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27 results for “parietal cortex”
Adaptive memory distortions are predicted by feature representations in parietal cortex
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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>
Dataset accompanying 'The Representation of Priors and Decisions in The Human Parietal Cortex'
<p>This dataset accompanies the manuscript 'The Representation of Priors and Decisions in The Human Parietal Cortex', published in PLoS Biology.</p><p>This dataset consists of:</p><p>1) Biophysical model simulations of a neural mass model performing a 2-alternative perceptual decision making task. </p><p>2) MEG data from 26 human volunteers performing the same task (see fig1 of the manuscript). MEG data were combined with anatomical MRI scans (not shared) to source localise brain activity in parietal cortex. The neural source time courses are shared. Full details can be found in the methods section of the manuscript.</p><p>3) Analysis code to reproduce all main data figures in the manuscript. Details of how to run the analyses are included in the analysis code documentation.</p><p>4) .txt files including the summary data necessary to reproduce figures in the manuscript. This is to allow users who do not have access to matlab to access the data underlying the figures without running the entire analysis pipeline.</p>
Dog neuroimaging data from: Action observation reveals a network with divergent temporal and parietal cortex engagement in dogs compared to humans
<p>Action observation is a fundamental pillar of social cognition. Neuroimaging research has revealed a human and non-human primate action observation network (AON) encompassing fronto-temporo-parietal areas with links to the species’ imitation tendencies and relative lobe expansion. Dogs (Canis familiaris) have good action perception and imitation skills and a less expanded parietal than temporal cortex, but their AON remains unexplored. We conducted a functional MRI study with 28 dogs and 40 humans and found functionally analogous involvement of somatosensory and temporal brain areas of both species’ AONs and responses to transitive and intransitive action observation in line with their imitative skills. Employing a functional localizer, we also identified functionally analogous agent-responsive areas within both species’ AONs. However, activation and task-based functional connectivity measures suggested significantly less parietal cortex involvement in dogs than in humans. These findings advance our understanding of the neural bases of action understanding and the convergent evolution of social cognition, with analogies and differences resulting from similar social environments and divergent brain expansion, respectively.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 28 dogs</li> <li>structural scans of the same dogs including brain masks & skull-stripped versions</li> <li>eventfiles containing the condition names, onsets and durations for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for group-level imaging data of both species (https://osf.io/z479k/).</p>
Data from: Dynamic reorganization of neuronal activity patterns in parietal cortex dataset
<p>Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells' activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.</p>
Dataset supporting "Human posterior parietal cortex encodes the movement goal in a pro-/anti-reach task."
<p>Here we provide fMRI data used for the following project (for details see data description file):</p> <p>Gertz, H., & Fiehler, K. (2015). Human posterior parietal cortex encodes the movement goal in a pro-/anti-reach task. <em>Journal of Neurophysiology</em>, <em>114</em>(1), 170–83. http://doi.org/10.1152/jn.01039.2014</p> <p>Previous research on reach planning in humans has implicated a fronto-parietal network, including the precuneus (PCu), a putative human homologue of the monkey parietal reach region (PRR), and the dorsal premotor cortex (PMd). Using a pro-/anti-reach task, electrophysiological studies in monkeys have demonstrated that the movement goal rather than the location of the visual cue is encoded in PRR and PMd. However, if only the effector but not the movement goal is specified (underspecified condition) the PRR and PMd have been shown to represent all potential movement goals. In this fMRI study, we investigated whether the human PCu and PMd likewise encode the movement goal, and whether these reach-related areas also engage in situations with underspecified compared to specified movement goals. By using a pro-/anti-reach task, we were able to spatially dissociate the location of the visual cue from the location of the movement goal. In the specified conditions, pro- and anti-reaches activated similar parietal and premotor areas. In the PCu contralateral to the moving arm, we found directionally selective activation fixed to the movement goal. In the underspecified conditions, we observed activation in reach-related areas of the posterior parietal cortex, including PCu. However, the activation was substantially weaker in parietal areas and lacking in PMd. Our results suggest that human PCu encodes the movement goal rather than the location of the visual cue if the movement goal is specified and even engages in situations when only the visual cue but not the movement goal is defined.</p>
Evidence accumulation occurs locally in the parietal cortex
<p>We uploaded electrophysiological data collected from the lateral intraparietal area (LIP) of two macaque monkeys when they performed a probabilistic reasoning task. The relevant paper (Evidence accumulation occurs locally in the parietal cortex, <a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-022-32210-6&data=05%7C01%7C%7Cd48eee4c9d4f47238f7a08da720be1af%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637947692608533539%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=IfRirBDis37vOCpejmH8jTORoybd%2BV7%2Fcz2ieFmD%2Bhc%3D&reserved=0">https://doi.org/10.1038/s41467-022-32210-6</a>) is published in Nature Communications. </p> <p>The description of the data and codes for reproducing results in the paper can be found at https://github.com/zwzhangi36/sensory-evidence_transformation_in_LIP .</p>
Data from: Dynamic reorganization of neuronal activity patterns in parietal cortex dataset
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Data from: Cross-modal representation of chewing food in posterior parietal and visual cortex
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Changes in the Posterior Parietal Cortex - Primary Motor Cortex Pathway Induced by Motor Training
ClinicalTrials.gov study NCT00904332. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Posterior parietal cortex drives inferotemporal activations during three-dimensional object vision
The primate visual system consists of a ventral stream, specialized for object recognition, and a dorsal visual stream, which is crucial for spatial vision and actions. However, little is known about the interactions and information flow between these two streams. We investigated these interactions within the network processing three-dimensional (3D) object information, comprising both the dorsal and ventral stream. Reversible inactivation of the macaque caudal intraparietal area (CIP) during functional magnetic resonance imaging (fMRI) reduced fMRI activations in posterior parietal cortex in the dorsal stream and, surprisingly, also in the inferotemporal cortex (ITC) in the ventral visual stream. Moreover, CIP inactivation caused a perceptual deficit in a depth-structure categorization task. CIP-microstimulation during fMRI further suggests that CIP projects via posterior parietal areas to the ITC in the ventral stream. To our knowledge, these results provide the first causal evidence for the flow of visual 3D information from the dorsal stream to the ventral stream, and identify CIP as a key area for depth-structure processing. Thus, combining reversible inactivation and electrical microstimulation during fMRI provides a detailed view of the functional interactions between the two visual processing streams.
Original data and code for Kang, Mooshagian, and Snyder (2024) Functional organization of posterior parietal cortex circuitry based on inferred information flow
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Interactions of Fronto-Parietal High Frequency rTMS on Anterior Cingulate Cortex Activation in Schizophrenia
ClinicalTrials.gov study NCT04309370. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Lesions to right posterior parietal cortex impair visual depth perception from disparity but not motion cues
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Data from: Decision and navigation in mouse parietal cortex
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Data from: Posterior parietal cortex drives inferotemporal activations during three-dimensional object vision
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A cell type specific error correction signal in posterior parietal cortex
GEO Series GSE232200. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Repetitive Transcranial Magnetic Stimulation of the Posterior Parietal Cortex in Patients Suffering From Gilles de la Tourette Syndrome
ClinicalTrials.gov study NCT01043549. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Role of the Posterior Parietal Cortex in the Processing of Sensory Information
ClinicalTrials.gov study NCT03386188. IPD Sharing: NO. Countries: 1. Publications: 0.
rTMS Posterior Parietal Cortex Modulation and Upper Limb Movement After Stroke
ClinicalTrials.gov study NCT03323255. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.