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156 results for “autonomic nervous system”
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</p>
SPARC Connectivity Knowledge base of the Autonomic Nervous System
<p>The SPARC Knowledge base of the Autonomic Nervous System (SCKAN) is an integrated graph database composed of three parts: the SPARC dataset metadata graph, ApiNATOMY and NPO models of connectivity, and the larger ontology used by SPARC which is a combination of the NIF-Ontology and community ontologies.</p> <p>The fastest way to get querying is to follow the instructions in the <a href="https://github.com/SciCrunch/sparc-curation/blob/master/docs/sckan/README.org#getting-started">SCKAN readme file</a>.</p> <p>For background information please see <a href="https://scicrunch.org/sawg/about/SCKAN">https://scicrunch.org/sawg/about/SCKAN</a> and <a href="https://sparc.science/resources/6eg3VpJbwQR4B84CjrvmyD">the SPARC portal resource page about SCKAN.</a></p> <p>This release contains the raw and compiled data for SCKAN. The release-*.zip contains raw data inputs along with the Blazegraph journal file, the sparc-sckan-graph-*.zip contains the SciGraph database, and sckan-data-*.tar.gz is a Docker image that contains the Blazegraph journal file and the SciGraph database along with the configuration files for running each of the servers. The image is intended to be used as a data volume with another Docker container that runs the SciGraph and Blazegraph server software.</p> <p>The Docker image containing this data is available live and is likely easier to use than the archived image included in this release. See the <a href="https://github.com/SciCrunch/sparc-curation/blob/master/docs/sckan/README.org#getting-started">SCKAN readme file</a> for the most up-to-date instructions.</p> <p>We would like to thank the members of the SAWG (SPARC Anatomy Working Group, RRID:SCR_018709) for their work on the various connectivity models included in this release.</p> <p>This work was funded by the NIH Common Fund under 3OT2OD030541-01S1.</p>
Hypoglycemia and Autonomic Nervous System Function
ClinicalTrials.gov study NCT01816893. IPD Sharing: NO. Countries: 1. Publications: 4.
Relationship between the autonomic nervous system and cerebral autoregulation during controlled breathing
<h2>Version 3 of the database</h2> <p>The updated version of the database includes autonomic nervous system data (HRV metrics) estimated using the ECG signal.</p> <p>The previous versions of the database included data estimated from non-invasive, photoplethysmography-based ABP signals.</p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (database 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (database1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes <strong>49 healthy volunteer</strong>s (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes <strong>12 healthy volunteers</strong> (8 females, 4 males, median age: 25 years, range: 20-26 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in dataset 1 and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>A three-lead surface electrocardiogram (ECG) was used to record the heart's electrical activity</li> <li>CBv was measured in the MCA using transcranial Doppler ultrasonography (Doppler BoxX, DWL, Compumedics Germany GmbH, Singen, Germany in database 1; EMS-9PB, Delica, Shenzhen, China in database 2).</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li><strong>Protocol:</strong> After a resting epoch lasting at least 5 minutes (baseline, referred to in the aliases as "B"), a controlled breathing session was initiated. Five-minute recordings were collected at each of the following respiratory rates: 6, 10, or 15 breaths per minute (corresponding to 0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome (referred to in aliases as "6", "10", "15")</li> </ul> <h1>Data description</h1> <ul> <li>ID</li> <li>Type of database (database 1/database 2)</li> <li>Type of device used for ABP measurement (ECG was measured in the same way in both databases, using a built-in module, attached to photoplethysmography)</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Physiological parameters measured during controlled breathing, including:</li> <ul> <li>end-tidal carbon dioxide: ETCO2</li> <li>Respiratory rate: RR</li> <li>Carbon dioxide concentration: CO2</li> <li>Heart rate: HR</li> <li>Arterial blood pressure: ABP</li> <li>Cerebral blood flow velocity: CBv</li> </ul> <li>Autonomic Nervous System metrics, including:</li> <ul> <li>joint symbolic dynamics, estimated as the relative frequency of baroreflex-like word types (JSD<sub>sym</sub>) and the relative frequency of patterns that are opposed to baroreflex behaviour (JSD<sub>diam</sub>) (Baumert et al., 2015)</li> <li>entropy metrics: MSEn, multiscale entropy; ApEn, approximate entropy; SampEn, sample entropy, FuzzyEn, fuzzy entropy</li> <li>frequency-domain metrics: LFn, HFn, normalized power spectral density of the R-R interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz), obtained by dividing the respective power spectra by a total power (TP, 0.04–0.40 Hz); LF/HF; low-to-high frequency ratio; </li> <li>Baroreflex sensitivity estimated using cross-correlation method (xBRS) </li> <li>time-domain metrics: SDNN, standard deviation of the R-R intervals; RMSSD, square root of the mean of the squared successive differences between adjacent R-R intervals; meanNN, mean intervals between normal R-peaks, pNN20 and pNN50, proportion of R-R intervals greater than 20 ms or 50 ms, respectively;</li> </ul> <li>Cerebral autoregulation metrics, including TFA metrics were provided for two frequency ranges: VLF, very low frequency (0.02–0.07 Hz), BF, breathing frequency (determined for each of the participants for spontaneous breathing and 0.10; 0.17; 0.25 Hz±0.02 Hz for controlled breathing); </li> <ul> <li>coherence,</li> <li>phase shift (PS)</li> <li>gain</li> </ul> </ul> <p> </p> <p> </p> <p> </p>
