dataesr/scientific-paragraphs-categorization huggingface dataset:
Overview of the MR Analyses Flow Data were analyzed in subsequent steps as detailed in the flowchart of Figure  2  and as described below. We first determined the FFA region from the functional localizer for each individual subject. The right FFA-mask was then applied for the BOLDimages of the Sternberg main paradigm, and (1) FFA activation conventionally extracted for estimation of global activation of 2 http://www. fil. ion. ucl. ac. uk/spm this region in both groups for all memory conditions and then (2) an RSA analysis performed to examine the microscale functional architecture of the FFA during the paradigm. The RSAmatrices were then probed for the level of maintenance of neural representations over time by determining the level of correlations as a function of the trial distances. Additionally, functional activation was further linked to behavioral performance of all the subjects.
Beta images of these trials were then used for extracting single-voxel activation clusters from the individual FFA masks based on the functional localizer. For each subject, we then correlated the single-trial beta values within the FFA with the beta values from every other trial. Considering the data of a single memory load condition (low, medium, high load), the elements γ P i,j of the per-subject RSA matrices represent the Spearman's rank correlation coefficient of subject P between the subject's correctly answered trials i and j over all voxels of the subject's specific FFA mask. Note that for the different memory load conditions, the size of the RSA matrices may differ, as their size is determined by the lowest number of correctly answered trials T: 0 ≤ i, j < T, with T = min ∀ p T P and T P being the number of correctly answered trials of subject P.
If we can build such domain, we will accept the null hypothesis when an observed random walk is entirely contained with the domain and reject it otherwise. Donsker's theorem guarantees that a properly normalized version of our random walk converges toward a canonical Brownian motion process under the null hypothesis. The work of  Kendall et al. (2007)  gives the expression of the boundaries of the minimal surface domain containing a given fraction of the observed random walks. They also show that a boundary of the form ± (a + b * √ t) gives rise to an almost minimal surface domain while being much easier to compute. We used the latter in this work  (Kendall et al. , 2007; Pouzat et al. , 2015). All the details, codes and data related to this test are available at the following address: https://github.
Enrolled volunteers The two ears of healthy volunteers (n = 41 subjects, 27 females and 14 males) aged from 24 to 35 years were tested at the University Medical Center of Groningen (UMCG). For 78 ears out of 82, there was no history of otological disease. Otoscopic examination ensured the absence of earwax accumulation and of tympanicmembrane abnormality. Ten of these 78 ears were excluded due to the instability of the measuring probe when the body was tilted, which impeded complete data collection. Experiments with the remaining 68 ears were carried out in a soundproof room. Subjects had signed their informed consent to the study and its method before starting the tests (local ethical committee, authorization 2017/679).
After body tilt, ICP reaches a stable value after a few seconds  ( Lawley et al. , 2017 ) , and past experiments with OAEs have shown that it takes about 10 seconds after an ICP step in humans for OAE characteristics to plateau  ( Buki et al. , 20 0 0 ). Volunteers were instructed to behave naturally and swallow when they felt the need, which was seen to occur once or twice per minute. In the core protocol to which all 68 ears were submitted, WBT data collection in one posture took about 30 seconds including probe-fit check. The WBT measurement started 10 seconds after each position change, thus subjects remained 40 seconds in each position before changing to the next. Pilot tests with repeated WBT in the subsample made of the first 10 enrolled subjects controlled the stability of tympanometric patterns when each body position was prolonged a few minutes.
Data analysis Following previous studies of the influence of posture on EA  ( Voss et al. , 2010 ) , data were first presented as plots of aEA. Next, tympanograms were built showing EA(Pec, f n ) against Pec, with n increasing from 1 to 73 to represent the frequency bins between 226 and 30 0 0 Hz sampled by the WBT device. For quantitative comparison of EAs in different postures, stacks were built of tympanograms, i. e. , graphs of the dimensionless EA against Pec at a given frequency, each graph at f n + 1 being shifted upward by 0. 03 with respect to the graph at f n. From these representations, TPP and its frequency dependence showed up more clearly than from the inclined surface plots provided by the WBT device. For comparisons of 3D plots of EA in different postures, the volume between these plots (VBP) was computed as: VBP = [|EA-posture-X(Pec m , f n ) -EA-upright- posture(Pec m , f n )|].
