dataesr/scientific-paragraphs-categorization huggingface dataset:
The bundles were lowered in the dorsal hippocampi at stereotaxic coordinates AP = -4. 0 mm, ML = +/-2. 5 mm and DV = -1. 5 mm to -4. 5 mm relative to the Bregma. LFP acquisition LFP signals were collected from video-EEG device for offline processing. Intracranial electrode signals were fed through a high input impedance, DC-cut at 1 Hz, gain of 1000, 16-channel amplifier and digitized at 20 kHz (Xcell, Dipsi, Cancale, France), together with a synchronization signal from the ultrasound scanner. Custom-made software based on LabVIEW (National Instruments, Austin, TX, USA) simultaneously acquired video from a camera pointed at the recording stage. A regular amplifier was used, and no additional electronic circuit for artifact suppression was necessary. A large bandwidth amplifier was used, which can record local field potentials in all physiological bands (LFP, 0.
Ultrasound Acquisition Vascular images were obtained via the ultrafast compound Doppler imaging technique  53. The probe was driven by a fully programmable GPU-based ultrafast ultrasound scanner (Aixplorer, Supersonic Imagine, Aix-en-Provence, France) relying on 24-Gb RAM memory. We acquired 6000 ultrasound images at 500 Hz frame rate for 12 seconds, repeating every 52-60 s (refractory period of 40 seconds during which no data can be acquired). Each frame is a compound plane-wave frame, that is, a coherent summation of beamformed complex in phase/quadrature images obtained from the insonification of the medium with a set of successive plane waves with specific tilting angles  54. This compound plane wave imaging technique enabled the re-creation of a dynamic transmit focusing at all depths a posteriori in the entire field of view with few ultrasound emissions.
LFP/VIDEO analysis All analysis was performed in MATLAB. For each recording, the position of each recording site on the probe tract (four recording sites per probe) was identified by measuring its impedance while lowering the bundle in saline solution (Na-Cl 0. 9%) before implantation. Hippocampal theta and gamma rhythms were confirmed by phase inversion across recording sites in successive hippocampal layers, time-frequency decomposition and phase-amplitude crossfrequency coupling  18. Though we cannot completely exclude potential contamination of highfrequency LFP recordings by unit activity, we observed that fast gamma oscillations displayed typical phase-amplitude coupling patterns for all recordings robustly across animals. The size of our electrode diameters (25 to 50 microns) and the stability of our recordings throughout sessions decrease the probability that fast gamma events arose from correlated spiking activity.
CA1 recording sites were identified when peak gamma power occurs at the peak of the theta phase, corresponding to the CA1 stratum lacunosum-moleculare  18. Videos were processed frame by frame, first by drawing a fixed rectangle to isolate all pixels within the track. We then performed thresholding on the histogram of the pixels inside the track to isolate bright pixels of the rat's body and compute their barycenter. Animal's position is then computed frame by frame and smoothed with a gaussian kernel (half-width 500 ms). Speed vectors are then extracted in two dimensions and re-projected into the actual track coordinate to extract relevant body speed. Baseline evaluation & spatial averaging fUS data have been shown to be proportional to local CBV. Because it is not possible to derive absolute CBV levels, we normalized ultrasound data.
Atlas registration Coronal recordings were registered to two-dimensional sections from the Paxinos atlas 56 using anatomical landmarks, such as cortex edges, hippocampus outer shape and large vessels below brain surface as a reference. We performed manual scaling and rotation along each of the 3 dimensions to recover the most probable registration. Once performed, regions of interest were extracted using binary masks. To register diagonal planes that had no direct coronal sections, we segmented each two-dimensional recording plane into anatomical regions based on a 3D MRI-based whole-brain atlas, which provided labeling for 52 brain regions  57. To derive the functional regions in our ultrasound image, we designed a customized registration algorithm  31  , which projected our two-dimensional ultrasound plane onto a three-dimensional volumetric dataset.
