Automated device for simultaneous photometry and electrophysiology in freely moving animals

Automated device for simultaneous photometry and electrophysiology in freely moving animals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Automated device for simultaneous photometry and electrophysiology in freely moving animals Benjamin De Corte, Youngcho Kim, Kelsey Heslin, John Freeman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4249277/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 May, 2026 Read the published version in Nature Methods → Version 1 posted You are reading this latest preprint version Abstract Photometry and electrophysiology are powerful tools for investigating brain-behavior relationships. Combining these techniques would allow us to ask previously un-addressable questions, such as how neuromodulators impact neuronal firing rates. Current options are highly limited—requiring a substantial loss in data-quality or severely restricting naturalistic-movement. These drawbacks arise from engineering-limits on devices that allow optically-tethered subjects to move freely. Here, we introduce a device that overcomes these challenges. Its automated orientation-tracking system allows subjects to move freely for multiple-hours with minimal supervision and without sacrificing data-quality. The device is modular and adaptable, being compatible with most recording systems and equipped for added functionality (e.g., optogenetics). To demonstrate its utility, we simultaneously tracked extracellular striatal dopamine and single-neuron firing as mice performed a reward-learning task. Mice showed excellent mobility, and we observed robust trial-by-trial correlations between striatal firing and dopamine signaling. This device provides a powerful tool that outperforms current commercial solutions. Biological sciences/Neuroscience/Learning and memory Biological sciences/Neuroscience/Molecular neuroscience Photometry Electrophysiology Commutator Simultaneous Dopamine Striatum dLight Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Measuring neural activity in behaving subjects provides detailed insight into the nature of brain-behavior relationships. Recent methodological advances have markedly improved both the quality and type of neural data that we can collect. For example, novel electrophysiology probes have vastly increased the number of neurons and brain areas that we can simultaneously record from 1 – 4 . Furthermore, with improvements in fluorescent biosensors for fiber photometry, we can now easily track neural signals that were previously either difficult or impossible to monitor, such as various neuromodulators 5 – 11 and even components of intracellular signaling cascades 12 – 14 . Moreover, with viral-approaches available for both methods, we can even selectively record these signals from specific cell-populations 5 , 15 , 16 . Electrophysiology and fiber photometry are powerful techniques when used individually. However, given that they often provide complementary data, being able to conduct both methods simultaneously would carry strong advantages. As one example, various theories make core assumptions regarding how neuromodulators should impact the spiking of individual neurons to guide behavior 17 – 20 . However, as we have traditionally lacked the ability to simultaneously monitor neuromodulator levels and spiking in vivo , these hypotheses are often based on indirect data (e.g., in vitro experiments, focal drug infusions, etc.). With a robust approach for combining these methods in freely-moving subjects, we could interrogate these predictions directly. Currently, our ability to conduct simultaneous photometry and electrophysiology in vivo is highly limited. Ideally, one would simply tether subjects to an implant that integrates an optical cannula (for photometry) and an electrode array (for electrophysiology) and begin recording. However, as subjects move, the two data tethers will quickly tangle, immobilizing the subject and introducing a variety of other problems (e.g., data loss/corruption, implant damage, etc.). This ‘tangling problem’ is a concern for any method involving a tether, yet unlike most techniques, it cannot be easily solved with additional equipment in the specific case of photometry and electrophysiology 5 . In fact, as addressed below, we are not aware of any open source or commercial solutions that do not incur a substantial loss in data-quality. This includes wireless systems, where battery-life and implant-weight place strong limits on the number of neural signals that can be recorded simultaneously and the overall length of recording-sessions themselves. Here, we describe a novel device that overcomes these hurdles. We confirm that mice can perform behavioral tasks for arbitrarily long sessions (1.5-2 hours, in our case) with no impact on photometric, electrophysiological, or behavioral data-quality. Furthermore, we have explicitly designed the device to be modular, allowing users to easily adapt it to fit their specific equipment/experimental needs and extend its functionality. Results In the main text, we will focus on giving a high-level overview of our approach and validation data; however, we provide detailed assembly instructions in the supplemental materials. Key challenge and approach . Combining any optical method with electrophysiology will be difficult, yet photometry is a particularly challenging case. As noted above, both electrophysiology and photometry require connecting subjects to a tether that transmits neural data to an external recording system. As subjects move during an experiment, the tethers will become tangled, impeding natural behavior and risking more severe consequences. For some techniques, additional equipment can help avoid this problem. For example, during solo-electrophysiology recordings, tangling can be prevented using an electrical commutator–a device that rotates with the subject as it moves, while preserving electrical contact in the data-tether’s wires 21 , 22 . However, at present, no analogous solutions exist for photometry 5 , representing the primary obstacle toward combining these methods. Figure 1. Overview. a) Standard photometry recording setup. Here and below, all illustrations are built to-scale using 3D CAD software (Blender), with the subject being roughly the size of a 250g rat. b) Problem of light-leak when using an optical swivel to allow for free-movement. c) Leakless data acquisition by passing photometry voltage signals through an electrical commutator and rotating all optical components with the subject during movement. d) Broad overview of final design with integrated electrophysiology. Figure 1a helps clarify the problem, giving a simplified overview of how standard photometry systems operate. Briefly, LEDs send light to an optical filter. The filter passes the light to the brain and captures any that returns. The returning light—typically reflecting the neural signal of interests—is sent to a photocollector (also known as a photodetector or photoreceiver). The photocollector converts the light-intensity into a voltage signal, and the acquisition system records this data. Critically, in standard setups, the patch-cord connection between the filter and the subject’s implant is continuous. As such, subjects can indeed become tangled during recordings. Many attempt to resolve this problem by placing an optical swivel (also known as an optical rotary-joint) between the filter and the subjects’ implant (Fig. 1b). Analogous to an electrophysiology commutator, optical swivels transmit light while allowing the optical tether to rotate freely. However, due to difficulties in optical transmission, a substantial amount of light will leak out of a swivel in the process, resulting in a marked reduction in signal-quality 5 . In virtually all cases, the light-leak is too severe for effective recordings to be taken—typically causing a 50% reduction in overall return-signal and an even greater reduction in the signal-to-noise ratio (i.e., any neural signals of interest will only deviate from baseline by a few percent). Therefore, experimenters typically opt for a continuous connection and engage in a variety of non-ideal strategies to offset this limitation, such as untangling subjects by-hand, using extra-long tethers, keeping recording sessions brief, among others. While these solutions can be generally effective for solo-photometry recordings, our goal is to conduct long-term recordings and add a second data-tether for electrophysiology, making them untenable here. Our solution to this problem centers on the fact that input to the LEDs and output from the photocollector are fully electrical signals. Therefore, both can be routed through an electrical commutator, and if all photometry components are mounted to the commutator itself, the entire assembly can rotate with the subject as it moves. This keeps the connection between the filter and subject continuous, avoiding signal-loss seen with optical swivels. Figure 1c gives a more specific view of our application. The LEDs, filtercube, and photocollector are mounted on a 3D printed frame that fixes to the bottom of an electrical commutator. All of the components still interact as described above. However, they can now rotate with the subject, with the commutator maintaining electrical connections between the LEDs, photocollector, and recording system. Below, we show that this solution easily integrates with electrophysiology acquisition for simultaneous recordings (Fig. 1d). Electrophysiology acquisition and orientation tracking . Small subjects would be too weak to rotate the commutator on their own, so we designed an automated system that tracks how subjects move and rotates the commutator accordingly. We integrated this process within the electrophysiology-side of our design, building upon a popular method proposed by Fee and Leonardo 21 for solo-electrophysiology recordings. The general approach is to attach a magnet to the electrophysiology data-cable and fix a Hall-effect sensor—a device that measures the strength of magnetic fields—to the commutator itself. As the subject moves, the magnet will rotate, and the Hall-effect sensor’s output will reflect the orientation-change, increasing/decreasing as the magnet turns toward/away from it, respectively. The sensor’s output feeds to an automated motor system that rotates the commutator as needed. In our device, the motor-system consists of a stepper motor and pulley mounted to the commutator via 3D printed parts—all designed to rotate the frame at the bottom of the commutator (Fig. 2 b). The electrophysiology data-cable runs through the center of the frame and is used to implement orientation-tracking (Fig. 2 c). Specifically, a magnet appends to the cable via a 3D printed holder that sits vertically within a ball-bearing, allowing the magnet to rotate with the subject. We take two steps to ensure small subjects will be able to easily rotate the assembly. First, the cable runs through a torque-arm, which boosts force by horizontally offsetting the cable from the axis of rotation at the bearing (Fig. 2 c). Second, note that, if were to plug the data-cable directly into the bottom of the commutator, any stiffness in the cable would place resistance on the subject as it moves. To resolve this, we bridge the connection between the cable and the bottom of the commutator with magnet wire, also known as ‘winding wire’ (Fig. 2 d). Magnet wire is extremely durable, yet highly flexible, effectively eliminating any resistance that the cable itself would introduce. All wires send data to the electrophysiology system via a connector fixed to the top of the commutator (Fig. 2 d). To detect subject-rotation, two Hall-effect sensors track the orientation