Effect of body tilt on EA at ambient pressure The first possible readout of 3D EA data extracted by the WBT device is aEA(f) at ambient pressure against frequency. Fig. 1 (a-d  These examples show that aEA(f) depends on posture. At a given frequency, there is a tendency for aEA to be largest in upright posture (solid lines), either before or after body tilt. Both the size and frequency dependence of the change in aEA in supine or Trendelenburg positions show a large intersubject variability. In the ears depicted in Fig. 1  a and b, the largest aEA decrease occurred at frequencies < 1. 2 kHz while aEA changed little above this corner frequency. The frequency dependence of aEA in cases of Fig. 1 c  and d  was less systematic, almost absent in Fig. 1  c and greatly exaggerated in Fig. 1 d.
Acknowledgments The first author of this work, SGT, was funded by the  Horizon 2020 Framework Programme of the European Union  in the framework of a  Marie Sklodowska-Curie Innovative Training Networks program entitled Egret +  (project # 675 033 ,  H2020-MSCA- ITN-2015 ). The authors also thank  Nomdo Jansonius ,  Frans Cornelissen  and  Pim van Dijk , at the  UMCG, Groningen (The Netherlands ) for steering Egret + and welcoming SGT in their departments for conducting experiments. The technical help of  Romain Moura at Echodia (St Beauzire, France)  is gratefully acknowledged.
Methods: The learning database included clinical outcomes and post-operative imaging from which the coordinates of the active contacts and those of anatomical landmarks were extracted. We used machine learning regression methods to build three different prediction models. External validation was performed according to a leave-one-out cross-validation. Results: Fifteen patients (29 leads) were included, with a median tremor improvement of 72% on the Fahn-Tolosa-Marin scale. Kernel ridge regression, deep neural networks, and support vector regression (SVR) were used. SVR gave the best results with a mean error of 1. 33 ± 1. 64 mm between the predicted target and the active contact position.
Active Contact Locations on the DISTAL Atlas in MNI Space Active contacts were normalized on the DISTAL atlas in MNI space [ICBM 2009b template] (11) then the DISTAL atlas was merged onto the MNI template to obtain the overlay of active contacts on the anatomical structures segmented according to the atlas. All these operations were carried out using the open-source Lead-DBS software © (www. lead-dbs. org) running on MATLAB © (MATHWORKS TM ).
Metamodel Evaluation: External Validation by Cross-Validation and Lead Location on Atlas We provided two approaches for evaluating the performance of the three methods. The first was based on cross-validation statistical testing. We measured the distance between the predicted and the clinical targets. In this approach, we removed one patient (both hemispheres) from the training set and compared the prediction calculated by the metamodels with the (n -1) patients to the active contact (leave-one-out crossvalidation, LOOCV). This step was performed iteratively for all the patients of the database. The distances (mean ± SD) between the predicted target and active contacts were then calculated. In the second approach, we compared the position of the predicted target to the anatomical structure of the VIM from the DISTAL atlas  (11)  in MNI space.
Statistical Analysis Quantitative variables were presented as mean with standard deviation (SD) or as median with interquartile range (IQR) depending on their distribution (tested by the Kolmogorov-Smirnov test) and were compared using the two-tailed Student's test (5% alpha level). Qualitative variables were presented as values (%). Differences were considered to be statistically significant if p < 0. 05. SPSS STATISTICS v. 24 © (IBM TM ) software was used.
Data Augmentation Effect on DNN and SVR Performance and Prediction 95% Confidence Interval Each database input sample was augmented by adding random noise in the range of the inter-operator variability. We showed that for the DNN method, the optimum multiplicity factor value was 100. This multiplicity value provided a good compromise between the mean value and the standard deviation; while for  the SVR method, the optimum values of both mean and standard deviation were achieved with an augmentation factor of 10, as shown in Figures  8, 9. Given the intra-observer variability in landmark positioning, the mean values of the confidence interval of the predicted target were equal to 0. 5 mm for the DNN method and 0. 35 mm for the SVR method (Supplementary Material).
