DOI: 10.5061/dryad.tb2rbp0bq
title: Type-I nNOS neurons orchestrate cortical neural activity and vasomotion

description: It is unknown how the brain orchestrates the coordination of global neural
 and vascular dynamics. We sought to uncover the role of a sparse but
 unusual population of genetically-distinct interneurons known as type-I
 nNOS neurons, using a novel pharmacological strategy to unilaterally
 ablate these neurons from the somatosensory cortex of mice.
 Region-specific ablation produced changes in both neural activity and
 vascular dynamics, decreased power in the delta-band of the local field
 potential, reduced sustained vascular responses to prolonged sensory
 stimulation, and abolished the post-stimulus undershoot in cerebral blood
 volume. Coherence between the left and right somatosensory cortex
 gamma-band power envelope and blood volume at ultra-low frequencies was
 decreased, suggesting that type-1 nNOS neurons integrate long-range
 coordination of brain signals. Lastly, we observed decreases in the
 amplitude of resting-state blood volume oscillations and decreased
 vasomotion following the ablation of type-I nNOS neurons. This
 demonstrates that a small population of nNOS-positive neurons is
 indispensable for regulating both neural and vascular dynamics in the
 whole brain and implicates disruption of these neurons in diseases ranging
 from neurodegeneration to sleep disturbances.
description: # Type-I nNOS neurons orchestrate cortical neural activity and vasomotion
 This document outlines the steps necessary to generate the figures for the
 manuscript **Type-I nNOS neurons orchestrate cortical neural activity and
 vasomotion** by Kevin L Turner, Dakota F Brockway, Md Shakhawat Hossain,
 Keith R Griffith, Denver I Greenawalt, Qingguang Zhang, Kyle W Gheres,
 Nicole A Crowley, and Patrick J Drew. --- ## Generating the figures ---
 The code in this repository generates all main figures, supplemental
 information, and statistics. It also contains the analysis code for all
 pre-processing steps. Begin by downloading the code repository by clicking
 the green **Code** button at the top of the page and selecting **Download
 ZIP**. * Code repository location:
 [https://github.com/KL-Turner/Turner-eLife2025](https://github.com/KL-Turner/Turner-eLife2025) ![](https://raw.githubusercontent.com/KL-Turner/Turner-eLife2025/refs/heads/master/Utility%20functions/Images/Image1.png) Next, the data (~7 GB) can be downloaded from the following location: [https://doi.org/10.5061/dryad.tb2rbp0bq](https://doi.org/10.5061/dryad.tb2rbp0bq) ![](https://raw.githubusercontent.com/KL-Turner/Turner-eLife2025/refs/heads/master/Utility%20functions/Images/Image2.png) After downloading both the code and data, unzip both folders by right-clicking and selecting *Extract all* or double-clicking each folder. The unzipped folders should look something like this. ![](https://raw.githubusercontent.com/KL-Turner/Turner-eLife2025/refs/heads/master/Utility%20functions/Images/Image3.png) The Dryad link contains several pre-analyzed structures that can be used to immediately generate the figures without re-analyzing any data. To generate the figures, begin by moving all of the files from the unzipped Dryad folder (the **Results_** folders) into the code repository. It should look something like this: ![](https://raw.githubusercontent.com/KL-Turner/Turner-eLife2025/refs/heads/master/Utility%20functions/Images/Image4.png) ## Results\_Turner (.mat - MATLAB2024b; .xlsx - MS Excel) The repository includes pre-analyzed structures for each analysis performed in the paper; each of these has the prefix "Results_" followed by the analysis performed. \_Ephys, \_GCaMP, \_Pulse, \_2P refers to animals in different data sets depending on experimental configuration.Descriptions of each analysis can be found in the