Presentation on theme: "Multielectrode Arrays Joe Kostansek, Greg Loney, Ariel Simonton."— Presentation transcript:
Multielectrode Arrays Joe Kostansek, Greg Loney, Ariel Simonton
“Bundles” of electrodes situated together to facilitate recording of field potentials and in some instances stimulation of brain areas These bundles are capable of recording from hundreds to thousands of neurons at a time and are typically considered chronic Typically PCA is used to segregated individual neurons or individual neuronal phenotypes
1958 – Strumwasser Utilized single 80- m stainless steel wires to record from awake, behaving squirrels Recordings lasted for a week or longer Strumwasser concluded that the constant waveform and amplitude implied that he was recording from the same neurons repeatedly Techniques have largely remained unchanged for the last several decades
Due to the small diameter of microwires, position is not fixed and fluctuates with movement, BP, etc… With the advent of silicon based electronics and reduced price in the 1970’s, fixed arrays now become possible Due to strength of silicon and thus increased surface tension, more electrodes are able to be implemented and implanted Michigan-Array is one of the first silicon –based arrays. Due to increased density of probes, researchers are now able to record from soma and dendrites simultaneously
Utah-array: Developed in the late 1980’s-early 1990’s Each Individual silicon ‘tong’ contains a platinum insulated microelectrode thus allowing for higher resolution at each individual recording point Notice the scaled position of each tong. Due to increased resolution, researchers are know able to record from various layers of cortex, which may contain different cell types, with a greater degree of accuracy Rigidity of array causes some problems in vivo
Polymide-based electrode cuff Highly flexible Rings of electrodes allow for continuous recordings, especially of peripheral nerves Sieve electrode Nerve is cut and allowed to regenerated through the holes in the ‘sieve’ Often coated with BDNF or other NTF’s in order to facilitate regeneration and approach
Advantages Ability to record or stimulate hundreds to thousands of neurons Can correlate activity across many neurons Can be used in awake, behaving animals to study higher processes: – Memory formation – Sensory integration Chronic or Acute Potential human clinical uses: – Deep brain stimulation – Seizure control – Restoring motor control
Disadvantages Cannot look at single ion channels – Better suited for recording action potentials Typically implanted chronically – Can cause immune response and inflammation – May lead to neurodegenerative effects Not a disadvantage, but: Provides a LOT of data, it’s necessary to have sophisticated sorting software to properly analyze results
Sample recording from the amygdala Individual spikes are recorded into a program and can be bundled (as seen here) depending on waveform properties. Some arrays have the ability to individually move each wire. – Beneficial for recording from multiple layers of cortex
Single Neuron Analysis First step: analysis of individual recorded neurons – Commonly used: raster plots and peristimulus time histograms (PSTH) These figures graph individual firing patterns over time so that they may be correlated to either a stimulus or behavioral event. Example data: Shows an individual neuron’s response to mechanical stimulation of a digit on the hand of an owl monkey
Neural Ensemble Response Next step: Visualizing neural activity as a whole – Population Peristimulus Time Histograms (PPSTH) – Spatiotemporal Maps – Linear Time Series Analysis – Artificial Neural Networks
Population Peristimulus Time Histogram X-axis – Individual neurons arranged rostral/caudal or some other method of organization Y-axis – Peristimulus time Z-axis – Instantaneous firing rate (spikes/sec) – Spike counts per time bin
Spatiotemporal Population Map Magnitude of neuronal firing = # of standard deviations away from spontaneous firing rate
Linear Time Series Analysis Best for continuous stimuli or behavioral variables Uses neuronal inputs and behavioral outputs in a modified linear regression equation X(t) matrix – Columns: Single neurons – Rows: Time segments If data is 3D, then x,y,z is analyzed in a separate matrix and incorporated with X(t) matrix
Artificial Neural Networks (ANNs) Very important tool to look at neural ensemble activity in relation to sensory input or behavioral output Require no a priori assumptions Useful for both categorical and continuous stimuli
Optimized Learning Vector Quantization Three layers: input, hidden, output – Input: Data – Hidden: Two artificial neural units – Output: Prediction A method of analysis in which the program “learns” which neural patterns are associated with a given output Can follow with principal component analysis
