Faculty Bibliography

Soft and conductive nanomaterials like carbon nanotubes, graphene, and nanowire scaffolds have expanded the family of ultraflexible microelectrodes that can bend and flex with the natural movement of the brain, reduce the inflammatory response, and improve the stability of long-term neural recordings. However, current methods to implant these highly flexible electrodes rely on temporary stiffening agents that temporarily increase the electrode size and stiffness thus aggravating neural damage during implantation, which can lead to cell loss and glial activation that persists even after the stiffening agents are removed or dissolve. A method to deliver thin, ultraflexible electrodes deep into neural tissue without increasing the stiffness or size of the electrodes will enable minimally invasive electrical recordings from within the brain. Here we show that specially designed microfluidic devices can apply a tension force to ultraflexible electrodes that prevents buckling without increasing the thickness or stiffness of the electrode during implantation. Additionally, these "fluidic microdrives" allow us to precisely actuate the electrode position with micron-scale accuracy. To demonstrate the efficacy of our fluidic microdrives, we used them to actuate highly flexible carbon nanotube fiber (CNTf) microelectrodes for electrophysiology. We used this approach in three proof-of-concept experiments. First, we recorded compound action potentials in a soft model organism, the small cnidarian Hydra. Second, we targeted electrodes precisely to the thalamic reticular nucleus in brain slices and recorded spontaneous and optogenetically evoked extracellular action potentials. Finally, we inserted electrodes more than 4 mm deep into the brain of rats and detected spontaneous individual unit activity in both cortical and subcortical regions. Compared to syringe injection, fluidic microdrives do not penetrate the brain and prevent changes in intracranial pressure by diverting fluid away from the implantation site during insertion and actuation. Overall, the fluidic microdrive technology provides a robust new method to implant and actuate ultraflexible neural electrodes.

Information processing in the rodent hippocampus is fundamentally shaped by internally generated sequences (IGSs), expressed during two different network states: theta sequences, which repeat and reset at the ∼8 Hz theta rhythm associated with active behavior, and punctate sharp wave-ripple (SWR) sequences associated with wakeful rest or slow-wave sleep. A potpourri of diverse functional roles has been proposed for these IGSs, resulting in a fragmented conceptual landscape. Here, we advance a unitary view of IGSs, proposing that they reflect an inferential process that samples a policy from the animal's generative model, supported by hippocampus-specific priors. The same inference affords different cognitive functions when the animal is in distinct dynamical modes, associated with specific functional networks. Theta sequences arise when inference is coupled to the animal's action-perception cycle, supporting online spatial decisions, predictive processing, and episode encoding. SWR sequences arise when the animal is decoupled from the action-perception cycle and may support offline cognitive processing, such as memory consolidation, the prospective simulation of spatial trajectories, and imagination. We discuss the empirical bases of this proposal in relation to rodent studies and highlight how the proposed computational principles can shed light on the mechanisms of future-oriented cognition in humans.

Hippocampal place cells are key to episodic memories. How these cells participate in memory retrieval remains unclear. After rats acquired a fear memory by receiving mild footshocks in a shock zone on a track, we analyzed place cells when the animals were placed on the track again and displayed an apparent memory retrieval behavior: avoidance of the shock zone. We found that place cells representing the shock zone were reactivated, despite the fact that the animals did not enter the shock zone. This reactivation occurred in ripple-associated awake replay of place cell sequences encoding the paths from the animal's current positions to the shock zone but not in place cell sequences within individual cycles of theta oscillation. The result reveals a specific place-cell pattern underlying inhibitory avoidance behavior and provides strong evidence for the involvement of awake replay in fear memory retrieval.

In most biological tissues, light scattering due to small differences in refractive index limits the depth of optical imaging systems. Two-photon microscopy (2PM), which significantly reduces the scattering of the excitation light, has emerged as the most common method to image deep within scattering biological tissue. This technique, however, requires high-power pulsed lasers that are both expensive and difficult to integrate into compact portable systems. Using a combination of theoretical and experimental techniques, we show that if the excitation path length can be minimized, selective plane illumination microscopy (SPIM) can image nearly as deep as 2PM without the need for a high-powered pulsed laser. Compared to other single-photon imaging techniques like epifluorescence and confocal microscopy, SPIM can image more than twice as deep in scattering media ( ? 10 times the mean scattering length). These results suggest that SPIM has the potential to provide deep imaging in scattering media in situations in which 2PM systems would be too large or costly.

