dataesr/scientific-paragraphs-categorization huggingface dataset:
Results organisation The quantitative analysis results will be organised according to the following elements: ► The cohort data will be compared between patients identified as 'questionable ability' according to the Open access clinical evaluation (reference) and the neuroimaging evaluation, in order to see if the imaging allows to reclassify patients as 'able'. To do this, the neuroimaging examinations will be performed blinded to the clinician's assessment. ► In addition, the results of the functional neuroimaging tests will be analysed in comparison with the neuropsychological tests performed, to verify the correlation between the results of the neuropsychological tests of decision-making ability and the results of the neuroimaging tests, and thus to identify, among the battery of tests used, those that are the most discriminating in the evaluation of decision-making ability.
A thematic analysis will be conducted to build a thematic tree. The analysis of the qualitative data from the semistructured interviews follows the logic of grounded theory, which is based on the data collected, from which an explanatory theory answering the initial question is constructed. 37 38 lot study To implement our study, it was necessary to carry out a primary inclusion of archetypal patients in a pilot study. This pilot study was conducted at the Research Centre of the Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec from January 2020. In accordance with the legislation in application in Quebec, the protocol for the pilot study has been validated by the ethical committee (Comité d'Ethique de la Recherche Viellissement -Neuroimagerie) in Montreal, Quebec, under the identification CER VN 18-19 -42, since 3 April 2019.
The data collected are not part of the main study presented in this article. The challenge of a pilot study was to proof our concept by ROI activation in fMRI during our task and the concordance with neuropsychological assessment. Other objectives were to verify the clinical feasibility of our protocol with older adults with cognitive impairment; and to verify that all relevant tasks and information can be recorded, both clinically and in fMRI. Nevertheless, due to the consequences of the COVID-19 epidemic, the data collected before the successive lockdowns, of the frail older adults, were only complete for 4 older healthy subjects and 2 patients corresponding to the inclusion criteria for an objective of 12 older adults in each group. Although these data do not cover all the objectives of the pilot study, they are sufficient to prove our concept.
Patient and public involvement Patients and the public were not formally involved in the IMAGISION study except through the participation of their representative in the ethical committees. Although the research question stems from clinical interactions with patients, they were not directly involved in the design of the protocol. The pilot study allowed, based on the feedback from the participants, an adaptation of the procedures, resulting in particular in a better organisation of fMRI time. Nevertheless a general public conference will take place at the end of the research, for which all participants will be invited. The data collected will be individually accessible on request, and may be transmitted by the referring geriatrician.
S12A ). Based on this simulated data, we measured two other variables: the percentage of time spent freezing and the number of freezing bouts. We then compared these variables obtained from simulations to those in the data. For GFP and ChR2 mice as well as sham and bulbectomized mice, the model prediction and data showed an excellent match (Fig. S12B, C ), which validates the Markov model as a good description of freezing behaviour. Using this framework, we analysed the effect of both bulbectomy and our optogenetic perturbation of OB activity. We found that in both cases freezing bout duration was reduced but active bout duration was not changed (Fig. 3d, f ). These differences are also clear in the cumulative distributions of freezing and active bout durations (Fig. 3g-j ): active bout distributions overlap, whereas freezing bout distributions are Fig.
Local field potentials were stored at 1250 Hz. Analyses were performed with custom made Matlab programs, based on generic code that can be downloaded at www. battaglia. nl/computing/ and http://fmatoolbox. sourceforge. net/. Local field potentials were recorded using tungsten wires with PFA isolation (0. 002" bare) and single units using home-made tetrodes formed by twisting insulated Nichrome Wire (0. 001" bare). Tetrodes were plated to reduce impedances to around 100 kΩ using gold solution (Neuralynx). For spike sorting, extracted waveforms were sorted via a semi-automated cluster cutting procedure using KlustaKwik and Klusters (http://neurosuite. sourceforge. net/) Recordings were visualized and processed using NeuroScope and NDManager (http://neurosuite. sourceforge. net/). Fear conditioning.
