Faculty Bibliography

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.

A major challenge for miniature bioelectronics is wireless power delivery deep inside the body. Electromagnetic or ultrasound waves suffer from absorption and impedance mismatches at biological interfaces. On the other hand, magnetic fields do not suffer these losses, which has led to magnetically powered bioelectronic implants based on induction or magnetothermal effects. However, these approaches have yet to produce a miniature stimulator that operates at clinically relevant high frequencies. Here, we show that an alternative wireless power method based on magnetoelectric (ME) materials enables miniature magnetically powered neural stimulators that operate up to clinically relevant frequencies in excess of 100 Hz. We demonstrate that wireless ME stimulators provide therapeutic deep brain stimulation in a freely moving rodent model for Parkinson's disease and that these devices can be miniaturized to millimeter-scale and fully implanted. These results suggest that ME materials are an excellent candidate to enable miniature bioelectronics for clinical and research applications.

The hippocampal activity supporting trace fear conditioning has long been mysterious, but a leading hypothesis posits "time-cell"-like sequential patterns. In this issue of Neuron, Ahmed et al. (2020) present new data suggesting that, at least during the first session of learning, a subset of neurons coalesce to selectively encode the task but without expressing reliable sequences.

An emerging corpus of research seeks to use virtual realities (VRs) to understand the neural mechanisms underlying spatial navigation and decision making in rodents. These studies have primarily used visual stimuli to represent the virtual world. However, auditory cues play an important role in navigation for animals, especially when the visual system cannot detect objects or predators. We have developed a virtual reality environment defined exclusively by free-field acoustic landmarks for head-fixed mice. We trained animals to run in a virtual environment with 3 acoustic landmarks. We present evidence that they can learn to navigate in our context: we observed anticipatory licking and modest anticipatory slowing preceding the reward region. Furthermore, we found that animals were highly aware of changes in landmark cues: licking behavior changed dramatically when the familiar virtual environment was switched to a novel one, and then rapidly reverted to normal when the familiar virtual environment was re-introduced, all within the same session. Finally, while animals executed the task, we performed in-vivo calcium imaging in the CA1 region of the hippocampus using a modified Miniscope.org system. Our experiments point to a future in which auditory virtual reality can be used to expand our understanding of the neural bases of audition in locomoting animals and the variety of sensory cues which anchor spatial representations in a new virtual environment.

OBJECTIVE:Recording electrical activity from individual cells in vivo is a key technology for basic neuroscience and has growing clinical applications. To maximize the number of independent recording channels as well as the longevity, and quality of these recordings, researchers often turn to small and flexible electrodes that minimize tissue damage and can isolate signals from individual neurons. One challenge when creating these small electrodes, however, is to maintain a low interfacial impedance by applying a surface coating that is stable in tissue and does not significantly complicate the fabrication process. APPROACH:Here we use a high-pressure Pt sputtering process to create low-impedance electrodes at the wafer scale using standard microfabrication equipment. MAIN RESULTS:We find that direct-sputtered Pt provides a reliable and well-controlled porous coating that reduces the electrode impedance by 5-9 fold compared to flat Pt and is compatible with the microfabrication technologies used to create flexible electrodes. These porous Pt electrodes show reduced thermal noise that matches theoretical predictions. In addition, we show that these electrodes can be implanted into rat cortex, record single unit activity, and be removed all without disrupting the integrity of the coating. We also demonstrate that the shape of the electrode (in addition to the surface area) has a significant effect on the electrode impedance when the feature sizes are on the order of tens of microns. SIGNIFICANCE:Overall, porous Pt represents a promising method for manufacturing low-impedance electrodes that can be seamlessly integrated into existing processes for producing flexible neural probes.

