dataesr/scientific-paragraphs-categorization huggingface dataset:
= -0. 553, p = 0. 003; contractile muscle volume: r = 0. 403, p = 0. 041; figure 5, C and D), but 6MWT did not correlate with muscle volume (r = 0. 245, p = 0. 184) and T2 water (r = -0. 251, p = 0. 261). For the individual muscle groups after Bonferroni correction, 6MWT correlated with the fat fraction of the ankle dorsiflexors (r = -0. 498, p = 0. 007) and ankle plantarflexors (r = -0. 574, p = 0. 001). The 6MWT did not correlate with any of the other MRI outcome measures of the functional muscle groups (table e-3 available from Dryad, doi. org/10. 5061/dryad. 4nb96c1).
Acknowledgment The authors thank all participants for participating in this study. They thank the  OPTIMISTIC Consortium  for critical remarks on the data analysis and interpretation. In addition, they thank  Barbara Janssen  for help with the MRI protocol,  Ferroudja Daidj  for participant management in Paris, and  Sjaak van Asten ,  Lydia Overtoom , and  Justine van Eerden  for their help with data acquisition or processing.
r = -0. 553, p = 0. 003; contractile muscle volume: r = 0. 403, p = 0. 041; figure 5, C and D), but 6MWT did not correlate with muscle volume (r = 0. 245, p = 0. 184) and T2 water (r = -0. 251, p = 0. 261). For the individual muscle groups after Bonferroni correction, 6MWT correlated with the fat fraction of the ankle dorsiflexors (r = -0. 498, p = 0. 007) and ankle plantarflexors (r = -0. 574, p = 0. 001). The 6MWT did not correlate with any of the other MRI outcome measures of the functional muscle groups (table e-3 available from Dryad, doi. org/10. 5061/dryad. 4nb96c1).
Overall, there are both a significant post-TMS reduction for the contralateral hemifield and a difference between pre-/post-TMS ratios between the two hemifields. Therefore, the post-TMS increase in contrast sensitivity was significant for the retinotopically stimulated hemifield, but not for the ipsilateral, and the pre-/post-TMS ratio was significantly different between the two hemifields. Figure  1a  shows not only a change in the mean of the populations but also changes in the distribution of the dataset. In order to test whether the group effect obtained stands when analyzed on a subject by subject basis and to characterize the changes in the distribution of the dataset under different conditions, we ran a permutation test and then plotted the empirical cumulative distribution functions of the 12 hemispheres analyzed (Fig.
The model then hypothesizes an iterative feedback-feedforward iteration, which gives rise to the final representation, discarded of the prediction. Indeed, our results show that the inhibition of extrastriate areas such as V2 prevents such a local inhibition from taking place, increasing contrast sensitivity as a result. Given the difficulty in running neuroimaging-guided brain stimulation studies, the design of the present study has some limitations, mostly concerning stimulation propagation and lack of a control condition for the collinear configuration: Regarding the former, one could expect that the short eccentricity at which the experiment was conducted could add as a confound a potential stimulation of the striate cortex during the rTMS procedure. However, analysis of the TMS spread conducted with the software SimNIBS showed that the stimulation did not reach V1 (see Figure  1  supplementary material).
Apparatus Stimuli were generated with MATLAB Psychtoolbox  (Brainard 1997; Pelli 1997 ) and displayed on a 17″ Dell M770 CRT monitor, refresh rate 60 Hz. Screen resolution was 1024 × 768 pixels, each pixel subtending 1. 9 arcmin. Mean luminance as measured with a Minolta CS110 (Konica Minolta, Canada) was 47. 6 cd/m 2. Bits# videocard (Cambridge Research Systems, Cambridge, UK) was used to increase the contrast range to 12 bits of luminance resolution. Images were linearized through a 12-bit gamma-corrected lookup table (LUT).