Optogenetic stimulation. Fear conditioning experiment: On day 1, during the habituation session, mice received 13 Hz blue light stimulations (45 s). The stimulation intensity was chosen to see clear LFP oscillations in the OB and the PiCx, an output structure of the OB. No stimulation was delivered on day 2, during the habitation to sounds and the conditioning phase. During the test session on day 3, mice first received 3 short bouts of light stimulation (10 s, inter-stim interval 10 s) before the onset of the first CS. No stimulation was delivered during the CS-and the first pair of CS+. 13 Hz blue light stimulations were delivered during subsequent CS+ (45 s, locked at the CS+ onset, i. e. extending 15 s after the end of each sound). Sleep stimulation experiment: after an initial light-phase day of baseline recording in their homecage to habituate to sleeping with electrophysiological and optic cables, mice were tracked using homemade Matlab software to identify periods of prolonged immobility corresponding to sleep.
Brains were extracted and placed in PFA for postfixation for 24 h, transferred to PBS for at least 48 h, and then cut into 50-μm-thick sections using a freezing microtome. Slides were mounted and stained with hard set vectashield mounting medium with DAPI (Vectorlabs). Location of electrodes was identified and mapped onto the stereotaxic atlas (Paxinos and Watson, 2004). Spectral and coherence analysis. Electrophysiological data were analysed with custom-written Matlab (The MathWorks Inc. , Natick, MA, 2000) codes that made use of the Chronux toolbox (http://www. chronux. org). Spectrograms and coherograms were calculated with multi-taper Fourier analysis to diminish finite windowing effects. For all analysis, we applied 5 tapers and a window size of 3 s with a 0. 1 s shift between bins. Changes in power of specific bands were calculated only after identification of a peak in the power spectrum (3-6 Hz for the respiratory rhythm) in at least one condition using the signal to noise ratio defined as the ratio between power in the band of interest to the power in the rest of the spectrum.
For each statistical analysis provided in the manuscript, the Kolmogorov-Smirnov normality test was first performed on the data. If the data failed to meet the normality criterion, statistics relied on non-parametric tests. We, therefore, represent the median and quartiles of data in boxplots in all figures, in accordance with the use of non-parametric tests. For comparison of proportions, the standard chi2 test was used. Two-way mixed repeated anova was performed using GraphPad Prism, GraphPad Software, La Jolla, California USA. Ranksum and signed rank: we report the signed rank statistic if the number of replicates is too weak to provide the normal Z statistic. All statistical tests were two-sided. On all figures, the following conventions are used *p < 0. 05, **p < 0. 01, ***p < 0. 001.
Acknowledgements We thank  L. Roux  and  A. Peyrache  for discussion and critical readings of an earlier version of the manuscript. This work was supported by the  Fondation pour la Recherche sur le Cerveau  ( AP FRC 2016 ), by the  French National Agency for Research   ANR-12-BSV4-0013-02  ( AstroSleep ) and  ANR-16-CE37-0001  (Cocode), by the  CNRS: ATIP-Avenir  ( 2014 ) and by the  city of Paris  (Grant Emergence  2014 ). This work also received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR , with the references:  ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02 PSL* Research University  and was supported by the  ERC   consolidator grant (Grant/Award Number: ERC-CoG-726169-MNEMOSYNE ) G. d. L. and M. M. L. were funded by the  Ministere de l'Enseignement Supeŕieur et de la Recherche, France.
Inclusion criteria: patients aged 18 years and older, presenting with an ischemic left-sided stroke and a chronic (onset > three years) aphasia-related severe communication disability, defined as a complete inability to communicate with the physician at the medical visits, without the help of proxies. Inclusion were concurrent (i. e. all patients started baseline at the same time). There were no exclusion criteria. The interventions and evaluation were performed at the patients' homes. The study conformed to Helsinki Declaration. Patients gave a written informed consent. Procedural fidelity was measured based on video-recording of the sessions by a speech therapist who did not participate in the session. Data analysis was performed using both visual aids (using https://manolov. shinyapps. io/Overlap/) and statistical methods adapted to single-case experimental designs : randomization tests  10   gesture intervention versus second gesture intervention.