Acknowledgements The authors would like to thank  S. Hubatz  for help with the track running protocol,  T. Watson  for technical help with current-lesioning protocol. A. B. received funding from the  Ecole Doctorale Frontières du Vivant ,  Programme Bettencourt. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n°  339244-FUSIMAGINE. This work was also partly supported by the  Fondation pour la Recherche sur le Cerveau (FRC)  (Program Rotary -Espoir en tête) and by the  AXA Research Fund  under the chair New hopes in medical imaging with ultrasound.
Participants Sixty-five healthy undergraduates enrolled in our study. Two participants' baseline salivary cortisol levels were more than three standard deviations away from the mean of the sample. Therefore, these two participants were considered as outliers, and their data were not included in the analyses. Three participants were also excluded from the analyses because they did not provide sufficient saliva for cortisol analysis. Additionally, as following instructions in the cued ASRT task (for details, see section 2. 4) is crucial, seven participants were excluded as they did not meet this requirement (for details, see section 2. 5; for analysis on the sample containing these participants, see Supplementary Material). The final sample consisted of 53 participants (15 men, 38 women; M age = 20.
Statistical analysis Statistical analyses were carried out by SPSS version 25. 0 software (SPSS, IBM). As participants were instructed to find the alternating sequence and report that during the task, performance on the sequence report (calculated as the average percentage of correctly reporting the sequence after the ASRT task blocks) could show whether participants adequately followed the instructions of the task. In the present study, seven participants exhibited below 50% performance, suggesting that they did not follow the instructions properly and were therefore excluded from the analyses (see also section 2. 1; for analysis on the sample containing these participants, see Supplementary Material). Statistical analysis of the effectiveness of stress induction was the same as in prior studies (e.
Each epoch consists of five blocks, thus, we analyzed five epochs of the ASRT task. As described in section 2. 4, each trial was determined as the last element of a pattern high, random high, or random low triplet. The median of RT data for correct responses was calculated for each participant in each epoch, separately for the three trial types. As presented above, two aspects of regularity extraction, sequence learning and statistical learning can be assessed by the ASRT task  (Németh et al. , 2013;  for further details, see Introduction and the task description above). To examine the two aspects, RT data were analyzed in a mixed design ANOVA. At first, as statistical learning is a rapid process  (Kóbor et al. , 2018; Simor et al. , 2019) , we focused on the early phase of the task. We used a mixed design ANOVA with PROBABILITY (random high vs random low trials) as a within-subject factor and GROUP (stress vs control) as a between-subject factor on the RT data showed in the first epoch.
Similarly, at first, sequence learning was compared only in the last epoch of the task as this learning process shows a gradual trajectory  (Kóbor et al. , 2018; Simor et al. , 2019) , and therefore, the effect of stress may emerge in this later phase of learning. Hence, first, we used a mixed design ANOVA with ORDER (pattern vs random high trials) as a within-subject factor and GROUP (stress vs control) as a between-subject factor on the RT data showed in the last epoch. Next, to investigate learning in the learning phase (i. e. , on the whole task), statistical learning was quantified with a mixed design ANOVA with PROBABILITY (random high vs random low trials) and EPOCH (1-5) as within-subject factors and GROUP (stress vs control) as a between-subject factor. Similarly, sequence learning was quantified with a mixed design ANOVA with ORDER (pattern vs random high trials) and EPOCH (1-5) as within-subject factors and GROUP (stress vs control) as a between-subject factor.
Acknowledgements This research was supported by the  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 );  Hungarian Scientific Research Fund  ( NKFIH-OTKA K 128016 , to D. N. ,  NKFIH-OTKA PD 124148  to K. J. );  János Bolyai Research Scholarship of the Hungarian Academy of Sciences  (to K. J. );  IDEXLYON Fellowship  of the  University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) (to D. N. ). We thank  Kate Schipper  for her comments and suggestions on the previous version of the manuscript.
ACKNOWLEDGMENTS Sincere thanks are extended to  Drs. L. Carver ,  E. Head ,  W. Poon ,  H. LeBlanc ,  F. Culicchia ,  C. Eicken , and  C. Hebel  for short postmortem interval (PMI) human brain and/or retinal tissues or extracts, miRNA array work and initial data interpretation, and to  D Guillot  and  AI Pogue  for expert technical assistance. Thanks are also extended to the many neuropathologists, physicians and researchers of  Canada  and the  United States  who have provided high quality, short  PMI , human  CNS , retinal tissues or extracted total brain and retinal RNA and biofluids (ECF,  CSF , and blood serum sample) for scientific study.