manuscript, and units are consistent with the figures used by each analysis. If short, a description of the data and units is as follows: * Results_ArousalStateProb_Ephys.mat - probability (%) of each animal being in a given classification state; Fig. S2 * Results_ArousalStateProb_GCaMP.mat - probability (%) of each animal being in a given classification state; Fig. S2 * Results_Baseline_2P.mat - baseline diameter (μm) of each arteriole; Fig. 7 * Results_BaselineShift_2P.mat - diameter shift (μm) during isoflurane of each arteriole; Fig. 7 * Results_BilatCoher_Ephys.mat - coherence (unitless) between bilateral gamma power (ΔP/P) or HbT (μM); Fig. 6 * Results_BilatCoher_GCaMP.mat - coherence (unitless) between bilateral GCaMP (ΔF/F) or hemodynamics HbT (μM); Fig. 6 * Results_CrossCorr_Ephys.mat - cross correlation (unitless) between hemodynamic HbT (μM) and/or gamma power (ΔP/P); Fig. 5 * Results_CrossCorr_GCaMP.mat - cross correlation (unitless) between HbT (μM) and/or GCaMP (ΔF/F); Fig. 5 * Results_Diameter_2P.mat - arteriole diameter (ΔD/D) during different arousal states; Fig. 7 * Results_Evoked_2P.mat - arteriole diameter (ΔD/D) following vibrissae stimulation; Fig. 3 * Results_Evoked_Ephys.mat - HbT (μM)and gamma power (ΔP/P) following vibrissae stimulation; Fig. 3 * Results_Evoked_GCaMP.mat - HbT (μM) and GCaMP (ΔF/F) following vibrissae stimulation; Fig. 3 * Results_Evoked_Pulse.mat - HbT (μM) following vibrissae stimulation; Fig. 3 * Results_HRF_Ephys.mat - hemodynamic response functions (unitless) between HbT (μM) and gamma power (ΔP/P), Fig. S5 * Results_IntSig_Ephys.mat - intrinsic optical imaging of HbT (μM) data; Fig. 7, Fig. S7 * Results_IntSig_GCaMP.mat - intrinsic optical imaging of HbT (μM) data and GCaMP (ΔF/F); Fig. 7, Fig. S7 * Results_IntSig_Pulse.mat - intrinsic optical imaging of HbT (μM) data; Fig. S6 * Results_ModelAccuracy_Ephys.mat - sleep scoring model accuracy (Awaken, NREM, REM classes) as percentages; Fig. S3 * Results_ModelAccuracy_GCaMP.mat - sleep scoring model accuracy (Awaken, NREM, REM classes) as percentages; Fig. S3 * Results_NeuralHemoCoher_Ephys.mat - coherence (unitless) single hemisphere gamma power (ΔP/P) and HbT (μM); Fig. 5 * Results_NeuralHemoCoher_GCaMP.mat - coherence (unitless) single hemisphere GCaMP (dF/F) and hemodynamics HbT (μM); Fig. 5 * Results_PearsonCorr_Ephys.mat - Pearson's correlation coefficient (unitless) between bilateral HbT (μM) and gamma power (ΔP/P); Fig. S6 * Results_PearsonCorr_GCaMP.mat - Pearson's correlation coefficient (unitless) between bilateral HbT (μM) and GCaMP (ΔF/F); Fig. S6 * Results_PowerSpec_2P.mat - power spectrum (unitless) of arteriole diameters (ΔD/D); Fig. S4 * Results_PowerSpec_Ephys.mat - power spectrum (unitless) of HbT(μM) and gamma power (ΔP/P); Fig. S4 * Results_PowerSpec_GCaMP.mat - power spectrum (unitless) of HbT (μM) and GCaMP (ΔF/F); Fig. S4 * Results_PowerSpec_LFP.mat - power spectrum (unitless) of local field potentials (power, a.u.); Fig. 4 * Results_PupilArea_Ephys.mat - change in pupil size (mm, mm^2) during different arousal states; Fig. S2 * Results_PupilBlinkInterval_Ephys.mat - time between blinking events (seconds); Fig. S2 * Results_PupilEvoked_Ephys.mat - change in pupil size (mm, mm^2) following vibrissae stimulation; Fig. S2 * Results_Transitions_Ephys.mat - change in HbT (μM) and neural activity (power, a.u.) of arousal transitions; Fig. S7 * Results_Transitions_GCaMP.mat - change in HbT (μM) and GCaMP (ΔF/F) of arousal transitions; Fig. S7 * Results_WhiskBehav_Ephys.mat - duration and temporal characteristics of individual whisking events; Fig.S2 * Results_WhiskBehav_GCaMP.mat - duration and temporal characteristics of individual whisking events; Fig.S2 Data sets used for representative examples * T165_210222_12_45_38_ProcData.mat - hemodynamics, behavior, vibrissae