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In JoVE (2)
- Whole Animal Perfusion Fixation for Rodents
- Surgical Implantation of Chronic Neural Electrodes for Recording Single Unit Activity and Electrocorticographic Signals
Other Publications (8)
- Journal of Neural Engineering
- Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
- Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
- Journal of Neuroscience Methods
- IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
- Physical Biology
- Neuron
- Neuron
Articles by Gregory J. Gage in JoVE
JoVE Neuroscience
Whole Animal Perfusion Fixation for Rodents
1Biomedical Engineering, University of Michigan, 2Department of Neurological Surgery, University of Washington School of Medicine
Here we describe a low-cost, rapid, controlled and uniform fixation procedure using 4% paraformaldehyde perfused via the vascular system: through the heart of the rat to obtain the best possible preservation of the brain.
JoVE Neuroscience
Surgical Implantation of Chronic Neural Electrodes for Recording Single Unit Activity and Electrocorticographic Signals
1Biomedical Engineering, University of Michigan, 2Biomedical Engineering, University of Wisconsin-Madison, 3NeuroNexus Technologies
We provide useful information for surgeons who are learning the process of implanting chronic neural recording electrodes. Techniques for both penetrating and surface electrode systems are described in a rodent animal model.
Other articles by Gregory J. Gage on PubMed
Naive Coadaptive Cortical Control
The ability to control a prosthetic device directly from the neocortex has been demonstrated in rats, monkeys and humans. Here we investigate whether neural control can be accomplished in situations where (1) subjects have not received prior motor training to control the device (naive user) and (2) the neural encoding of movement parameters in the cortex is unknown to the prosthetic device (naive controller). By adopting a decoding strategy that identifies and focuses on units whose firing rate properties are best suited for control, we show that naive subjects mutually adapt to learn control of a neural prosthetic system. Six untrained Long-Evans rats, implanted with silicon micro-electrodes in the motor cortex, learned cortical control of an auditory device without prior motor characterization of the recorded neural ensemble. Single- and multi-unit activities were decoded using a Kalman filter to represent an audio "cursor" (90 ms tone pips ranging from 250 Hz to 16 kHz) which subjects controlled to match a given target frequency. After each trial, a novel adaptive algorithm trained the decoding filter based on correlations of the firing patterns with expected cursor movement. Each behavioral session consisted of 100 trials and began with randomized decoding weights. Within 7 +/- 1.4 (mean +/- SD) sessions, all subjects were able to significantly score above chance (P < 0.05, randomization method) in a fixed target paradigm. Training lasted 24 sessions in which both the behavioral performance and signal to noise ratio of the peri-event histograms increased significantly (P < 0.01, ANOVA). Two rats continued training on a more complex task using a bilateral, two-target control paradigm. Both subjects were able to significantly discriminate the target tones (P < 0.05, Z-test), while one subject demonstrated control above chance (P < 0.05, Z-test) after 12 sessions and continued improvement with many sessions achieving over 90% correct targets. Dynamic analysis of binary trial responses indicated that early learning for this subject occurred during session 6. This study demonstrates that subjects can learn to generate neural control signals that are well suited for use with external devices without prior experience or training.
Laminar Analysis of Movement Direction Information in Local Field Potentials of the Rat Motor Cortex
Local field potentials (LFPs) have been proposed for use in controlling neural prosthetic devices because they can provide reliable motor and sensory-related information, and can easily be recorded over long periods of time. While studies have shown that directional information about motor movements can be inferred from LFPs, it is not known at what depth these signals should be recorded from in order to maximize the amount of movement information. Towards this end, we used a directional motor task in Long Evans rats, while sampling LFPs with an electrode consisting of 16 vertical recording sites that were evenly-spaced 100 microm apart. This allowed for simultaneous recording of all layers of the motor cortex. The frequency components of LFPs were then analyzed using k-means clustering to determine directional information as a function of depth. Here we report our initial findings that superficial layers (II/III) of motor cortex may provide more information about movement directions then deeper layers (V).