of the magnet’s face (Fig. 2 c and Fig. 3 ). Note that the original design only incorporates one Hall-effect sensor 21 . However, a single sensor will only be able to track the magnet around 180 degrees. Therefore, it will miss rapid turns beyond this range, which can be common during behavioral tasks (e.g., when a subject must traverse between opposite sides of a conditioning chamber). Conceptually, when using two Hall-sensors that are offset by 90-degrees, their outputs can work together like the sine and cosine functions, allowing us to track the magnet’s orientation around a 360-degre axis (Fig. 3 b,c). We have not observed missed-rotations with this improvement. The process of translating the Hall-sensor inputs to a motor output is handled by a simple feedback circuit, composed of a microcontroller that implements a rotation-detection algorithm and turns the motor via a driver-circuit (Fig. 3 ). For convenience, we use a Teensy 4.0 (ARM Cortex-M7) microcontroller, which is cheap and fully compatible with the Arduino coding environment. Furthermore, as it is arguably the most powerful microcontroller currently available, it can readily accommodate more advanced equipment/algorithms for users hoping to extend our system’s functionality (see Discussion). We have used/improved upon this system for solo-electrophysiology recordings over the past 6 years and find that it is virtually errorless, even for very long sessions (e.g., > 5 hours, without human-intervention). Nonetheless, we still implement two safety/convenience features. First, the algorithm contains an ‘auto-shutoff’ mode that pauses the motor if it makes 5 continuous rotations in one direction, usually indicating an error has occurred. Second, as shown Fig. 3 c, the system’s circuit-board contains two buttons that allow users to manually override the motor, permitting them to correct errors without disturbing subjects. Photometry . The next step is to mount all photometry components to the frame and route the LED/photocollector signals through the commutator (Fig. 4 ). When designing the device, we realized a key challenge would be allowing the frame to accommodate equipment from different recording systems. For example, the shape/size of LEDs varies across vendors, and we wanted our design to be compatible across models. To resolve this, we gave the frame a ‘breadboard-like’ design, containing three arms with a standardized grid of screw holes (Fig. 4 b). Each photometry component (LEDs, filter, photocollector) fits within a 3D-printed attachment that screws into the grid. We provide attachments for common photometry components (Doric LEDs/minicube, Newport 2151 photocollector). However, if researchers hoping to use the device have different equipment, they only need to design an attachment for their given model. We encourage users to share their designs via the project’s GitHub page. Input to the LEDs and output from the photocollector route through the commutator via a circuit board that screws into the frame (Fig. 4 c). The wires connect to the circuit board at the top, providing access for the photometry system. The final construction step is to build a mount that can suspend the device above the subject, and we describe how we constructed ours in the supplemental materials (see Fig. S7; also Fig. 1d). As detailed below, the device provides excellent neural and behavioral data for long-sessions (1.5-2 hours, in our projects). While we focus on single-sensor photometry recordings here (one LED for control-excitation and one LED for our signal of interest), the commutator is already equipped for a third LED, allowing users to record from two sensors simultaneously (Fig. 4 c). Furthermore, despite the complexity of motion-tracking with two tethers, faults in the automated anti-tangling process are rare (without supervision, an average of 1 tangle every 2 hours). We find that checking subjects every 20–30 minutes and correcting any minor errors—using the push-buttons to prevent subject distraction—is more than sufficient to avoid tangling. Data. Finally, we characterized the quality and utility of in vivo data collected with the commutator device. Specifically, we expressed a fluorescent dopamine-sensor 23 (dLight-1.3b) in the nucleus accumbens of 6 mice, and recorded with an ‘optetrode’ implant, composed of 8 tetrodes surrounding an optic fiber (Fig. 5 a,b). We offset the tetrode-tips to be 200um from the tip of the optic fiber (Fig. 5 a), putting them within the recordable range of the photometry signal 24 . We first performed fundamental checks on data-quality. For example, Fig. 5 c highlights that, relative to standard photometry recordings, optical swivels produce a severe signal-drop, due to light-leak (-50%; Fig. 5 c). As intended, the commutator resolves this issue, with any differences being due to standard fluorophore-depletion within a session (Fig. 5 c; -3% over this particular recording). Next, we ensured that light from the photometry system would not interfere with the electrophysiology recordings. Specifically, light can scatter electrons at the tip of a recording electrode, producing a voltage-artifact called a ‘photoelectric effect.’ This is a substantial concern for combined optogenetics and electrophysiology, as these artifacts are often severe enough to obscure spike-detection without proper precautions (e.g., increased spacing between optic and electrodes) 15 , 25 . However, we observe no evidence of photoelectric effects in our recordings, even when explicitly pulsing the LEDs (Fig. 5 d). This likely relates to the fact that light-power used for photometry is usually orders of magnitude lower than that used for optogenetics (microwatt vs milliwatt scale, respectively). Finally, during early protypes of the device, we noted that running the motor would occasionally produce noise in both the photometry and electrophysiology data. The electrophysiology contamination was simply due to 60Hz noise and was easily resolvable with standard steps (i.e., common ground for recording and motor systems; Fig. 5 e). The photometry contamination resulted from mild vibration on the photocollector during rotation. Fortunately, padding the photocollector holder with foam solved this issue (Fig. 5 e; also see Fig. 4 b). Our final question was whether the device would provide useful data regarding brain-behavior relationships. After all, photometry typically measures ‘bulk’ neural signals, and some data question the degree to which they correlate with single-neuron activity 26 . Importantly, these critiques primarily apply to calcium indicators intended to measure net spike-activity in a given area (e.g., GCaMP), as they confound somatic activity and dendritic inputs. To our knowledge, no data have addressed how indicators that reflect neuromodulator-levels or intracellular signaling molecules map to neuronal firing rates in vivo . In our view, this is where the true value of combining the two methods lies, and most concerns surrounding spike-indicators do not readily generalize to these sensors. To address this, we tracked striatal dopamine-levels, single-neuron firing rates, and behavior during a reward learning task, given the substantial theoretical and clinical interest in how these processes relate to one another. Specifically, we recorded while mice learned a task where a cue signaled reward could be earned for pressing a lever after variable time-intervals elapsed (see Methods). Despite the complexity of the equipment and number of tethers, mice were able to perform the task well, suggesting the automated-anti-tangling system permits free-movement (Fig. 6 b,c). As a point of reference, we compared their behavior to a separate group of mice trained on the same task while tethered to a Doric dual-optical swivel (FRJ_1x2i_FC-2FC). Like the commutator, this swivel model also incorporates two tethers, yet as it rotates passively with remarkably low-torque, subjects can rotate it easily, without motor-based assistance. As shown in Fig. 6 c, the two groups were indistinguishable. Next, we evaluated trial-by-trial covariance between the dopamine signal and the firing of striatal neurons (Fig. 6 d). We focused on how each signal varied around key task events—the onset of the cue, lever presses, and reward (Fig. 6 e). Striatal neurons were modulated around all events. Consistent with prior reports, clear dopamine-peaks emerged at cue-onset and reward 7 , 27 , though some weak suppression can be noted around unrewarded lever-presses. More importantly, we note robust trial-by-trial correlations between dopamine-levels and striatal firing rates (Fig. 6 f,g). While a full analysis is beyond the scope of this report, we ran a simple correlation analysis (see Methods) between dopamine peaks at reward-onset and single-unit firing (e.g., Fig. 6 f). Reliable correlations were common among the population (39.8% of neurons; Fig. 6 g). Notably, as expected by canonical direct/indirect pathway theorization, positive and negative correlations occurred at roughly equal frequency (Fig. 6 g, χ 2 (1, N = 118) = 0.383, p = 0.536). Discussion We developed a modular, open-source device for accomplishing combined electrophysiology and photometry. The device prevents the ‘tangling problem’ inherent to standard photometry setups while also avoiding light-leak related to optical swivels 5 . This allows subjects to move naturalistically during experiments, with no loss in neural data-quality. We establish the utility of this approach by providing what we believe are the first-data showing robust correlations between striatal firing and dopamine-levels in behaving subjects, yet the method can be applied in a variety of contexts. While some commercial and open-source systems for combined recordings are beginning to emerge 28 , ours is the first to permit movement while eliminating the need for an optical swivel—the key to obtaining full behavioral/neural data-quality. Furthermore, should commercial systems emerge that can match this level of performance, we assume the price of our device (~$500 USD) will be considerably lower. The device functions exceptionally well, and there are a variety of avenues for expanding its functionality and improving performance, which we address below. The most obvious direction is whether the current device can be adapted for solo-photometry recordings, where researchers still lack a reliable method to prevent tangling, particularly for multi-site experiments. After all, while subjects often perform ‘well enough’ for data collection in standard setups, the lack of a swivel/commutator still a key drawback of the method, placing strain on the subject, demanding time from the researcher for manual monitoring/untangling, and making longer recording sessions difficult. Unfortunately, simply swapping the electrophysiology cable in the current design for the photometry patch-cord will not work, as the optical tether will be too stiff to effectively rotate the magnet. However, a simple solution would be to incorporate a second ‘dummy’ tether that appends to the bottom of the torque-arm in place of the electrophysiology cable. Another direction is evaluating whether the motion tracking hardware on the device can be modified to provide movement-data that syncs with neural activity. Specifically, OpenEphys recently developed an electrophysiology commutator that implements motion tracking by attaching an ‘intertial measurement-unit’ (IMU) to the subject’s headstage. An IMU is effectively a ‘digital compass’ that can track a subject’s orientation in 3D-space at high temporal-resolution. This gives a more direct readout of the animal’s position for motor-feedback, and moreover, provides highly useful data of its own for relating neural data to motor activity. At present, we prefer the