tracking, vibrissae stimulation; (Fig. 2) * T165_210222_12_45_38_SpecDataA.mat - Spectrograms of neural and hippocampal power; (Fig. 2) * T165_210223_12_37_03_ProcData.mat - hemodynamics, behavior, vibrissae tracking, vibrissae stimulation; (Fig. 2) * T165_210223_12_37_03_SpecDataA.mat - Spectrograms of neural and hippocampal power; (Fig. 2) * T192_RH_210518_11_41_23_005_A08_MergedData.mat - hemodynamics, behavior, vibrissae tracking, vibrissae stimulation; (Fig. 2) * T192_RH_210518_11_41_23_005_A08_SpecData.mat - Spectrograms of neural and hippocampal power; (Fig. 2) * T233_220206_13_07_08_ProcData.mat - hemodynamics, behavior, vibrissae tracking, vibrissae stimulation; (Fig. 2) * T233_220206_13_07_08_SpecDataA.mat - Spectrograms of neural and hippocampal power; (Fig. 2) * T233_220206_14_10_44_ProcData.mat - hemodynamics, behavior, vibrissae tracking, vibrissae stimulation ; (Fig. 2) * T233_220206_14_10_44_SpecDataA.mat - Spectrograms of neural and hippocampal power; (Fig. 2) Immunohistochemistry and Histological characterization data * DAPI_Counts.xlsx - DAPI IHC cell counts; Fig. 1, Fig. S1 * DAPI_Fluorescence.xlsx - DAPI IHC (fluorescence, a.u.); Fig. 1, Fig. S1 * Diaphorase_Counts.xlsx - NADPH Diaphorase cell counts; Fig. 1, Fig. S1 * GFAP_Fluorescence.xlsx - GFAP IHC (fluorescence, a.u.); Fig. 1, Fig. S1 * IBA1_Counts.xlsx - IBA1 IHC cell counts; Fig. 1, Fig. S1 * MasterMouseList.xlsx - Notes; Fig. 1, Fig. S1 * NeuN_Fluorescence.xlsx - NeuN IHC 9fluorescence, a.u.); Fig. 1, Fig. S1 * nNOS_Counts.xlsx - nNOS IHC cell counts; Fig. 1, Fig. S1 ## Results\_Zhang (.mat - MATLAB 2024b) 14 Folders corresponding to experimental IDs: T192, T200, T205, T206, T208, T209, T211, T212, T213, T216, T217, T218, T219, T225. Each folder contains the analyzed data from three runs (all .mat structures with name, condition, file date) for spectroscopy experiments HbT, HbO, HbR (μM) during locomotion; Fig. 3 Text. ## Results Hossain (.mat - MATLAB 2024b; .png - ImageJ/FIJI; .xlsx - MS Excel;) * 55 data sets (AnimalID_Run1.mat or AnimalID_Run1.png) of open-field, exploratory * behavior. * ExperimentalRecords_Keys.xlsx - summary of animal information, Notes * OFTTable.mat - Analysis of open field exploratory behavior, position/location analysis of animal within ROI; Fig S2. From here, open MATLAB and navigate to the code folder. Open the function **MainScript_eLife2025.m**. Click the green **Run** button, and the figures will then take a few minutes to generate. **Software/System Requirements:** The Code was written and tested with MATLAB 2024b. Running **MainScript_eLife2025.m** took ~5 minutes to run on a 2021 MacBook Pro with M1 Pro chipset and 16 GB RAM. The Mainscript will automatically save all of the MATLAB figures and statistical read-outs, which will be saved in a folder called MATLAB Figs & Stats. We are happy to provide any raw data and unprocessed histological images upon request. LabVIEW code used to acquire the data can be found at: [https://github.com/DrewLab/LabVIEW-DAQ](https://github.com/DrewLab/LabVIEW-DAQ) --- ## Acknowledgements --- * Chronux Toolbox: [http://chronux.org/](http://chronux.org/) * ConvertTDMS.m by Brad Humphreys: [https://github.com/humphreysb/ConvertTDMS](https://github.com/humphreysb/ConvertTDMS) * colors.m by John Kitchin: [http://matlab.cheme.cmu.edu/cmu-matlab-package.html](http://matlab.cheme.cmu.edu/cmu-matlab-package.html) * BrainMesh.m by Yaoyao Hao: [https://github.com/Yaoyao-Hao/BrainMesh](https://github.com/Yaoyao-Hao/BrainMesh) * multiWaitbar.m by Ben Tordoff: [https://www.mathworks.com/matlabcentral/fileexchange/26589-multiwaitbar-label-varargin](https://www.mathworks.com/matlabcentral/fileexchange/26589-multiwaitbar-label-varargin) #### Contact Patrick Drew or Kevin Turner ([kevin\_turner@brown.edu](mailto:kevin_turner@brown.edu)) with any questions/comments.