Analysis Letswave 5, an open source toolbox (https://www. letswave. org) running over MATLAB R2013b (The MathWorks, USA), was used for the data analysis. Preprocessing-Data were bandpass filtered from 0. 05 to 140 Hz with a fourth-order zero-phase Butterworth filter. Sequences were coarsely segmented from 2 s before to 65 s after sequence onset. To correct for blinks, an independent component analysis was applied for the six participants who blinked more than 0. 2 times/s on average throughout the testing sequences (M = 1. 20 blinks/s; SD = 0. 22; as in, e. g. ,  Retter & Rossion, 2016a). Channels with abrupt deflections of ± 100 μV or above in more than one epoch were linearly interpolated with the neighboring 3-4 channels (M = 0. 75 channels; SE = 0. 86 channels; range = 0-3 channels, i.
e. , up to no more than 3% of channels). Data were re-referenced to the average of all 128 EEG channels. Each 7-s presentation duration step was re-segmented in two ways: 1) beginning from -200 ms before the first face stimulus onset, to capture face-categorization responses; and; 2) beginning exactly at face onset, in order to isolate the stimulus-presentation response to the full 7 s of each presentation duration step. Regions-of-interest-To determine significance of the face-categorization responses, a ten-channel bilateral occipito-temporal (OT) ROI was defined a priori based on previous studies with the generic face categorization paradigm (e. g. ,  Retter & Rossion, 2016b; Retter et al. , 2018 ; see also  Dzhelyova & Rossion, 2014). To decompose the responses further, the average amplitude of the right and left OT regions were examined separately (right: channels P10; P8; PO8; PO10; PO12; and left: P9; P7; PO7; PO9; PO11).
Additionally, the same approach was used at the individual participant level, with a Z-Score threshold of 1. 64 (p<. 05). To describe the data further, the amplitude and scalp topographies were assessed across presentation conditions. Scalp topographies were visualized in terms of both their original amplitude and their normalized amplitude  (McCarthy & Wood, 1985). To assess the extent of right lateralization for face categorization, a lateralization index was computed using the amplitudes of the right and left OT ROIs (R and L, respectively) as follows: (R-L)/(R +L)*100 (as in  Retter & Rossion, 2016a). Statistical tests on response amplitudes were applied independently for face-categorization and stimulus-presentation responses in the form of repeated measures analysis-of-variance tests (ANOVAs), with factors of Región and Condition.
To reduce the likelihood of false positives, only groups of at least 5 consecutive time bins (i. e. , about 10 ms) were reported. In Experiment 1, data were analyzed up to 600 ms post facestimulus onset; in Experiment 2, face-categorization responses were confounded with behavioral detection (pre-)motor activity, with response times occurring on average at 535 ms over these presentation rates (SE = 15. 1 ms). We thus focused the analyses on the first two face-selective components, the P1-face (onset at about 100 ms, 144-ms peak latency) and N1-face (202-ms peak latency), as defined in Experiment 1 (Section 4. 2; and reported in previous studies with this paradigm, see e. g. ,  Rossion et al. , 2015; Retter & Rossion, 2016a; Quek & Rossion, 2017; Or et al. , 2019). To further probe a potential null result, we obtained the Bayes factors corresponding to tests of the samples' means (t-tests;  Rouder et al.
Spatio-Temporal Dynamics The face-categorization responses in Experiment 1 were examined in the time domain to explore further potential qualitative differences across presentation rates, which would not be predicted by an all-or-none neural response account. The following analyses use data selectively filtering out the stimulus-presentation frequency (see Methods; for unfiltered data, see Supplemental Figure  4 ). Four prominent time-windows of interest appeared across conditions, corresponding to those reported previously as P1-face, N1-face, P2-face, and P3face (at a 12. 5-Hz stimulus-presentation rate;  Retter & Rossion, 2016a). These timewindows encompassed nearly all significant deviations over the bilateral OT ROI (Figure  8A ). Each of these deflections, or "components", was significant across three to six frequency conditions, without any clear trend of component differences as stimulus presentation duration varied.