Laminar Characterization of Spiking Activity in the Rat Motor Cortex
The neocortex is a six-layered tissue consisting of different cell types. How does unit activity in the different layers of the motor cortex relate to movement? Does implantation in a particular layer improve direction decoding ability for a neuroprosthetic device? We simultaneously recorded unit activity in different layers of the rat motor cortex using chronic multi-site silicon electrodes. We used a combination of histology and electrophysiological signatures of Local Field Potentials (LFPs) to accurately localize the electrode sites in the different layers of the cortex. We analyzed 142 units from two animals and found that 40 units (28%) in Layers II to V showed significant modulation with respect to movement. Of these units that showed significant modulation, 9/20 (45%) of units in Layers II/III encoded directional information as compared to 15/19 (79%) of the units in Layers IV/V. These preliminary results suggest that units in Layers IV/V relatively contain more directional information than other layers of the cortex.
Wavelet Filtering Before Spike Detection Preserves Waveform Shape and Enhances Single-unit Discrimination
The isolation of single units in extracellular recordings involves filtering. Removing lower frequencies allows a constant threshold to be applied in order to identify and extract action potential events. However, standard methods such as Butterworth bandpass filtering perform this frequency excision at a cost of grossly distorting waveform shapes. Here, we apply wavelet decomposition and reconstruction as a filter for electrophysiology data and demonstrate its ability to better preserve spike shape. For the majority of cells, this approach also improves spike signal-to-noise ratio (SNR) and increases cluster discrimination. Additionally, the described technique is fast enough to be applied real-time.
Development of Closed-loop Neural Interface Technology in a Rat Model: Combining Motor Cortex Operant Conditioning with Visual Cortex Microstimulation
Closed-loop neural interface technology that combines neural ensemble decoding with simultaneous electrical microstimulation feedback is hypothesized to improve deep brain stimulation techniques, neuromotor prosthetic applications, and epilepsy treatment. Here we describe our iterative results in a rat model of a sensory and motor neurophysiological feedback control system. Three rats were chronically implanted with microelectrode arrays in both the motor and visual cortices. The rats were subsequently trained over a period of weeks to modulate their motor cortex ensemble unit activity upon delivery of intra-cortical microstimulation (ICMS) of the visual cortex in order to receive a food reward. Rats were given continuous feedback via visual cortex ICMS during the response periods that was representative of the motor cortex ensemble dynamics. Analysis revealed that the feedback provided the animals with indicators of the behavioral trials. At the hardware level, this preparation provides a tractable test model for improving the technology of closed-loop neural devices.
Local Dynamics of Gap-junction-coupled Interneuron Networks
Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making.
Selective Activation of Striatal Fast-spiking Interneurons During Choice Execution
Basal ganglia circuits are essential for the organization and execution of voluntary actions. Within the striatum, fast-spiking interneurons (FSIs) are thought to tightly regulate the activity of medium-spiny projection neurons (MSNs) through feed-forward inhibition, yet few studies have investigated the functional contributions of FSIs in behaving animals. We recorded presumed MSNs and FSIs together with motor cortex and globus pallidus (GP) neurons, in rats performing a simple choice task. MSN activity was widely distributed across the task sequence, especially near reward receipt. By contrast, FSIs showed a coordinated pulse of increased activity as chosen actions were initiated, in conjunction with a sharp decrease in GP activity. Both MSNs and FSIs were direction selective, but neighboring MSNs and FSIs showed opposite selectivity. Our findings suggest that individual FSIs participate in local striatal information processing, but more global disinhibition of FSIs by GP is important for initiating chosen actions while suppressing unwanted alternatives.
Basal Ganglia Beta Oscillations Accompany Cue Utilization
Beta oscillations in cortical-basal ganglia (BG) circuits have been implicated in normal movement suppression and motor impairment in Parkinson's disease. To dissect the functional correlates of these rhythms we compared neural activity during four distinct variants of a cued choice task in rats. Brief beta (∼20 Hz) oscillations occurred simultaneously throughout the cortical-BG network, both spontaneously and at precise moments of task performance. Beta phase was rapidly reset in response to salient cues, yet increases in beta power were not rigidly linked to cues, movements, or movement suppression. Rather, beta power was enhanced after cues were used to determine motor output. We suggest that beta oscillations reflect a postdecision stabilized state of cortical-BG networks, which normally reduces interference from alternative potential actions. The abnormally strong beta seen in Parkinson's Disease may reflect overstabilization of these networks, producing pathological persistence of the current motor state.
