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The persistence of back pain following acute back “sprains” is a serious public health problem with poorly understood pathophysiology. The recent finding that human subjects with chronic low back pain (LBP) have increased thickness and decreased mobility of the thoracolumbar fascia measured with ultrasound suggest that the fasciae of the back may be involved in LBP pathophysiology. This study used a porcine model to test the hypothesis that similar ultrasound findings can be produced experimentally in a porcine model by combining a local injury of fascia with movement restriction using a “hobble” device linking one foot to a chest harness for 8 weeks. Ultrasound measurements of thoracolumbar fascia thickness and shear plane mobility (shear strain) during passive hip flexion were made at the 8 week time point on the non-intervention side (injury and/or hobble). Injury alone caused both an increase in fascia thickness (p = .007) and a decrease in fascia shear strain on the non-injured side (p = .027). Movement restriction alone did not change fascia thickness but did decrease shear strain on the non-hobble side (p = .024). The combination of injury plus movement restriction had additive effects on reducing fascia mobility with a 52% reduction in shear strain compared with controls and a 28% reduction compared to movement restriction alone. These results suggest that a back injury involving fascia, even when healed, can affect the relative mobility of fascia layers away from the injured area, especially when movement is also restricted. pigpaper_thicknessUltrasound Thickness measurementspigpaper_SSUltrasound shear strain measurementspigpaper_wtPig Weightspigpaper_gait_dataGait measurementspigpaper_cgrp_dataSpinal cord substance P and CGRP measurementspigpaper_cortisolSalivary cortisol measurements

Experiment Description: This experiment involved 12 healthy subjects with no prior experience on neurofeedback or BCI, and without any known neurological disorders. All participants are right-handed, except one ambidextrous (participant #5). All participants have provided their signed informed consent for participating in the study in accordance with the 1964 Declaration of Helsinki. The experiment had been conducted in a laboratory environment under controlled conditions. The subjects went through three sessions lasting maximum two hours, during three consecutive days and each day at approximately at the same hour. During each session, participants underwent three different conditions. The first condition was always the ���resting-state���: the user was asked to keep the eyes open for two minutes staring at a screen with a green cross and a red arrow pointing up, and then closed for the other two minutes. After this, two more conditions followed related to a Motor Imagery (MI) task performed in a randomized order between left|right-hand movement. The two MI conditions consisted of two phases each: a training phase and a test phase. The general experimental routine for both of them was the same: each trial lasted 6 seconds (2 seconds baseline and 4 seconds MI), forewarned by the appearance of a green cross on the screen and a concomitant beep-sound a second before the onset of the task. Then, an arrow was appearing pointing left or right, and the subject had to imagine the movement of the corresponding arm reaching an object in front of the Baxter Robot (Rethink Robotics, Bochum, Germany). For both phases, 20 trials from left and 20 trials for right MI were generated in a randomized order, for a total of 40 trials. Finally, there was an inter-trial interval that extended randomly between 1.5 and 3.5 seconds. Overall, this study resulted into 180 EEG datasets. Data Description: Data Format General Data Format (GDF) Sampling Rate 250 Hz Channels 32 EEG + 3 ACC. EEG system LiveAmp 32 with active electrodes actiCAP (Brain Products GmbH, Gilching, Germany) Events: Code Description 32775 Baseline Start 32776 Baseline Stop 768 Start of Trial, Trigger at t=0s 786 Cross on screen (BCI experiment) 33282 Beep 769 class1, Left hand - cue onset 770 class2, Right hand - cue onset 781 Feedback (continuous) - onset 800 End Of Trial 1010 End Of Session 33281 Train 32770 Experiment Stop Directory Tree: ROOT | chanlocs.locs | | +--- USER # | +---SESSION # | | +---CONDITION # | | | \---RESTING_STATE | | | +---1st_PERSON | | | | TRAINING | | | | ONLINE | | | +---3rd_PERSON | | | | TRAINING | | | | ONLINE Approved by the Ethics Committee of CHULN and CAML (Faculty of Medicine, University of Lisbon) with reference number: 245/19.

In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user’s ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person’s actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control. TrajectoriesAFCProcessedDataDataReadMe

Background: Several life history and ecological variables have been reported to affect the likelihood of species becoming urbanized. Recently, studies have also focused on the role of brain size in explaining ability to adapt to urban environments. In contrast, however, little is known about the effect of colonization pressure from surrounding areas, which may confound conclusions about what makes a species urban. We recorded presence/absence data for birds in 93 urban sites in Oslo (Norway) and compared these with species lists generated from 137 forest and 51 farmland sites surrounding Oslo which may represent source populations for colonization. Results: We found that the frequency (proportion of sites where present) of a species within the city was strongly and positively associated with its frequency in sites surrounding the city, as were both species breeding habitat and nest site location. In contrast, there were generally no significant effects of relative brain mass or migration on urban occupancy. Furthermore, analyses of previously published data showed that urban density of birds in six other European cities was also positively and significantly associated with density in areas outside cities, whereas relative brain mass showed no such relationship. Conclusions: These results suggest that urban bird communities are primarily determined by how frequently species occurred in the surrounding landscapes and by features of ecology (i.e. breeding habitat and nest site location), whereas species’ relative brain mass had no significant effects. Primary data on presence/absence of bird species in Oslo, NorwayThe data file contains information on presence/absence of 90 bird species in 93 urban sites in Oslo and 176 rural sites in the surroundings of Oslo, together with information on location and size of each site.Daleetal-datafile.xlsx

