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14 results for “Self-motion”
Dataset for "Development of allocentric representations using self-motion information"
<p>The present .csv file contains the raw data from a 2017 data collection. Specifically, children from six to 11 years old were tested with a non-visual spatial orientation task, in which they were required to i) observe animal-shaped landmarks located in each of the 4 angles of the experimental room; ii) be guided by the experimenter along a two-legged segment; iii) indicate the location of the four landmarks.</p>
Data from Churan et al. Action-dependent processing of self-motion in parietal cortex of macaque monkeys
<p><strong>Animals</strong></p> <p>Two adult male monkeys (macaca mulatta) participated in the study. Single-unit recordings were done using standard tungsten microelectrodes (FHC, Bowdoin, USA) with an impedance of ~2 MΩ at 1 kHz that were positioned by an hydraulic micromanipulator (MO-95, Narishige, Tokyo, Japan). A stainless-steel guiding tube was used for transdural penetration and support of the electrode. The neuronal signal was processed using a commercial system (Alpha Omega, Nof HaGalil, Israel). It was band-pass filtered (cut-off frequencies at 500 Hz and 8000 Hz) and sampled at 44 kHz.</p> <p><strong>Apparatus</strong></p> <p>During recordings, the monkeys were sitting head-fixed in a primate chair in a dark room, and their eye-position was monitored at 1000 Hz using a video-based eye tracker (EyeLink 1000, SR Research, Ottawa, Canada). The chair was positioned at a distance of 97 cm from a semi-transparent screen (size 160 cm x 90 cm, subtending the central 79 deg x 50 deg of the visual field) on which the visual stimuli were back-projected using a PROPixx-projector (VPixx Technologies, St-Bruno de Montarville, Canada) running at a resolution of 1920 x 1080 pixels and at a frame rate of 100 Hz. A custom-made touch sensor (length 10 cm, diameter 1 cm) was integrated into the monkey chair in front of the monkey and its status was monitored online at a sampling rate of 1 kHz.</p> <p><strong>Data processing</strong></p> <p>Single units were isolated using a semi-manual spike sorter (Plexon Inc, Dallas, Texas). To this end we used a threshold on the electrode signal that was set manually to separate the action potentials from noise. The samples that exceeded the threshold were further analyzed using principal components as well as other features that were derived from the signal (like local maxima and minima). Then clusters of samples with similar properties were identified visually and each defined as representing a single unit. For a detailed description of the sorting process see the offline User Guide (Plexon, 2020).</p> <p>Further description of the Methods, see: Churan et al. 2021, doi: 10.1152/jn.00049.2021</p> <p><strong>Data:</strong></p> <p>The file '<strong>data_active_passive.mat</strong>' contains following variables:</p> <p>monkey: code for the tested monkey (1=monkey S, 2=monkey O)</p> <p>baseline: Mean and standard deviation of the activity in a time window of 150 ms to 20 ms before the press of the button.</p> <p>reaction: Mean time between the switch of the color of the fixation point from red to green and the time of the button press.</p> <p>anti_p: Significance of a one sided t-test between the baseline activity and activity 200 ms to 0 ms prior to the onset of stimulus motion.</p> <p>p_win (a (1-3),b (1-3),c (1-3),n(1-110)): 4D matrix containing p-values of t-tests</p> <p>a:</p> <p>1: Was preparatory activity significantly higher in the passive relative to the active condition?</p> <p>2: Was preparatory activity significantly lower in the passive relative to the active condition?</p> <p>3: Was the tonic motion response (200 ms to 500 ms after motion onset) significantly different between the active and the passive conditions?</p> <p>b:</p> <p>1: Calculation was made based on all motion directions</p> <p>2: Calculation was made based on the preferred motion direction</p> <p>3: Calculation was made based on the flanking motion directions</p> <p>c:</p> <p>1: Calculation was made based on all presented delays</p> <p>2: Calculation was made based on the shorter set of delays (500 ms to 700 ms)</p> <p>3: Calculation was made based on the longer set of delays (701 ms to 1000 ms)</p> <p>n: number of the investigated neuron</p> <p>psth_alldir: cell array containing the PSTHs (obtained by convolving each spike with a Gaussian as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). PSTHs were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>psth_bestdir: same as above - using only the preferred direction</p> <p>psth_nbestdir: same as above - using only the flanking directions</p> <p>d_alldir: cell array containing the continuous d-prime (as described in the manuscript) in a time window between 1000 ms before and 800 ms after the onset of motion (in 1 ms steps). d' were calculated based on data from all tested directions. Each cell array consists of 4 elements containing different conditions:</p> <p>1: active condition</p> <p>2: passive condition shorter set of delays (500 ms to 700 ms)</p> <p>3: passive condition longer set of delays (701 ms to 1000 ms)</p> <p>4: passive condition all delays</p> <p>d_bestdir: same as above - using only the preferred direction</p> <p>d_nbestdir: same as above - using only the flanking directions</p> <p>The file '<strong>timecourse_preparatory.mat</strong>' contains the cell array 'd_alldir_preparatory' that consists of 201 elements. Each of the elements contains PSTHs of 23 neurons that have exhibited significant preparatory activity in the passive condition in a time window 1000 ms to 0 ms before the motion onset. Each of the 201 elements describes a specific range of delays between button press and motion onset. This delay range is always a 100 ms wide sliding window, e.g. the element 1 represents delays between 500 and 600 ms, in element 2, the delays are between 501 and 601 ms and so on with the last element (201) representing delays between 700 and 800 ms.