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1,155 results for “nervous system”
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>
Lipoic acid protects the central nervous system by immunomodulation and antioxidation
<p>Lipoic acid protects the central nervous system by immunomodulation and antioxidation. In the periphery, LA prevents inflammatory cells from crossing the BBB and protects brain endothelial cells. In the CNS, LA inhibits the activity of T cells/microglia, decreases the expression of TNF-α and IFN-γ, and neutralizes ROS and NO.</p>
Paleoneurology of stem palaeognaths clarifies the plesiomorphic condition of the crown bird central nervous system
<p>This dataset contains additional brain and endosseous labyrinth endocasts generated by Widrig et al. (2024) Paleoneurology of stem palaeognaths clarifies the plesiomorphic condition of the crown bird central nervous system.</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>
data set related to article A Nervous System-Specific Model of Creatine Transporter Deficiency Recapitulates the Cognitive Endophenotype of the Disease: a Longitudinal Study
<p>This record contains raw data related to article A Nervous System-Specific Model of Creatine Transporter Deficiency Recapitulates the Cognitive Endophenotype of the Disease: a Longitudinal Study</p>
Figure 1 in Investigations of the nervous system biomarkers in the brain and muscle of freshwater fish (Oreochromis niloticus) following accumulation of nanoparticles in the tissues
Figure 1. TEM images of muscle (A) and brain (B) tissue samples of control fish (O. niloticus).
Small brains: Body shape constrains tissue allocation to the central nervous system in ant-mimicking spiders
<p>In Batesian mimicry, mimetic traits are not always as convincing as predicted by theory – in fact, inaccurate mimicry with only a superficial model resemblance is common and taxonomically widespread. The ‘selection trade-offs hypothesis’ proposes a life-history trade-off between accurate mimetic traits and one or more vital biological functions. Here, using an accurate myrmecomorphic (ant-mimicking) jumping spider species, <em>Myrmarachne smaragdina</em>, we investigate how myrmecomorphic modifications to the body shape impact the internal anatomy in a way that could be functionally limiting. Specifically, via X-ray micro-computed tomography (microCT), we quantify how the spider’s constricted prosoma, which emulates the head and thorax of ants, impacts the size of the central nervous system (CNS) and the venom glands. We found that, relative to their whole-body mass, the CNS of the ant-mimicking jumping spider was smaller when compared with a relatively closely related non-mimic jumping spider, indicating that some trade-off between mimic accuracy and size of neural anatomy, as articulated by the ‘selection trade-offs hypothesis’, is a possibility. Our explorative evidence enables and encourages broader investigation of how variable mimic accuracy impacts the neuroanatomy in ant mimics as a direct test of the ‘selection trade-offs hypothesis’. </p>
Recordings from the C. borealis Stomatogastric Nervous System at different temperatures in the decentralized condition
<p>As a courtesy please inform us if you are planning to work with these data (marder@brandeis.edu).</p> <p>These data are collected from the stomatogastric nervous system of the crab <em>Cancer borealis</em> in the decentralized condition (modulatory inputs cut) at different temperatures. For data collection methods please see Haddad & Marder, 2018 (DOI: <a href="https://doi.org/10.1016/j.neuron.2018.08.035">10.1016/j.neuron.2018.08.035</a>). These data are also among the larger data set described in Gorur-Shandilya et al, 2021 (https://www.biorxiv.org/content/10.1101/2021.07.06.451370v1.full.pdf). </p> <p>In data sets 845_082 and 845_078 there are two preparations recorded from in each file. Please see notes to clarify which channels belong to which preparations. Please see end for descriptions of nerve/neuron abbreviations. </p> <p>Data set 845_082_0044 (11°C) and 845_082_0064 (27°C):</p> <p> Preparation 1 = Channels 1 (lvn), 6 (lpn), 2 (pyn), 4 (pdn)</p> <p> Preparation 2 = Channels 7 (lvn), 10 (lpn), i2/9 (pdn), 13 (lgn)</p> <p>Data set 857_016_0049 (11°C) and 857_016_0069 (27°C):</p> <p> Preparation 1 = Channels 2 (LG), 7 (lvn), 4 (lpn), 9 (pyn), 8 (pdn), 15 (lgn), 6 (mvn), 11 (dgn)</p> <p>Data set 845_078_0027 (11°C) and 845_078_0040 (27°C)</p> <p> Preparation 1 = Channels 1 (lvn, upper), 6 (lpn), 4 (pyn), 2 (lvn lower)</p> <p> Preparation 2 = Channels 7 (lvn), 10 (lpn), i2/9 (pyn), 13 (pdn)</p> <p>lvn = lateral ventricular nerve, lpn = lateral pyloric nerve, pyn = pyloric nerve, pdn = pyloric dilator nerve, lgn = lateral gastric nerve, mvn = median ventricular nerve, dgn = dorsal gastric nerve, LG= lateral gastric neuron.</p>