Impact of TMS at the population level The distributions of ratios z-scores obtained for the test ipsilateral and contralateral were compared to each other with a two-sample Kolmogorov-Smirnov test. They were also, respectively, compared to the normal distribution expected in case of differences simply caused by random Gaussian noise (mean = 0 and standard deviation = 1) with one-sample Kolmogorov-Smirnov tests. The same procedure was applied with the related distributions of ratios p values, which were compared to each other and to the uniform distribution expected in case of Gaussian noise through onesample and two-sample Kolmogorov-Smirnov tests. The analyses described above were implemented by using the Scipy 0. 14 and Numpy 1. 8. 1 packages for Python (http:// www. scipy. org) (Oliphant 2007a, b).
2012) (Fig. 3c-e ). Additionally, the spread of the TMS was estimated with the software Sim-NIBS to ensure that the stimulation did not spread to visual areas other than V2. This system uses ultrasound markers in a 3D space to track the position of the coil relative to the participant's head. This is achieved by specifying fixed landmarks on the participant's head, namely inion, nasion, and left and right ear. The neuronavigation system thus provides topographic information of the head-based transmitters relative to a participant-based coordinate frame. The same landmarks digitized on the participant's head are specified on the head reconstruction of the anatomical MR data to achieve TMS-MRI coregistration. After coregistration, coil and head movements are registered online and are visualized in real time at correct positions relative to the anatomical reconstruction of the participant's brain.
This mapping allowed us to define a portion of the visual field corresponding to each region within a single degree of eccentricity. For each of these four conditions, a run lasted 204 s and consisted of six full cycles of 32 s plus 12 initial s that permitted the establishment of the visual responses steady state. All the data were collected on a 3T scanner (Philips Achieva), using a standard 32 channels head coil. The functional data were acquired using (T2* weighted) echoplanar imaging (EPI). For the retinotopic mapping, we used: TR: 2 s; TE: 30 ms; field of view (FOV): 210 mm; voxel size 2 × 2 × 2 mm; no gap thickness; and SENSE factor: 2. 5. A run comprised either 102 volumes (retinotopic mapping). A volume contained 33 slices that covered occipital and parietal cortices. We recorded eight runs for the retinotopic mapping experiment (two runs for each condition).
The session also included the acquisition of a high-resolution anatomical image using a T1-weighted magnetizationprepared rapid gradient-echo (MPRAGE) sequence (160 slices; TR: 2300 ms; TE: 3. 93 ms; FA: 12°; FOV: 256 mm; and voxel size 1 × 1 × 1 mm). This anatomical image was used as a reference to which the functional images from all the experiments were aligned. Preprocessing fMRI data were analyzed using the BrainVoyager QX software (v2. 8, Brain Innovation) and MATLAB in-house built scripts. Preprocessing included slice scan time correction, 3D motion correction using trilinear/sinc interpolation, and high-pass filtering (0. 01 Hz). For each individual participant, functional data were coregistered on the anatomy. Functional and anatomical data were brought into ACPC space using cubic spline interpolation.
An in-house MATLAB script was used in order to obtain the averaged signal of the four runs of each condition in the wedges (two clockwise and two anticlockwise) and rings (two expanding and two contracting) and to create the file containing the values to be projected in the surface maps in BrainVoyager, where the central V2 spot was manually extracted based on the observed activations. Once marked the surface, this was projected back on the anatomical T1 using BrainVoyager (Fig. 3a, b ).
Acknowledgements Author MM was supported by the " Fondation Fouassier " ( 2013 00039351  and  2014 00048124 ). Author FAJ was supported by the  CRC1193 : Neurobiology of resilience grant from  German Research Foundation. Authors would like to thank to  Ruffin Van Rullen  for the facilitation of the Neuronavigation system,  Pascal Girard  and  Jean-Michel Hupé  for the critical review of this manuscript and the organizers of ECVP 2014 in Belgrade, for the stimulating atmosphere that originated this work.