DISCUSSION In this study, we aimed to define an effective target for essential tremor, not based on anatomical data, but rather on postoperative clinical outcome. We sought to predict the position of this functional target, called "Vim, " with reference to the conventionally stimulated anatomical structure, according to the radiological characteristics of a given patient. For this, we used and optimized various methods of machine learning, from a database including, on the one hand, the position of 18 landmarks per hemisphere in 15 patients (29 electrodes), and on the other hand, the position of the DBS-lead contacts whose activation led to an optimal clinical result in these same patients. We were able to predict the position of the functional target with a minimum mean error of 1. 33 ± 1.
64 mm. Moreover, all the models studied positioned the predicted targets at an average distance of <1. 5 mm from the anatomical Vim. The database was constructed exclusively with data from patients with optimal post-operative outcome, for the reason that there is no way of knowing, for patients whose result is suboptimal or poor, if the reason is due to poor electrode positioning or to true physiological tolerance to the anti-tremor effect of thalamic DBS (non-responder patients) as explained by Papavassiliou et al. (20). Regarding the 18 landmarks, no intra-or inter-observer error >2 mm was observed, except for the X coordinate of the BPT (4. 14 mm between two observers) certainly linked to an overly vague definition of this landmark. Most regression models, however, especially in the feature selection stage, eliminate from the outset the reference points whose variability is outliers.
Furthermore, we created the "Optim-DBS" software to calculate the target "Vim" from the metamodels developed in this study, by manually positioning the 18 landmarks. It is intended to automate the step of selecting landmarks to minimize landmark positioning variability. At present, the procedure (from loading the patient's imaging to export of the imaging marked with the predicted target) takes from 5 to 10 min on average (a video is available on the internet public page https://www. youtube. com/ watch?v=Hlf7edlWhHg). Our learning approach implied that our database should theoretically satisfy two requirements: (1) to contain the largest number of different examples (i. e. , as many patients as possible) and (2) to contain the largest possible number of representations of each characteristic of the data.
The data augmentation technique applied to the DNN and SVR models made it possible to meet this second requirement. We have seen that this improved the performance of these two models. Our database may not, however, have met the first requirement, hence underlining the need to include many more patients to refine the models, and thus to set up distributed registers between the various DBS centers. Indeed, low sample size is a major issue and is explained by the relative low number of surgical procedures for essential tremor and the strict inclusion criteria in our study. Since the number of patients included in the database was 15, our initially preferred method was RKHS, which is suitable for small databases. Since the inter-operator variability allowed us to generate artificial data, however, the DNN and SVR methods became more suitable and performed "better" than RKHS in terms of prediction error.
The performance of the DNN method is much more difficult to explain since there is no clear (in the expression of F) causality between proximity in the landmarks space and proximity of active contact positions. Despite the lack of evidence of causality in DNN, the filters that are included in the DNN network layers allow good performance to be achieved. The DNN method, however, generally requires a higher data size, as can be observed in Figure  8. We expect that DNN would provide better performance with a larger dataset size, considering the variability of patient brain anatomy. An initial validation step in the modeling process was crossvalidation, intended to give an idea of model robustness. Given the small number of data, we opted for a leave-one-out crossvalidation (LOOCV) method rather than k-folds in order to keep enough data for the training set.
By showing, however, that active contacts (thus "real" clinically effective targets) are not necessarily those closest to VIM, reflecting a degree of anatomo-functional discordance (which may be real or artifactual due to the use of atlases), we illustrated the fact that a simple anatomical validation (whether radiological, electrophysiological, or atlas-based) is insufficient to prejudge the validity of our modeling. The only way to validate this target is to be able to test it in the  operating room and to compare the clinical results obtained when the electrode is implanted in the predicted target to that obtained by the conventional method. With this objective, we have initiated a multi-center prospective study (Opti-VIM study, NCT03760406). To illustrate the potential clinical interest of this targeting method, we provided the first results of a cohort of consecutive patients operated on under general anesthesia without MER with the RKHS metamodel (using the OptimDBS software) and not included in the Opti-VIM trial.
