dataesr/scientific-paragraphs-categorization huggingface dataset:
We will briefly summarize how these properties interact, before describing a protocol for assessing opsin properties with regard to how to best activate or suppress a neuron. While many techniques are employed in this evaluation process, we will focus on those steps that are germane to opsin evaluation and two-photon optogenetics while directing the reader elsewhere for some technical procedures. While we will focus on selection of activating opsins, we will briefly discuss the additional concerns surrounding selection of a suppressing opsin. We will also include, where possible and relevant, data on existing opsins. While many studies focus on individual features of opsin behavior, proper evaluation of an opsin requires a holistic understanding of many properties of those opsins. There are relatively few commonly used activating opsins used with multiphoton optogenetics the most common variants are ChR2  [28] , C1V1  [8, 13] , ChrimsonR, Chronos  [15] , ChroME  [11] , ReaChR  [57] , CoChR  [56] , and ChRmine  [14]  each with their own advantages and disadvantages.
Integrating multichannel-data-acquisition hardware in the microscope itself is also a viable solution offered by many major microscope companies. Connecting this device with a suitable integration software generates a master file for each experiment, containing individual traces for all relevant signals, such as breathing rate, temperature, frame trigger, and certainly optogenetic or sensory stimulation pulses. Referring to the mentioned manufacturers, LABView is used for National Instruments interfaces, whereas DAQ boards from Cambridge Electronic Design use the software package Spike2. Particular care has to be exerted in terms of gathering all relevant information important for the current experiment in this master file. For example, the frame trigger signal of a microscope can give insights at which absolute time point images were acquired.
Here, we present a pipeline based on the idea of an event-related analysis: We put forward the notion that any functional circuit imaging of a neuronal ensemble at the end only serves as a tool to extract the underlying action potential train of each individual neuron. Consequently, we provide a step-by-step guide to extract putatively AP-related calcium transients from the raw images. Once this optically derived matrix of AP-related events is generated, e. g. , by transforming an extracted intensity trace to a binarized train of zeros and ones, already well-established correlation or interaction concepts can be employed on these dimensionality-reduced datasets. All-optical experiments require a tailored pipeline for each technical implementation and mandate the avoidance of falsepositive identification of putatively AP-related calcium transients.
Upon applying a threshold to exclude low-probability regions from each probability map, individual somata are extracted from high-probability regions  [67]. Ultimately, all somata positions from each image from the image sequence are combined to acquire a final output of active somata areas. However, manual segmentation of functional calcium imaging data by marking neuronal somata with polygon shapes still prevails due to the applicability on datasets of varying quality, e. g. , datasets of a particularly low signal-to-noise ratio, when deep learning algorithms reach their limits. Depending on the strength of GCaMP6 expression and the overall SNR ratio, if targeting somatic changes in calcium concentrations, a decontamination of the ROIs containing the neuronal soma from neuropil signal might be useful.
This effect is illustrated in Fig. 1. Botcherby et al. describe the consequences of this on imaging properties  [18]. In lens design, optical engineers typically optimize for either transverse or axial focusing. Most commonly transverse imaging is preserved because imaging lenses are expected to have uniform performance throughout the field-of-view (FOV). The consequence is an accumulation of spherical aberrations along the axial range of the lens, shown in Fig. 1c. One can also design for the Herschel condition, which instead forms perfect focus spots along the optical axis but accumulates aberrations in the transverse dimension. The FCM imaging system designs presented here are optimized primarily for the Herschel condition, by constraining the optimization parameters in Zemax optical design software to A lens producing an ideal diffraction limited focus at the design working distance (b) will not produce a diffraction limited focus when the input wavefront is divergent (c) due to spherical aberrations as a result of the Abbe sine condition maintain consistent imaging properties throughout the full focal range of the imaging system.
The 10Â objective collects the fluorescence emission from the CIFB and is separated from the excitation laser path by a primary dichroic mirror (T670LPXR, Chroma Technology). A second dichroic mirror (FF562-Di02, Semrock) splits the red and green fluorescence that is focused using an achromatic doublet lens (LB1761-A, Thorlabs) on separate large-area photon counting photo-multiplier tubes (PMT) (H7422P-40, Hamamatsu). The output pulses from the PMTs are amplified by a high-bandwidth amplifier (ACA-4-35db, Becker & Hickl GmbH) and converted to logic-level pulses by a timing discriminator (6915, Phillips Scientific). The pulses are counted by a data-acquisition card (PCIe-6259, National Instruments) at a rate of 20 MHz. The counts are sampled and binned by pixels and converted into an image in custom software in Labview (National Instruments) that also controls the EWTL driver.