Results fUS reveals brain activation related to cognitive tasks. We first imaged cue-evoked hemodynamic responses in the SEF of awake behaving primates as they were instructed to start an oculomotor task. Twenty successive trials (fixation, saccade, and antisaccade trials) were performed. We observed the responses using fUS imaging with a 15 MHz ultrasonic array (see Fig. 1a ) inserted into the 20 × 20 mm 2 electrophysiological chamber. The high temporal sampling rate (10 ms) enabled by fUS imaging means that cardiac pulsatility and respiratory motion artifacts occurring at different Doppler frequencies could be unambiguously subtracted from the collected data (Fig. 1b , see Supplementary Fig. 1a    between the no-rule and rule trials reveals a strong activation of the SEF region during the rule trials (Fig.
During data acquisition, the eye position of the primate was monitored at 1 kHz with an infrared video eye tracker (Eyelink 1k, SR-Research), which enabled live control of the behavioral paradigm and the delivery of a reward based on the success or failure of a visual task. The designation 'behavior signal' refers to an oscillating signal created with a period equal to the time between the start of trials, as shown in Fig. 1c. This period contains information about the time response of the primate. Implant and probe for fUS imaging in awake behaving monkeys. The head fixation system was a titanium headpost (Non-dental acrylic implant, Crist Instrument, MD, USA). After behavioral training of the animals, a craniotomy of 20 mm × 20 mm above the supplementary region was performed (Medio-Lateral: 0; Antero-Posterior: +26), and an electrophysiological recording chamber was implanted (CILUX chamber, Crist instrument).
A custom miniature 15-MHz ultrasound probe (Fig. 1a ) (128 elements, 15 MHz, 100 × 100 μm² of spatial resolution) with acoustic coupling gel was placed in the chamber. The acquired images had a pixel size of 100 × 100 μm, a slice thickness of 400 μm and a field of view (FOV) of 14 mm × 10 mm (see Supplementary Fig. 4a  for different FOVrelated to different probe frequency). The FOV of our miniature probe allowed imaging of superficial and deep cortical regions including the SEF and the ACC. Functional ultrasound (fUS). We measured CBV variations with an fUS sequence modified from a previously used Power Doppler sequence  53. As fUS signals are proportional to CBV  5  , we refer to the acquired images as 'CBV images'. Data were acquired by emitting continuous groups of 11 planar ultrasonic waves tilted at angles varying from -10 to 10 degrees using an ultrafast ultrasound research scanner (256 electronic channels, 60 MHz sampling rate).
Ultrasonic echoes were summed to create a single compound image acquired every 2 ms. After spatial temporal filtering based on the singular value decomposition of these ultrasonic images  54  for fine discrimination between blood flow and tissue motion, final Doppler images were created by averaging 125 compound ultrasonic images over a sliding 250 ms window with 10 ms overlaps. At a frame rate of 100 Hz, each 300 s acquisition period generated a final sequence of 30,000 Doppler images. Data processing. The SEF was spatially located by mapping activated pixels obtained from computing the normalized correlation coefficient between the local Power Doppler signal obtained from fUS and the temporal block pattern (no-rule vs. rule) of the visual stimulus. The power Doppler signal was then averaged within a large region of interest (ROI) (~240 pixels) and a 1-pixel ROI in the SEF and in similar ROIs in a contra-lateral controlled region (Fig.
3b ), either in the frequency domain (peak frequency) or time domain (mean CBV oscillation period). For all experiments, the correlation between the SEF activity and behavior was plotted against the related success rate (Fig. 3d ). Spectrograms were drawn using the inbuilt MatLab function with a Hamming window of 15% for the overall acquisition time and 80% for overlapped samples (Fig. 3a , Supplementary Fig. 2a-d ) to study the shift frequency of CBV oscillations in regions of interest during visual tasks.
