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Neurophotometrics got in touch with its roots recently when the Oberlin Neuroscience Newsletter featured our President Dr. Sage Aronson. Sage graduated from Oberlin in 2012, and loves to stay in touch with fellow Obies!
The data presented here were collected and analyzed by Kauê M. Costa, a postdoctoral fellow from Geoffrey Schoenbaum’s laboratory at the National Institute on Drug Abuse.
These dopamine transients (dLight1.2) were recorded in the ventromedial striatum (nucleus accumbens core) during a classical conditioning paradigm. The gray rectangle at time point 0 indicates the conditioned stimulus (CS; a cue), whereas the green rectangle at time point 10s indicates the unconditioned stimulus (US; a sucrose reward).
A reward prediction error signal (RPE) can be seen at both time points. As the sessions progress, you can see how this RPE signal transfers from the US to the CS as the subject begins to associate reward with cue. Each session trace represents an average of only six trials from a single animal — no need to average many trials from several animals to observe meaningful effects. Though we should note, these data were recorded while simultaneously recording from two rats, so it would be possible to have a side-by-side comparison of two individual subjects in this case!
The signal from this subject is so stable that you can follow the shift in RPE from US to CS in the heat map to the left. The signal at time point 0 (CS response) becomes considerably stronger as trials progress down the Y-axis. Concurrently, the strong signal observed at time point 10s (US response) gradually diminishes.
“With this kind of single trial resolution I can now figure out how individual neural and behavioral learning curves relate to each other.” said Kauê. “[I can] see what can disrupt one but maybe not the other, fit them to specific models… all kinds of cool stuff. I think it would be nice for people to know what can be done.”
Data presented here are published with permission from the lab of Geoffrey Schoenbaum at the National Institute on Drug Abuse. This figure and all of the beautiful data that went into it were produced by Kauê Machado Costa. This highlight was written by Caroline E. Sferrazza.
Congratulations to our friends in Fritjof Helmchen’s lab at the University of Zurich! They have published an excellent study in Nature this summer on the use of high-density multi-fiber arrays in freely-moving animals.
Neurophotometrics CEO Dr. Sage Aronson gives a talk on fiber photometry at Tabor Academy on February 11, 2019.
This data highlight features experiments from Kamran Khodakhah’s laboratory at the Albert Einstein College of Medicine.
A) Scheme of the experimental configuration. Channelrhodopsin was injected in the ventral tegmental area (VTA) and the fluorescent dopamine sensor dLight1.1 was injected in the left nucleus accumbens (NAc) and right medial prefrontal cortex (mPFC). Fibers were implanted in the right VTA, left NAc, and right mPFC.
B) Raw data of fiber photometry recordings (Neurophotometrics, Constant mode, 40 Hz) of dLight1.1 fluorescence, collected simultaneously in NAc and mPFC while optically stimulating the VTA with 440 nm light at different power intensities (trains of 14 pulses, 1 ms length, at 20 Hz).
C) Average dLight1.1 signal in the NAc evoked with optical stimulation in the contralateral VTA at four light power levels (10 sweeps each, average +/- SE) extracted from the signal in B.
D) Average dLight1.1 signal in the mPFC evoked with optical stimulation in the ipsilateral VTA at four light power levels (10 sweeps each, average +/- SE) extracted from the signal in B.
The analysis was performed with Igor Pro 7. The dopamine sensor (AAV9.CAG.dLight1.1) was kindly provided by Dr. Lin Tian (UC Davis).
Data presented here are published with permission from the lab of Kamran Khodakhah at the Albert Einstein College of Medicine. This figure was produced by Jorge Vera and Maritza Oñate. If you have any questions or comments about the data shown here, you may contact firstname.lastname@example.org.
In this data highlight, we present an example of a FIP signal that predicts the duration of a drinking bout with nearly 100% accuracy. These data were reproduced with permission from the authors who requested to keep their names and favorite brain region anonymous.
These are raw data that are intended to demonstrate what our software will live-plot while you are recording. They have not been corrected for bleaching (which is minimal). In this example, a virus expressing GCaMP6s was injected into a nucleus and an optical fiber was implanted over a specific projection. During this recording, the animal was placed in an operant conditioning chamber and was trained to press a lever to receive a sucrose reward. Arrows in the above plot indicate when the animal began consuming the reward, referred to as a drinking bout. In this raw trace, the x-axis represents time (seconds) and the y-axis represents mean pixel value.
On the right, the mean SEM is aligned to the onset of a drinking bout. Note the nearly identical kinetics for the first 5 seconds. More variance exists later in the trace as some bouts lasted less than 5 seconds.
With a simple linear regression, the authors are able to predict — with astounding accuracy — the duration of a drinking bout from the duration of the inhibition seen with the fiber photometry. This would not be possible without the single trial resolution and high signal-to-noise ratio that the Neurophotometrics FIP system provides.
This data highlight features experiments from Christophe Proulx’s laboratory at the CERVO Brain Research Center of Université Laval.
A) Scheme of the experimental configuration. Fibers were implanted in three brain regions (X, Y, and Z) to allow recording at axon terminals projecting from a distinct GCaMP-expressing region (HUB).
B) Signals were recorded simultaneously at axon terminals in regions X, Y, and Z while mice were allowed to explore an open field. Highlighted bars indicate periods of increased mobility. A coherent “escape” signal can be observed broadcasting from the Hub to all three of these distant brain regions.
Data presented here are published with permission from the lab of Christophe Proulx. This figure was produced by Christophe Proulx, Ekaterina Martianova, and Alicia Pageau. If you have any questions or comments about the data shown here, you may contact email@example.com.
In this data highlight, we present a sample of raw fiber photometry data recorded from axon terminals using the Neurophotometrics system. These data were reproduced with permission from the authors who requested to keep their names and favorite brain region anonymous.
A virus expressing GCaMP6s was injected into a brain region and an optic fiber cannulae was implanted over a specific projection from that nucleus. These data represent the mean value of an ROI drawn over the face of an optical fiber over time. They have not been manipulated or corrected in any way.
The trace above is a snippet of a long recording session. The animal was placed in an open field and air puffs were administered manually. Recording sessions with our system can vary in length, and if light power is dialed in then researchers can expect multi-hour recordings with minimal bleaching.
The colorful traces to the left represent the data cut up into event-related fluorescence traces. This reveals similarities in temporal kinetics. Notably, the faster rise and slower decay is typical with GCaMP6s.
In this summary graph, you can observe a decrease in the peak response to an air puff as a function of time — potentially due to habituation of the animal. The response increased after a short period of no intervention.
With sufficient viral expression and cannula placement, our FIP system routinely offers single trial resolution. This decreases the time-course of many experiments and allows for unique insights into the dynamics of individual trials.
Note: Recording trials were interleaved with 470nm and 410nm excitation allowing for near-simultaneous calcium-dependent and calcium-independent (isosbestic) measurements. The 410nm trace was a flat line and is not shown with these data.