This sample of patients is particular because of their age and comorbidities (mainly cerebral atrophy) that precluded for awake surgery with MER and clinical testing and thus was an exclusion criterion for the Opti-VIM trial. We offered the possibility of an asleep surgery without electrophysiology as an "off-label" targeting procedure and 9 patients were operated on. We chose the RKHS model because it was the first developed in the OptimDBS software and used at the beginning of the Opti-VIM clinical trial. As the model cannot be changed during the study, which is still running with this model, we also used this model to operate on the patients outside the trial. The clinical results of this case series, with a median improvement rate in the FTM scores between pre-and post-operative assessments of 64% (IQR 40-75) at a mean follow-up of 14 months (±9), are within the same range as the improvement rates compiled in a recent literature review  (47)  which were between 66 and 78% at 1 year postoperatively for bilateral implantation under local anesthesia with MER.
CONCLUSION In this study, using machine-learning algorithms, we predicted a clinical target for DBS in essential tremor. The learning database consisted of clinical and radiological features of patients previously operated on with optimal outcome. A prospective clinical trial (Opti-VIM) is now ongoing to validate this approach. Other perspectives include automatic landmark localization by the machine and the creation of a multi-center registry to enlarge the learning database. This would allow more variables to be included in the models, such as patient and disease characteristics, brain MRI data (e. g. , functional connectivity), more post-operative data (side effects, stimulation parameters and suboptimal outcomes), etc. , to optimize the models and make classifications to study whether different models can apply to different patient groups with different targets.
Outlook In this review, we focused on three main aspects of honeybee olfactory coding: (1) the role different odorants play in the life of a honeybee; (2) the neuroanatomy and neurophysiology of the olfactory system; (3) the principal neurophysiological mechanisms leading to memory formation and learning related plasticity. Decades of neuroanatomy, neurophysiology and behavioral experiments provided us with a wealth of knowledge about the logic behind olfactory coding, and of the architecture of the global neuronal network of the olfactory system as well as of the local networks guiding olfactory coding within individual neuropils. Still, a lot remains to do. Luckily, research in honeybee olfaction is alive and vivid, supported by the recent development of largescale flight tracking, higher resolution optical physiology, and the extensive application of computational tools for the analysis of large behavioral, morphological and neurophysiological datasets.
Cognition Participants undertook three randomized cognitive tasks assessing three main executive functions: shifting (Switch), working memory (3-Back), and inhibition (Stroop) (Fig. 2 ; Appendix Text A). Behavioral data were collected using the software PsyToolkit  [38, 39]. For all tasks, the maximal response time was 2 s, and feedback was provided. For the Switch task, the participants made a cued binary decision about one of two possible tasks (color or shape). For the 3-Back task, participants had to determine if a letter had earlier appeared three times among 15 possible letters that were presented separately in a randomized order. For the Stroop task, participants were asked to identify the colors of written words while ignoring the meaning of the word. We assessed the global effect on executive performance by calculating two general performance scores based on error rates (ERs) and response times (RTs; in milliseconds) in each task: (1) error score: (ER Switch þ ER 3-Back þ ER Stroop )/3 (2) response time score: (RT Switch þ RT 3-Back þ RT Stroop )/3.
Autonomic nervous system measures The participants' average heart rates were recorded during the five experimental periods (BASAL, TSAS, INTERVENTION, STRESS, COGNITION S2). According to taskforce recommendations, heart-rate variability parameters were extracted from 5-min segments in the BASAL, INTERVENTION, STRESS, and COGNITION S2 periods. The detection of artifacts and extraction of time and frequency domains were performed using MATLAB software (R2019a®). For the time domain, we calculated the root mean square successive difference (RMSSD, in ms), the standard deviation of ReR intervals (SDRR, in ms), and the percentage of successive normal ReR intervals differing by more than 50 ms (pNN50, in ms). For the frequency domain, we quantified the power of low (LF: 0. 04e0. 15 Hz) and high (HF: 0.