Then, the otoferlin sequence was edited in silico by defining excision sites. The absolute boundaries for each of the six C2 domains of the mouse otoferlin protein were selected based on several factors. The approximate location of each C2 domain could be delineated based on the output from the SMART database. This server (http://smart. emblheidelberg. de/) can identify the presence of each domain; however, the structural boundaries are not necessarily clear. With the approximate boundaries defined, each domain was aligned and sorted against an extensive library of known type 1 and type 2 C2 domain ferlins. The resulting phylogenetic tree sorted each otoferlin C2 domain according to its most likely topology. Once the topology was defined, each of the eight ␤-strands was located by aligning the known "in-out-in-out" periodicity characteristic of C2-domain ␤-strands.
3 to 5 mM to enhance the amplitude of Ca 2ϩ currents to levels nearby body temperature. All experiments were performed at room temperature (22°C-24°C). Patch-clamp recording and capacitance measurement. All IHC recordings were performed in the 20 -40% normalized distance from the apex, an area coding for frequencies ranging from 8 to 16 kHz, using an EPC10 amplifier controlled by Patchmaster pulse software (HEKA Elektronik). For the mini-Otof C2-EF experiments, only eGFP-expressing IHCs were recorded. For the other mini-Otof experiments, in the absence of eGFP fluorescent signaling, IHCs were randomly recorded in the tissue preparation. Patch pipettes were pulled with a micropipette puller (P-97 Flaming/Brown; Sutter Instrument) and fire polished with an MF-830 microforge (Narishige) to obtain a resistance range of 2 to 4 M⍀.
Patch pipettes were filled with an intracellular cesium-based solution containing the following (in mM): 145 CsCl, 1 MgCl 2 , 5 HEPES, 1 EGTA, 20 TetraEthylAmmonium chloride, 2 ATP, and 0. 3 GTP, pH 7. 2, 300 mOsm. Changes in cell membrane capacitance (⌬C m ) were used to monitor fusion of synaptic vesicles during exocytosis. The C m was measured accordingly to the  Lindau and Neher (1988)  technique by using the lock-in amplifier Patchmaster software (HEKA) and applying a 1 kHz command sine wave (20 mV amplitude) at holding potential (Ϫ80 mV) before and after the pulse experiment. Because recording conditions can greatly influence capacitance measurements, only IHC patch-clamp recordings with low series resistance below 10 m⍀ and a maximum leak current of 25 pA (at V h ϭ Ϫ80 mV) were considered in the present study.
Images were analyzed with ImageJ software (W. S. Rasband, NIH). RT-PCR to detect C2-ACEF and C2-ACDF transcripts. Two organ of Corti apical turns from either P50 injected with otoferlin C2-ACEF or C2-ACDF construct were freshly dissected and stored in separate low-DNA-binding tubes containing l l of RNase inhibitor (Amresco, catalog #E633) maintained in dry ice. Total RNA was extracted from tissues using the RNAzol protocol  (Molecular Research Center, catalog #RN 190). mRNA were purified using the Dynabeads mRNA Purification Kit (ThermoFisher Scientific, catalog # 61006) and reverse transcribed to cDNA by using the oligodT primers linked to the magnetic beads and AffinityScript Multiple Temperature Reverse Transcriptase (Stratagene, catalog #600107). Reverse transcription was performed at 37°C overnight.
Specific PCR amplification was performed by using 0. 45 g of cDNA and Herculase II Fusion DNA Polymerase (Stratagene, catalog #600677). Specific sense and antisense primers (Eurofins Genomics) are summarized in Table  1. Data analysis. Data were analyzed using ImageJ, Patchmaster (HEKA Elektronik), and Origin 9. 1 (OriginLab). Statistical analyses were performed using independent two-sample Student t tests and, for multiple comparisons, either one-way ANOVA (two-way ANOVA in the case of Fig. 3 B, C, G ) followed by Tukey's post hoc test for normally distributed data (Shapiro test) or the nonparametric Kruskal-Wallis test followed by Dunn's test for not normally distributed data. Means are expressed as ϮSEM. In the figures, asterisks indicate statistical significance with p Ͻ 0. 05. A statistical F test was used to compare the exocytotic curve efficiency in Figure  3E.
