An implantable device for wireless monitoring of diverse physio-behavioral characteristics in freely behaving small animals and interacting groups.

Comprehensive, continuous quantitative monitoring of intricately orchestrated physiological processes and behavioral states in living organisms can yield essential data for elucidating the function of neural circuits under healthy and diseased conditions, for defining the effects of potential drugs and treatments, and for tracking disease progression and recovery. Here, we report a wireless, battery-free implantable device and a set of associated algorithms that enable continuous, multiparametric physio-behavioral monitoring in freely behaving small animals and interacting groups. Through advanced analytics approaches applied to mechano-acoustic signals of diverse body processes, the device yields heart rate, respiratory rate, physical activity, temperature, and behavioral states.

A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals.

Fully implantable wireless systems for the recording and modulation of neural circuits that do not require physical tethers or batteries allow for studies that demand the use of unconstrained and freely behaving animals in isolation or in social groups. Moreover, feedback-control algorithms that can be executed within such devices without the need for remote computing eliminate virtual tethers and any associated latencies. Here we report a wireless and battery-less technology of this type, implanted subdermally along the back of freely moving small animals, for the autonomous recording of electroencephalograms, electromyograms and body temperature, and for closed-loop neuromodulation via optogenetics and pharmacology.

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images.

Photocurable bioresorbable adhesives as functional interfaces between flexible bioelectronic devices and soft biological tissues.

Flexible electronic/optoelectronic systems that can intimately integrate onto the surfaces of vital organ systems have the potential to offer revolutionary diagnostic and therapeutic capabilities relevant to a wide spectrum of diseases and disorders. The critical interfaces between such technologies and living tissues must provide soft mechanical coupling and efficient optical/electrical/chemical exchange. Here, we introduce a functional adhesive bioelectronic-tissue interface material, in the forms of mechanically compliant, electrically conductive, and optically transparent encapsulating coatings, interfacial layers or supporting matrices.

Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment.

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images.