Acknowledgments The authors thank the participants who volunteered to participate in this study. We also thank  L. Djeavelou ,  D. Saboul ,  V. Pialoux  for technical and theoretical support, as well as  A. Metais ,  E. Gouraud ,  B. Canada ,  Y. Blache ,  A. Foure ,  M. Degot ,  M. Binay ,  M. Gallot ,  B. Gillet  for they help at different times in the study. Finally, our thoughts turn to  C. Faes , who was of great help for this project, as well as her family.
Besides a thorough immunofluorescence study on oligodendrocytes, we applied AxonDeepSeg, an open source software, to automatically segment and characterise myelin thickness and axon area. We found that axon calibre, the main contributor to changes in conduction speed, is specifically reduced in the associative striatum of the Sapap3-KO mouse; myelination per se seems unaffected in associative and sensorimotor cortico-striatal circuits.
e. , lOFC and primary/secondary motor cortex (M1/M2). In these candidate regions, we morphologically characterised myelinated axons in the Sapap3-KO mouse model, measuring both axon area and myelin thickness as the determinant parameters of axon conductivity. To achieve this, we took advantage of the open source software AxonDeepSeg  [49] , which automatically segments axon and myelin compartments using deep learning methods and performs various morphometric measurements (axon area, myelin thickness, g-ratio). AxonDeepSeg is fast (a few seconds per image), allowing for the analysis of hundreds of images in only a few minutes. As myelin coverage is performed by oligodendroglial cells, i. e. , the glial cells that wrap around the fibres to produce the myelin sheaths, we additionally screened this cell type in the same regions of interest for potential abnormalities.
Immunohistochemistry analysis. For the quantification of oligodendrocyte density, we used Fiji, ImageJ2  [52, 53] , and manually counted all cells that were positively stained for Olig2 and CC1. Positive immunolabelling for both Olig2 and CC1 (Olig2+/CC1+ cells) allowed for the identification of mature oligodendrocyte; positive immunolabelling of Olig2 but negative labelling of CC1 (Olig2+/CC1-cells) allowed for the identification of immature oligodendroglial cells. Data analysis and statistics. All data and statistical analysis were performed using Matlab R2017b. Given the small sample size, we exclusively performed non-parametric Mann-Whitney-U tests. We furthermore used two open source software packages available on GitHub for the visualisation of our data: raincloud plots  [54]  as well as estimation plots  [55].
Funding: This work was realised with the following funding:  Agence Nationale de la Recherche  ( ANR-16-INSERM-SINREP ,  ANR-19-ICM-DOPALOOPS ) (EB), the  ICM Big Brain Theory Program (BBT-ACTIMYEL; EB, BN-O) , the  L'Oréal-UNESCO  Fellowship 2016 (CS), the  Canada Research Chair in Quantitative Magnetic Resonance Imaging  ( 950-230815 ) (J. C. -A. ), the  Canadian Institute of Health Research  ( CIHR FDN-143263 ) (J. C. -A. ), the  Canada Foundation for Innovation  ( 32454 ,  34824 ) (J. C. -A. ), the  Fonds de Recherche du Québec-Santé  ( 28826 ) (J. C. -A. ), the  Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2019-07244 ) (J. C. -A. ), the  Canada First Research Excellence Fund (IVADO and TransMedTech)  (J. C. -A. ), the  Quebec BioImaging Network  ( 5886 ,  35450 ) (J.
Data Availability Statement: The datasets and analyses of this study are available from the corresponding author on reasonable request. Acknowledgments: All animal work was conducted at the PHENO-ICMice facility. This work furthermore benefitted from the equipment and services from the iGenSeq core facility at the ICM for the genotyping of the animals. All electron microscopy imaging was performed using the equipment of the ICM. QUANT core facility, with particular regards to all the advice and help provided by Dominique Langui. All fluorescent microscopy was conducted using the equipment at the CELIS core facility. Moreover, sample preparation and immunohistochemistry were possible using the equipment at the HISTOMICS core facility. We thank G. Feng and Ann M. Graybiel for kindly providing founders of the Sapap3-KO colony, Christian Perone for helping us apply AxonDeepSeg, Dorien Maas for her guidance with immunohistochemistry protocols, Sami Beaumont for advice on analytical and statistical tools, as well as Marine Euvrard for her help with animal experiments.