3A-C ; data from four mice of three different litters). Note that in this latter set of experiments with Otof-C2-DEF, in the absence of eGFP (because of the size limit of the cDNA insertion in the AAV vector; see Materials and Methods), randomly selected IHCs were recorded in the organ of Corti. Only one of seven IHCs did not show a significant increase in exocytosis compared with noninjected Otof Ϫ/Ϫ mice. This high rate of exocytosis rescue in AAV-Otof-C2-DEF-injected cochleae was consistent with the high percentage (Ͼ90%) of IHCs expressing the mini-otoferlin protein (Fig. 1E ). The kinetics of exocytosis for Otof-C2-DEFtransduced IHCs were significantly higher than in Otof Ϫ/Ϫ IHCs (two-way ANOVA, p ϭ 6. 8 10 Ϫ13 ; post hoc Tukey's test, p ϭ 1. 7 10 Ϫ3 ; Fig. 3C ). Remarkably, recordings from Otof Ϫ/Ϫ IHCs of AAV-Otof-C2-ACEF-injected cochlea displayed a more efficient rescue of exocytosis when stimulated with brief depolarizing impulses lasting Ͻ20 ms (Fig.
5 Ϯ 6. 5 ms. In our experimental conditions, whereas the data for Otof-C2-EF (RRP ϭ 5. 0 Ϯ 0. 8 fF with ϭ 15. 5 Ϯ 7. 3 ms), Otof-C2-ACEF (RRP ϭ 5. 9 Ϯ 0. 3 fF with ϭ 8. 5 Ϯ 1. 9 ms), and Otof-C2-DEF (RRP ϭ 4. 5 Ϯ 0. 5 fF with ϭ 19. 0 Ϯ 5. 2 ms) could be fitted with a single exponential, we could not accurately fit the data for Otof-C2-ACDF and Otof Ϫ/Ϫ with such a function and therefore determine RRP values for these conditions. Therefore, to be able to statistically compare all mini-Otof conditions and controls within a single one-way ANOVA, we instead compared the initial rate of RRP exocytosis. This RRP rate was obtained for each condition by fitting the first four time points of each of the kinetics with a linear function. All mini-Otofs, except Otof-C2-ACDF, showed a significant increase in the rate of the RRP exocytosis compared with Otof Ϫ/Ϫ IHCs (Fig.
This work was supported by a grant from the  Fondation Pour l'Audition  (D. D. ),  European Commission  Grant  ERC-2011-ADG_294570 ,  LabEx Lifesenses  Grant  ANR-10-LABX-65 , and  French government  funds managed by the  Agence Nationale de la Recherche (EargenCure). *S. S. and D. D. contributed equally to this work. The authors declare no competing financial interests.
Materials and Methods The perceptual discrimination task The study was approved by the Hebrew University Committee for the Use of Human Subjects in Research. Recruitment was based on the online labor market Amazon Mechanical Turk  48. Data were collected from 100 participants (51 males, 49 females; 91 dextrals, 7 sinistral, 2 ambidextrous; mean age = 39 years, min = 22 years, max = 71 years). All participants were Mechanical Turk's Masters, located in the United States of America. All participants reported normal or corrected to normal vision and no history of neurological disorders. The experiment was described as an academic survey of visual acuity. A base monetary compensation was given to all applied participants for the participation. In order to encourage good performance throughout the experiment, an additional bonus fee was given for every correct response and another bonus was guaranteed to 10% of participants with highest scores.
