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30 results for “auditory brainstem”
Dataset: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity
<p>Dataset for the Dataset Publication: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity</p> <p>There are two datasets available: 1) the measured ABRs from Experiments 1 and 2 and 2) the extracted IOIs:</p> <ol> <li>ABR measurements:</li> </ol> <p>The filename of the ABR recordings from Experiment 1 include the species name, individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyyddmm). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “Carollia_perspicillata_cp6male_modrate6_20190905T125903”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”).</p> <p>The filename of the ABR recordings from Experiment 2 include the place of the Experiments (Bad Segeberg) and species name (<em>C. perspicillata</em>), individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyymmdd; be aware, that the date format is different between Experiment 1 and 2). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “BadSegeberg_cper_1_male_modrate6_20200622T140952_stimulus_ST01_short ”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”), the individual was presented with stimulus example 01 of the short stimuli.</p> <ol> <li>IOI recordings</li> </ol> <p>The recordings of Inter-Onset-Intervals are all in one single csv file and species and sequence ID is given per row, to be able to analyze the data further.</p> <p> </p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Auditory brainstem development of Naked Mole-Rats (Heterocephalus glaber)
<p>Life underground often leads to animals having specialized auditory systems to accommodate the constraints of acoustic transmission in tunnels. Despite living underground, naked mole-rats use a highly vocal communication system, implying that they rely on central auditory processing. However, little is known about these animals' central auditory system, and whether it follows a similar developmental time course as other rodents. Naked mole-rats show slowed development in the hippocampus suggesting they have altered brain development compared to other rodents. Here, we measured morphological characteristics and voltage-gated potassium channel Kv3.3 expression and protein levels at different key developmental time points (postnatal days 9, 14, 21, and adulthood) to determine whether the auditory brainstem (lateral superior olive (LSO) and medial nucleus of the trapezoid body (MNTB)), develops similarly to two common auditory rodent model species: gerbils and mice. Additionally, we measured the hearing onset of naked mole-rats using auditory brainstem response (ABR) recordings at the same developmental timepoints. In contrast to other work in naked mole-rats showing that they are highly divergent in many aspects of their physiology, we show that naked mole-rats have a similar hearing onset, between P9-P14, to many other rodents. On the other hand, we show some developmental differences, such as a unique morphology and Kv3.3 protein levels in the brainstem.</p>
RAW DATA form - Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs
<p><strong>Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs</strong></p> <p>Ghada BinKhamis, Agnès Léger, Steven L. Bell, Garreth Prendergast, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>doi: 10.1097/AUD.0000000000000648</strong></p> <p><em>(<strong>Please site above article)</strong></em></p> <p> </p> <p><strong>Description of raw EEG (speech-ABR) data main folder, subfolders, and raw EEG files</strong></p> <p><strong>Folder Information</strong></p> <p><strong>Main folder:</strong></p> <ul> <li>Contains 144 subfolders with raw data from 12 participants</li> </ul> <p><strong>Subfolder names:</strong></p> <ul> <li>Each subfolder starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12</li> </ul> </li> </ul> <ul> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>And finally the background condition <ul> <li>quiet, noise</li> </ul> </li> </ul> <p><strong>Example subfolder names:</strong></p> <ul> <li><em>S01 40ms da noise:</em>Participant number 1, speech-ABRs in response to the 40ms [da] in background noise</li> <li><em>S07 170ms ga quiet:</em>Participant number 7, speech-ABRs in response to the 170ms [ga] in quiet</li> </ul> <p><strong>Each participant has 12 subfolders:</strong></p> <ol> <li>S__ 40ms da quiet </li> <li>S__ 40ms da noise</li> <li>S__ 50ms da quiet</li> <li>S__ 50ms da noise</li> <li>S__ 50ms ba quiet</li> <li>S__ 50ms ba noise</li> <li>S__ 50ms ga quiet</li> <li>S__ 50ms ga noise</li> <li>S__ 170ms da quiet</li> <li>S__ 170ms da noise</li> <li>S__ 170ms ba quiet</li> <li>S__ 170ms ga quiet</li> </ol> <p><strong>Each participant subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong></p> <ul> <li>Each ‘.mat’ file starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12 </li> </ul> </li> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>Next is ‘noise’ if background condition was noise</li> <li>Next is the stimulus polarity <ul> <li>Pos for positive/standard</li> <li>Neg for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally is the recording number for that polarity <ul> <li>R1 is the first recording</li> <li>R2 is the second