Hall-sensor approach, to avoid requiring researchers to add weight/equipment to their implants. However, when designing the motor-circuit, we deliberately wired the Hall sensors to the pins on the microcontroller that can be used to communicate with most IMUs (i.e., i2c clock/data). Therefore, users interested in this approach can begin piloting without modifying the electronics. While IMUs require considerably more compute-power than Hall-sensors, we again emphasize that, despite being inexpensive, the Teensy 4.0 microcontroller used here is highly advanced (e.g., ARM Cortex M7 processor, 600 MHz clock-speed, 64-bit doubles, etc.), making it an excellent choice for this application. Researchers might also be interested in integrating optogenetics with the device. This would add substantial causal power to any experiment 29 , when proper precautions are taken 30 . While not explicitly highlighted above, the gear-pin at the top of the commutator already contains an opening specifically for adding optogenetic patch-cords. Therefore, while we have not explicitly tested this method at present, the device should be able to accommodate optogenetics with minimal to no modification. Finally, there may be applications that require more wires than the current design provides. Examples would include added electrical devices/methods like electrical stimulation, or photometry systems that use a camera as the photocollector. If these experiments do not require dual-sensor photometry, a simple solution would be to use wires from the third LED-port for any added signals, as all wires are electrically isolated from one another on the circuit-boards. However, in the Supplemental Materials, we outline simple steps for freeing added wires if needed (see ‘Tips and general notes’ section). In short, this is the first device to allow for naturalistic behavior while conducting simultaneous photometry and electrophysiology. This will propel important new discoveries regarding the nature of brain-behavior relationships, as researchers can now quantify the in vivo relationship between signals that were previously either difficult or impossible to measure simultaneously. This project will have a dedicated GitHub page, and we encourage users who make improvements to submit their modifications and improvements. Methods Design materials and software. The supplemental files associated with this manuscript contain all necessary information and software needed to replicate the current design. This includes an itemized-list of all materials that need to be ordered (Bill-of-Materials), files for 3D-printed parts (stl files), files for printed-circuit-board files (gerber folder/files), and all microcontroller programs (ino files). We generated figures using Blender and used cloud-based platforms for designing 3D-printed parts and printed-circuit boards (Onshape and EasyEDA, respectively). All 3D-printed parts were generated using a Formalbs 3D-printer (Form3) at a 25 micron resolution, rinsed with 99% isopropyl alcohol (FormWash), and UV-cured (FormCure). Surgery . All animal work was approved by the Institute for Animal Care and Use Committees of the New York State Psychiatric Institute and Columbia University and was performed according to PHS Policy on Humane Care and Use of Laboratory Animals. For combined photometry and electrophysiology experiments, adult C57BL/6J mice (3 male / 3 female; 12-weeks old) were anesthetized with isoflurane and unilaterally injected with a virus encoding dLight-1.3b (.3ul, AAV9-syn-dLight1.3b, Addgene) into the nucleus accumbens (NAC, AP: 1.0, ML: 1.4, DV: -4.1 from bregma/skull-surface). Then, an optetrode implant (200um optic surrounded by 8 tetrodes) was lowered into the same site (DV -4.3 to account for the 200um distance between the optic/tetrode tips). The implant was sealed with epoxy (Slo-Zap) and dental cement (Ortho-Jet, Black). Subjects serving as a behavioral comparison to these mice (Fig 6c) came from a control group of an optogenetics experiment for the same project. They received identical surgical procedures. However, these were DAT-ires-cre mice (Jax Strain #:006660; 4 male / 4 female), received bilateral optic implants into the NAC, and received infusions of a cre-dependent reporter-virus (AAV9-Ef1a-DIO EYFP) into the ventral tegmental area (AP: -3.2, ML: +/- 0.5, DV: -4.6 from bregma/skull-surface). Photometry recordings . Photometry recordings were performed using an RZ5P Tucker Davis Technologies system. The system controlled light-delivery via a Doric current-regulator (LEDD_2) that powered two Doric connectorized LEDs (see below). A Doric minicube (FMC4_IE(400-410)_E(460-490)_F(500-550)_S) served as the filter, and we used a Newport photoreceiver to measure returning light (Newport 2151). Time-frequency demodulation was used to measure dLight responses to both dopamine-dependent (465nm, 330Hz) and dopamine-independent (i.e., isosbestic, 405nm, 210Hz) excitation, delivered by Doric connectorized-LEDs. Summated light-levels from the LEDs were tuned to 30uW. The signals were demodulated online and written to disc. Before analysis, we corrected for photobleaching and movement artifacts by subtracting the isosbestic signal from the excitation signal, after scaling them to the same level with regression 31 . Electrophysiology recordings . We recorded electrophysiology data using a first-generation Open Ephys system 32 . Specifically, we used an Intan RHD2132 headstage to sample each channel at 30kHz. The raw wide-band signals were written to disc and post-processed with custom Python routines (see SpikeInterface package 33 for analogous workflows). Specifically, we used local common-average referencing (20% trimmed-mean), band-pass filtered each signal (Butterworth, 600-6000Hz), extracted spikes (Quiroga threshold 34 , 4s), performed automated spike-sorting (Mountainsort 35 ), and finally manually vetted/curated each purported single-unit with standard approaches (inspecting waveform-shape, cross-correlograms, ISI-histograms, refractory-period violations, and isolation-distance 36 ). Behavior . All mice were trained in Med-Associates operant chambers on a task in which one of two cues (identical houselights) were presented across different trials. Each cue signaled occasional reward delivery across time for lever-pressing, determined by a geometric/Poisson schedule (i.e., variable-interval procedure). Each cue signaled a different reward-rate (high rate = 1/10s, low-rate = 1/40s). The high-rate cue was presented for 20s, and the low-rate cue was presented for 80s. All trials were separated by a dark, 120s inter-trial interval, in which no rewards were available. Sessions lasted 90 minutes. The primary behavioral measure of interest were response rates during each cue. Note that, because our focus is on simple-verification for this publication, we only detail data from the high-rate cue trials here, reserving a full analysis for a later publication. Statistics . For behavioral analyses, lever-press rates were compared with a mixed-model ANOVA for acquisition, with group (commutator vs dual-swivel) as a between-subjects factor and session as a within-subjects factor. We compared post-acquisition performance across groups with independent-samples t-tests. For analyses correlating dopamine-levels to single-neuron firing, we focused on how each signal varied around reward, putting both signals on a common scale by expressing them as standard-deviations from a 10-second pre-trial baseline period. As our measure of reward-evoked extracellular dopamine, we took the maximum of the phasic burst in the dLight-signal within the first 3-seconds following reward. Single-neurons fire heterogeneously across time around reward (e.g., some burst/suppress, others show more non-linear dynamics, etc.), making it more difficult to define a simple-measure of modulation than the dopamine-data. As a straightforward approach, we computed each neuron’s mean rate over time during trials where the dopamine was high or low (upper/lower quartiles of the reward-evoked maxima, respectively). We then identified the time-point where the two firing-rate vectors differed maximally (absolute values). Finally, we centered a 500ms bin around this time-value and computed the spearman correlation between the dopamine-level and the firing-rate across all rewards. Declarations Declaration of Interest. Authors have no conflicts of interest to report. References Buzsáki, G. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7 , 446–451 (2004). Steinmetz, N. A. et al. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science 372 , eabf4588 (2021). Luo, T. Z. et al. An approach for long-term, multi-probe Neuropixels recordings in unrestrained rats. eLife 9 , e59716 (2020). Hultman, R. et al. Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability. Cell 173 , 166-180.e14 (2018). Simpson, E. H. et al. Lights, fiber, action! A primer on in vivo fiber photometry. Neuron 112 , 718–739 (2024). Ma, L. et al. Locomotion activates PKA through dopamine and adenosine in striatal neurons. Nature 611 , 762–768 (2022). Kalmbach, A. et al. Dopamine encodes real-time reward availability and transitions between reward availability states on different timescales. Nat. Commun. 13 , 3805 (2022). Kubitschke, M. et al. Next generation genetically encoded fluorescent sensors for serotonin. Nat. Commun. 13 , 7525 (2022). Jing, M. et al. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods 17 , 1139–1146 (2020). Blanco-Pozo, M., Akam, T. & Walton, M. E. Dopamine-independent effect of rewards on choices through hidden-state inference. Nat. Neurosci. 27 , 286–297 (2024). Dong, C. et al. Psychedelic-inspired drug discovery using an engineered biosensor. Cell 184 , 2779-2792.e18 (2021). Massengill, C. I. et al. Sensitive genetically encoded sensors for population and subcellular imaging of cAMP in vivo. Nat. Methods 19 , 1461–1471 (2022). Lee, S. J. et al. Cell-type specific asynchronous modulation of PKA by dopamine in learning. Nature 590 , 451–456 (2021). Sippy, T. & Tritsch, N. X. Unraveling the dynamics of dopamine release and its actions on target cells. Trends Neurosci. (2023) doi:10.1016/j.tins.2022.12.005. Cardin, J. A. et al. Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2. Nat. Protoc. 5 , 247–254 (2010). Matikainen-Ankney, B. A. et al. Nucleus Accumbens D1 Receptor–Expressing Spiny Projection Neurons Control Food Motivation and Obesity. Biol. Psychiatry 93 , 512–523 (2023). Albin, R. L., Young, A. B. & Penney, J. B. The functional anatomy of basal ganglia disorders. Trends Neurosci. 12 , 366–375 (1989). Yu, A. J. & Dayan, P. Acetylcholine in cortical inference. Neural Netw. 15 , 719–730 (2002). Bouret, S. & Sara, S. J. Network reset: a simplified overarching theory of locus coeruleus noradrenaline function. Trends Neurosci. 28 , 574–582 (2005). Aosaki, T., Miura, M., Suzuki, T., Nishimura, K. & Masuda, M. Acetylcholine-dopamine balance hypothesis in the striatum: an update. Geriatr. Gerontol. Int. 10 Suppl 1 , S148-157 (2010). Fee, M. S. & Leonardo, A. Miniature motorized microdrive and commutator system for chronic neural recording in small animals. J. Neurosci. Methods 112 , 83–94 (2001). Barbera, G. et al. An open source motorized swivel for in vivo neural and behavioral recordings. MethodsX 7 , 101167 (2020). Patriarchi, T. et al. Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science 360 , (2018). Pisanello, M. et al. The Three-Dimensional Signal Collection Field for Fiber Photometry in Brain Tissue. Front. Neurosci. 13 , 82 (2019). Kravitz, A. V., Owen, S. F. & Kreitzer, A. C. Optogenetic identification of striatal projection neuron subtypes during in vivo recordings. Brain Res. 1511 , 21–32 (2013). Legaria, A. A. et al. Fiber photometry in striatum reflects primarily nonsomatic changes in calcium. Nat. Neurosci. 