by a wireless, battery-free optoelectronic device.

Wireless activation of the enteric nervous system (ENS) in freely moving animals with implantable optogenetic devices offers a unique and exciting opportunity to selectively control gastrointestinal (GI) transit in vivo, including the gut-brain axis. Programmed delivery of light to targeted locations in the GI-tract, however, poses many challenges not encountered within the central nervous system (CNS). We report here the development of a fully implantable, battery-free wireless device specifically designed for optogenetic control of the GI-tract, capable of generating sufficient light over large areas to robustly activate the ENS, potently inducing colonic motility ex vivo and increased propulsion in vivo. Use in in vivo studies reveals unique stimulation patterns that increase expulsion of colonic content, likely mediated in part by activation of an extrinsic brain-gut motor pathway, via pelvic nerves. This technology overcomes major limitations of conventional wireless optogenetic hardware designed for the CNS, providing targeted control of specific neurochemical classes of neurons in the ENS and brain-gut axis, for direct modulation of GI-transit and associated behaviours in freely moving animals.

Remote control of muscle-driven miniature robots with battery-free wireless optoelectronics.

Bioengineering approaches that combine living cellular components with three-dimensional scaffolds to generate motion can be used to develop a new generation of miniature robots. Integrating on-board electronics and remote control in these biological machines will enable various applications across engineering, biology, and medicine. Here, we present hybrid bioelectronic robots equipped with battery-free and microinorganic light-emitting diodes for wireless control and real-time communication. Centimeter-scale walking robots were computationally designed and optimized to host on-board optoelectronics with independent stimulation of multiple optogenetic skeletal muscles, achieving remote command of walking, turning, plowing, and transport functions both at individual and collective levels. This work paves the way toward a class of biohybrid machines able to combine biological actuation and sensing with on-board computing.

Preparation and use of wireless reprogrammable multilateral optogenetic devices for behavioral neuroscience.

Wireless battery-free optogenetic devices enable behavioral neuroscience studies in groups of animals with minimal interference to natural behavior. Real-time independent control of optogenetic stimulation through near-field communication dramatically expands the realm of applications of these devices in broad contexts of neuroscience research. Dissemination of these tools with advanced functionalities to the neuroscience community requires protocols for device manufacturing and experimental implementation. This protocol describes detailed procedures for fabrication, encapsulation and implantation of recently developed advanced wireless devices in head- and back-mounted forms. In addition, procedures for standard implementation of experimental systems in mice are provided. This protocol aims to facilitate the application of wireless optogenetic devices in advanced optogenetic experiments involving groups of freely moving rodents and complex environmental designs. The entire protocol lasts ~3-5 weeks.

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment.

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.