2P-FCM Miniature Optical System Design The imaging system for the 2P-FCM was designed in Zemax optical design software. Models for the stock lenses and for the EWTL were obtained from the manufacturers (Edmund Optics, Thorlabs, and Varioptic). The CIFB (Fujikura Ltd. FIGH-15-600 N) has an outer diameter of 700 μm, an active image diameter of 550 μm, and a length of 1. 0 m. There are ~15,000 cores with core-to-core spacing of 4. 5 μm and core diameter of 3. 2 μm, as previously reported by Chen et al. measured with scanning electron microscopy  [55]. The miniature optics contained in the head-mounted 2P-FCM imaging system are shown in Fig. 7. The fiber-coupling lens is an asphere (FL: 6. 2-mm, diameter: 4. 7-mm, Edmund Optics 83-710) to collimate the light diverging from the fiber bundle. The electrowetting lens is placed in the collimated beam and the light is refocused onto the sample by an objective lens consisting of a plano-convex lens (FL: 7.
3D-Printed Miniature Head-Mount Design The enclosure for the 2P-FCM optics is designed in Solidworks 3D CAD software (Dassault Systemes). The packaging is split into three sections: top, bottom, and baseplate, shown in Fig. 9. The top-section contains the CIFB ferrule, held in place by two set-screws, and the fiber-collimating asphere. The bottom-section contains the objective lenses. The unmounted lenses are held in by friction in precisely sized openings. The top section has two curved tabs that interface with slots in the bottom section, which help to ensure reproducible alignment. The EWTL and the electrode are sandwiched between the bottom section and the top section with an O-ring that ensures good electrical contact. The flat-flex  electrode cable exits the enclosure through a small slot between the sections.
3D Imaging To demonstrate volumetric imaging, a 1 mm thick sample of fixed mouse cortical tissue was imaged with the 2P-FCM. The sample was fixed with 4% paraformaldehyde and has cells that express enhanced green fluorescent protein (eGFP) driven by proteolipid protein promoter. The eGFP labels oligodendrocyte cell bodies in the cortex. Axial scanning was accomplished by sequentially tuning the EWTL across the full focal range in 36 discrete steps, acquiring 36 image planes to form a Z-stack. The images were processed to remove the fiber-core pattern as described in the Methods section. A 3D-volume image was created using ImageJ/Fiji software and is shown in Fig. 15. Over 200 cells were observed in this volume.
GCaMP6s was expressed virally using AAV_CWSL. hSyn. DIO. Synaptophysin-GCaMP6s. P2A. mRuby. Imaging data was processed to remove the fiber bundle artifact using the custom Matlab software and then analyzed by manually selecting ROIs where ΔF/F transients exceeded 6 standard deviations threshold. Behavioral video was analyzed with the DeepLabCut  [90]  software package which provides markerless tracking of body parts. The mouse was allowed to explore the environment for 5 min while recording behavioral video and imaging neural activity with the 2P-FCM. Piriform cortex is the largest recipient of projections from olfactory bulb, and it is expected to show an increase in neural activity when the mouse smells novel odors. DeepLabCut was used to track the snout of the mouse during the image sequence.
Materials Pulse Pre-compensator l 1x Polarization Maintaining Fiber -Thorlabs Custom Fiber Patch Cable: PM780-HP, FC/APC connectors both ends, keyed to slow axis, FT800SM-Blue Tubing, 0. 5 m in length  The following includes detailed notes on how to align the pre-compensation optics before the microscope and align the 2P-FCM for freely moving mouse imaging. Details including Zemax optical design, solidworks files, and software can be found at https://github. com/CUNeurophotonics/2PFCM. One of the challenges is setting up the single-mode fiber and grating pair compressor in front of the microscope. In particular, aligning the beam through the single-mode fiber can be challenging. Make sure to terminate the fiber with an APC (angled physical contact) on both fiber ends. APC termination minimizes back reflection because the fiber face is cut at an angle as opposed to a flat surface which can reflect back along the same optical path and cause the ultrafast laser to stop modelocking.