25 , 1124–1128 (2022). Schultz, W. Dopamine reward prediction-error signalling: a two-component response. Nat. Rev. Neurosci. 17 , 183–195 (2016). Patel, A. A., McAlinden, N., Mathieson, K. & Sakata, S. Simultaneous Electrophysiology and Fiber Photometry in Freely Behaving Mice. Front. Neurosci. 14 , (2020). Formozov, A., Dieter, A. & Wiegert, J. S. A flexible and versatile system for multi-color fiber photometry and optogenetic manipulation. Cell Rep. Methods 3 , 100418 (2023). Taniguchi, J. et al. Comment on ‘Accumbens cholinergic interneurons dynamically promote dopamine release and enable motivation’. 2023.12.27.573485 Preprint at https://doi.org/10.1101/2023.12.27.573485 (2024). Bruno, C. A. et al. pMAT: An open-source software suite for the analysis of fiber photometry data. Pharmacol. Biochem. Behav. 201 , 173093 (2021). Siegle, J. H. et al. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J. Neural Eng. 14 , 045003 (2017). Buccino, A. P. et al. SpikeInterface, a unified framework for spike sorting. eLife 9 , e61834 (2020). Quiroga, R. Q., Nadasdy, Z. & Ben-Shaul, Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16 , 1661–1687 (2004). Chung, J. E. et al. A Fully Automated Approach to Spike Sorting. Neuron 95 , 1381-1394.e6 (2017). Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. & Redish, A. D. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131 , 1–11 (2005). Additional Declarations There is NO Competing Interest. Supplementary Files 3DPrintFiles.zip Pubcommutator3DModeltoSubmitNM.zip PubcommutatorBOMtoSubmitNM.xlsx PubcommutatorsupplementtoSubmitNM.docx arduinoCodes.zip Cite Share Download PDF Status: Published Journal Publication published 11 May, 2026 Read the published version in Nature Methods → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4249277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":293336409,"identity":"716c630e-8829-4ee2-9812-621aeb5a3124","order_by":0,"name":"Benjamin De Corte","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACA4bkgw8kftjA+BYMDBKMDUAGMx4tackGlj1pML4EMVpyzAQq2A4jawEzcGsxZ08wY7jBc17e4NrhBww/KiQS185ubvzAUGGd2IBDi2XPg7SHMyxuG264nWbA2HNGInHbnYPNEgxn0nFqMbiRcNxYgud2gsHtHAZmxjaglhuJDRKMbYfxaElsk/7Ddg5FS/MPxn/4tCSzSUiwHUDR0gYMNNxaLHueMRtI9iQbzgT65SDQL8ZAv7RZJBxLN8alxZw9/yMwKu3k+W4nP3zwo8JGdtvt9sc3PtRYy+LSggIOwFkJxCgfBaNgFIyCUYATAADuSGHnUOuA7wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6741-6324","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"De Corte","suffix":""},{"id":293336410,"identity":"27474f77-ef8e-481a-afa9-507129bbdb22","order_by":1,"name":"Youngcho Kim","email":"","orcid":"","institution":"University of Iowa","correspondingAuthor":false,"prefix":"","firstName":"Youngcho","middleName":"","lastName":"Kim","suffix":""},{"id":293336411,"identity":"8fb8900b-b74a-43ba-9b25-b4aab048c48e","order_by":2,"name":"Kelsey Heslin","email":"","orcid":"","institution":"Icahan School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Kelsey","middleName":"","lastName":"Heslin","suffix":""},{"id":293336412,"identity":"2895f5a1-401e-4ce0-97c8-6cacc5e98adc","order_by":3,"name":"John Freeman","email":"","orcid":"","institution":"University of Iowa","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Freeman","suffix":""},{"id":293336413,"identity":"94c2174a-e0e1-442a-ab7a-6617e83f119c","order_by":4,"name":"Eleanor Simpson","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Eleanor","middleName":"","lastName":"Simpson","suffix":""},{"id":293336414,"identity":"b887e862-03ac-4f13-80c6-7498e3885207","order_by":5,"name":"Krystal Parker","email":"","orcid":"","institution":"University of Iowa","correspondingAuthor":false,"prefix":"","firstName":"Krystal","middleName":"","lastName":"Parker","suffix":""},{"id":293336415,"identity":"788376ad-67d5-41e0-9797-fcb0d230bab4","order_by":6,"name":"Peter Balsam","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Balsam","suffix":""}],"badges":[],"createdAt":"2024-04-10 21:30:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4249277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4249277/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41592-026-03092-z","type":"published","date":"2026-05-11T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55177292,"identity":"951ae344-4c88-45c5-85ab-1cffd1652d9f","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":448119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview.\u003c/strong\u003e a) Standard photometry recording setup. Here and below, all illustrations are built to-scale using 3D CAD software (Blender), with the subject being roughly the size of a 250g rat. b) Problem of light-leak when using an optical swivel to allow for free-movement. c) Leakless data acquisition by passing photometry voltage signals through an electrical commutator and rotating all optical components with the subject during movement. d) Broad overview of final design with integrated electrophysiology.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/511fa3ae0e6f03c833c40fe1.png"},{"id":55177293,"identity":"88ac19cb-b1b1-4b46-9489-5bebc5b4bff7","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":693438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectrophysiology acquisition and automated anti-tangling.\u003c/strong\u003e a) Overview of relevant components. b) Motor system hardware-components. c) Orientation-tracking hardware-components. D) Electrophysiology hardware components, highlighting the magnet-wire bridge connection related to the orientation-tracking system.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/2152d2ff6901e808c8013b81.png"},{"id":55177291,"identity":"6b2616cf-4a3c-42b8-a6d6-0ab22815788f","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":236533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAutomated anti-tangling hardware and algorithm.\u003c/strong\u003e a) Black box clarifies relevant components. b) Hardware summary during movement. Magnet’s face rotates with subject, while Hall-effect sensors remain in fixed-position on the frame. c) Software summary. Hall effect sensors measure the strength of magnetic fields. Here, the field’s strength is determined by the angle of the magnet’s face to the sensor, being strongest when the magnet points directly toward it. With two sensors offset by 90 degrees, the anti-tangling algorithm can track rotation around a full 360-degree axis. Pseudo-code on bottom right summarizes how the algorithm uses the sensor-inputs to run the motor, always rotating the frame toward a fixed ‘null’ position with respect to the magnet (and by consequence, the subject’s orientation).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/f07b60b83a98d946ca73de14.png"},{"id":55178121,"identity":"59abe669-a576-4f27-806a-761af0cd811d","added_by":"auto","created_at":"2024-04-23 16:37:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":754708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrating photometry-recordings. \u003c/strong\u003e\u0026nbsp;a) Overview of relevant components. b) Attachments for LEDs, photocollector, and filter screw into ‘breadboard-like’ arms on the commutator’s frame. Screw-grid dimensions are depicted on the far right. c) LED-power and photocollector output route through a circuit board secured to the frame. Wires on the board run through the commutator to the circuit board at the top, which connects to the photometry system.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/ca4b21ef1ce5cf0d8482b045.png"},{"id":55177298,"identity":"89c0a95e-9104-45a1-90d4-f9a472f3f1a6","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":638454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBasic performance verification. \u003c/strong\u003e\u0026nbsp;a) Overview of implant for combined dLight and tetrode recordings in the nucleus accumbens. Zoomed image highlights light-level and distance between the base of the optic and tetrodes. b) Five-minutes of simultaneous photometry (top) electrophysiology (bottom) recordings. c) Compares signal quality for standard photometry recordings, those taken with an optical swivel, and those taken with the current commutator design. Traces show 1-minute of non-normalized light-measurements at the photocollector for the dLight excitation wavelength when using each method. Bars show percent change in mean signal, relative to the standard (swivel-less) design. d) No apparent photoelectric effects with combined photometry/electrophysiology. Heat map shows voltage traces from a representative electrode when delivering 1-second LED pulses, with the mean response plotted underneath. Bars shows no apparent differences in signal quality when the LEDs are on/off, quantified in median absolute deviations (MAD). e). Motor does not impact data-quality. Panels are equivalent to D (now summarizing effects of motor being on/off), with photometry on left and electrophysiology on right.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/e298f8e585a14a82cfb3df71.png"},{"id":55177299,"identity":"63c33993-60ab-4cac-8ec6-cb569ebdc185","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":501275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain-behavior relationships during combined photometry and electrophysiology. \u003c/strong\u003eA) Task-diagram. Light-cue signals reward-delivery for lever-pressing at variable intervals during a trial. B) Representative task-performance from a mouse connected to the commutator. C) Mice connected to commutator perform as well as mice connected to a Doric optical swivel (i.e., a device that places virtually no resistance on subject-movement). D) Single-unit isolation. Peak-plot from a pair of tetrode wires. E) Task-related dLight and single-unit activity around key task events (see pictures next to heat maps) during simultaneous recordings. All signals normalized to 10-second pre-trial baseline period (expressed as standard-deviations) and sorted by dLight amplitude around event-onset. F) Dopamine-spike correlations around reward. Heat maps sorted by dLight-maximum, and we split the neuron’s mean rate by high/low dopamine (upper/lower quartiles of the dLight maxima). Scatterplot shows correlation between dLight maximum and spike-magnitude (see methods), with a line of best fit. While a linear relationship appears appropriate for this neuron, our analysis uses the spearman-correlation for sake of other neurons that might have non-linear relationships. G) Population-level summary of dopamine-spike correlations. Left panel shows the distribution of spearman correlation coefficients between the dopamine signal and firing rates across all neurons. Pie chart shows fraction of reliable correlations in dataset, and bar chart breaks this population down by the direction of the correlations.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/ce6db458c8f89e1e59a2448b.png"},{"id":109052696,"identity":"a26a037a-3602-4107-85a5-98b3cf765597","added_by":"auto","created_at":"2026-05-12 07:06:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3998105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/2de9822d-bcc5-4973-a3b1-1a29d9ab480d.pdf"},{"id":55177296,"identity":"544b5334-611a-4efc-a934-12996ed64418","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":484099,"visible":true,"origin":"","legend":"","description":"","filename":"3DPrintFiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/cc080d787a8890f0a42bbe7c.zip"},{"id":55177312,"identity":"277e9fed-323d-4ba6-b7ee-e338f0055ec1","added_by":"auto","created_at":"2024-04-23 16:29:32","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":131036814,"visible":true,"origin":"","legend":"","description":"","filename":"Pubcommutator3DModeltoSubmitNM.zip","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/5d717d1b7973ab4e0dd5aea1.zip"},{"id":55177295,"identity":"c542dd72-4933-4413-b30e-c830213febd0","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20804,"visible":true,"origin":"","legend":"","description":"","filename":"PubcommutatorBOMtoSubmitNM.