Acknowledgments This research was supported by grants from the  Belgian National Foundation for Scientific Research  [http:// www. fnrs. be/;grant number  FC7159  to TR], the  European Research Council  [https://erc. europa. eu/;grant facessvep number  284025  to BR], the  University of Nevada, Reno Integrative Neuroscience Center of Biomedical Research Excellence  [https://www. nigms. nih. gov/Research/DRCB/IDeA/pages/COBRE. aspx;grant number  P20 GM103650 ], and the  National Eye Institute  [https://nei. nih. gov/;grant number  EY010834  to MW, and grant number  EY023268  to FJ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to  Andrea Conte  for the stimulation codes; to  Caroline Michel , for collecting the stimuli; and to  Charles Or  for generating the color key for plotting electrode location on a scalp map.
However, it is likely that either or both of the discussed factors contributed to our findings. In addition to the slow loss of phenotype, our behavioral data present two other unexpected findings. One was the detection of reduced anxiety, as measured by the increased amount of time spent in the center of the open field in 103Q-injected mice at 3 months. While statistical analysis revealed that this change was also transient, the difference between group means was stable throughout the tested time period. In general, increased anxiety is common in HD animals, however decreased anxiety has been previously reported in R6/2 mice  (File et al. , 1998). The reason for decreased anxiety in the current study is unknown. The second was the lack of behavioral changes at later time points when astrogliosis and changes to WT HTT were detected.
Acknowledgments FC is a recipient of a Researcher Chair from the  Fonds de Recherche du Québec en Santé (FRQS) ,  Canada ; providing salary support and operating funds and receives funding from the  Canadian Institutes of Health Research (CIHR) ,  Canada ; to conduct her HD-related research. The primate studies were made possible by a  CAMS Innovation Fund for Medical Sciences (CIFMS), China ; grant ( 2016-I2M-2-006 ), the  SAFEA , China; :  Introduction of Overseas Talents in Cultural and Educational Sector  ( G20190001626 ) as well as the  National Natural Science Foundation of China, China ; Grant ( 31970510 ). GS and AM are supported by  FRQS  doctoral research awards and MA by a post-doctoral fellowship from the same funding agency. At the time of the study, GC was awarded a fellowship from the  Huntington's Disease Society of America.
, 2018). We then assessed the contributions of each individual gene to the observed correlations, and identified a consensus set of top genes (showing positive contribution scores) across all six analyses (i. e. , three definition strategies for the sentence-processing regions by two rs-fMRI data sets for estimating the functional connectivity). Finally, using several bioinformatics databases, we explored the biological roles and expression specificity of these genes, and tested whether they showed an enrichment for association signals with ASD, schizophrenia, or intelligence, using genome-wide association study (GWAS) data. In addition, we analyzed three other functional networks by way of comparison to these language-related networks: the spatial navigation network, frontoparietal multiple demand network, and default mode network.
BIL&GIN This data set included 144 healthy right-handed adults (mean age ± SD 27 ± 6 years; 72 female) drawn from the larger BIL&GIN database, which is roughly balanced for handedness  (Mazoyer et al. , 2016). Each participant completed three slow-event fMRI runs (gradient echo planar imaging, repetition time [TR] = 2. 0 s, acquisition voxel size = 3. 75 × 3. 75 × 3. 75 mm 3 ; 3T Philips Intera Achieva scanner) in which they were asked to complete three different sentence tasks including covertly producing, listening to, or reading sentences and familiar word lists as reference. Of these participants, 137 also completed rs-fMRI scans using the same imaging sequence as that used for the tasks, which lasted 8 minutes (240 volumes). Immediately before rs-fMRI scanning, the participants were instructed to "keep their eyes closed, to relax, to refrain from moving, to stay awake, and to let their thoughts come and go.
