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1,389 results for “multimodality”
Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment
<p>The dataset is related to the paper Piazza, C.; Pirovano, I.; Mastropietro, A.; Genova, C.; Gagliardi, C.; Turconi, A.C.; Malerba, G.; Panzeri, D.; Maghini, C.; Reni, G.; Rizzo, G. and Biffi E. Development and Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment. Electronics 2021, 10, 2838. https://doi.org/10.3390/electronics10222838. </p> <p>It includes multimodal data (EEG, EMG, kinematics and kinetics) of a healthy child (HC) and a child with hemiparesis (CP01) during walking in a virtual environmment (GRAIL, Motek).</p> <p>Data can be opened by using EEGLab and Matlab </p> <p> </p> <p> </p>
Analysis on Multimodal Imaging of 'stealth' Choroidal Neovascularization in Central Serous Chorioretinopathy
<p><strong>Purpose:</strong> To investigate the prevalence of concurrent choroidal neovascularization (CNV) in patients with clinically diagnosed of central serous chorioretinopathy (CSC) and analyze the multimodal imaging characteristics of CSC with ‘stealth’ CNV (CSC-sCNV). <strong>Design: </strong>Retrospective, observational cross-sectional study. <strong>Methods</strong><strong>:</strong> The current study analyzed the clinical data of 111 clinically diagnosed of CSC patients (134 eyes) who received fluorescein fundus angiography (FFA), indocyanine green angiography (ICGA), and optical coherence tomography angiography (OCTA) at their initial visit. The CSC-CNV that can be detected by FFA+ICG was defined as classic CSC-CNV (CSC-cCNV), and CSC-CNV that cannot be detected by FFA+ICG but can be detected by OCTA was defined as CSC-sCNV. The multimodal imaging characteristics of CSC-sCNV were analyzed.<strong> Results: </strong>The concurrent CNV was found in 29 eyes (21.6%) of 28 patients (25.2%), who were significantly older than those without CNV (P=0.039). Among the 29 eyes, 6 eyes (20.7%) were CSC-cCNV and 23 (79.3%) were CSC-sCNV. All CSC-sCNV eyes were type 1 CNV, of which, 22 had photoreceptor layer defects of varying severities. OCTA showed 4 (17.4%) were active CNV and 19 (82.6%) were inactive CNV based on the morphology features. CSC-sCNV lesions manifested as smoke-stack leakage, diffuse RPE changes with/without ink-blot leakage and normal fluorescence on FFA (1 eye, 7 eyes, 10 eyes and 5 eyes respectively). ICGA showed no typical CNV features in CSC-sCNV but choroidal vasodilation beneath CNV lesions and choroidal hyperpermeability at the posterior pole.<strong> Conclusion:</strong> CSC-sCNV may be the primary phenotype of CSC-CNV and its inactivity might be the reason that it cannot be detected by FFA.</p>
Datasets for Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection
<p>These are the datasets about the experimental group (N=20, where N is the sample size, that is, 20 participants in total) and control group (N=20, where N is the sample size, that is, 20 participants in total) for our published paper entitled "Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection", DOI: 10.1109/JBHI.2021.3134024. </p> <p>1. What do these datasets include?</p> <p>There are 40 participants' data in 40 folders, respectively. Each folder includes EEG data, Questionnaires, and answers as well as other non-EEG biometrics and memory test result of the cognitive task.</p> <p><br> 1.1 EEG data<br> All EEG data is either *.easy file or *.info file. The *.easy file is the raw EEG data. The *.info file is the information (e.g., EEG montage) paired with those raw EEG data. The *.info file can be simply readable by any Notepad software. The *.easy file can be readable by Neuroelectrics's plugin for EEGLAB (https://www.neuroelectrics.com/wiki/index.php/EEGLAB). Note, most of the participants' EEG data is a single easy file, but due to technical problems, few participants' EEG data consists of some separated easy files. Please use the function 'merge dataset' in the EEGLAB to merge them, and then use the following markers to extract segments of interest.</p> <p>All received EEG markers are integer numbers. Specifically, the markers are as follows:</p> <p>For control group:<br> a) The 'target' stimuli in the cognitive task: 16<br> b) The 'distractor' stimuli in cognitive task:32<br> c) The end of Baseline_1(that is, before the first cognitive task): 37<br> d) The end of Baseline_2(that is, before the round 1 neutral task): 47<br> e) The end of Baseline_3(that is, before the round 2 neutral task): 57<br> f) The end of Baseline_4(that is, before the second cognitive task): 67<br> g) The end of FMS reporting during the round 1 neutral task: 100<br> h) The end of FMS reporting during the round 2 neutral task: 101</p> <p>For experimental group:</p> <p>a) The 'target' stimuli in the cognitive task: 16<br> b) The 'distractor' stimuli in cognitive task:32<br> c) The end of Baseline_1(that is, before the first cognitive task): 37<br> d) The end of Baseline_2(that is, before the tunnel travel task): 47<br> e) The end of Baseline_3(that is, before the rollercoaster task): 57<br> f) The end of Baseline_4(that is, before the second cognitive task): 67<br> g) The end of FMS reporting during the Tunnel travel task: 100<br> h) The end of FMS reporting during the Rollercoaster task: 101</p> <p>1.2 Questionnaires</p> <p>1.2.1 SSQ<br> All SSQ questionnaires are named as xxx_QuestionnaireResult.csv, where xxx stands for the timestamps.