When the number of measures at baseline is seven or less, Tarlow's Tau-U method may fail to detect even high degrees of trend. For this reason, in case of significant results, we checked whether Tau U effect sizes were maintained if a systemic baseline correction was used for phases of length of seven of less (i. e. using a conservative and overcorrecting approach where even non-significant trends are corrected for). Randomization test was performed using ExPRT Package  15  , using a within-cases comparisons of means, following a restricted Marscuilo-Busk procedure  10. The three patients included were males, at least 4 years post middle cerebral artery ischemic stroke, presenting with a right-sided hemiplegia, severe apraxia and severe communication disability due to aphasia (Aphasia Severity Rating Scale of 0 in all patients) but also due to executive dysfunction.
Two of the most well-known embedding techniques, DeepWalk  [19]  and node2vec  [20] , are based on such random walks. Methods based on random walks seem particularly appropriate in our framework. Indeed, in the supra-adjacency representation, these random-walks will explore for each active node both the self-coupling edges leading to other versions of the same original node, and the edges representing the interactions between nodes. As written above, these edges encode the paths along which dynamical processes occur, meaning that the final embedding will preserve structural similarities relevant to these dynamical processes. Here we will use DeepWalk  [19] , as it is a simple and paradigmatic algorithm, and it is known to yield high performance in node classification tasks  [16]. Note that DeepWalk does not consider weighted edges, but it can easily be generalized so that the random walks take into account edge weights  [20].
Whenever multiple proximity events were registered between two individuals within a time window, we used the number of such events as the weight of the corresponding temporal edge. Table  I  shows some basic statistics for each data set. We simulated the SIR model on each such weighted temporal network, using the following five combinations of epidemic parameters: (β, µ) = {(0. 25, 0. 055), (0. 13, 0. 1), (0. 13, 0. 055), (0. 13, 0. 01), (0. 01, 0. 055)}. In each case, we consider as initial state a single randomly selected node as seed, setting its state as infectious, with all others susceptible. Given the stochastic nature of the model, in some cases the infectious state barely spreads, with a large majority of the nodes remaining susceptible. The prediction task would then be trivial, and we restrict our study to non-trivial cases in which there is still at least one infectious node when more than half of the total data set time span has elapsed (i.
, 2007). When searching for the keywords "body-dissatisfaction" and "state scale" and "French, " no relevant results are found on the PsycNet or ScienceDirect databases. Cash et al. (2002)  also underlined the issue that tools used to measure state body satisfaction in previous research have often been developed from trait-scales, or constructed without prior validation. As said earlier, these scales may lack sensitivity. For this purpose,  Cash et al. (2002)  developed the Body-Image State Scale. This 6-item state-measure of body satisfaction is non-specific, exclusively text-based and originally available in English. Its 9-point scale with specific body-related statements to choose from should allow participants to give a precise answer. It is less prone to the unrealistic body representation associated with contour-drawing based scales, as participants are asked about their own body, without being shown one.
Statistical Analyses Construct validity was assessed using exploratory and confirmatory factor analyses, as well as inter-item correlations. The exploratory factor analysis was conducted on SPSS 25, while the confirmatory factor analysis was carried out on RStudio using the lavaan package  (Rosseel, 2012). Both used a maximum likelihood estimation method. For the factor analyses, a "fair" item loading cut-off (0. 45) was set  (Tabachnick and Fidell, 2007). A 0. 2 cut-off was set for communalities  (Child, 2006). The Kaiser criterion was used, meaning valid factors should have an eigenvalue greater than one  (Costello and Osborne, 2005). For the exploratory factor analysis, examination of the scree-plot and a parallel analysis were also used to corroborate factor solutions. Data from one random split-half of the sample (n = 156) was selected for the exploratory factor analysis.