recording</li> </ul> </li> </ul> <p><strong>Example ‘.mat’ file name:</strong></p> <ul> <li><em>S04 50 ba Neg R1:</em>Participant number 4, speech-ABR in response to the 50ms [ba] in quiet, reversed polarity stimulus, recording number one </li> <li><em>S02 40 da noise Pos R2:</em>Participant number 2, speech-ABR in response to the 40ms [da] in background noise, standard/positive stimulus, recording number two</li> </ul> <p> </p> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example: <ul> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: <strong>1800</strong>is the number of sample points for speech-ABRs to the 40ms da, this number will be <strong>2200</strong>for the speech-ABRs to the 50ms stimuli (ba, da, ga), and <strong>4600</strong>for the speech-ABRs to the 170ms stimuli (ba, da, ga)</li> <li>chans: 2 is the number of channels (channel 2 is the ipsilateral channel)</li> <li>frames: 3000 is the number of epochs</li> </ul> </li> <li>The last filed <strong>‘values’</strong>is what contains the raw EEG data (1800x2x3000) <ul> <li><strong>1800 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>3000 </strong>is the number of epochs</li> <li>The field <strong>‘values’ </strong>for speech-ABRs to the 50ms stimuli is <strong>2200x2x3000 </strong>and for speech-ABRs to the 170ms stimuli is <strong>4600x2x3000</strong>.</li> </ul> </li> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> </ul> <p><strong>Data is recorded in Volts and will need to be converted to Micro Volts</strong></p> <p><strong>Date of data collection</strong>: May to November 2016</p>
Optimizing parameters for using the parallel auditory brainstem response (pABR) to quickly estimate hearing thresholds
<p><b>Objectives: </b>Timely assessments are critical to providing early intervention and better hearing and spoken language outcomes for children with hearing loss. To facilitate faster diagnostic hearing assessments in infants, the authors developed the parallel auditory brainstem response (pABR), which presents randomly timed trains of tone pips at five frequencies to each ear simultaneously. The pABR yields high-quality waveforms that are similar to the standard, single-frequency serial ABR but in a fraction of the recording time. While well-documented for standard ABRs, it is yet unknown how presentation rate and level interact to affect responses collected in parallel. Furthermore, the stimuli are yet to be calibrated to perceptual thresholds. Therefore, this study aimed to determine the optimal range of parameters for the pABR and to establish the normative stimulus level correction values for the ABR stimuli.</p> <p><b>Design: </b>Two experiments were completed, each with a group of 20 adults (18 – 35 years old) with normal hearing thresholds (≤ 20 dB HL) from 250 to 8000 Hz. First, pABR electroencephalographic (EEG) responses were recorded for six stimulation rates and two intensities. The changes in component wave V amplitude and latency were analyzed, as well as the time required for all responses to reach a criterion signal-to-noise ratio of 0 dB. Second, behavioral thresholds were measured for pure tones and for the pABR stimuli at each rate to determine the correction factors that relate the stimulus level in dB peSPL to perceptual thresholds in dB nHL.</p> <p><b>Results:</b> The pABR showed some adaptation with increased stimulation rate. A wide range of rates yielded robust responses in under 15 minutes, but 40 Hz was the optimal singular presentation rate. Extending the analysis window to include later components of the response offered further time-saving advantages for the temporally broader responses to low frequency tone pips. The perceptual thresholds to pABR stimuli changed subtly with rate, giving a relatively similar set of correction factors to convert the level of the pABR stimuli from dB peSPL to dB nHL.</p> <p><b>Conclusions: </b>The optimal stimulation rate for the pABR is 40 Hz, but using multiple rates may prove useful. Perceptual thresholds that subtly change across rate allow for a testing paradigm that easily transitions between rates, which may be useful for quickly estimating thresholds for different configurations of hearing loss. These optimized parameters facilitate expediency and effectiveness of the pABR to estimate hearing thresholds in a clinical setting.</p>
A Safety Study of the Auditory Brainstem Implant for Pediatric Profoundly Deaf Patients
ClinicalTrials.gov study NCT02102256. IPD Sharing: NO. Countries: 1. Publications: 4.
Auditory Brainstem Implant (ABI) in Adult Non-Neurofibromatosis Type 2 Subjects
ClinicalTrials.gov study NCT01736267. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Screening for Autism Spectrum Disorders Using Auditory Brainstem Responses
ClinicalTrials.gov study NCT03971578. IPD Sharing: NO. Countries: 1. Publications: 1.
Auditory Brainstem Implant (ABI) in Pediatric Non-Neurofibromatosis Type 2 Subjects
ClinicalTrials.gov study NCT01864291. IPD Sharing: NO. Countries: 1. Publications: 7.
Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech
Open the record for dataset details and reuse information.
Optimizing parameters for using the parallel auditory brainstem response (pABR) to quickly estimate hearing thresholds
Open the record for dataset details and reuse information.