</p> <p>1.2.2 FMS</p> <p>For control group:<br> All FMS questionnaires are named as xxx_Nature_FMS_backup.csv or xxx_Bio-data and FMS Nature.csv, where xxx stands for the timestamps and 'Nature' refers to the vection-free neutral task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read.</p> <p>For experimental group:</p> <p>All FMS questionnaires are named as xxx_Gabor_FMS_backup.csv or xxx_Rollercoaster_FMS_backup.csv or xxx_Bio-data and FMS GaborRacer.csv or xxx_Bio-data and FMS Rollercoaster.csv, where xxx stands for the timestamps and Gabor or GaborRacer refer to the tunnel travel task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read.</p> <p>1.3 Non-EEG biometrics<br> All non-EEG biometrics are named as xxx_Bio-data 'Task name'.csv, where 'Task name' refers to 'VA' (that is the cognitive task), or 'Nature' (for the control group) or 'GaborRacer' (for the experimental group) or 'Rollercoaster' (for the experimental group). The column information is as follows:<br> Column A: time<br> Column Q: PPG raw data (the left index finger)<br> Column R: Fingertip temperature (the left middle finger)<br> Column S: Forehead temperature<br> Column T: VR hermetic space temperature</p> <p>The data in other columns are not used in this study.</p> <p>1.4 The memory test result of the cognitive task</p> <p>Memory test result: xxx_CheckMemoryResult.csv, where xxx stands for the timestamps.</p> <p>Column A: time</p> <p>Column B: memory test result (Correct/Wrong)</p> <p>Column C: the number of the target (green sunfish)</p> <p>Column D: the reported number of the target in the memory test</p>
Multimodal Sensory Learning for Object Manipulation
<p><strong>Multimodal Manipulation Learning Database</strong></p> <p>The dataset consists of data recordings for object manipulation with audio-tactile sensory feedback for object handover. It captures the auditory and tactile signals of a Kuka IIWA robot with an Allegro hand holding a plastic container containing different materials. The robot manipulates the container with vertical shaking and rotation motions. The data consists of force/pressure measurements on the Allegro hand using a Tekscan tactile skin sensor, auditory signals from a microphone, and the joints data of the IIWA robot and the Allegro hand joints. </p> <p><strong>Dataset</strong></p> <p>Each datafile is a rosbag file containing the data recording from one trial of a robot motion with one material, with rostopics on the following data:</p> <ul> <li>Kuka IIWA 7 Joint data: /iiwa/TorqueController/command /iiwa/eePose /iiwa/joint_states</li> <li>Allegro hand joint data: /allegro_hand_right/joint_states</li> <li>Tekscan sensor recording (tactile force/pressure sensor data on hand): /tekscan/frame</li> <li>Audio data (for microphone attached to hand): /audio/audio /audio/audio_info</li> <li>Experiment information: /trialInfo <ul> <li>which contains: <ul> <li>trial information (motion type, speed, etc.)</li> <li>start/stop of different phases of the trials</li> </ul> </li> </ul> </li> </ul> <p><strong>Motion Types</strong></p> <p>The database contains recordings for the robot executing two different motion types: vertical shaking of the object and rotation of the object.</p> <p><strong>Materials</strong></p> <p>The database contains recordings for 5 different material classes in the plastic container, as shown below: empty, vitamins, gummies, cornflakes, and rice. We used approximately the same volume of each material for each trial. We tested each material class and motion combination for a total of 10 different experimental conditions and collected 30 trials for each condition.</p> <p>The vertical motion dataset was entirely collected on 2021/08/25. The rotation dataset was split into two day. The empty, gummies and rice class data was collected on 2021/08/26. The vitamins and cornflakes classes were collected on 2021/09/13.</p> <p><strong>Database Setup</strong></p> <p>The database consists of the data in two formats: annotated ('annotated_bags_mml.zip') and unannotated/numbered filenames ('numbered_bags_mml.zip') datasets. The data in the two datasets are identical- the annotated filename dataset has the experimental descriptions in the filename directly (as described below).</p> <p>The annotated filenames dataset ('annotated_bags_mml.zip') consists of a single directory with all 300 rosbag datafiles (10 experimental conditions, 30 trials each). Each rosbag (<code>.bag</code>) is saved in the directory, with filename specified ('Date Recorded YYYYMMDD' + '_motion' + '_material' + '_trialID' + '.bag'). Motion Types are: {'vertical', 'rotation'}. Materials are: {'empty', 'cornflakes', 'gummies', 'rice', 'vitamins'}. For each experimental condition, there are 30 datafiles with trial IDs from 0-29.