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148 results for “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>
data set related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms
<p>This record contains raw data related to article Brainstem enlargement in preschool children with autism Results from an intermethod agreement study of segmentation algorithms</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>
Control of feeding by a bottom-up brainstem-subthalamic pathway
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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>
A case report on intensive, robot-assisted rehabilitation program for brainstem radionecrosis
<p>Radiotherapy is a valid treatment option for nasopharyngeal carcinoma. However, complications can occur following irradiation of the closest anatomical structures, including brainstem radionecrosis (BRN). The rehabilitation is poorly described in patients with BRN, despite its usefulness in improving functional independence in patients with brain tumors. We aimed at testing the usefulness of intensive, robot-assisted neurorehabilitation program to improve functional independence in a 57-year-old male with BRN.</p> <p>Patient concerns: </p> <p>A 57-year-old male diagnosed with a nasopharyngeal carcinoma, received a radiation total dose of 72 Gy. Owing to the appearance of a severe symptomatology characterized by dysphagia, hearing loss, and left sided hemiparesis, the patient was hospitalized to be provided with intensive pharmacological and neurorehabilitation treatment.</p> <p>Diagnosis: </p> <p>Follow-up brain magnetic resonance imaging disclosed no residual cancer, but some brainstem lesions compatible with BRN areas were appreciable.</p> <p>Intervention: </p> <p>The patient underwent a 2-month conventional, respiratory, and speech therapy. Given that the patient only mildly improved, he was provided with intensive robot-aided upper limb and gait training and virtual reality-based cognitive rehabilitation for other 2 months.</p> <p>Outcomes: </p> <p>The patient reported a significant improvement in functional independence, spasticity, cognitive impairment degree, and balance.</p> <p>Conclusion: </p> <p>Our case suggests the usefulness of neurorobotic intensive rehabilitation in BRN to reduce functional disability. Future studies should investigate whether an earlier, even multidisciplinary rehabilitative treatment could lead to better functional outcome in patients with BRN.</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>
Understanding degraded speech leads to perceptual gating of a brainstem reflex in human listeners
<p>The ability to navigate "cocktail-party" situations by focussing on sounds of interest over irrelevant, background sounds is often considered in terms of cortical mechanisms. However, subcortical circuits such as the pathway underlying the medial olivocochlear (MOC) reflex modulate the activity of the inner ear itself, supporting the extraction of salient features from auditory scene prior to any cortical processing. To understand the contribution of auditory subcortical nuclei and the cochlea in complex listening tasks, we made physiological recordings along the auditory pathway while listeners engaged in detecting non(sense)-words in lists of words. Both naturally spoken and intrinsically noisy, vocoded speech—filtering that mimics processing by a cochlear implant—significantly activated the MOC reflex, but this was not the case for speech in background noise, which more engaged midbrain and cortical resources. A model of the initial stages of auditory processing reproduced specific effects of each form of speech degradation, providing a rationale for goal-directed gating of the MOC reflex based on enhancing the representation of the energy envelope of the acoustic waveform. Our data reveals the co-existence of two strategies in the auditory system that may facilitate speech understanding in situations where the signal is either intrinsically degraded or masked by extrinsic acoustic energy. Whereas intrinsically degraded streams recruit the MOC reflex to improve representation of speech cues peripherally, extrinsically masked streams rely more on higher auditory centres to de-noise signals.</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
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Optimizing parameters for using the parallel auditory brainstem response (pABR) to quickly estimate hearing thresholds
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Understanding degraded speech leads to perceptual gating of a brainstem reflex in human listeners
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Examining relationship between auditory brainstem responses, cognitive ability, and speech-in-noise perception among young adults with normal hearing thresholds
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Somatotopic organization of brainstem analgesic circuitry
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Data from: Sequential appetite suppression by oral and visceral feedback to the brainstem
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An early surge of norepinephrine along brainstem pathways drives sensory-evoked awakening
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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.