Examining relationship between auditory brainstem responses, cognitive ability, and speech-in-noise perception among young adults with normal hearing thresholds
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Auditory brainstem development of Naked Mole-Rats (Heterocephalus glaber)
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Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures
<p><em><strong>Folder and Data Description for dataset of:</strong></em></p> <p><strong>Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures</strong></p> <p>Ghada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>Please site the paper when using this dataset</strong> (DOI: 10.1177/2331216519848297)</p> <p> </p> <p><strong>Shared dataset is as follows:</strong></p> <ul> <li><strong>Behavioral data is in the excel spread sheet entitled:</strong> “BinKhamis_et_al_behavioral_data .xlsx”<br> </li> <li><strong>Speech-ABRs (raw EEG (speech-ABR) data) are contained within the five 'zip' folders.</strong></li> </ul> <p><strong>Description of the “Speech-ABRs” folders, subfolders, and raw EEG files:</strong></p> <p><strong>“Speech-ABRs” Folder Information:</strong></p> <ul> <li><strong>Each Speech_ABR folder</strong> contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number 1 to participant number 20)</li> <li><strong>Subfolders:</strong> <ul> <li>Each subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the background condition: noise or quiet</li> <li>Next is whether recordings were: aided or unaided</li> </ul> </li> <li><strong>Example subfolder names:</strong> <ul> <li><strong><em>HA1 noise aided:</em></strong> participant number 1, aided speech-ABRs in background noise</li> <li><strong><em>HA4 noise unaided:</em></strong> participant number 4, unaided speech-ABRs in background noise</li> <li><strong><em>HA55 quiet aided:</em></strong> participant number 55, aided speech-ABRs in quiet</li> <li><strong><em>HA97 quiet unaided</em></strong>: participant number 97, unaided speech-ABRs in quiet</li> </ul> </li> <li>Each participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise) <ul> <li><strong>Each subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong> <ul> <li>Each ‘.mat’ file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the stimulus: 40 da</li> <li>Next is ‘unaided’ only if recordings were without HA</li> <li>Next is ‘noise’ only if the background condition was noise</li> <li>Next is the stimulus polarity: <ul> <li>‘Pos’ for positive/standard</li> <li>‘Neg’ for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally the test ear and recording number for that polarity <ul> <li>R1 is the first recording from the right ear, R2 is the second recording from the right ear</li> <li>L1 is the first recording from the left ear, L2 is the second recording from the left ear</li> </ul> </li> <li><strong>Example ‘.mat’ file name:</strong> <ul> <li><strong><em>HA1 40 da Neg Noise R1.mat: </em></strong>participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.</li> <li><strong><em>HA4 40 da unaided Pos Noise L2.mat:</em></strong> participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.</li> <li><strong><em>HA7 40 da Neg R2.mat:</em></strong> participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.</li> <li><strong><em>HA10 40 da unaided Pos Noise L1.mat:</em></strong> participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example:</li> <li><strong><em>xunits</em></strong>: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li><strong><em>start: </em></strong>‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li><strong><em>points:</em></strong> <strong>2200</strong> is the number of sample points</li> <li><strong><em>chans:</em></strong> 2 is the number of channels <ul> <li><em>Right ear:</em> channel 2, <em>Left ear:</em> channel 1</li> </ul> </li> <li><strong><em>frames:</em></strong> 2500 is the number of epochs</li> <li>The last filed <strong>‘values’</strong> is what contains the raw EEG data (2200x2x2500) <ul> <li><strong>2200 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>2500 </strong>is the number of epochs <ul> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> <li>Data are in Volts; conversion to <strong>μVolts </strong>(multiply by 1000) is required.</li> </ul> </li> </ul> </li> </ul> <p><strong>Date of data collection: </strong>October 2017 to July 2018</p>
Impact of Maternal Iron Status on Neonatal Iron Status and Auditory Brainstem Response in the Newborn
ClinicalTrials.gov study NCT01019902. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Implantation of an Auditory Brainstem Implant for the Treatment of Incapacitating Unilateral Tinnitus
ClinicalTrials.gov study NCT02630589. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Auditory Brainstem Implant (ABI) in Children With No Cochleae or Auditory Nerves
ClinicalTrials.gov study NCT02310399. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Sedation for Brainstem Evoked Auditory Response (BEAR) Testing Using Intravenous Pentobarbital
ClinicalTrials.gov study NCT01282112. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Auditory Brainstem Response as a Diagnostic Tool in Schizophrenia and Bipolar Disorder
ClinicalTrials.gov study NCT01629355. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Gene Delivery to Neurons in the Auditory Brainstem of Barn Owls using Standard Recombinant Adeno-associated Virus Vectors
<div> <p>Recombinant adeno-associated virus (rAAV) vectors are a commonly used tool for gene delivery. There is a large choice of different serotypes whose transduction efficiency varies for different animal species. In this study, three rAAV vectors were tested for transduction efficiency in the auditory brainstem of adult barn owls (<i>Tyto alba</i>) which are not standard laboratory animals. Injections with rAAV serotypes 2/1 and 2/5 resulted in reliable expression in various nuclei of the auditory brainstem of barn owls. Both vectors showed evidence of being spread by axonal transport. However, only rAAV2/5 also showed expression in regions far distant from the injection site, suggesting long-range axonal transport in connections along the auditory pathway. In contrast, injections with rAAV2/9 resulted in no expression. Our results demonstrate for the first time that commercially available rAAV vectors can be used for reliable gene expression in the barn owl auditory brainstem and pave the way toward optogenetic manipulation of neural activity in this important animal species in neuroethology and auditory physiology.</p> </div>
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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.