Many of these tools have been developed into transgenic animal strains  [16, 17]. Early demonstrations of optogenetic manipulations in combination with light-sheet microscopy include optogenetic control over a variety of physiological phenomena, especially in larval zebrafish, from the beating of the heart  [18]  to cellular control of reflexive behaviors  [19]. In this chapter, we describe how optogenetics can be added to a home-built microscope inspired by the open-source project Open-Spim  [20]. As an example, we describe in detail a microscope configuration suitable for cellular or sub-cellular optogenetics in larval zebrafish. The method involves adding two-photon photostimulation shaped by computer-generated holography (2P-CGH). The stimulation module exploits the high numerical aperture (NA) detection objective of the light-sheet microscope to simultaneously excite multifocal points targeted either to sub-cellular regions or to multiple somata.
Analysis of Large-Scale Ca 2+ Data Set Figure  4  shows the analysis of a representative data set measuring spontaneous neural activity in a 5 days past fertilization (dpf) zebrafish larvae expressing nuclear localized GCaMP6s in neurons, as acquired by volumetric calcium imaging on the described Besselbeam light-sheet microscope. Fluorescence was captured in 20 volume sections of the forebrain, spaced 8 μm apart, with an acquisition rate of 0. 67 Hz. Motion correction and the extraction of activity traces from individual neurons were implemented with the open-source calcium image analysis package CaImAn  [60]. Motion artifacts were corrected through piecewise-rigid registration. Active neurons were then detected in the motion-corrected data through constrained non-negative matrix factorization (CNMF).
Using this approach, the estimated image x is recovered as In other words, we assume that s is sparse if its l 1 -norm is small; thus, we find the s with the smallest l 1 -norm that still satisfies the imaging process Θs = y. This optimization can be solved using basis pursuit methods  [66] [67] [68]. A convenient MATLAB toolbox is provided by Candes et al. 1  alongside their original publications  [66, 69]. Several alternative efficient algorithms have been proposed  [70] [71] [72] [73]. Typically, these methods sacrifice some aspect of accuracy to gain a substantial advantage in speed. We examine one of such algorithms in a later section. Let us now consider the choice of the measurement matrix. The CS problem can be solved with high accuracy using Eq. 4 provided that the number of measurements taken exceeds the sparsity, i.
Alternatively, a digital micromirror device (DMD) can be used to directly deflect a binary pattern. DMDs, typically, have faster projection rates in the 10-kHz range but are limited to binary (on-off) light shaping and may exhibit high loss. Nematic liquid crystal SLMs enable greater control over the light field, including grayscale patterns, however, at sub-kHz rates. Despite the high maximum speed of these devices, a practical limit exists in the speed with which the test patterns can be generated and sent to the hardware. This throughput limit is set by the efficiency of the software, which is unlikely to be met by scientific toolsets such as MATLAB or ImageJ. The plane of the DLS forms the first image plane of the system. The image plane is relayed to a diffraction grating that enables TF.
Philip Wijesinghe and Kishan Dholakia The DEEP or CHI operates by projecting a sequence of small Hadamard codes (e. g. , 16 × 16 pixels), repeated and tessellated over a large-pixel-count image. The mapping of the patterns onto the camera pixels is performed by imaging a calibration phantom (a thin fluorescent layer). The calibration set is then used to demodulate patterned recording sequences. In effect, this method realizes a parallel version of single-pixel imaging and is equivalently amenable to CS  [15, 79]. Additionally, this can be performed in a linescanning configuration  [57]. The trade-off exists in choosing the size of the Hadamard code. The length of the code establishes the distance in the camera over which the scattering cross-talk can be eliminated; however, a larger length requires more total measurements.
As shown in Fig. 4 , to ensure that r max is located at the center of the annulus, without changing D o (i. e. , the NA of the Bessel beam), the period of the SLM pattern, S, can be concurrently altered with D i. In summary, the two cases discussed above represent typical examples of long and short Bessel focus and can be used as a starting point for the mask design. If space permits, inserting a beam expander or a beam reducer before the SLM can further vary the axial length of the Bessel focus (see note 3 in Subheading 2. 4). The MATALB code to facilitate this module design can be found in Ref. [15].