Patterns of neural activity that occur spontaneously during sharp-wave ripple (SWR) events in the hippocampus are thought to play an important role in memory formation, consolidation and retrieval. Typical studies examining the content of SWRs seek to determine whether the identity and/or temporal order of cell firing is different from chance. Such 'first-order' analyses are focused on a single time point and template (map), and have been used to show, for instance, the existence of preplay. The major methodological challenge in first-order analyses is the construction and interpretation of different chance distributions. By contrast, 'second-order' analyses involve a comparison of SWR content between different time points, and/or between different templates. Typical second-order questions include tests of experience-dependence (replay) that compare SWR content before and after experience, and comparisons or replay between different arms of a maze. Such questions entail additional methodological challenges that can lead to biases in results and associated interpretations. We provide an inventory of analysis challenges for second-order questions about SWR content, and suggest ways of preventing, identifying and addressing possible analysis biases. Given evolving interest in understanding SWR content in more complex experimental scenarios and across different time scales, we expect these issues to become increasingly pervasive. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

OBJECTIVE:The ability to modulate neural activity in a closed-loop fashion enables causal tests of hypotheses which link dynamically-changing neural circuits to specific behavioral functions. One such dynamically-changing neural circuit is the hippocampus, in which momentary sharp-wave ripple (SWR) events-≈  100 ms periods of large 150-250 Hz oscillations-have been linked to specific mnemonic functions via selective closed-loop perturbation. The limited duration of SWR means that the latency in systems used for closed-loop interaction is of significant consequence compared to other longer-lasting circuit states. While closed-loop SWR perturbation is becoming more wide-spread, the performance trade-offs involved in building a SWR disruption system have not been explored, limiting the design and interpretation of paradigms involving ripple disruption. APPROACH:We developed and evaluated a low-latency closed-loop SWR detection system implemented as a module to an open-source neural data acquisition software suite capable of interfacing with two separate data acquisition hardware platforms. We first use synthetic data to explore the parameter space of our detection algorithm, then proceed to quantify the realtime in vivo performance and limitations of our system. MAIN RESULTS:We evaluate the realtime system performance of two data acquisition platforms, one using USB and one using ethernet for communication. We report that signal detection latency decomposes into a data acquisition component of 7.5-13.8 ms and 1.35-2.6 ms for USB and ethernet hardware respectively, and an algorithmic component which varies depending on the threshold parameter. Using ethernet acquisition hardware, we report that an algorithmic latency in the range of  ≈20-66 ms can be achieved while maintaining  <10 false detections per minute, and that these values are highly dependent upon algorithmic parameter space trade-offs. SIGNIFICANCE:By characterizing this system in detail, we establish a framework for analyzing other closed-loop neural interfacing systems. Thus, we anticipate this modular, open-source, realtime system will facilitate a wide range of carefully-designed causal closed-loop experiments.

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Several research groups have developed head-mounted fluorescence microscopes as a modality for recording neural activity in freely behaving mice. The current designs have shown exciting results from in vivo imaging of the bright dynamics of genetically encoded calcium indicators (GECI). However, despite their potential, head-mounted microscopes are not in use with genetically encoded voltage indicators (GEVI) or bioluminescence indicators. Due to its ability to match the temporal resolution of neuron spiking, GEVIs offer great benefits to experiments designed to provide feedback after real-time detection of specific neural activity such as the less than 250ms replay events that can occur in the hippocampus. Orthogonally, the emerging bioluminescence activity reporters have the potential to eliminate autofluorescence and photobleaching that can occur in fluorescence imaging. There are two important properties of the head-mounted microscope's image sensor affecting the ability to image GEVIs and bioluminescence indicators. First, the low signal to noise ratio (SNR) characteristics of GEVIs and bioluminescent indicators make signal detection difficult. Second, in order to take advantage of the GEVIs faster fluorescence kinetics, the image sensor must be capable of matching frame rates. Here, we present the design of a new imaging module for head-mounted microscopes incorporating the latest CMOS sensor technology aimed at increasing image sensor sensitivity and frame rates for use in real-time detection experiments. The design builds off an existing open-source project and can integrate into the existing data acquisition hardware and microscope housing.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.