</p> <p>All data recordings for the vertical motion have filenames: '20210825_vertical_+ 'material' + 'trialID' +'.bag). For the rotation motion, the empty, gummy and rice classes have filenames: '20210826_rotation_+ 'material' + 'trialID' +'.bag). For cornflakes and vitamins classes, the filenames are: '20210913_rotation_+ 'material' + 'trialID' +'.bag).</p> <p>The numbered/unannotated file dataset ('numbered_bags_mml.zip') consists of the same 300 data files as in the annotated dataset except here the filenames are numbered '{000-299}.bag'. The directory contains a spreadsheet ('annotations.csv') listing the experimental descriptions for each file name. The columns of the xls spreadsheet are {'Bagfile name', 'Year', 'Month', 'Day', 'Motion/Movement (mvt_type)', 'Material', 'Trial ID'}, where {Year, Month, Day} refer to the date that trial data was collected (either 2021/08/25, 2021/08/26, or 2021/09/13). </p>
Schematic Multimodal Network In the Ohio River Basin
<p>The topological connectivity of the components of Ohio Basin, including the waterway segments, highways, and railways serving the OD demand of commodities</p>
Supporting data for "Telecom-heralded entanglement between multimode solid-state quantum memories"
<p>This repository contains the data supporting the article "Telecom-heralded entanglement between multimode solid-state quantum memories" by Dario Lago-Rivera, Samuele Grandi, Jelena V. Rakonjac, Alessandro Seri and H. de Riedmatten, Nature 2021.</p> <p>The individual .zip files contain the raw detection files for all the storage times and losses reported in the article, for the measurement of diagonal and off-diagonal elements of the density matrix.</p>
Multimodal Electric Guitar Data
<p>A dataset of thirty-six student and semiprofessional electric guitarists performing a set of basic sound-producing actions as well as free improvisations. The multimodal dataset consists of EMG and motion capture data; additionally, video and sound recordings of each performer were made.</p>
Light environment interacts with visual displays in a species-specific manner in multimodal signaling wolf spiders
<p>Light availability is highly variable, yet predictable, over various timescales and the light environment is expected to play an important role in the evolution of visual signals. Courtship displays within the wolf spider genus Schizocosa always involve the use of substrate borne vibrations, however, there is substantial variation between species in the use of visual displays. We assessed the impact of light intensity on the courtship of four species of Schizocosa that vary in their use of visual signals during courtship. To examine the effects of the light environment on mating success and courtship effort in each species, we ran behavioral trials at three light intensities (bright, dim and dark). We also examined each species' circadian activity patterns. We found multiple effects of the light environment and these varied between species. Circadian activity patterns also differed between species and sexes. Our results suggest that dim-light might favor more conspicuous visual displays, while less conspicuous displays might be favored in bright light conditions. Additionally, we found evidence for light-dependent changes in selection on male traits, illustrating that short-term changes in light intensity have the potential for strong effects on the dynamics of sexual selection.</p>
MMFlood: A Multimodal Dataset for Flood Delineation from Satellite Imagery
<p>MMFlood is remote sensing dataset derived from Sentinel-1 (VV-VH), MapZen (DEM) and OpenStreetMap (Hydrography). It provides a complete and well-rounded set of data specifically designed for flood events, focusing on three main features: worldwide distribution, manually validated annotations and multiple modalities.</p> <ul> <li>1748 pairs of SAR images and pixel-level annotations exclusively based around flooded areas and taking into account 95 flood events in 42 different countries around the world, in a time span ranging from 2014 to 2021.</li> <li>Image size <2000x2000, image resolution of 20m .</li> <li>Manually verified flood events across the world with raw SAR acquisitions derived from Sentinel-1.</li> <li>Includion of Digital Elevation Model (DEM) images.</li> </ul>
Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease
<p>RDS files of SingleCellExperiment objects containing raw single cell RNA-seq count data required to replicate the analyses presented at: <a href="https://oshlacklab.com/paed-cf-cite-seq/">https://oshlacklab.com/paed-cf-cite-seq/</a> and described in the pre-print titled: <em>"</em>Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease<em>"</em>.<br> Instructions for how to incorporate the raw data into the analysis can be found at: <a href="https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html">https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html</a> and the complete analysis code and additional data files can be cloned/downloaded from: <a href="https://github.com/Oshlack/paed-cf-cite-seq">https://github.com/Oshlack/paed-cf-cite-seq</a>.</p>
Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI
<p>The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration</p> <p>The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.</p>
PiH Dataset for Determining Exception Context in Assembly Operations form Multimodal Data
<p>PiH Dataset used in Simonič, M.; Majcen Hrovat, M.; Džeroski, S.; Ude, A.; Nemec, B. Determining Exception Context in Assembly Operations from Multimodal Data. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 7962. https://doi.org/10.3390/s22207962</p> <p>The dataset consists of color images of different outcomes of the PiH task as well as the corresponding Cartesian pose of the robot end-effector and force torque data. Data is organized into two folders, representing one of the two possible insertion slots. In each of the folders, data is further split into the following cases:<br> - error in insertion target position ranging from -10 to 10 mm in x direction in 1 mm steps,<br> - error in insertion target position ranging from -10 to 10 mm in y direction in 1 mm steps,<br> - no positional error.</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises: RGB image (.jpg) and .mat file with <em>states </em>object that contains reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. </p>
Rings Insertion Dataset for Determining Exception Context in Assembly Operations form Multimodal Data
<p>Rings Insertion Dataset used in Simonič, M.; Majcen Hrovat, M.; Džeroski, S.; Ude, A.; Nemec, B. Determining Exception Context in Assembly Operations from Multimodal Data. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 7962. https://doi.org/10.3390/s22207962</p> <p>The dataset consists of color images of different outcomes of the ring insertion task as well as the corresponding Cartesian pose of the robot end-effector and force torque data. Data is organized into four folders, representing one of the possible insertion slots. In each of the folders, data is further split into the following cases:<br> - error in insertion target position ranging from -3 to 3 mm in x direction in 1 mm steps,<br> - error in insertion target position ranging from -3 to 3 mm in y direction in 1 mm steps,<br> - no positional error,<br> - other unidentified error (bad insertion).</p> <p>Multiple attempts were made for each case.</p> <p>Each entry has unique date-time tag and comprises: RGB image (.jpg) and .mat file with <em>states </em>object that contains reference and measured target pose in Cartesian space (positions and quaternions) and raw force-torque sensor data and force-torque data transformed to the tool frame.</p> <p>The experiments were performed with Franka Emika Panda collaborative robot. For acquisition of image data an Intel Realsense D435 RGB-D camera has been utilized. </p>
Multimodal data set for the investigation of the early stage of plasticity in a polycrystalline titanium sample
<p>This dataset is the result of several experiments to study the early stage of plasticity in a polycrystalline sample of a commercially pure alpha phase grade 2 titanium (CP-Ti family). The study aimed to achieve three critical goals: first, the acquisition of a 3D representation of the microstructure; second, the conduction of in situ measurements capturing grain-scale plasticity dynamics during a controlled tensile test; and third, a rigorous comparison of these experimental observations against the predictions derived from a microstructure-sensitive crystal plasticity simulation. This simulation was conducted on a digital twin of the titanium sample, aiming to assess the predictive accuracy of the model at the local scale. The data set was assembled from the different sources using the Pymicro package. </p>
Data for: Multimodal convergence in the pedunculopontine tegmental nucleus: motor, sensory, and theta-frequency inputs influence the activity of single neurons
<p>The pedunculopontine tegmental nucleus of the brainstem (PPTg) has extensive interconnections and neuronal-behavioural correlates. It is implicated in movement control and sensorimotor integration. We investigated whether single neuron activity in freely moving rats is correlated with components of skilled forelimb movement and whether individual neurons respond to both motor and sensory events. We found that individual PPTg neurons showed changes in firing rate at different times during the reach. This type of temporally specific modulation is like activity seen elsewhere in voluntary movement control circuits, such as the motor cortex, and suggests that PPTg neural activity is related to different specific events occurring during the reach. In particular, many neuronal modulations were time-locked to the end of the extension phase of the reach, when fine distal movements related to food grasping occur, indicating strong engagement of PPTg in this phase of skilled individual forelimb movements. In addition, some neurons showed brief periods of apparent oscillatory firing in the theta range at specific phases of the reach-to-grasp movement. When movement-related neurons were tested with tone stimuli, many also responded to this auditory input, allowing for sensorimotor integration at the cellular level. Together, these data extend the concept of the PPTg as an integrative structure in the generation of complex movements, by showing that this function extends to the highly coordinated control of the forelimb during skilled reach to grasp movement and that sensory and motor-related information converges on a single neuron, allowing for direct integration at the cellular level.