Design of an Axicon-Based Bessel Module When using an axicon for Bessel beam generation, one should start with selecting a high-quality axicon (e. g. , 1-APX-2-H254-P, ALTECHNA Inc. ; XFL25-010-U-B, ASPHERICON). Once the axicon is selected, the apex angle α is determined (see Subheading 2. 3. 3. 2); thus, the radius of the ring at the mask plane is only determined by the focal length of L1 (Fig. 2b ). From here, one can simulate the electric field at the mask plane and the 3D PSF (code can be found in Ref. [16] ) to guide the selection of L1, L2, and L3. Example simulation results are presented in Fig. 5. In this simulation, lens L2 is translated along the optical axis. The location where the front focal plane of L2 coincides with the back focal plane of L1 (i. e. , the mask plane) is indicated as D ¼ 0 mm (Fig.
Alignment of Bessel Module (Adapted from Ref. [15]) The Bessel focus module is located between the laser and the two-photon fluorescence microscope. First, obtain locations of the front and back focal planes of lenses L1-L3 (e. g. , from their Zemax models or vendor specifications). Second, place lenses so that the back focal plane of L3 is at the first scanner (or between the two scanners, if they are not conjugated), the front focal plane of L3 is superimposed with the back focal plane of L2, and the front focal plane of L2 is superimposed with the back focal plane of L1 (i. e. , the mask). At last, place the SLM or axicon at the front focal plane of L1 (see note 4 in Subheading 2. 4). It is helpful to have two mirrors with tip/tilt mounts between L3 and the first scanner to co-align Gaussian and Bessel paths.
Control Electronics and Software (3. 1) The electronic control system typically includes analog inputs/outputs to control the Pockels cells, electrically tunable lens, spatial light modulator, and scanners in both beam paths, as well as high-speed digitizers to record the imaging data from the PMT. For home-built microscopes, ScanImage  [79]  is one commonly used control software. (3. 2) The phase pattern on the spatial light modulator can be calculated through a superposition algorithm or a Gerchberg-Saxton algorithm as detailed in Subheading 2. 2. 2 so a userdefined excitation pattern can be projected to the sample. An open source control software can be found in  [80]. Coordinate Calibration (4. 1) It is critical to register the photostimulation beam's target coordinate with the imaging laser's image coordinate.
Introduction A fundamental question in systems neuroscience is how sensory stimuli are encoded by the activity of neurons. This question has been the subject of intense scrutiny across sensory systems, leading to fundamental discoveries describing the nature of neural representations across many levels of information processing and abstraction  [1, 2]. The rapid pace of this progress has been matched with an improvement in tools used for recording neuronal activity at a large scale (e. g. , two-photon (2P) calcium imaging), leading to the now-routine generation of datasets comprised of hundreds to thousands of simultaneously recorded neurons in awake behaving animals  [3, 4]. A consistent observation made from recordings taken across sensory systems is that neural responses to sensory stimuli include changes in both the rate and timing of action potentials within populations of neurons, as well as modulation with respect to ongoing, internally generated rhythms (sniffing, whisking, locomotion, etc.
Online Motion Correction For behavioral experiments that require targeting the same neurons for photostimulation across days, it is useful to employ an online motion correction method (see Note 2). In the experiments described by Gill, Lerman et al. , 2020, the FOV was first aligned to a reference image manually, then the position was fine-tuned automatically using a custom-designed closed-loop algorithm, implemented as a module within Scan-Image software  [25, 55]  (Vidrio Technologies). This algorithm attempted to minimize the difference between the reference image and the FOV by iteratively moving the microscope stage (Sutter 285) to reduce the residual displacement computed using a rigid motion correction package (NoRMCorre, Flatiron Institute  [56] ). The optimization typically converged within 10-15 s once the residual displacement vector was reduced to <0.
Acknowledgments We thank  Christianne Grimm  for critical reading of the chapter. We acknowledge financing from the ' Agence Nationale de la Recherche ' ( ANR ) project  ANR-17-CE16-0021 SLALLOM  and  ANR-19-CE16-0026   HOLOPTOGEN , the  IHU FOReSIGHT grant  (Grant  P-ALLOP3-IHU-000 ), the  National Institutes of Health  (Grant  NIH   1UF1NS107574 -01 ), the  ERC Advanced Grant HOLOVIS  ( ERC-2019-AdG ; Award no. 885090 ), and the  FPSU-Chaire   AXA-C18/1276.