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/0e203c0f1e714bcc8ffa153b.xlsx"},{"id":55177305,"identity":"ebb31f54-8ae5-4e7f-af74-227d3722965f","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4736225,"visible":true,"origin":"","legend":"","description":"","filename":"PubcommutatorsupplementtoSubmitNM.docx","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/e0fc5c43459c1f272b0e1128.docx"},{"id":55177300,"identity":"57359e57-b755-4015-bbfd-cdd520e4e78e","added_by":"auto","created_at":"2024-04-23 16:29:24","extension":"zip","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":491356,"visible":true,"origin":"","legend":"","description":"","filename":"arduinoCodes.zip","url":"https://assets-eu.researchsquare.com/files/rs-4249277/v1/a501b3f5de71b1fce67bcd5a.zip"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Automated device for simultaneous photometry and electrophysiology in freely moving animals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMeasuring neural activity in behaving subjects provides detailed insight into the nature of brain-behavior relationships. Recent methodological advances have markedly improved both the quality and type of neural data that we can collect. For example, novel electrophysiology probes have vastly increased the number of neurons and brain areas that we can simultaneously record from\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Furthermore, with improvements in fluorescent biosensors for fiber photometry, we can now easily track neural signals that were previously either difficult or impossible to monitor, such as various neuromodulators\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and even components of intracellular signaling cascades\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Moreover, with viral-approaches available for both methods, we can even selectively record these signals from specific cell-populations\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eElectrophysiology and fiber photometry are powerful techniques when used individually. However, given that they often provide complementary data, being able to conduct both methods simultaneously would carry strong advantages. As one example, various theories make core assumptions regarding how neuromodulators should impact the spiking of individual neurons to guide behavior\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, as we have traditionally lacked the ability to simultaneously monitor neuromodulator levels and spiking \u003cem\u003ein vivo\u003c/em\u003e, these hypotheses are often based on indirect data (e.g., \u003cem\u003ein vitro\u003c/em\u003e experiments, focal drug infusions, etc.). With a robust approach for combining these methods in freely-moving subjects, we could interrogate these predictions directly.\u003c/p\u003e \u003cp\u003eCurrently, our ability to conduct simultaneous photometry and electrophysiology \u003cem\u003ein vivo\u003c/em\u003e is highly limited. Ideally, one would simply tether subjects to an implant that integrates an optical cannula (for photometry) and an electrode array (for electrophysiology) and begin recording. However, as subjects move, the two data tethers will quickly tangle, immobilizing the subject and introducing a variety of other problems (e.g., data loss/corruption, implant damage, etc.). This \u0026lsquo;tangling problem\u0026rsquo; is a concern for any method involving a tether, yet unlike most techniques, it cannot be easily solved with additional equipment in the specific case of photometry and electrophysiology\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In fact, as addressed below, we are not aware of any open source or commercial solutions that do not incur a substantial loss in data-quality. This includes wireless systems, where battery-life and implant-weight place strong limits on the number of neural signals that can be recorded simultaneously and the overall length of recording-sessions themselves.\u003c/p\u003e \u003cp\u003eHere, we describe a novel device that overcomes these hurdles. We confirm that mice can perform behavioral tasks for arbitrarily long sessions (1.5-2 hours, in our case) with no impact on photometric, electrophysiological, or behavioral data-quality. Furthermore, we have explicitly designed the device to be modular, allowing users to easily adapt it to fit their specific equipment/experimental needs and extend its functionality.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn the main text, we will focus on giving a high-level overview of our approach and validation data; however, we provide detailed assembly instructions in the supplemental materials.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKey challenge and approach\u003c/b\u003e. Combining any optical method with electrophysiology will be difficult, yet photometry is a particularly challenging case. As noted above, both electrophysiology and photometry require connecting subjects to a tether that transmits neural data to an external recording system. As subjects move during an experiment, the tethers will become tangled, impeding natural behavior and risking more severe consequences. For some techniques, additional equipment can help avoid this problem. For example, during solo-electrophysiology recordings, tangling can be prevented using an electrical commutator\u0026ndash;a device that rotates with the subject as it moves, while preserving electrical contact in the data-tether\u0026rsquo;s wires\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, at present, no analogous solutions exist for photometry\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, representing the primary obstacle toward combining these methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Overview.\u003c/b\u003e a) Standard photometry recording setup. Here and below, all illustrations are built to-scale using 3D CAD software (Blender), with the subject being roughly the size of a 250g rat. b) Problem of light-leak when using an optical swivel to allow for free-movement. c) Leakless data acquisition by passing photometry voltage signals through an electrical commutator and rotating all optical components with the subject during movement. d) Broad overview of final design with integrated electrophysiology.\u003c/p\u003e \u003cp\u003eFigure 1a helps clarify the problem, giving a simplified overview of how standard photometry systems operate. Briefly, LEDs send light to an optical filter. The filter passes the light to the brain and captures any that returns. The returning light\u0026mdash;typically reflecting the neural signal of interests\u0026mdash;is sent to a photocollector (also known as a photodetector or photoreceiver). The photocollector converts the light-intensity into a voltage signal, and the acquisition system records this data.\u003c/p\u003e \u003cp\u003eCritically, in standard setups, the patch-cord connection between the filter and the subject\u0026rsquo;s implant is continuous. As such, subjects can indeed become tangled during recordings. Many attempt to resolve this problem by placing an optical swivel (also known as an optical rotary-joint) between the filter and the subjects\u0026rsquo; implant (Fig.\u0026nbsp;1b). Analogous to an electrophysiology commutator, optical swivels transmit light while allowing the optical tether to rotate freely. However, due to difficulties in optical transmission, a substantial amount of light will leak out of a swivel in the process, resulting in a marked reduction in signal-quality\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In virtually all cases, the light-leak is too severe for effective recordings to be taken\u0026mdash;typically causing a 50% reduction in overall return-signal and an even greater reduction in the signal-to-noise ratio (i.e., any neural signals of interest will only deviate from baseline by a few percent). Therefore, experimenters typically opt for a continuous connection and engage in a variety of non-ideal strategies to offset this limitation, such as untangling subjects by-hand, using extra-long tethers, keeping recording sessions brief, among others. While these solutions can be generally effective for solo-photometry recordings, our goal is to conduct long-term recordings and add a second data-tether for electrophysiology, making them untenable here.\u003c/p\u003e \u003cp\u003eOur solution to this problem centers on the fact that input to the LEDs and output from the photocollector are fully electrical signals. Therefore, both can be routed through an electrical commutator, and if all photometry components are mounted to the commutator itself, the entire assembly can rotate with the subject as it moves. This keeps the connection between the filter and subject continuous, avoiding signal-loss seen with optical swivels. Figure\u0026nbsp;1c gives a more specific view of our application. The LEDs, filtercube, and photocollector are mounted on a 3D printed frame that fixes to the bottom of an electrical commutator. All of the components still interact as described above. However, they can now rotate with the subject, with the commutator maintaining electrical connections between the LEDs, photocollector, and recording system. Below, we show that this solution easily integrates with electrophysiology acquisition for simultaneous recordings (Fig.\u0026nbsp;1d).\u003c/p\u003e \u003cp\u003e \u003cb\u003eElectrophysiology acquisition and orientation tracking\u003c/b\u003e. Small subjects would be too weak to rotate the commutator on their own, so we designed an automated system that tracks how subjects move and rotates the commutator accordingly. We integrated this process within the electrophysiology-side of our design, building upon a popular method proposed by Fee and Leonardo\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e for solo-electrophysiology recordings.