Linear vs. non-linear metrics of Autonomic Nervous System: study on healthy volunteers during controlled breathing
<h1>Please cite this article as reference article:</h1> <p>Uryga A, Najda M, Berent I, Mataczyński C, Urbański P, Kasprowicz M, Buchner T. The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability. Physiol Meas. 2024 Sep 16;45(9). doi: 10.1088/1361-6579/ad7778. </p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (dataset 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (dataset 1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes 49 healthy volunteers (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes 21 healthy volunteers (14 females, 7 males, median age: 22 years, range: 18-31 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in all subjects in dataset 1; CNAP, CNSystems Medizintechnik GmbH, Graz, Austria and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li>Protocol: after a resting epoch lasted at least 5 minutes, a controlled breathing session was initiated with 5-minute recordings at each of the respiratory rate: 6, 10 or 15 breaths/min (0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome.</li> </ul> <h1>Data description</h1> <ul> <li>Type of database (database 1/database 2)</li> <li>Type of device used to ABP measurement</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Autonomic Nervous System parameters including:</li> </ul> <p>- <strong>Phase-Rectified Signal Averaging</strong> - a non-linear approach used to quantify the acceleration (AC) and deceleration (DC) capacity of the heart; more details could be found here: <em>Campana L M, Owens R L, Clifford G D, Pittman S D and Malhotra A 2010 Phase-rectified signal averaging as a sensitive index of autonomic changes with aging J Appl Physiol 108 1668–73</em></p> <p>- <strong>Entropy</strong>: multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn) functions calculated for R-R intervals, which were implemented in NeuroKit2</p> <ul> <li> <strong>Heart rate variability (HRV) metrics</strong>: In the frequency domain, the Lomb–Scargle periodogram was used to determine the power spectral density of the interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz). Additionally, the total power of the HRV signal (TP, 0.04–0.40 Hz) and the ratio between low and high-frequency components (LF/HF) were calculated. In the time domain, the following metrics were determined: the standard deviation of the R-R intervals (SDNN) and the square root of the mean of the squared successive differences between adjacent R-R intervals (RMSSD), mean of the R-R intervals (meanNN), and the proportion of R-R intervals greater than 20 ms or 50 ms, out of the total number of R-R intervals (pNN20 and pNN50, respectively); appropriate functions were implemented in NeuroKit2</li> </ul> <p> </p> <p>Update ------version 2</p> <p>After the revision process, SDNNref was added, defined according to formula presented in paper of Monfredi et al. (Monfredi O, Lyashkov AE, Johnsen AB, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014 Dec;64(6):1334-43)</p>
Effect of a Sequence of Specific Manual Therapy Techniques Targeting the Autonomic Nervous System in Healthy Adults
ClinicalTrials.gov study NCT06477822. IPD Sharing: NO. Countries: 1. Publications: 18.
Effects of Salmeterol on Autonomic Nervous System
ClinicalTrials.gov study NCT01536587. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Anxiety-mediated Impairments in Large Elastic Artery Function and the Autonomic Nervous System
ClinicalTrials.gov study NCT03109795. IPD Sharing: NO. Countries: 1. Publications: 64.
Hypoglycemia and Autonomic Nervous System Function- B2
ClinicalTrials.gov study NCT03422471. IPD Sharing: YES. Countries: 1. Publications: 1.
SANICS II Trial: Stimulation of the Autonomic Nervous System in Colorectal Surgery by Perioperative Nutrition
ClinicalTrials.gov study NCT02175979. IPD Sharing: Not stated. Countries: 2. Publications: 11.
Nitric Oxide and the Autonomic Nervous System
ClinicalTrials.gov study NCT00178919. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effect of PADMA 28 on Endothelial Function, Autonomic Nervous System and Biomarkers in Patients With Coronary Artery Disease
ClinicalTrials.gov study NCT01581034. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Vagus Nerve Stimulation for Autonomic Nervous System Activity
ClinicalTrials.gov study NCT05906940. IPD Sharing: NO. Countries: 1. Publications: 3.
Pranayama Practice on the Autonomic Nervous System
ClinicalTrials.gov study NCT03280589. IPD Sharing: NO. Countries: 1. Publications: 1.
Autonomic Nervous System Dysfunction in Patients With End-stage Kidney Disease
ClinicalTrials.gov study NCT05278702. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Stroke - Sleep Disorders, Dysfunction of the Autonomic Nervous System and Depression
ClinicalTrials.gov study NCT02111408. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Autonomic Nervous System Profile in Hereditary Angioedema
ClinicalTrials.gov study NCT06408805. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Investigation of the Rewarming og the Fingers After Cooling and the Autonomic Nervous System in Raynaud's Phenomenon
ClinicalTrials.gov study NCT03094910. IPD Sharing: NO. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.