Data from the other random split-half of the sample (n = 156) was selected for the confirmatory factor analysis. While no clear recommendation exists on what constitutes a satisfactory percentage of explained variance, values between 50 and 60% were chosen to be retained  (Peterson, 2000). Goodness-of-fit indices were established prior to testing  (Hooper et al. , 2008). RMSEA values close to 0. 06 were considered indicators of good fit. SRMR values under 0. 05 were retained, as well-fitting models tend to obtain similar values. Finally, as it is recommended that a CFI index should be > 0. 95, it was decided to keep that value for this testing. Model improvement was assessed through modification index values (MI), implemented in R by the lavaan package. Higher MI values indicate a better fit of the corresponding model, using the LaGrange multiplier.
Materials and Methods Experiment 1 -voice versus object sounds Participants EEG was recorded in 20 participants (age range 19-26 years, 10 female) in experiment 1. Data from four participants were excluded because of the presence of EEG artefacts. All participants were right-handed and reported normal or corrected to normal vision, normal hearing and no history of psychiatric or neurologic disorders. The experiment was approved by the local ethical committee of the University of Louvain (project 2016-25), all participants provided written informed consent and received financial compensation for their participation.
Stimuli Individual sounds used to create the standard sequences were 250 ms long, leading to a base stimulation frequency (BF) of 4 Hz (1/250 ms) and a target frequency (TF) of 1. 333 Hz (4 Hz/3: vocal stimuli presented each third sound) and were selected in an effort to be as heterogeneous and variable as possible. We selected 137 nonvocal stimuli including environmental sounds (e. g. , water pouring, rain), musical instruments, sounds produced by manipulable and non-manipulable objects (e. g. , telephone ringing, ambulance siren). We selected 55 vocal stimuli including speech and non-speech vocalizations pronounced by speakers of different sex, age and emotional states. Stimuli were extracted from various sources including online databases, extracts from audiobooks and the Montreal Affective Voices dataset  (Belin et al.
Stimuli Sounds were extracted from a database provided by Agus and collaborators  (Goto et al. , 2003; Agus et al. , 2017). Stimuli were 128 ms long, therefore the base and target frequencies of what we will refer to as harmonic sequences were approximately 7. 813 Hz and 2. 604 Hz, respectively. Vocal stimuli (16 exemplars) consisted in vowels /a/, /e/, /i/ and /o/ sung in the note A3 by two male and two female singers. Non-vocal sounds consisted in 16 different musical instruments playing the note A3 (oboe, clarinet, bassoon, saxophone, trumpet, trombone, horn, guitar, mandolin, ukulele, harpsichord, piano, marimba, violin, viola, and cello, for a total of 16 stimuli). Importantly, vocal and non-vocal sounds implemented for this sequence type were matched for pitch (mean = 223. 5 Hz, SD = 7.
Analysis Data were analyzed as in experiment 1 with the epochs being re-segmented from 2. 048 s after the onset of the sequences (to exclude fade-in and to start segmenting from the onset of the first sound at 100% volume) and had a final length of 59. 9 s to contain an integer of voice presentation cycles and avoid overspill of the target-rate response in the frequency domain. The number of significant harmonics for the responses at the target and base frequencies were calculated as in the previous experiment considering all four stimulation sequences or the first sequence alone. We then performed a region-of-interest analysis by averaging the sum of the baseline subtracted amplitude of the significant harmonics of the target of the electrodes of the ROIvoice as defined in experiment 1 and contrasted the resulting activity against 0.
For the harmonic sequence, we presented all 16 vocal and 16 non-vocal stimuli. Sounds from each of the three conditions were presented once in a randomized order, for a total of 132 trials (one sound = one trial). The sequence and isolation tasks were presented one after another. Each trial consisted in the presentation of one short sequence (sequence task)/one sound (isolation task) after which participants had to indicate whether they heard a voice or not with a button press and a response was required to initiate the following trial. Participants performed the task blindfolded. The experiment was implemented on MATLAB _R2016b (MathWorks) using the Psychophysics Toolbox extensions  (Brainard, 1997; Pelli, 1997; Kleiner et al. , 2007).