Summary Multielectrode arrays are very useful tools for recording data from many neurons Creates a spatiotemporal summary of neural activity Information recorded from multielectrode arrays may be analyzed for individual cells as well as populations Advances in understanding of sensory perception, learning and memory, and other higher processes can be contributed to the introduction of multielectrode arrays
Nucleus accumbens neurons are innately tuned for rewarding and aversive taste stimuli… Roitman, MF, Wheeler, RA & Carelli, RM
Methods Rats were implanted with intraoral catheters, and microelectrode arrays in the anterior digastric muscle and nucles accumbens Rats received 30-cue trials (light & tone) of both sucrose and QHCl, presented after a variable delay EMG activity recorded from anterior digastric muscle Individual neuronal activity recorded from nucles accumbens
A & B: EMG activity data in response to intraoral infusions of sucrose and QHCl, respectively C & D: Sample EMG traces indicating “learning” of taste-cue pairings for sucrose and QHCl, respectively E & F: Average latency to first EMG burst for cue- paired sucrose and QHCl, respectively. Bars below the line indicate that first burst occurred during cue, bars above the line indicate that first burst occurred during infusion G: Note large amplitude bursts following QHCl infusion H: EMG activity displayed a trend to decrease as a function of infusion duration C
Four classes of cells: Raster plot and spike frequency bins (histograms) of four representative cells A: Sucrose inhibitory B: Sucrose excitatory C: QHCl inhibitory D: QHCl excitatory
Average firing rates of the 4 identified classes of cells: Sucrose inhibitory (39 0f 102) Sucrose excitatory (13 of 102) QHCl inhibitory (10 of 98) QHCl excitatory (30 of 98)
Representative sucrose inhibitory cell: Note the opposite pattern of firing for both types of stimuli
Firing rates for all four identified cells plotted against EMG activity for each 100 ms bin of pre-infusion (6 s; black) and post-infusion (6 s; gray). Sucrose inhibitory: negatively correlated Sucrose excitatory: positively correlated QHCl inhibitory: negatively correlated QHCl excitatory: positively correlated
Four new classes of cells: Raster plot and spike frequency bins (histograms) of four representative cells A: Sucrose-cue inhibitory B: Sucrose-cue excitatory C: QHCl-cue inhibitory D: QHCl-cue excitatory
Average firing rates of the 4 identified new classes of cells: Sucrose inhibitory (16 0f 102) Sucrose excitatory (26 of 102) QHCl inhibitory (12 of 98) QHCl excitatory (27 of 98)
Cue-invoked firing is significantly correlated with “learning” A: Firing rate increases as a function of trial repetitions B: Firing rate is negatively correlated with latency to first burst
Conclusions Individual, naïve neurons in the nucleus accumbens demonstrate unique patterns of firing to prototypically rewarding an aversive stimuli and are hihgly correlated with reflexive behaviors associated with these stimuli These same neurons demonstrate a reversed pattern of activation in response to stimuli of the opposite valence Neuronal activation demonstrates a pattern of adaptation reminiscent of learning Nucleus accumbens neurons may be innately tuned to encode predictions and aggregate motor output associated with rewarding and aversive taste stimuli
Cortical Excitation and Inhibition following Focal Traumatic Brain Injury Ming-Chieh Ding, Qi Wang, Eng H. Lo, and Garrett B. Stanley
Background Brain injuries causing swelling of the cortex leads to: – Increased extracellular K + – Altered firing rates – Neuronal injury/death – Stroke Changes from brain injury can lead to overall changes in network inhibition and excitation Purpose: To assess the effects of compression injury on excitatory and inhibitory networks in vivo.
Methods Male Long-Evans rats Microarray is implanted into the barrel cortex (primary somatosensory cortex) – 90 minute recovery time Array – 8 x 8 silicon electrodes – 1mm length – 400um spacing – 100-400 kohm impedance Stimulus – Mechanical stimulation of vibrissae Compression – 1mm steel cylinder, 1mm of compression
A.Experimental setup B.PSTH of all electrodes with stimulus of C2 vibrissae C.Recordings from two single electrode channels D.Cortical activation
A: Stimulus delivery B: Channels responsive to vibrissae deflection at different inter- deflection intervals D: Attenuation of second stimulus response depending on IDI
Neurons have an attenuated response to the second stimulus at shorter IDIs
Neural response after compression After compression, there is a slow recovery of baseline neural activity. Postcompression intensity often exceeded precompression intensity
Neural response after compression Increased activity post-compression, after a specific amount of time.
