DOI: 10.5061/dryad.ffbg79d4h
title: Data from: Distinct catecholaminergic pathways projecting to hippocampal CA1 transmit contrasting signals during navigation in familiar and novel environments

description: Neuromodulatory inputs to the hippocampus play pivotal roles in modulating
 synaptic plasticity, shaping neuronal activity, and influencing learning
 and memory. Recently it has been shown that the main sources of
 catecholamines to the hippocampus, ventral tegmental area (VTA) and locus
 coeruleus (LC), may have overlapping release of neurotransmitters and
 effects on the hippocampus. Therefore, to dissect the impacts of both VTA
 and LC circuits on hippocampal function, a thorough examination of how
 these pathways might differentially operate during behavior and learning
 is necessary. We therefore utilized 2-photon microscopy to functionally
 image the activity of VTA and LC axons within the CA1 region of the dorsal
 hippocampus in head-fixed male mice navigating linear paths within virtual
 reality (VR) environments. We found that within familiar environments some
 VTA axons and the vast majority of LC axons showed a correlation with the
 animals' running speed. However, as mice approached previously
 learned rewarded locations, a large majority of VTA axons exhibited a
 gradual ramping-up of activity, peaking at the reward location. In
 contrast, LC axons displayed a pre-movement signal predictive of the
 animal's transition from immobility to movement. Interestingly, a
 marked divergence emerged following a switch from the familiar to novel VR
 environments. Many LC axons showed large increases in activity that
 remained elevated for over a minute, while the previously observed VTA
 axon ramping-to-reward dynamics disappeared during the same period. In
 conclusion, these findings highlight distinct roles of VTA and LC
 catecholaminergic inputs in the dorsal CA1 hippocampal region. These
 inputs encode unique information, with reward information in VTA inputs
 and novelty and kinematic information in LC inputs, likely contributing to
 differential modulation of hippocampal activity during behavior and
 learning.
description: 2-photon calcium imaging of axons in the hippocampus of mice
 navigating through virtual environments. Images were motion corrected,
 ROIs were hand drawn around axons, and fluorescent traces were extracted.
 Fluorescent and behavioral traces were processed as described in the
 methods section. 
description: # Distinct catecholaminergic pathways projecting to hippocampal CA1
 transmit contrasting signals during navigation in familiar and novel
 environments
 [https://doi.org/10.5061/dryad.ffbg79d4h](https://doi.org/10.5061/dryad.ffbg79d4h) ## Description of the data and file structure Fluorescent and behaivoral data was collected as described in the methods section of the associated paper. The .mat file provided here contains all Fluorescent traces and behavioral data used in thepaper. All associated matlab scripts used to process and analyze this data can be found at [https://github.com/chadheer/LC_VTA_paper](https://github.com/chadheer/LC_VTA_paper). ### Files and variables #### File: LC\_VTA\_summary.mat **Description:** All fluorescent data was collected using a neurolabware 2-photon microscope using scanbox. Images were motion corrected using suite2p and fluorescence was extracted using hand drawn ROIs in ImageJ. ##### Variables: VTA_analysis_out: Experimental data sets collected from DAT-Cre mice injected in the VTA with an AAV for expression of GCaMP6s or GCaMP7b in Dopaminergic (DA) neurons. VTA DA axons were recorded in the hippocampus of head fixed mice as they navigated virtual environments.  LC_analysis_out: Experimental data sets collected from NET-Cre mice injected in the LC with an AAV for expression of GCaMP6s in Dopaminergic (DA) neurons. VTA DA axons were recorded in the hippocampus of head fixed mice as they navigated virtual environments.  LC_analyis_out and VTA_analysis_out have the following organization: analysis_out.MouseID.measure where MouseID corresponds to the mouse and session number, and measure are the different variables collected and analyzed Measures include: * F,FC, F2 = FrameN x roi, extracted raw (F) and delta F/F (FC)  traces for axon ROIs.  * blebs = contains F, Fc, and F2 for autofluorescent bleb ROIs.  * behavior = FrameN, contains all of the behavioral measures recorded during experiments; ybinned = position on track, lick = binary licking variable, reward = binary variable for when reward was delivered, velocity = speed of animal on treadmil, acceleration = acceleration of animal on treadmil, t = time (s) since start of recording, fr = frame rate of recording, lap = number of traversals completed, moving = binary variable of when the mouse is moving or stationary, good_beh = indexes of frames when mouse was moving (good_beh) or stationary (bad_beh). The above three variables were used to generate the following: * fam = contains fluorescent data for just the frames recorded in the familiar VR environment * nov = contains fluorescent data for just the frames recorded in the novel VR environment * tonic_rois = indexes of ROIs identified as tonically active and excluded from analysis as defined in the paper. * novel_aligned_F,Fc, or F2 = 400000 x ROIs: data aligned to the transition to the novel environment where idx 20001 is the first frame in the novel environent.  * novel_aligned_F_good/badbeh = same as above, but only including the frames where the mice are moving or not moving respectively * novel_aligned_F_good/badbeh_idxed = same as above but maintaining the original indexes of each frame * novel_aligned_pupil = same as above but using pupil diameter instead of fluorescence * novel_aligned_velocity = same as above but using velocity instead of fluorescence * dark_aligned_F,Fc, or F2 =  400000 x ROIs: data aligned to the transition from darkness to the familiar environment where idx 20001 is the first frame in the familiar environment * fam*_*lap_mean= lap x ROIs : mean F for each ROI for each lap in the familiar environment * fam_lap_good/badbeh: mean F for each ROI for each lap in the familiar environment using just the frames where the mice are moving or not moving respectively * fam*_*lap_mean2 = lap x ROIs : mean F2 for each ROI for each lap in the familiar environment * fam_lap_v = lap x ROIs: mean velocity for each lap in the familiar environment * fam*lap* * nov_lap_* = same variables as for above but for laps in the novel environment. * freezing ratio = ratio of time spent immobile/ total time * binnedA = Frames x bin x ROI: the F for all frames in 60 acceleration bins  * binnedV = Frames x bin x ROI: the F for all frames in 60 velocity bins  * lap_binnedV = lap x bin x ROi: the mean F for across 60 velocity bins for each lap * lap_freezing = the ratio of frames immobile/ total frames for each lap * initiation_F = initiation x Frame x ROI: F aligned to each motion initiation period where Frame 60 is the start of motion * initiation_V = initiation x Frame: velocity aligned to each motion initiation period where Frame 60 is the start of motion * initiation_id = the starting frame, time, and index of each motion initiation period. * terminationF = termination x Frame x ROI: F aligned to each motion termination period where Frame 60 is the end of motion * terminationV = termination x Frame: velocity aligned to each motion termination period where Frame 60 is the end of motion * pos_binned_F = lap x bin x roi : the mean F for 60 positional bins across each lap for each roi * pos_binned_F_a = lap x bin x ROI: the mean F for 5cm positional bins across each lap for each roi * time_binned_F = lap x bin x ROI:  the mean F for 60 time bins across each lap for each roi * lap_activity = lap x frame x roi: the F trace for each lap for each roi * fr_from_reward = lap x frame x roi: the number of frames from reward delivery for each frame for each lap * pos_binnedV = lap x bin: the mean velocity for 60 positional bins across each lap * pos_bin_V_a = lap x bin: the mean velocity for 5cm positional bins across each lap * time_binned_V = lap x bin: the mean velocity for 60 time bins across each lap * lap_velocity = the velocity trace for all frames in a lap * velocity_from_reward = the number of frames from reward delivery of each frame for every lap ## Code/software All code was written in matlab. Code is available at [https://github.com/chadheer/LC_VTA_paper](https://github.com/chadheer/LC_VTA_paper).