We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states - virtual place fields - in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence of states naturally lead to the development of a two component sequence score in which the sequential and contextual information are decoupled. We also show how this score can discriminate between true and shuffled sequences of hippocampal neural activity.

The striatum mediates a variety of functions including movement, decision-making, motivation, and reward learning. In vivo recording is a powerful technique that allows for the interrogation of these striatal functions while an animal is awake and behaving. Here, we describe equipment needed and general setup for performing in vivo electrophysiology experiments, data processing, and quantification of recording quality. While this protocol is focused on striatal recordings, concepts should translate to other structures as well.

The development of microelectrodes capable of safely stimulating and recording neural activity is a critical step in the design of many prosthetic devices, brain-machine interfaces, and therapies for neurologic or nervous-system-mediated disorders. Metal electrodes are inadequate prospects for the miniaturization needed to attain neuronal-scale stimulation and recording because of their poor electrochemical properties, high stiffness, and propensity to fail due to bending fatigue. Here we demonstrate neural recording and stimulation using carbon nanotube (CNT) fiber electrodes. In vitro characterization shows that the tissue contact impedance of CNT fibers is remarkably lower than that of state-of-the-art metal electrodes, making them suitable for recording single-neuron activity without additional surface treatments. In vivo chronic studies in parkinsonian rodents show that CNT fiber microelectrodes stimulate neurons as effectively as metal electrodes with 10 times larger surface area, while eliciting a significantly reduced inflammatory response. The same CNT fiber microelectrodes can record neural activity for weeks, paving the way for the development of novel multifunctional and dynamic neural interfaces with long-term stability.

Parkinson's disease (PD) is a neurodegenerative disorder which follows from cell loss of dopaminergic neurons in the substantia nigra pars compacta (SNc), a nucleus in the basal ganglia (BG). Deep brain stimulation (DBS) is an electrical therapy that modulates the pathological activity to treat the motor symptoms of PD. Although this therapy is currently used in clinical practice, the sufficient conditions for therapeutic efficacy are unknown. In this work we develop a model of critical motor circuit structures in the brain using biophysical cell models as the base components and then evaluate performance of different DBS signals in this model to perform comparative studies of their efficacy. Biological models are an important tool for gaining insights into neural function and, in this case, serve as effective tools for investigating innovative new DBS paradigms. Experiments were performed using the hemi-parkinsonian rodent model to test the same set of signals, verifying the obedience of the model to physiological trends. We show that antidromic spiking from DBS of the subthalamic nucleus (STN) has a significant impact on cortical neural activity, which is frequency dependent and additionally modulated by the regularity of the stimulus pulse train used. Irregular spacing between stimulus pulses, where the amount of variability added is bounded, is shown to increase diversification of response of basal ganglia neurons and reduce entropic noise in cortical neurons, which may be fundamentally important to restoration of information flow in the motor circuit.

The globus pallidus internus (GPi) is the main output nucleus of the basal ganglia, which is associated with a variety of functions including motor performance and cognition. The GPi is one of the primary targets of deep brain stimulation (DBS) in patients with movement disorders. However, the therapeutic mechanism of GPi-DBS is poorly understood and rodent models have not been characterized. Cognitive side effects, such as impulsivity and depression, of DBS treatment for Parkinson's disease are known, but their relationship to the efficacy of the treatment is not well explained. The goal of this study is to illuminate the effects of GPi-DBS on both motor and cognitive function in a hemi-Parkinsonian rat model. In this work, we study the motor performance of the rodents in multiple behaviors, as well as of impulsivity and depression, and consider the relationship between these behavioral variables and the stimulation frequency of the DBS signal. For the first time, the connection is directly established between stimulating the GPi, motor performance and cognition is directly established in the hemi-Parkinsonian rodent model.

Neural activity during sharp wave ripples (SWR), short bursts of co-ordinated oscillatory activity in the CA1 region of the rodent hippocampus, is implicated in a variety of memory functions from consolidation to recall. Detection of these events in an algorithmic framework, has thus far relied on simple thresholding techniques with heuristically derived parameters. This study is an investigation into testing and improving the current methods for detection of SWR events in neural recordings. We propose and profile methods to reduce latency in ripple detection. Proposed algorithms are tested on simulated ripple data. The findings show that simple realtime algorithms can improve upon existing power thresholding methods and can detect ripple activity with latencies in the range of 10-20 ms.