Low temperature approach for high density electrical feedthroughs for neural implants using maskless fabrication techniques.

Implantable electronic packages for neural implants utilize reliable electrical feedthroughs that connect the inside of a sealed capsule to the components that are exposed to the surrounding body tissue. With the ongoing miniaturization of implants requiring ever higher integration densities of such feedthroughs new technologies have to be investigated. The presented work investigates the sealing of vertical feedthroughs in aluminum-oxide-substrates with gold stud-bumps. The technology enables integration densities of up to 1600/cm 2 while delivering suitable water leak rates for realistic implantation durations of miniaturized packages (feedthrough-count $>50$, package-volume $<2$ cm $^{3})$ of more than 50 years. All manufacturing steps require temperatures below $420 ^{\circ}\mathrm {C}$ and are suitable for maskless rapid prototyping.

Neurophysiological Evaluation of a Customizable µECoG-based Wireless Brain Implant.

The number of implantable bidirectional neural interfaces available for neuroscientific research applications is still limited, despite the rapidly increasing number of customized components. We previously reported on how to translate available components into "ready-to-use" wireless implantable systems utilizing components off-the-shelf (COTS). The aim of the present study was to verify the viability of a micro-electrocorticographic ($\mu $ECoG) device built by this approach. Functionality for both neural recording and stimulation was evaluated in an ovine animal model using acoustic stimuli and cortical electrical stimulation, respectively. We show that auditory evoked responses were reliably recorded in both time and frequency domain and present data that demonstrates the cortical electrical stimulation functionality. The successful recording of neuronal activity suggests that the device can compete with existing implantable systems as a neurotechnological research tool.

Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves.

The development of realistic neuroanatomical models of peripheral nerves for simulation purposes requires the reconstruction of the morphology of the myelinated fibres in the nerve, including their nodes of Ranvier. Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments. In this contribution, we propose a supervised machine learning approach for the detailed reconstruction of the geometry of fibres inside a peripheral nerve based on its high-resolution serial section images. Learning from sparse expert annotations, the algorithm traces myelinated axons, even across the nodes of Ranvier. The latter are detected automatically. The approach is based on classifying the myelinated membranes in a supervised fashion, closing the membrane gaps by solving an assignment problem, and classifying the closed gaps for the nodes of Ranvier detection. The algorithm has been validated on two very different datasets: (i) rat vagus nerve subvolume, SBFSEM microscope, 200 × 200 × 200 nm resolution, (ii) rat sensory branch subvolume, confocal microscope, 384 × 384 × 800 nm resolution. For the first dataset, the algorithm correctly reconstructed 88% of the axons (241 out of 273) and achieved 92% accuracy on the task of Ranvier node detection. For the second dataset, the gap closing algorithm correctly closed 96.2% of the gaps, and 55% of axons were reconstructed correctly through the whole volume. On both datasets, training the algorithm on a small data subset and applying it to the full dataset takes a fraction of the time required by the currently used semiautomated protocols. Our software, raw data and ground truth annotations are available at http://hci.iwr.uni-heidelberg.de/Benchmarks/. The development version of the code can be found at https://github.com/RWalecki/ATMA.