, 2014; Kong, Song, et al. , 2017; Kong, Wang, et al. , 2017; Yarkoni et al. , 2011) , due to the high number of studies included. We used database version 0. 6 (current as of July 2018), which included 413,429 activation peaks reported in 11,406 studies (see Defining Cortical Regions for Sentence Processing).
, 2017; Wang et al. , 2016). AHBA AHBA (Allen Human Brain Atlas; http://www. brain-map. org) is a publicly available online resource for gene-expression data. The atlas characterizes gene expression (i. e. , messenger RNA [mRNA] quantification) in postmortem human brain based on genome-wide microarray-based measurement, for more than 20,000 genes at ∼500 sampling sites distributed over the whole brain. (See  Hawrylycz et al. , 2012  for more detail about the data collection. ) Normalized expression data were used in the present study. To date (search conducted on March 30, 2017), six adult donors with no history of neuropsychiatric or neurological conditions were available in the database  (ages 24, 31, 34, 49, 55, and 57 years; one female). Left hemisphere cerebral cortical data are available for all six donors, whereas right-hemisphere data are available for only two.
Detailed information on donors and analysis methods is available at http://www. brain-map. org. Structural brain imaging data of each donor were used to align sampling sites into standard coordinate space. We also used gene-expression data from the Allen Institute's BrainSpan project (http://www. brainspan. org/), which includes human brain tissues from (embryo) 8 weeks postconception to (adult) 40 years of age, sampling an average 13 regions (range, 1-17) from one to three brains per time point, and measured using RNA sequencing  (Li et al. , 2018). We used these latter data for examining the expression of specific genes of interest across human brain development. Genome-wide association study results for ASD, schizophrenia, and intelligence We downloaded GWAS summary statistics from the Psychiatric Genomics Consortium (2018; PGC) for ASD with up to 7,387 cases and 8,567 controls (Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium, 2017), and schizophrenia with up to 36,989 cases and 113,075 controls (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).
Defining Cortical Regions for Sentence Processing We employed three strategies and data sources for defining the cortical sentence-processing network. To refer to the regions defined under each of these three approaches, we use the terms Supramodal Sentence Areas (SmSAs), Synthesized Sentence Areas (SSAs), and Onecontrast Sentence Areas (OcSAs). Given left-hemisphere dominance of the language network (see Introduction) and limited postmortem gene-expression data for the right hemisphere (see AHBA), we focused on the left hemisphere in the present study. Note that we made use of existing, previously published task-fMRI data sets for this study (see below), and do not repeat all of the method details here.
SmSAs We applied the definition of left-hemispheric high-order and supramodal sentence areas provided by The SENSAAS atlas  (Labache et al. , 2018). This atlas of language integrative and supramodal areas, involving 142 healthy right-handers, is based on the conjunction of activation across sentence production, listening, and reading, as contrasted with activation for lists of overlearned words (again presented as production, listening, or reading tasks) in the same participants. Then, a second criterion was applied whereby leftward activation asymmetry was required during the three sentence minus words contrasts. (See  Labache et al. , 2018  for a full description of this definition approach. ) Task fMRI data were used from the BIL&GIN data set (see Data Sets) for this purpose. To obtain accurate measures of functional asymmetry, this work made use of the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) atlas, including left-right homotopic regions of interest based on resting-state functional connectivity data  (Joliot et al.
OcSAs OcSAs were defined based on the probabilistic activation map of a single fMRI contrast from the EvLabN60 data set (see Data Sets). The passively reading task has been shown previously to reliably activate language-sensitive regions and to be robust to the materials, task, and modality of presentation  (Fedorenko et al. , 2011; Fedorenko et al. , 2010; Mahowald & Fedorenko, 2016; Scott et al. , 2017). The map was created by overlapping the statistical maps from all participants for the contrast of sentences versus nonwords (t > 2. 3) onto the MNI152 template (Montreal Neurological Institute template 152), and then dividing by the total number of participants (e. g. ,  Zhen et al. , 2017). The value for each voxel in the obtained map indicated the probability of the voxel showing a significant contrast activation across the population.