Neuron Response Profiles Principal Channel – Channel with the largest response magnitude to a given stimulus Precompression Significant – Significant channel before compression Postcompression Significant – Significant channel post-compression, but not pre- compression
Spike magnitudes significantly different between principal, pre-compression and post-compression significant channels Post-compression significant channels showed the largest relative change in spike magnitude over time
“Paired pulse” whisker stimulation after compression, 50ms IDI Before compression, response at this IDI is suppressed After compression, response to the same stimulus IDI eventually leads to excitation
A: Principal channel latency does not change before and after stimulus C: Vector strength – a measure of temporal precision – does not change in principal or precompression neurons D: Vector strength increases in postcompression significant channels
Conclusions After compression in the rat brain, neurons displayed a change in response to vibrissae stimulation – Neurons displayed a period of inhibition after compression, followed by excitation greater than seen pre-compression. – Some neurons were not responsive at all before compression, but became active after compression Na+ and K+ levels unbalance after injury – May cause lower threshold for depolarization – Hyperexcitability
What is it? Instead of implanting into the organism and dealing with the difficulties of live animals, the in vitro approach allows for cultured cells/tissues to be used. First done in myoneural junctions and gastropods (1980s, linear method) technology has improved technique dramatically (planar, 3D, perforated, thin, etc.). Extracellular recordings field potentials, spikes Two types: Acute slices neurons dissociated, spontaneously form networks Organotypic slices ** network integrity remains In vitro Multi(Micro)-Electrode Arrays (MEAs) The cells/tissues grow directly onto the recording electrodes Advantages/Disadvantages Long term recordings (weeks to months if done carefully) Works like most electrophyisology differences = array, analysis of data Multiple electrodes – some experimental, some controls, simultaneously stimulate/record from different sites Non-invasive to the cell (no rupturing of cells) High spatial resolution (very low for single cells) Expensive, tough to maintain/clean
Want to start using this technique? Microscope - One challenge among in vitro MEAs has been imaging them with microscopes that use high power lenses, requiring low working distances on the order of micrometers. In order to avoid this problem, “thin”-MEAs have been created using cover slip glass. These arrays are approximately 180 μm allowing them to be used with high-power lenses.
Want to start using this technique? Amplifier Temperature Controller MEA and Base 64 Channel Stimulator PCI Data Acquisition Card Software, Air tables, computers, oscilloscopes, audio, amplifiers, perfusion systems, analog/digital converters etc. Capable of recording from up to 128 channels simultaneously Exceeding transfer rates of 6 MHz. Channels sampled at 50 kHz Use to stimulate your cultures With a variety of factors – electricity, Solutions, etc. 60 channel Upright/inverted Blanking circuit Pelltier device Heating element
Want to start using this technique? The MEA Standard Set-ups: 8 x 8 or 6 x 10 electrodes. Titanium oxide electrodes that have diameters between 10 and 30 μm. These arrays are normally used for single-cell cultures or acute brain slices. 60 electrodes are split into 6 x 5 arrays separated by 500 μm. Electrodes within a group are separated by 30 um with diameters of 10 μm. These can be used to examine local responses of neurons while also studying functional connectivity of organotypic slices ** Want good spatial resolution? HD-MEA is your answer. It allows signals sent over a long distance to be taken with higher precision. These arrays usually have a square grid pattern of 256 electrodes that cover an area of 2.8 by 2.8 mm. Other types: Perforated: The perforated MEA design applies negative pressure to openings in the substrate so that tissue slices can be positioned on the electrodes to enhance contact and recorded signals. Thin, multi-welled, hexagonal, 3D (penetrates farther into cultures)
Too expensive to buy, you can make your own! Want to start using this technique?
So what now? epilepsy synaptic plasticity (LTP, PPF, etc.) development regeneration biological rhythms ** network oscillators cardiac physiology robotics Other techniques: Histology/ICC Calcium imaging Patch-clamp optogenetics neural networks Animat A computer generated animal, in a virtual world. Cortical neurons from rats are dissociated and placed on a MEA capable of both recording and stimulating neural activity. Distributed patterns of neural activity are used to control the animat’s behavior in a simulated environment. The computer acts as its sensory system providing electrical feedback to the network about the Animat’s movement within its environment. Changes in behavior neural plasticity.