Dataset associated with the original research article published in "Frontiers in Human Neuroscience", by Weinman et al., doi: 10.3389/fnhum.2021.639773 The data is stored in a MATLAB file named EMG.mat. The workspace file contains the following variables: ECU FCR ECULLR FCRLLR time Variable description: ECU and FCR: A 5-dimensional cell array with normalized EMG tracks measured during perturbations. Each combination of condition was repeated 10 times, for each subject, so accessing a specific cell in this variable will provide a 10x2049 array of EMG data, with each row corresponding to one of the 10 repetitions. ECULLR and FCRLLR: A 5-dimensional cell array with LLR amplitude (average of the processed EMG signal during the time window corresponding to a long-latency response: 50 to 100 ms), also measured 10 times for each combination of conditions. Accessing a cell in this data will provide a 10x1 array of average EMG values, with each row corresponding to the average LLR for one of the 10 repetitions. Each of the variables listed above has 5 dimensions, each dimension corresponding to one factor. As an example, the dimensions of variable ECU are ECU{sub,v,d,t,inst}, defined as follows: sub = subject number (1 thru 11). Subject 10 has blank cells in the FCR dataset, due to data corruption from noise. v = velocity (1, 2, 3). 1=50 deg/s, 2=125 deg/s, 3=200 deg/s d = direction (1 or 2). 1=shorten, 2=stretch t = torque (1 or 2). 1=0 mNm, 2=200 mNm inst = instruction (1 or 2), 1=“yield”, 2=“do not intervene” As such, the variable EMG.ECU{1,1,1,1,1} is a 10x2049 variable including 10 repetitions of the timeseries of ECU EMG signal measured from subject 1, for perturbations at 50 deg/s, shortening the ECU (extension perturbations), with 0 mNm background torque, when the instruction was “yield”. time: A 1x2049 array of time values corresponding to the EMG readings in milliseconds. Values in this array are from 0 ms to 200 ms. The time series have 2049 datapoints (sampling frequency: 1024 Hz) representing up to the 200 ms from the perturbation onset. LLR averages were taken from the indexes representing 50 to 100 ms, or 513:1024.

DB1, Subject 1DB1_s1.zipDB1, Subject 2DB1_s2.zipDB1, Subject 3DB1_s3.zipDB1, Subject 4DB1_s4.zipDB1, Subject 5DB1_s5.zipDB1, Subject 6DB1_s6.zipDB1, Subject 7DB1_s7.zipDB1, Subject 8DB1_s8.zipDB1, Subject 9DB1_s9.zipDB1, Subject 10DB1_s10.zipDB1, Subject 11DB1_s11.zipDB1, Subject 12DB1_s12.zipDB1, Subject 13DB1_s13.zipDB1, Subject 14DB1_s14.zipDB1, Subject 15DB1_s15.zipDB1, Subject 16DB1_s16.zipDB1, Subject 17DB1_s17.zipDB1, Subject 18DB1_s18.zipDB1, Subject 19DB1_s19.zipDB1, Subject 20DB1_s20.zipDB1, Subject 21DB1_s21.zipDB1, Subject 22DB1_s22.zipDB1, Subject 23DB1_s23.zipDB1, Subject 24DB1_s24.zipDB1, Subject 25DB1_s25.zipDB1, Subject 26DB1_s26.zipDB1, Subject 27DB1_s27.zipDB2, Subject 1DB2_s1.zipDB2, Subject 2DB2_s2.zipDB2, Subject 3DB2_s3.zipDB2, Subject 4DB2_s4.zipDB2, Subject 5DB2_s5.zipDB2, Subject 6DB2_s6.zipDB2, Subject 7DB2_s7.zipDB2, Subject 8DB2_s8.zipDB2, Subject 9DB2_s9.zipDB2, Subject 10DB2_s10.zipDB2, Subject 11DB2_s11.zipDB2, Subject 12DB2_s12.zipDB2, Subject 13DB2_s13.zipDB2, Subject 14DB2_s14.zipDB2, Subject 15DB2_s15.zipDB2, Subject 16DB2_s16.zipDB2, Subject 17DB2_s17.zipDB2, Subject 18DB2_s18.zipDB2, Subject 19DB2_s19.zipDB 2, Subject 20DB2_s20.zipDB2, Subject 21DB2_s21.zipDB2, Subject 22DB2_s22.zipDB2, Subject 23DB2_s23.zipDB2, Subject 24DB2_s24.zipDB2, Subject 25DB2_s25.zipDB2, Subject 26DB2_s26.zipDB2, Subject 27DB2_s27.zipDB2, Subject 28DB2_s28.zipDB2, Subject 29DB2_s29.zipDB2, Subject 30DB2_s30.zipDB2, Subject 31DB2_s31.zipDB2, Subject 32DB2_s32.zipDB2, Subject 33DB2_s33.zipDB2, Subject 34DB2_s34.zipDB2, Subject 35DB2_s35.zipDB2, Subject 36DB2_s36.zipDB2, Subject 37DB2_s37.zipDB2, Subject 38DB2_s38.zipDB2, Subject 39DB2_s39.zipDB2, Subject 40DB2_s40.zipDB3, Subject 1DB3_s1.zipDB3, Subject 2DB3_s2.zipDB3, Subject 3DB3_s3.zipDB3, Subject 4DB3_s4.zipDB3, Subject 5DB3_s5.zipDB3, Subject 6DB3_s6.zipDB3, Subject 7DB3_s7.zipDB3, Subject 8DB3_s8.zipDB3, Subject 9DB3_s9.zipDB3, Subject 10DB3_s10.zipDB3, Subject 11DB3_s11.zip Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible.

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. openData_newxls files contain percent signal change per run.img/hdr files show tvalues for contrasts see readme.txt for more information