\u003c/p\u003e \u003cp\u003eThe general approach is to attach a magnet to the electrophysiology data-cable and fix a Hall-effect sensor\u0026mdash;a device that measures the strength of magnetic fields\u0026mdash;to the commutator itself. As the subject moves, the magnet will rotate, and the Hall-effect sensor\u0026rsquo;s output will reflect the orientation-change, increasing/decreasing as the magnet turns toward/away from it, respectively. The sensor\u0026rsquo;s output feeds to an automated motor system that rotates the commutator as needed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our device, the motor-system consists of a stepper motor and pulley mounted to the commutator via 3D printed parts\u0026mdash;all designed to rotate the frame at the bottom of the commutator (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The electrophysiology data-cable runs through the center of the frame and is used to implement orientation-tracking (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Specifically, a magnet appends to the cable via a 3D printed holder that sits vertically within a ball-bearing, allowing the magnet to rotate with the subject. We take two steps to ensure small subjects will be able to easily rotate the assembly. First, the cable runs through a torque-arm, which boosts force by horizontally offsetting the cable from the axis of rotation at the bearing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Second, note that, if were to plug the data-cable directly into the bottom of the commutator, any stiffness in the cable would place resistance on the subject as it moves. To resolve this, we bridge the connection between the cable and the bottom of the commutator with magnet wire, also known as \u0026lsquo;winding wire\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Magnet wire is extremely durable, yet highly flexible, effectively eliminating any resistance that the cable itself would introduce. All wires send data to the electrophysiology system via a connector fixed to the top of the commutator (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eTo detect subject-rotation, two Hall-effect sensors track the orientation of the magnet\u0026rsquo;s face (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Note that the original design only incorporates one Hall-effect sensor\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, a single sensor will only be able to track the magnet around 180 degrees. Therefore, it will miss rapid turns beyond this range, which can be common during behavioral tasks (e.g., when a subject must traverse between opposite sides of a conditioning chamber). Conceptually, when using two Hall-sensors that are offset by 90-degrees, their outputs can work together like the sine and cosine functions, allowing us to track the magnet\u0026rsquo;s orientation around a 360-degre axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,c). We have not observed missed-rotations with this improvement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe process of translating the Hall-sensor inputs to a motor output is handled by a simple feedback circuit, composed of a microcontroller that implements a rotation-detection algorithm and turns the motor via a driver-circuit (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For convenience, we use a Teensy 4.0 (ARM Cortex-M7) microcontroller, which is cheap and fully compatible with the Arduino coding environment. Furthermore, as it is arguably the most powerful microcontroller currently available, it can readily accommodate more advanced equipment/algorithms for users hoping to extend our system\u0026rsquo;s functionality (see Discussion).\u003c/p\u003e \u003cp\u003eWe have used/improved upon this system for solo-electrophysiology recordings over the past 6 years and find that it is virtually errorless, even for very long sessions (e.g., \u0026gt;\u0026thinsp;5 hours, without human-intervention). Nonetheless, we still implement two safety/convenience features. First, the algorithm contains an \u0026lsquo;auto-shutoff\u0026rsquo; mode that pauses the motor if it makes 5 continuous rotations in one direction, usually indicating an error has occurred. Second, as shown Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, the system\u0026rsquo;s circuit-board contains two buttons that allow users to manually override the motor, permitting them to correct errors without disturbing subjects.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhotometry\u003c/b\u003e. The next step is to mount all photometry components to the frame and route the LED/photocollector signals through the commutator (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When designing the device, we realized a key challenge would be allowing the frame to accommodate equipment from different recording systems. For example, the shape/size of LEDs varies across vendors, and we wanted our design to be compatible across models. To resolve this, we gave the frame a \u0026lsquo;breadboard-like\u0026rsquo; design, containing three arms with a standardized grid of screw holes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Each photometry component (LEDs, filter, photocollector) fits within a 3D-printed attachment that screws into the grid. We provide attachments for common photometry components (Doric LEDs/minicube, Newport 2151 photocollector). However, if researchers hoping to use the device have different equipment, they only need to design an attachment for their given model. We encourage users to share their designs via the project\u0026rsquo;s GitHub page.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInput to the LEDs and output from the photocollector route through the commutator via a circuit board that screws into the frame (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The wires connect to the circuit board at the top, providing access for the photometry system. The final construction step is to build a mount that can suspend the device above the subject, and we describe how we constructed ours in the supplemental materials (see Fig. S7; also Fig.\u0026nbsp;1d).\u003c/p\u003e \u003cp\u003eAs detailed below, the device provides excellent neural and behavioral data for long-sessions (1.5-2 hours, in our projects). While we focus on single-sensor photometry recordings here (one LED for control-excitation and one LED for our signal of interest), the commutator is already equipped for a third LED, allowing users to record from two sensors simultaneously (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Furthermore, despite the complexity of motion-tracking with two tethers, faults in the automated anti-tangling process are rare (without supervision, an average of 1 tangle every 2 hours). We find that checking subjects every 20\u0026ndash;30 minutes and correcting any minor errors\u0026mdash;using the push-buttons to prevent subject distraction\u0026mdash;is more than sufficient to avoid tangling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData.\u003c/b\u003e Finally, we characterized the quality and utility of \u003cem\u003ein vivo\u003c/em\u003e data collected with the commutator device. Specifically, we expressed a fluorescent dopamine-sensor\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (dLight-1.3b) in the nucleus accumbens of 6 mice, and recorded with an \u0026lsquo;optetrode\u0026rsquo; implant, composed of 8 tetrodes surrounding an optic fiber (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b). We offset the tetrode-tips to be 200um from the tip of the optic fiber (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), putting them within the recordable range of the photometry signal\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We first performed fundamental checks on data-quality. For example, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec highlights that, relative to standard photometry recordings, optical swivels produce a severe signal-drop, due to light-leak (-50%; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). As intended, the commutator resolves this issue, with any differences being due to standard fluorophore-depletion within a session (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec; -3% over this particular recording).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we ensured that light from the photometry system would not interfere with the electrophysiology recordings. Specifically, light can scatter electrons at the tip of a recording electrode, producing a voltage-artifact called a \u0026lsquo;photoelectric effect.\u0026rsquo; This is a substantial concern for combined optogenetics and electrophysiology, as these artifacts are often severe enough to obscure spike-detection without proper precautions (e.g., increased spacing between optic and electrodes)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, we observe no evidence of photoelectric effects in our recordings, even when explicitly pulsing the LEDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). This likely relates to the fact that light-power used for photometry is usually orders of magnitude lower than that used for optogenetics (microwatt vs milliwatt scale, respectively).\u003c/p\u003e \u003cp\u003eFinally, during early protypes of the device, we noted that running the motor would occasionally produce noise in both the photometry and electrophysiology data. The electrophysiology contamination was simply due to 60Hz noise and was easily resolvable with standard steps (i.e., common ground for recording and motor systems; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). The photometry contamination resulted from mild vibration on the photocollector during rotation. Fortunately, padding the photocollector holder with foam solved this issue (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ee; also see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eOur final question was whether the device would provide useful data regarding brain-behavior relationships. After all, photometry typically measures \u0026lsquo;bulk\u0026rsquo; neural signals, and some data question the degree to which they correlate with single-neuron activity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Importantly, these critiques primarily apply to calcium indicators intended to measure net spike-activity in a given area (e.g., GCaMP), as they confound somatic activity and dendritic inputs. To our knowledge, no data have addressed how indicators that reflect neuromodulator-levels or intracellular signaling molecules map to neuronal firing rates \u003cem\u003ein vivo\u003c/em\u003e. In our view, this is where the true value of combining the two methods lies, and most concerns surrounding spike-indicators do not readily generalize to these sensors.\u003c/p\u003e \u003cp\u003eTo address this, we tracked striatal dopamine-levels, single-neuron firing rates, and behavior during a reward learning task, given the substantial theoretical and clinical interest in how these processes relate to one another. Specifically, we recorded while mice learned a task where a cue signaled reward could be earned for pressing a lever after variable time-intervals elapsed (see Methods). Despite the complexity of the equipment and number of tethers, mice were able to perform the task well, suggesting the automated-anti-tangling system permits free-movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb,c). As a point of reference, we compared their behavior to a separate group of mice trained on the same task while tethered to a Doric dual-optical swivel (FRJ_1x2i_FC-2FC). Like the commutator, this swivel model also incorporates two tethers, yet as it rotates passively with remarkably low-torque, subjects can rotate it easily, without motor-based assistance. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, the two groups were indistinguishable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we evaluated trial-by-trial covariance between the dopamine signal and the firing of striatal neurons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). We focused on how each signal varied around key task events\u0026mdash;the onset of the cue, lever presses, and reward (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). Striatal neurons were modulated around all events. Consistent with prior reports, clear dopamine-peaks emerged at cue-onset and reward\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, though some weak suppression can be noted around unrewarded lever-presses. More importantly, we note robust trial-by-trial correlations between dopamine-levels and striatal firing rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ef,g). While a full analysis is beyond the scope of this report, we ran a simple correlation analysis (see Methods) between dopamine peaks at reward-onset and single-unit firing (e.g., Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Reliable correlations were common among the population (39.8% of neurons; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). Notably, as expected by canonical direct/indirect pathway theorization, positive and negative correlations occurred at roughly equal frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eg, χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;118)\u0026thinsp;=\u0026thinsp;0.383, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.536).