Acknowledgments We would like to thank  Ethan Hughes  for assistance with in vivo animal imaging work,  Juliet Gopinath ,  Victor Bright , and  Robert Cormack  for helpful discussions. We acknowledge  Nicole Arevalo  for assistance with animal care, and  Elizabeth Gould  and  Wendy Macklin  for providing brain tissue from Plp-EGFP mice. Funding for this work was provided by the  National Science Foundation   DBI-1353757 ,  CBET-1631912 , and  IIP-1602128  and the  National Institutes of Health BRAIN Initiative   NS099577.
Acknowledgements This work was supported by funding from  TU Delft  and The  Netherlands Organisation for Health Research and Development (ZonMw). Acknowledgments  CONACYT  ( INFR-2018-294756  [LCR],  CF6653  [LCR]),  Burroughs Wellcome Fund  ( Career Award  at the Scientific Interface # 1015761 ) [WY],  National Eye Institute  ( R01EY011787  [RY]),  National Institute of Mental Health  ( R01MH115900 ) [RY],  National Institute of Neurological Disease and Stroke  ( R01NS110422  [RY],  R34NS116740 [RY ],  R01NS118289  [WY]), the  US Army Research Office  ( W911NF-12-1-0594 MURI ) [RY], and the  National Science Foundation  ( CAREER 1847141 [WY] ,  CRCNS 1822550 [RY ] ). Rafael Yuste  is listed as an inventor of the patent: "Devices, apparatus and method for providing photostimulation and imaging of structures" (United States Patent 9846313).
Acknowledgments The authors would like to acknowledge the support of the  Department of Biomedical Sciences  ( SID2018 ,  Dal Maschio ) and the  Padua Neuroscience Center (ReTurnPD, Dal Maschio)  at the  University of Padua , the support of  EC Research Programs  ( VISGEN ,  Dal Maschio ;  NEURAM ,  Veres and Dal Maschio). The authors would like to thank the colleagues providing help, comments, and suggestions in drafting the content.
Using alcohol, we cleaned the skin on the right section of the OO, above the belly of the APB muscle, as well as the skin around the site of the ground electrode placed on the right temple. We then attached electrodes on these sites. We visually checked the recorded EMG signals. If the signals appeared to be noisy, we re-cleaned the skin and re-attached the electrodes. After data collection, we computed the area-under-the-curve (AUC) of the rectified EMG signal. The covered area was taken as an index of the MEP size  (Maegherman et al. , 2020). In each trial, the MEP AUC was computed in a window spanning from 8 to 35ms after the TMS pulse. As in Maegherman et al. (  2020 ), a pre-pulse section of 27ms (i. e. , from 35 to 8ms before the pulse) was extracted to allow a posteriori checks of equivalent baseline contraction across conditions.
Analysis plan All analyses were conducted in R (R Core Team, 2021). We fitted several Bayesian multilevel models using the brms package  (Bürkner, 2017)  with two categorical predictors (recoded using sum contrasts as -0. 5 vs. 0. 5): the inner speech mode (i. e. , inner speaking vs. inner hearing), the syllable (i. e. , /bu/ vs. /gi/), and the AUC of the lip MEP as a continuous dependent variable (for an introduction to Bayesian multilevel modelling, see  Nalborczyk et al. , 2019). For each effect of interest, we report the mean of the posterior distribution along with its 95% credible interval, as well as the mean of the posterior distribution of the standardised mean difference (Cohen's d) with its 95% credible interval, when appropriate (i. e. , when comparing two conditions). We also report the Bayes factor (BF), which quantifies the relative support (evidence) for either the null or the alternative hypothesis (e.
Confirmatory (preregistered) analyses Before moving to the statistical results, we represent the distribution of standardised MEP amplitudes across conditions in Figure  2. This figure shows that the MEP amplitude recorded in the two inner hearing conditions was at similar levels as those recorded in the control condition. Conversely, MEPs were larger in the two inner speaking conditions. Moreover, MEPs were larger for the /bu/ syllable than for the /gi/ syllable. To estimate these effects while accounting for the skewness of the collected data (for more details, see the online supplementary materials), we fitted a multilevel Skew-Normal model. The Skew-Normal distribution is a generalisation of the Normal distribution with three parameters ξ (xi), ω (omega), and α (alpha) for location, scale, and skewness (shape), respectively.