</p> <p>Some example code that re-creates most of the figures from the manuscript and that may serve as a starting point for further exploration of the data is available on request from the corresponding author.</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
The joint representation of self-motion and touch in superior colliculus neurons supporting active sensation
<p><span><span>Localizing</span><span> objects using active </span><span>sensing</span><span> requires </span><span>the</span><span> brain </span><span>to</span> <span>integrate</span> <span>a </span><span>map </span><span>sensory</span><span> space against</span><span> an ongoing </span><span>estimate</span><span> of </span><span>body position</span><span>. </span><span>While </span><span>such computations </span><span>are</span><span> often</span> <span>ascribed to </span><span>the </span><span>cerebral </span><span>corte</span><span>x</span><span>, we examined the </span><span>midbrain</span><span> superior colliculus (SC), due to its </span><span>close relationship</span><span> with</span> <span>the sensory periphery </span><span>as well as</span><span> higher, motor-related brain regions. </span><span>Using high-density electrophysiology and movement tracking, w</span><span>e discovered tha</span><span>t</span> <span>the </span><span>on-going </span><span>kinematics of </span><span>whisker motion</span> <span>and locomotion </span><span>speed </span><span>accurately predict </span><span>the firing rate of</span><span> mouse</span><span> SC neurons</span><span>. </span><span>Neural activity was best predicted by m</span><span>ovement</span><span>s </span><span>occurring</span> <span>either </span><span>in the past, present, or future, </span><span>revealing</span><span> that the SC population continuously </span><span>estimates</span> <span>a</span> <span>trajector</span><span>y of self-motion</span><span>. </span><span>Neural </span><span>selectivity</span><span> for </span><span>combinations </span><span>of kinematic features built </span><span>an accurate</span><span> representation of</span><span> whisker </span><span>position</span> <span>with</span><span> high temporal resolution.</span> <span>Half of all self-motion encoding</span> <span>neurons displayed a </span><span>touch</span><span> response </span><span>as</span><span> an object entered the active whisking field. </span><span>Trial-to-trial v</span><span>ariation in the size of this response was explained by the </span><span>position</span><span> of </span><span>the whisker</span><span> upon touch.</span><span> Taken together, these data </span><span>indicate</span><span> that SC neurons linearly combine an internal estimate of </span><span>body </span><span>movements</span><span> with </span><span>external</span><span> stimulation to </span><span>enable active tactile localization</span><span>.</span></span><span> </span></p>
Video Data: Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation
<p>Video dataset accompanying "Spatial Biases in Optic-Flow Sampling for Self-Motion Estimation in Natural Environments." See accompanying <a href="https://github.com/eacooper/AlexanderOpticFlow">Github repository</a> for more documentation and analysis code.</p>
Data from Schmitt et al. 2020: A causal role of area hMST for self-motion perception in humans. Cerebral Cortex Communications
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Bianca R Baltaretu, J Douglas Crawford, Frank Bremmer, A Causal Role of Area hMST for Self-Motion Perception in Humans, <em>Cerebral Cortex Communications</em>, Volume 1, Issue 1, 2020, tgaa042, <a href="https://doi.org/10.1093/texcom/tgaa042">https://doi.org/10.1093/texcom/tgaa042</a> Published 2020 July 20.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in one of three directions (30° to the left, straight ahead, 30° to the right) to eight human participants. In 57% of all trials TMS pulses were applied either to right hemisphere hMST or a control area while the optic flow stimulus was presented. Participants indicated the perceived heading of the self-motion stimulus.</p> <p>The dataset contains the responses (perceived headings) of all eight participants (s1 to s8). The responses are reported here in degrees with 0 degrees representing a movement straight ahead, negative values describing movements forward to the left and positive values describing movements forward to the right.</p> <p> </p>
Data from Schmitt et al. 2021: Preattentive processing of visually guided self-motion in humans and monkeys. Progress in Neurobiology