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe developed a modular, open-source device for accomplishing combined electrophysiology and photometry. The device prevents the ‘tangling problem’ inherent to standard photometry setups while also avoiding light-leak related to optical swivels\u003csup\u003e5\u003c/sup\u003e. This allows subjects to move naturalistically during experiments, with no loss in neural data-quality. We establish the utility of this approach by providing what we believe are the first-data showing robust correlations between striatal firing and dopamine-levels in behaving subjects, yet the method can be applied in a variety of contexts. While some commercial and open-source systems for combined recordings are beginning to emerge\u003csup\u003e28\u003c/sup\u003e, ours is the first to permit movement while eliminating the need for an optical swivel—the key to obtaining full behavioral/neural data-quality. Furthermore, should commercial systems emerge that can match this level of performance, we assume the price of our device (~$500 USD) will be considerably lower. The device functions exceptionally well, and there are a variety of avenues for expanding its functionality and improving performance, which we address below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most obvious direction is whether the current device can be adapted for solo-photometry recordings, where researchers still lack a reliable method to prevent tangling, particularly for multi-site experiments. After all, while subjects often perform ‘well enough’ for data collection in standard setups, the lack of a swivel/commutator still a key drawback of the method, placing strain on the subject, demanding time from the researcher for manual monitoring/untangling, and making longer recording sessions difficult. Unfortunately, simply swapping the electrophysiology cable in the current design for the photometry patch-cord will not work, as the optical tether will be too stiff to effectively rotate the magnet. However, a simple solution would be to incorporate a second ‘dummy’ tether that appends to the bottom of the torque-arm in place of the electrophysiology cable.\u003c/p\u003e\n\u003cp\u003eAnother direction is evaluating whether the motion tracking hardware on the device can be modified to provide movement-data that syncs with neural activity. Specifically, OpenEphys recently developed an electrophysiology commutator that implements motion tracking by attaching an ‘intertial measurement-unit’ (IMU) to the subject’s headstage. An IMU is effectively a ‘digital compass’ that can track a subject’s orientation in 3D-space at high temporal-resolution. This gives a more direct readout of the animal’s position for motor-feedback, and moreover, provides highly useful data of its own for relating neural data to motor activity. At present, we prefer the Hall-sensor approach, to avoid requiring researchers to add weight/equipment to their implants. However, when designing the motor-circuit, we deliberately wired the Hall sensors to the pins on the microcontroller that can be used to communicate with most IMUs (i.e., i2c clock/data). Therefore, users interested in this approach can begin piloting without modifying the electronics. While IMUs require considerably more compute-power than Hall-sensors, we again emphasize that, despite being inexpensive, the Teensy 4.0 microcontroller used here is highly advanced (e.g., ARM Cortex M7 processor, 600 MHz clock-speed, 64-bit doubles, etc.), making it an excellent choice for this application.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearchers might also be interested in integrating optogenetics with the device. This would add substantial causal power to any experiment\u003csup\u003e29\u003c/sup\u003e, when proper precautions are taken\u003csup\u003e30\u003c/sup\u003e. While not explicitly highlighted above, the gear-pin at the top of the commutator already contains an opening specifically for adding optogenetic patch-cords. Therefore, while we have not explicitly tested this method at present, the device should be able to accommodate optogenetics with minimal to no modification.\u003c/p\u003e\n\u003cp\u003eFinally, there may be applications that require more wires than the current design provides. Examples would include added electrical devices/methods like electrical stimulation, or photometry systems that use a camera as the photocollector. If these experiments do not require dual-sensor photometry, a simple solution would be to use wires from the third LED-port for any added signals, as all wires are electrically isolated from one another on the circuit-boards. However, in the Supplemental Materials, we outline simple steps for freeing added wires if needed (see ‘Tips and general notes’ section).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn short, this is the first device to allow for naturalistic behavior while conducting simultaneous photometry and electrophysiology. This will propel important new discoveries regarding the nature of brain-behavior relationships, as researchers can now quantify the \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003erelationship between signals that were previously either difficult or impossible to measure simultaneously. This project will have a dedicated GitHub page, and we encourage users who make improvements to submit their modifications and improvements.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eDesign materials and software.\u0026nbsp;\u003c/em\u003eThe supplemental files associated with this manuscript contain all necessary information and software needed to replicate the current design. This includes an itemized-list of all materials that need to be ordered (Bill-of-Materials), files for 3D-printed parts (stl files), files for printed-circuit-board files (gerber folder/files), and all microcontroller programs (ino files). We generated figures using Blender and used cloud-based platforms for designing 3D-printed parts and printed-circuit boards (Onshape and EasyEDA, respectively). All 3D-printed parts were generated using a Formalbs 3D-printer (Form3) at a 25 micron resolution, rinsed with 99% isopropyl alcohol (FormWash), and UV-cured (FormCure).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurgery\u003c/em\u003e. All animal work was approved by the Institute for Animal Care and Use Committees of the New York State Psychiatric Institute and Columbia University and was performed according to PHS Policy on Humane Care and Use of Laboratory Animals.\u003c/p\u003e\n\u003cp\u003eFor combined photometry and electrophysiology experiments, adult C57BL/6J mice (3 male / 3 female; 12-weeks old) were anesthetized with isoflurane and unilaterally injected with a virus encoding dLight-1.3b (.3ul, AAV9-syn-dLight1.3b, Addgene) into the nucleus accumbens (NAC, AP: 1.0, ML: 1.4, DV: -4.1 from bregma/skull-surface). Then, an optetrode implant (200um optic surrounded by 8 tetrodes) was lowered into the same site (DV -4.3 to account for the 200um distance between the optic/tetrode tips). The implant was sealed with epoxy (Slo-Zap) and dental cement (Ortho-Jet, Black). Subjects serving as a behavioral comparison to these mice (Fig 6c) came from a control group of an optogenetics experiment for the same project. They received identical surgical procedures. However, these were DAT-ires-cre mice (Jax Strain #:006660; 4 male / 4 female), received bilateral optic implants into the NAC, and received infusions of a cre-dependent reporter-virus (AAV9-Ef1a-DIO EYFP) into the ventral tegmental area (AP: -3.2, ML: +/- 0.5, DV: -4.6 from bregma/skull-surface).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhotometry recordings\u003c/em\u003e. Photometry recordings were performed using an RZ5P Tucker Davis Technologies system. The system controlled light-delivery via a Doric current-regulator (LEDD_2) that powered two Doric connectorized LEDs (see below). A Doric minicube (FMC4_IE(400-410)_E(460-490)_F(500-550)_S) served as the filter, and we used a Newport photoreceiver to measure returning light (Newport 2151). Time-frequency demodulation was used to measure dLight responses to both dopamine-dependent (465nm, 330Hz) and dopamine-independent (i.e., isosbestic, 405nm, 210Hz) excitation, delivered by Doric connectorized-LEDs. Summated light-levels from the LEDs were tuned to 30uW. The signals were demodulated online and written to disc. Before analysis, we corrected for photobleaching and movement artifacts by subtracting the isosbestic signal from the excitation signal, after scaling them to the same level with regression\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eElectrophysiology recordings\u003c/em\u003e. We recorded electrophysiology data using a first-generation Open Ephys system\u003csup\u003e32\u003c/sup\u003e. Specifically, we used an Intan RHD2132 headstage to sample each channel at 30kHz. The raw wide-band signals were written to disc and post-processed with custom Python routines (see SpikeInterface package\u003csup\u003e33\u003c/sup\u003e for analogous workflows). Specifically, we used local common-average referencing (20% trimmed-mean), band-pass filtered each signal (Butterworth, 600-6000Hz), extracted spikes (Quiroga threshold\u003csup\u003e34\u003c/sup\u003e, 4s), performed automated spike-sorting (Mountainsort\u003csup\u003e35\u003c/sup\u003e), and finally manually vetted/curated each purported single-unit with standard approaches (inspecting waveform-shape, cross-correlograms, ISI-histograms, refractory-period violations, and isolation-distance\u003csup\u003e36\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBehavior\u003c/em\u003e. All mice were trained in Med-Associates operant chambers on a task in which one of two cues (identical houselights) were presented across different trials. Each cue signaled occasional reward delivery across time for lever-pressing, determined by a geometric/Poisson schedule (i.e., variable-interval procedure). Each cue signaled a different reward-rate (high rate = 1/10s, low-rate = 1/40s). The high-rate cue was presented for 20s, and the low-rate cue was presented for 80s. All trials were separated by a dark, 120s inter-trial interval, in which no rewards were available. Sessions lasted 90 minutes. The primary behavioral measure of interest were response rates during each cue. Note that, because our focus is on simple-verification for this publication, we only detail data from the high-rate cue trials here, reserving a full analysis for a later publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistics\u003c/em\u003e. For behavioral analyses, lever-press rates were compared with a mixed-model ANOVA for acquisition, with group (commutator vs dual-swivel) as a between-subjects factor and session as a within-subjects factor. We compared post-acquisition performance across groups with independent-samples t-tests. For analyses correlating dopamine-levels to single-neuron firing, we focused on how each signal varied around reward, putting both signals on a common scale by expressing them as standard-deviations from a 10-second pre-trial baseline period. As our measure of reward-evoked extracellular dopamine, we took the maximum of the phasic burst in the dLight-signal within the first 3-seconds following reward. Single-neurons fire heterogeneously across time around reward (e.g., some burst/suppress, others show more non-linear dynamics, etc.), making it more difficult to define a simple-measure of modulation than the dopamine-data. As a straightforward approach, we computed each neuron’s mean rate over time during trials where the dopamine was high or low (upper/lower quartiles of the reward-evoked maxima, respectively). We then identified the time-point where the two firing-rate vectors differed maximally (absolute values). Finally, we centered a 500ms bin around this time-value and computed the spearman correlation between the dopamine-level and the firing-rate across all rewards.