Primary endogenous oscillator that controls circadian rhythms of numerous behavioural, endocrine and physiological processes. The SCN network synchronizes its component cellular oscillators, reinforces their oscillations, responds to light input (RHT) by altering their phase distribution, increases their robustness to genetic perturbations, and enhances their precision. The basis for cell-autonomous circadian oscillations are positive and negative feedback loops as shown here. These loops drive rhythms in protein expression of several clock components. Some Useful Background Knowledge http://people.usd.edu/~cliff/Courses/Behavioral%20Neuroscience/Biorhythm/BRfigs/BRAfferent%20SCN%20figures.html Welsh et al, Suprachiasmatic Nucleus: Cell Autonomy and Network Properties. Ann. Rev. Physiol. 2010
Glycine Glycine is present in the SCN can act as a classical inhibitory NT and an excitatory neuromodulator Circadian release of glycine In slices, high concentrations of glycine can reset the clock What is glycine’s function in the SCN? Some Useful Background Knowledge Welsh et al, Suprachiasmatic Nucleus: Cell Autonomy and Network Properties. Ann. Rev. Physiol. 2010
Figure 1. Voltage-clamp recording of glycine-induced current in neurons of acute SCN slices D – Agonists of glycine receptors (beta-alanine and taurine) induce currents with similar characteristics to glycine-induced currents (applied to the same cell) Evidence that SCN neurons in acute coronal brain slices of mice exhibit a glycine-induced current. A – Application of glycine (5 s) at a holding potential of 0mV generated an outward current in 83% of neurons – remaining neurons were insensitive. Concentration dependent effects (threshold 10µM). Characteristics of responses. B – Concentration response curve. Fitted to Hill equation – EC 50 at 780µM C - Extracellular recordings – cell-attached mode – showed a concentration dependent suppression of spontaneous firing activity in SCN neurons sensitive to glycine.
Figure 2. Glycine activates strychnine-sensitive GlyRs in SCN A – Typical response to 1mM glycine (outward current) at a holding potential at 0 mV (upper trace). Typical response is reduced by the coapplication of strychnine (5µM) (middle trace) Recovery after washout (lower trace) D - Suppression of the amplitude of the current decreases with increased concentrations of strychnine. B – Extracellular recordings in an acute SCN slice. Strychnine reduced the duration of the inhibition of spontaneous electrical activity C – Glycine antagonists strychnine (5µM), PMBA (100µM) and ginkgolide B (1µM) reduce glycine-induced currents by 51, 56 and 34% respectively. E - Gabazine knocks out the GABA component of the glycine responses.
Figure 3. Ion selectivity and specificity of the glycine-induced current A & B - Distinguish currents induced by glycine and GABA A receptors – application of 100µM GABA and 1mM glycine. I-V relationship – reversal potentials of the cells used in A: GABA-induced: -50.1mV, glycine- induced: -49.2mV. Average glycine trace: - 47.1mV Nernst potential for chloride at their conditions: -51mV. C – Co-application of saturating amounts of GABA and glycine resulted in currents that were smaller than the sum of glycine + GABA. Slow deactivation – glycine current. D - 3/71 neurons tested for GABA and glycine were insensitive to GABA but yielded a glycine- induced current. E - Strychnine + GABA had no effect on GABA responses effects of strychnine are caused by a specific block of GlyR. Gabazine suppressed GABA currents. F – Glycine does not act on nAChRs – no difference in current amplitude when tubocurarine (blocker) is applied.