Target frequency We observed a significant voice-selective response for the first two harmonics: at 2. 604 and 5. 208 Hz. Voice-selective responses peaked over central and superior temporal electrodes bilaterally, the scalp distribution of the response being highly similar to the voice-selective response found in the standard sequence (Fig. 4 ) and being reliable across participants. We then performed a ROI analysis considering the right superior temporal electrodes (ROIvoice) as defined in experiment 1. Baseline subtracted amplitudes at the two significant harmonics were summed for each electrode individually and the resulting responses at the electrodes of the ROIvoice were then averaged together for every participant. One data point was excluded as it deviated more than three standard deviations from the mean of the group and responses were significant against zero (mean = 0.
Experiment 3 -Behavioral detection of voices Responses to the sequence and isolation tasks were analyzed according to signal detection theory. Specifically, sensitivity indices related to the participants' ability to detect a voice when present was assessed using d-prime (Fig. 5 ); d-prime (d9) constitutes an unbiased quantification of performance in detection tasks as it takes into account both hits and false alarms  (Tanner and Swets, 1954; Macmillan and Creelman, 2004). Data points were considered as outliers when deviating from the mean of the group of more (or less) than three times the standard deviation of the group in at least one condition/task, leading to the exclusion of one participant. We first checked whether performance was above chance (d9 = 0) for each condition and for the sequence and the isolation task separately.
We conducted a similar factorial regression analysis that included mean-centered PLS scores, instead of mean-centered IOS scores, as factor. This analysis revealed that the main effect of PLS scores was not significant, b = -. 096, β = -. 166, t (66) = -1. 430, p =. 157. The main effect of shoulder width was not significant, b =. 240, β =. 107, t(66) =. 895, p =. 374, but it was qualified by a significant interaction with PLS scores, b = -. 046, β = -. 279, t(66) = -2. 369, p =. 021. The relationship between shoulder width and perceived critical aperture is positive and significant for low PLS score, b =. 846, β =. 379, t(66) = 2. 646, p =. 010, while this relationship is non-significant when PLS score is high, b = -. 366, β = -. 164, t(66) = -. 881, p =. 381 (Fig  1B ). We repeated these analyses on the 3-PP data obtained by romantically involved participants.
In line with this hypothesis, the analyses of performance of romantically involved participants in the 1-PP task revealed that the strength of the link between participants' shoulder width and perceived critical aperture was negatively affected by the amount of inclusion of romantic partner in the self, as indexed by IOS scores. Converging evidence was obtained when testing the modulatory role of PLS scores. We found that the relationship between participants' shoulder width and perceived critical aperture in the 1-PP task was negatively affected by the intensity of romantic feelings (as indexed by PLS score). The fact that IOS and PLS scores had a similar modulatory effect on 1-PP data is consistent with the relationship that has been established between inclusion of romantic partner in the self and romantic feelings  [12, 18, 29].
Participants The participants were 40 adults suffering from different types of motor speech disorders (MSD) (dysarthria or AoS) and 40 matched controls recruited in the framework of a larger research project on motor speech disorders (the Swiss National Science Found (SNF) Sinergia Project on Motor Speech Disorders (MoSpeeDi)). Both groups of participants were part of a larger data collection  (Fougeron et al. , 2018; Laganaro et al. , 2020). All participants were French native speakers, from various parts of French-speaking Switzerland and of northern France. The participants with MSD presented mild or moderate acquired or progressive speech difficulty noticed by the patient and by a speech and language pathologist. We included participants suffering from three types of dysarthria: hypokinetic dysarthria (participants with Parkinson's disease -PD -), mixed dysarthria (participants with amyotrophic lateral sclerosis -ALS-) and another type of mixed/dystonic dysarthria (participants with Wilson's disease -WD-, predominance of hypokinetic and hyperkinetic dystonic types).