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Interest.\u003c/strong\u003e Authors have no conflicts of interest to report.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBuzs\u0026aacute;ki, G. Large-scale recording of neuronal ensembles. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 446\u0026ndash;451 (2004).\u003c/li\u003e\n \u003cli\u003eSteinmetz, N. A. \u003cem\u003eet al.\u003c/em\u003e Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e372\u003c/strong\u003e, eabf4588 (2021).\u003c/li\u003e\n \u003cli\u003eLuo, T. Z. \u003cem\u003eet al.\u003c/em\u003e An approach for long-term, multi-probe Neuropixels recordings in unrestrained rats. \u003cem\u003eeLife\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e59716 (2020).\u003c/li\u003e\n \u003cli\u003eHultman, R. \u003cem\u003eet al.\u003c/em\u003e Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e173\u003c/strong\u003e, 166-180.e14 (2018).\u003c/li\u003e\n \u003cli\u003eSimpson, E. H. \u003cem\u003eet al.\u003c/em\u003e Lights, fiber, action! A primer on \u003cem\u003ein vivo\u003c/em\u003e fiber photometry. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 718\u0026ndash;739 (2024).\u003c/li\u003e\n \u003cli\u003eMa, L. \u003cem\u003eet al.\u003c/em\u003e Locomotion activates PKA through dopamine and adenosine in striatal neurons. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e611\u003c/strong\u003e, 762\u0026ndash;768 (2022).\u003c/li\u003e\n \u003cli\u003eKalmbach, A. \u003cem\u003eet al.\u003c/em\u003e Dopamine encodes real-time reward availability and transitions between reward availability states on different timescales. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 3805 (2022).\u003c/li\u003e\n \u003cli\u003eKubitschke, M. \u003cem\u003eet al.\u003c/em\u003e Next generation genetically encoded fluorescent sensors for serotonin. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 7525 (2022).\u003c/li\u003e\n \u003cli\u003eJing, M. \u003cem\u003eet al.\u003c/em\u003e An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1139\u0026ndash;1146 (2020).\u003c/li\u003e\n \u003cli\u003eBlanco-Pozo, M., Akam, T. \u0026amp; Walton, M. E. Dopamine-independent effect of rewards on choices through hidden-state inference. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 286\u0026ndash;297 (2024).\u003c/li\u003e\n \u003cli\u003eDong, C. \u003cem\u003eet al.\u003c/em\u003e Psychedelic-inspired drug discovery using an engineered biosensor. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e184\u003c/strong\u003e, 2779-2792.e18 (2021).\u003c/li\u003e\n \u003cli\u003eMassengill, C. I. \u003cem\u003eet al.\u003c/em\u003e Sensitive genetically encoded sensors for population and subcellular imaging of cAMP in vivo. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1461\u0026ndash;1471 (2022).\u003c/li\u003e\n \u003cli\u003eLee, S. J. \u003cem\u003eet al.\u003c/em\u003e Cell-type specific asynchronous modulation of PKA by dopamine in learning. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e590\u003c/strong\u003e, 451\u0026ndash;456 (2021).\u003c/li\u003e\n \u003cli\u003eSippy, T. \u0026amp; Tritsch, N. X. Unraveling the dynamics of dopamine release and its actions on target cells. \u003cem\u003eTrends Neurosci.\u003c/em\u003e (2023) doi:10.1016/j.tins.2022.12.005.\u003c/li\u003e\n \u003cli\u003eCardin, J. A. \u003cem\u003eet al.\u003c/em\u003e Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 247\u0026ndash;254 (2010).\u003c/li\u003e\n \u003cli\u003eMatikainen-Ankney, B. A. \u003cem\u003eet al.\u003c/em\u003e Nucleus Accumbens D1 Receptor\u0026ndash;Expressing Spiny Projection Neurons Control Food Motivation and Obesity. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, 512\u0026ndash;523 (2023).\u003c/li\u003e\n \u003cli\u003eAlbin, R. L., Young, A. B. \u0026amp; Penney, J. B. The functional anatomy of basal ganglia disorders. \u003cem\u003eTrends Neurosci.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 366\u0026ndash;375 (1989).\u003c/li\u003e\n \u003cli\u003eYu, A. J. \u0026amp; Dayan, P. Acetylcholine in cortical inference. \u003cem\u003eNeural Netw.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 719\u0026ndash;730 (2002).\u003c/li\u003e\n \u003cli\u003eBouret, S. \u0026amp; Sara, S. J. Network reset: a simplified overarching theory of locus coeruleus noradrenaline function. \u003cem\u003eTrends Neurosci.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 574\u0026ndash;582 (2005).\u003c/li\u003e\n \u003cli\u003eAosaki, T., Miura, M., Suzuki, T., Nishimura, K. \u0026amp; Masuda, M. Acetylcholine-dopamine balance hypothesis in the striatum: an update. \u003cem\u003eGeriatr. Gerontol. Int.\u003c/em\u003e \u003cstrong\u003e10 Suppl 1\u003c/strong\u003e, S148-157 (2010).\u003c/li\u003e\n \u003cli\u003eFee, M. S. \u0026amp; Leonardo, A. Miniature motorized microdrive and commutator system for chronic neural recording in small animals. \u003cem\u003eJ. Neurosci. Methods\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 83\u0026ndash;94 (2001).\u003c/li\u003e\n \u003cli\u003eBarbera, G. \u003cem\u003eet al.\u003c/em\u003e An open source motorized swivel for \u003cem\u003ein vivo\u003c/em\u003e neural and behavioral recordings. \u003cem\u003eMethodsX\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 101167 (2020).\u003c/li\u003e\n \u003cli\u003ePatriarchi, T. \u003cem\u003eet al.\u003c/em\u003e Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e360\u003c/strong\u003e, (2018).\u003c/li\u003e\n \u003cli\u003ePisanello, M. \u003cem\u003eet al.\u003c/em\u003e The Three-Dimensional Signal Collection Field for Fiber Photometry in Brain Tissue. \u003cem\u003eFront. Neurosci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 82 (2019).\u003c/li\u003e\n \u003cli\u003eKravitz, A. V., Owen, S. F. \u0026amp; Kreitzer, A. C. Optogenetic identification of striatal projection neuron subtypes during in vivo recordings. \u003cem\u003eBrain Res.\u003c/em\u003e \u003cstrong\u003e1511\u003c/strong\u003e, 21\u0026ndash;32 (2013).\u003c/li\u003e\n \u003cli\u003eLegaria, A. A. \u003cem\u003eet al.\u003c/em\u003e Fiber photometry in striatum reflects primarily nonsomatic changes in calcium. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1124\u0026ndash;1128 (2022).\u003c/li\u003e\n \u003cli\u003eSchultz, W. Dopamine reward prediction-error signalling: a two-component response. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 183\u0026ndash;195 (2016).\u003c/li\u003e\n \u003cli\u003ePatel, A. A., McAlinden, N., Mathieson, K. \u0026amp; Sakata, S. Simultaneous Electrophysiology and Fiber Photometry in Freely Behaving Mice. \u003cem\u003eFront. Neurosci.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, (2020).\u003c/li\u003e\n \u003cli\u003eFormozov, A., Dieter, A. \u0026amp; Wiegert, J. S. A flexible and versatile system for multi-color fiber photometry and optogenetic manipulation. \u003cem\u003eCell Rep. Methods\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 100418 (2023).\u003c/li\u003e\n \u003cli\u003eTaniguchi, J. \u003cem\u003eet al.\u003c/em\u003e Comment on \u0026lsquo;Accumbens cholinergic interneurons dynamically promote dopamine release and enable motivation\u0026rsquo;. 2023.12.27.573485 Preprint at https://doi.org/10.1101/2023.12.27.573485 (2024).\u003c/li\u003e\n \u003cli\u003eBruno, C. A. \u003cem\u003eet al.\u003c/em\u003e pMAT: An open-source software suite for the analysis of fiber photometry data. \u003cem\u003ePharmacol. Biochem. Behav.\u003c/em\u003e \u003cstrong\u003e201\u003c/strong\u003e, 173093 (2021).\u003c/li\u003e\n \u003cli\u003eSiegle, J. H. \u003cem\u003eet al.\u003c/em\u003e Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. \u003cem\u003eJ. Neural Eng.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 045003 (2017).\u003c/li\u003e\n \u003cli\u003eBuccino, A. P. \u003cem\u003eet al.\u003c/em\u003e SpikeInterface, a unified framework for spike sorting. \u003cem\u003eeLife\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e61834 (2020).\u003c/li\u003e\n \u003cli\u003eQuiroga, R. Q., Nadasdy, Z. \u0026amp; Ben-Shaul, Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. \u003cem\u003eNeural Comput.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1661\u0026ndash;1687 (2004).\u003c/li\u003e\n \u003cli\u003eChung, J. E. \u003cem\u003eet al.\u003c/em\u003e A Fully Automated Approach to Spike Sorting. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e95\u003c/strong\u003e, 1381-1394.e6 (2017).\u003c/li\u003e\n \u003cli\u003eSchmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. \u0026amp; Redish, A. D. Quantitative measures of cluster quality for use in extracellular recordings. \u003cem\u003eNeuroscience\u003c/em\u003e \u003cstrong\u003e131\u003c/strong\u003e, 1\u0026ndash;11 (2005).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Photometry, Electrophysiology, Commutator, Simultaneous, Dopamine, Striatum, dLight","lastPublishedDoi":"10.21203/rs.3.rs-4249277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4249277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhotometry and electrophysiology are powerful tools for investigating brain-behavior relationships. Combining these techniques would allow us to ask previously un-addressable questions, such as how neuromodulators impact neuronal firing rates. Current options are highly limited\u0026mdash;requiring a substantial loss in data-quality or severely restricting naturalistic-movement. These drawbacks arise from engineering-limits on devices that allow optically-tethered subjects to move freely. Here, we introduce a device that overcomes these challenges. Its automated orientation-tracking system allows subjects to move freely for multiple-hours with minimal supervision and without sacrificing data-quality. The device is modular and adaptable, being compatible with most recording systems and equipped for added functionality (e.g., optogenetics). To demonstrate its utility, we simultaneously tracked extracellular striatal dopamine and single-neuron firing as mice performed a reward-learning task. Mice showed excellent mobility, and we observed robust trial-by-trial correlations between striatal firing and dopamine signaling. This device provides a powerful tool that outperforms current commercial solutions.\u003c/p\u003e","manuscriptTitle":"Automated device for simultaneous photometry and electrophysiology in freely moving animals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 16:29:19","doi":"10.21203/rs.3.rs-4249277/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-methods","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nmeth","sideBox":"Learn more about [Nature Methods](http://www.nature.com/nmeth)","snPcode":"","submissionUrl":"","title":"Nature Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6d98cb2a-c29c-434c-a9d0-633c7493c73f","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30919513,"name":"Biological sciences/Neuroscience/Learning and memory"},{"id":30919514,"name":"Biological sciences/Neuroscience/Molecular neuroscience"}],"tags":[],"updatedAt":"2026-05-12T07:06:41+00:00","versionOfRecord":{"articleIdentity":"rs-4249277","link":"https://doi.org/10.1038/s41592-026-03092-z","journal":{"identity":"nature-methods","isVorOnly":false,"title":"Nature Methods"},"publishedOn":"2026-05-11 04:00:00","publishedOnDateReadable":"May 11th, 2026"},"versionCreatedAt":"2024-04-23 16:29:19","video":"","vorDoi":"10.1038/s41592-026-03092-z","vorDoiUrl":"https://doi.org/10.1038/s41592-026-03092-z","workflowStages":[]},"version":"v1","identity":"rs-4249277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4249277","identity":"rs-4249277","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}