Visualizing the organization and differentiation of the male-specific nervous system of C. elegans
<p>Image volumes and annotations for male NeuroPAL, flp-3, flp-27, and nlp-51 expression.</p> <p>The following image volumes were annotated by Tessa Tekieli, Chen Wang, and Robert Fernandez for:<br> "Visualizing the organization and differentiation of the male-specific nervous system of C. elegans".</p> <p>The publication is available here:<br> https://journals.biologists.com/dev/article-abstract/doi/10.1242/dev.199687/271902/Visualizing-the-organization-and-differentiation</p> <p>These image files can be viewed with the NeuroPAL ID software, available at:<br> https://www.hobertlab.org/neuropal/<br> OR<br> https://github.com/amin-nejat/CELL_ID</p> <p>This software was provided for the NeuroPAL publication, "NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. <em>elegans</em>".<br> The publication is available here:<br> https://www.cell.com/cell/fulltext/S0092-8674(20)31682-2</p> <p>Please cite the NeuroPAL publication when using the software.</p>
Data from: The endocast of Euparkeria sheds light on the ancestral archosaur nervous system
<p>Understanding the evolution of the tetrapod brain is essential to trace the history of ecomorphological diversification of modern clades. While previous studies focused on the morphological transformation of the nervous system along the dinosaur-bird transition, little is known about the brain anatomy of archosauriformes and early archosaurs. Here, we describe the endocast of <em>Euparkeria</em> <em>capensis</em>, a small-bodied, terrestrial archosauriform closely related to Archosauria, with the goal of resolving the current uncertainties surrounding the ancestral condition of the archosaurian nervous system. The endocast of <em>Euparkeria</em> is sigmoidal, with large olfactory bulbs, an expanded cerebral hemisphere and an elongated flocculus. We suggest that this pivotal taxon was an active predator with a remarkable olfactory acuity. Overall, the endocast of <em>Euparkeria</em> resembles the ones observed in phytosaurs, crocodilians and early dinosaurs, implying that modern crocodilians retain an archosaurian plesiomorphic brain morphology.</p>
Electron microscopy files (SBFSEM & TEM) for "Syncytial nerve net in a ctenophore sheds new light on the early evolution of nervous systems"
<p>4 electron microscopy datasets:</p> <p>1) SBFSEM data of 1-day old ctenophore <em>Mnemiopsis leidyi</em> (animal 1)</p> <p>2) SBFSEM data of 1-day old ctenophore <em>Mnemiopsis leidyi</em> (animal 2)</p> <p>3) SBFSEM data of 1-day old ctenophore <em>Mnemiopsis leidyi</em> (animal 3)</p> <p>4) TEM data of nerve net of 1-day old ctenophore <em>Mnemiopsis leidyi </em></p>
Rituximab and Combination Chemotherapy in Treating Patients With Primary Central Nervous System Lymphoma
ClinicalTrials.gov study NCT00335140. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study to Evaluate Central Nervous System (CNS) Pharmacodynamic Activity of TAK-653 in Healthy Participants Using Transcranial Magnetic Stimulation (TMS)
ClinicalTrials.gov study NCT03792672. IPD Sharing: YES. Countries: 1. Publications: 1.
Buparlisib (BKM120) In Patients With Recurrent/Refractory Primary Central Nervous System Lymphoma (PCNSL) and Recurrent/Refractory Secondary Central Nervous System Lymphoma (SCNSL)
ClinicalTrials.gov study NCT02301364. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Combination Chemotherapy, Radiation Therapy, and an Autologous Peripheral Blood Stem Cell Transplant in Treating Young Patients With Atypical Teratoid/Rhabdoid Tumor of the Central Nervous System
ClinicalTrials.gov study NCT00653068. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Stress and the Sympathetic Nervous System in Adults With Depression
ClinicalTrials.gov study NCT04838262. IPD Sharing: YES. Countries: 1. Publications: 1.
Acyclovir for Herpes Infections Involving the Central Nervous System in Neonates
ClinicalTrials.gov study NCT00031460. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Radiation Therapy in Preventing Central Nervous System (CNS) Metastases in Patients With Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00048997. IPD Sharing: Not stated. Countries: 1. Publications: 4.
A Study to Evaluate the Safety and Efficacy of Nivolumab Monotherapy and Nivolumab in Combination With Ipilimumab in Pediatric Participants With High Grade Primary Central Nervous System (CNS) Maligna
ClinicalTrials.gov study NCT03130959. IPD Sharing: NO. Countries: 15. Publications: 1.
Sunitinib in Sarcomas of the Central Nervous System
ClinicalTrials.gov study NCT03641326. IPD Sharing: NO. Countries: 1. Publications: 3.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.