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Jakob C.B. Schwenk, Adrian Schütz, Jan Churan, André Kaminiarz, Frank Bremmer.<br> Preattentive processing of visually guided self-motion in humans and monkeys. Progress in Neurobiology,<br> 2021,102117. https://doi.org/10.1016/j.pneurobio.2021.102117. Published 2021 July 2.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in an oddball EEG paradigm to 12 human participants and 2 macaque monkeys. We simulated two different headings (forward-left vs. forward-right) presented either as standard or deviant trials and tested for the occurrence of a visual mismatch negativity (vMMN) by comparing the visual-evoked potentials (VEPs). </p> <p>Human data:</p> <p>The dataset contains the preprocessed VEPs of all 12 human participants already averaged over all trials per participant. It contains data from all electrodes used for analysis (P1, P2, O1, O2, PO3, PO4, CP1, CP2) divided into data recordings in standard ('_std') or deviant ('_odd') trials. Each of these files consists of four subfiles representing the presented combinations of heading (forward to the right: '_r' or forward to the left: '_l') and attention condition (attention towards fixation target: 'fix' or attention towards the ground plane: 'plane'). The data matrices in these subfiles are sorted as [participants x time-points].</p> <p>Monkey data:</p> <p>Each .mat file contains the preprocessed VEPs from one monkey ('data'), the corresponding electrode labels ('elec') and the common time vector in seconds ('time'). The data struct contains VEPs for left- and rightwards heading in separate subfields, which again contain standard ('stan') and deviant ('odd') presentations of that heading. Within each subcondition, the actual data matrices are sorted as [electrodes x time-points x trials]. Here, the numbering of electrodes corresponds to the electrode labels contained in the 'elec' variable.</p>
Self-motion Perception in Parkinson's Disease
ClinicalTrials.gov study NCT03137238. IPD Sharing: NO. Countries: 1. Publications: 27.
Robust vestibular self-motion signals in macaque posterior cingulate region
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Data from: Estimation of self-motion duration and distance in rodents
Spatial orientation and navigation rely on information about landmarks and self-motion cues gained from multi-sensory sources. In this study, we focused on self-motion and examined the capability of rodents to extract and make use of information about own movement, i.e. path integration. Path integration has been investigated in depth in insects and humans. Demonstrations in rodents, however, mostly stem from experiments on heading direction; less is known about distance estimation. We introduce a novel behavioural paradigm that allows for probing temporal and spatial contributions to path integration. The paradigm is a bisection task comprising movement in a virtual reality environment in combination with either timing the duration ran or estimating the distance covered. We performed experiments with Mongolian gerbils and could show that the animals can keep track of time and distance during spatial navigation.
Self-Motion Perception in the Elderly
<p>Self-motion through space generates a visual pattern called optic flow. It can be used to determine one’s direction of self-motion (heading). Previous studies have already shown that this perceptual ability, which is of critical importance during everyday life, changes with age. In most of these studies subjects were asked to judge whether they appeared to be heading to the left or right of a target. Thresholds were found to increase continuously with age. In our current study, we were interested in absolute rather than relative heading judgements and in the question about a potential neural correlate of an age-related deterioration of heading perception. Two groups, older test subjects and younger controls, were shown optic flow stimuli in a virtual-reality setup. Visual stimuli simulated self-motion through a 3-D cloud of dots and subjects had to indicate their perceived heading direction after each trial. In different subsets of experiments we varied individually relevant stimulus parameters: presentation time, number of dots in the display, stereoscopic vs. non-stereoscopic stimulation, and motion coherence. We found decrements in heading performance with age for each stimulus parameter. In a final step we aimed to determine a putative neural basis of this behavioural decline. To this end we modified a neural network model which previously has proven to be capable of reproduce and predict certain aspects of heading perception. We show that the observed data can be modeled by implementing an age related neuronal cell loss in this neural network. We conclude that a continuous decline of certain aspects of motion perception, among them heading, might directly be based on an age-related progressive loss of groups of neurons being activated by visual motion. </p>
Data from: Estimation of self-motion duration and distance in rodents
Open the record for dataset details and reuse information.
Changing Vertical Self-motion Perception
ClinicalTrials.gov study NCT04200820. IPD Sharing: NO. Countries: 1. Publications: 0.
The Role of Perturbed Auditory Information for Self-motion in Gait
ClinicalTrials.gov study NCT05713383. IPD Sharing: NO. Countries: 1. Publications: 0.
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