Methods – in vitro Multi(Micro) Electrode Arrays (MEA’s) Organotypic Slices 250-350µm thick coronal slices containing the SCN from 2-5 day old animals Placed in a culture dish with culture medium (1mL DMEM/F12 – supplemented with 10% fetal calf serum, 2.5mM glutamax, 10mM Hepes, and 100µg/mL penicillin-streptomycin) which was exchanged 3x/week Incubated @37C in 5%CO 2 -95% air for more than 2 weeks Before recording, the slice was placed onto a nitrocellulose-coated MEA (Multichannel Systems, Reutlingen) Recording medium: same as culture but Hepes was elevated to 20mM and the NaHCO 3 was reduced to 0.56 g/L. Exchanged continously at 20µL/min using SP 260PZ syringe pump (WPI). Maintained on MEAs for up to 3 weeks under flow-through culture conditions http://www.staff.uni-mainz.de/golbs/Methods.html Can be kept for weeks in culture Can monitor the output signal of the circadian clock (electrical activity) for periods up to 3 weeks
Multi-electrode Array Recordings Methods – in vitro Multi(Micro) Electrode Arrays (MEA’s) Recorded long-term firing rate from organotypic slices of SCN/PVN using a MEA-1060 recording system (Multichannel Systems, Reutlingen) Two types of HD-MEA used with two different layouts: Two fields of 30 electrodes with a diameter of 10µm and 30µm spacing – fields separated by 500µm Only one field covered by the SCN (rendering other tissue (PVN) covered by other field) One field of 60 electrodes with 10µm diameter and 40µm spacing SCN covered entire field in this case Extracellular signals amplified 1200x and sampled at 32kHz on 60 channels simultaneously Noise detected and removed by threshold algorithms APs exceeding said voltage threshold were digitized and stored as time-stamped spike cut-outs using the MC Rack software (Multichannel Systems). Ehab Tousson and Hilmar Meissl, 2004, J Neuroscience
Figure 4. Glycine induced changes in the firing rate of SCN neurons in organotypic cultures A – An increase in firing rate of SCN neurons due to application of 1mM glycine C – A decrease in firing rate of SCN neurons due to application of 1mM glycine Suggests a counteraction of the effect of glycine on glycine receptors. Both responses are found throughout the circadian cycle B – In cells that were excited by glycine, application of 5µM strychnine reduced their spiking activity D – The proportion of cells that were inhibited by glycine was more prominent at CT 4 than at CT 16 (24 vs 6%) At CT 4 a small subset of SCN neurons (5%) had a biphasic response to glycine E & F – Simultaneous recording from the SCN and one of its targets (PVN) using high-density MEA’s with two recording fields revealed opposite responses to glycine. Excitation in the SCN and inhibition in the PVN. Possible that differences in response to glycine and other hypothalamus cells could depend on circadian time. Slice culture has rhythmic neuronal firing 23.86±0.37h
Circadian activity of the firing rate of the cells were measured for 3 days. Then 1mM glycine was applied to the bath and the activity was measured for 4 days. Phase shifts were calculated for the activity recorded on individual electrodes as well as for the average activity of all electrodes (in gray) both methods showed similar results. A – Vehicle application (aCSF) with no resulting phase shift (0.2±0.1 h) in circadian oscillation of the firing frequency. B – A phase advance (1.7±0.2 h) resulting from the application of 1mM glycine 3 h before (CT4) activity peak (CT7). C - A phase delay (-1.4±0.2 h) resulting from the application of 1mM glycine at CT 16 – shortly before the nadir of SCN neuronal activity D – Phase response histogram for vehicle and 1mM glycine at CT 4 and CT 16. Phase advance at CT 4 and phase delay at CT 16. E – Phase response histogram for glycine coapplied with glycine receptor antagonists strychnine and PMBA at CT 4 and CT 16 (applied for 5 s before application of glycine) Glycine has the ability to phase-shift rhythmic neuronal activity in the SCN by activation of strychnine-sensitive glycine receptors. Figure 5. Glycine phase-shifts the circadian rhythm of the firing rate of SCN neurons
Conclusions/Thoughts Glycine is able to phase-shift rhythmic neuronal activity in the master clock by the activation of strychnine-sensitive glycine receptors Glycine can function as both an inhibitory and excitatory NT in the SCN depending on circadian time (possible mechanism – circadian fluctuation of the chloride equilibrium potential) A weak glycinergic innervation of the SCN, as well as intrinsic release of glycine from the SCN could lead to a precise fine-tuning of GABA- and NMDA-mediated synchronization and influence phase resetting of the clock. MEA allowed the researchers to observe long-term oscillations in the SCN with/without the treatment of the SCN with glycine/drugs The organotypic slices of the SCN/PVN allowed the researchers to have a in vitro system that was very close to the in vivo system They were able to record throughout the entire SCN system (core/shell) and even extensions to the PVN to see how one system affects the other