</p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
Continental Reworking in the Eastern South China Block and Its Adjacent Areas Revealed by F-J Multimodal Ambient Noise Tomography
<p><span>It contains ZZ-component cross-correlation functions and the 3-D Vsv model in the eastern South China Block.</span></p>
A Mid-crustal Channel of Positive Radial Anisotropy Beneath the Eastern South China Block From F-J Multimodal Ambient Noise Tomography
<p><span>It contains the CCAB-FJpy, the Python code package for calculating the multi-component cross-correlation functions (CCFs) and extracting multimodal Love dispersions with multi-component frequency-Bessel (F-J) transform method. . Also, it contains multi-component CCFs (i.e., RR and TT), Vsh model and 3-D crustal radial anisotropy model of the eastern South China Block (ESCB).</span></p>
Source data for publication "A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory" in Journal of Hepatology
<p>Processed genomic and transcriptomic data for the publication <a href="https://doi.org/10.1016/j.jhep.2024.05.017">https://doi.org/10.1016/j.jhep.2024.05.017</a>.</p> <p>cnv_segmentation.tsv: CNV segmentation file from Sequenza.</p> <p>cnv_arm.tsv: Significant arm level CNV events called by GISTIC, from broad_values_by_arm.txt file.</p> <p>cnv_gene.tsv: Gene level CNV events called by GISTIC, from all_threshold_by_genes.txt file. </p> <p>RNA_raw_counts.tsv: Raw RNA-seq read counts from featureCounts.</p> <p>snv_indel.tsv: All SNV and Indel called with annotation from Funcotator.</p> <p> </p>
Data from: Changes in brain structure and function following exposure to oral LSD during adolescence: A multimodal MRI study
<p><em>Background</em>: LSD is a hallucinogen with complex neurobiological and behavioral effects. Underlying these effects are changes in brain neuroplasticity. This is the first study to follow the developmental changes in brain structure and function following LSD exposure in periadolescence. We hypothesized LSD given during a time of heightened neuroplasticity, particularly in the forebrain, would affect cognitive and emotional behavior and the associated underlying neuroanatomy and neurocircuitry. </p> <p><em>Methods:</em> Female and male mice were given vehicle, single, or multiple treatments of 3.3 µg of LSD by oral gavage starting on postnatal day 51. Between postnatal days 90-120 mice were imaged and tested for cognitive and motor behavior. MRI data from voxel-based morphometry, diffusion weighted imaging, and BOLD resting state functional connectivity were registered to a mouse 3D MRI atlas with 139 brain regions providing site-specific differences in global brain structure and functional connectivity between experimental groups.</p> <p><em>Results:</em> Motor behavior and cognitive performance were unaffected by periadolescent exposure to LSD. Differences across experimental groups in brain volume for any of the 139 brain areas were few in number and not focused on any specific brain region. Multiple exposures to LSD significantly altered gray matter microarchitecture across much of the brain. These changes were primary associated with the thalamus, sensory and motor cortices, and basal ganglia. The forebrain olfactory system and prefrontal cortex and hindbrain cerebellum and brainstem were unaffected. The functional connectivity between forebrain white matter tracts and sensorimotor cortices and hippocampus was reduced with multidose LSD exposure.</p> <p><em>Conclusion:</em> Does early exposure to LSD in periadolescence have lasting effects on brain development? There was no evidence of LSD having consequential effects on cognitive or motor behavior when animal were evaluated as young adults 90-120 days of age. Neither were there any differences in the volume of specific brain areas between experimental conditions. The pronounced changes in indices of anisotropy across much of the brain would suggest altered gray matter microarchitecture and neuroplasticity. The reduction in connectivity in forebrain white matter tracts with multidose LSD and consolidation around sensorimotor and hippocampal brain areas requires a battery of tests to understand the consequences of these changes on behavior.</p>
ScienceDex guides
Understand access before you commit
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.