The motor task The study was approved by the Hebrew University Committee for the Use of Human Subjects in Research. The experiment was described as an academic survey testing speed of motion. Data were collected from 20 participants (13 males, 7 females; all dextrals; mean age = 25 years, min = 19 years, max = 41 years) who were recruited using on-campus advertising. All participants reported normal or corrected to normal vision and no history of neurological disorders.
This model has a single parameter, the decision threshold,. To fit the model to the data, a second parameter, which accounts for the component of the reaction-time that is independent of the decision process,  , is added  56, 59, 60. By construction, there are no ICBs in this model and it was used as a baseline for comparison with the other three We fit each of the four DDM variants to each of the datasets using the HDDM Python toolbox, which allows for the construction of Bayesian hierarchical DDMs  61. HDDM uses Bayesian Markov-chain Monte Carlo sampling for generating posterior distributions over both subject-level and group-level model parameters, rather than point estimates of only subject-level parameters. To accomplish this, HDDM uses informative prior distributions on the group-level parameters, that constrain the parameters to a plausible range given past experiments  61, 62.
Because in the impossible trials there is no apriori reason to assume that one action is more likely than the other, we modified the code such that  = 0. Notably, comparable posteriors are obtained also when using  = 2 (not shown). (3) The assumptions regarding the width of the prior distribution of initial conditions can constrain the values of the estimated initial conditions in the HDDM fitting procedure, thus limiting the extent to which the initial conditions can capture the ICBs in the DDM. Therefore, we considered a wider prior distribution of initial conditions, by increasing the standard deviation of  in 61 from 0. 05 to 5. Notably, when keeping the standard deviation of  at 0. 05, the contribution of initial conditions to the ICBs in the resultant fitted DDMs is even smaller (not shown).
In the 'IC bias' DDM  = 0, as in the baseline DDM. However, by contrast,  is estimated from the data, this is in addition to  and. (3) In the 'drift bias' DDM, we assumed that  = 0. 5 and estimated ,  and. (4) In the 'IC+drift bias' DDM, both  and  were estimated from the data, in addition to  and. Hierarchical Bayesian estimation of the DDM parametersThe dataset of the vertical bisection task includes 20 impossible trials performed by 100 participants. The motor task includes 10 datasets (each pair of dots was considered a task and was analyzed separately). Each task was tested on 20 participants, each performing 20 decisions. The recurrent network simulations included 8 datasets, each corresponding to a different level of specific inhibition. Each of these dataset consisted of the responses made by 200 networks, each tested on 500 impossible trials.
Acknowledgements We thank  Thomas Boraud ,  Gianluigi Mongillo ,  Haim Sompolinsky  and  Talia Tron  for discussions. This work was conducted within the scope of the  France-Israel Laboratory of Neuroscience. D. H. thanks the  Department of Neurobiology at the Hebrew University  for its warm hospitality. This work was supported by the  Israel Science Foundation  (Y. LO. , Grant No. 757/16 ), the  DFG  (Y. LO. ), the  Gatsby Charitable Foundation  (Y. LO. ),  ANR-09-SYSC-002-01  (D. H. ) and the  France-Israel High Council for Science and Technology (D. H. and Y. LO. ).
models. ( 2 ) 2 In the 'IC bias' DDM  = 0, as in the baseline DDM. However, by contrast,  is estimated from the data, this is in addition to  and. (3) In the 'drift bias' DDM, we assumed that  = 0. 5 and estimated ,  and. (4) In the 'IC+drift bias' DDM, both  and  were estimated from the data, in addition to  and. Hierarchical Bayesian estimation of the DDM parametersThe dataset of the vertical bisection task includes 20 impossible trials performed by 100 participants. The motor task includes 10 datasets (each pair of dots was considered a task and was analyzed separately). Each task was tested on 20 participants, each performing 20 decisions. The recurrent network simulations included 8 datasets, each corresponding to a different level of specific inhibition. Each of these dataset consisted of the responses made by 200 networks, each tested on 500 impossible trials.