dataesr/scientific-paragraphs-categorization huggingface dataset:
M. );  French State/Agence Nationale de la Recherche  ( CaCoVi ,  ANR 18-CE16-0001-02 ;  MitObesity ,  ANR 18-CE14-0029-01 ;  ERA-Net Neuron CanShank ,  ANR-21-NEU2-0001-04 , to G. M),  Spanish Ministry of Science and Innovation  ( PID2019-109724RB-100  to C. M. ;  PGC2018-093990-A-I00 , to E. S. -G. ),  National Institutes of Health -MH (MH,  R01MH119355 ;  NINDS ,  R01NS097312 ;  NIDA ,  R01DA048822 , to A. A. ), Postdoctoral and Predoctoral Programs the  Basque Government  (to A. M. -B. , T. C. A. M. G. , and C. U),  Predoctoral Program  of the  UPV/EHU  (to E. S. ).
ACKNOWLEDGEMENTS We thank  Deniz Dalkara  and the viral core facility at the  Vision Institute (Paris)  for the AAV vectors. We thank  Lauren Reynolds  and  Benoît Girard  for careful reading of the manuscript. This work was supported by  Centre national de la recherche scientifique  ( CNRS UMR 8249 ),  Fondation pour la recherche médicale  ( FRM DEQ2013326488  to PF,  FRM FDT201904008060  to SM),  French National Cancer Institute  Grant  TABAC-16-022 , French state funds managed by  Agence nationale de la recherche  ( ANR-16  Nicostress to PF,  ANR-20  NICADO),.
Acknowledgments This work was supported by funding from the  Institut National de la Santé et de la Recherche Médicale , a grant from the  Agence Nationale de la Recherche  (SoAbsence, # 16-CE37-0021 2016 ) and a fellowship from the  Fondation Française pour la Recherche sur l'Epilepsie (G. Jarre). We thank all the staff of the INMED and GIN animal facilities for providing animal care. We also thank  Elodie Fino ,  Séverine Mahon ,  Alfonso Represa , and  Florian Studer  for their critical reading of the manuscript.
Acknowledgments: JGR is an  FCT/PhD Fellow  ( IMM LisbonBioMed PhD program   PD/BD/135518/2018 ); LVL is an FCT-CEEC Coordinator Investigator and is supported by  FCT. TS is an  FCT-CEEC  Principal Investigator also supported by  FCT  ( 2022. 03699. CEECIND). Funding from  CIBIT  was secured by  FCT/UIDB&P/4950. We would also like to acknowledge the  Rodent Facilities of Instituto de Medicina Molecular João Lobo   Antunes  and  ICNAS  for their technical support. We are also indebted to  Marcelo Dias  and  Tiago Gil-Oliveira  ( ICVS, Braga ),  Patrícia Figueiredo  ( IST, Portugal ),  Disha Shah  ( VIB Leuven ),  Ingrid Bethus  and  Sebastian P. Fernandez  ( IPMC, Nice ), and  Gwenaelle Catheline  ( Univ. Bordeaux ) for the availability and fruitful discussions/suggestions regarding data analysis.
Whole-body plethysmograph Respiration was measured by using a whole-body plethysmograph [diameter: 20cm, height: 30cm, EMKA Technologies, France ;  (Courtiol et al. 2014; Lefèvre et al. 2016) ]. It consists in a plexiglass cage composed of two independent chambers: the recording one where the rat is installed and the reference one. The changes in pressure resulting from the animals' breathing were detected using a differential pressure transducer located between the recording chamber and the reference one. The signal was sampled at 1 kHz, amplified (Amplipower, EMKA Technologies) and recorded using a home-made software developed in Python (pyacq, https://github. com/pyacq/pyacq). A constant airflow was delivered from the top (1100 ml/min) and from a central port situated on the wall of the plethysmograph (400ml/min) for a total of 1500 mL/min.
Respiratory signal Respiration appears to be a periodic function associating negative (inspiration) and positive (expiration) deflections. The analysis of the respiratory data relied on a python-coded algorithm allowing the detection of the transition point between inspiration and expiration  (Courtiol et al. 2014; Girin et al. 2021; Lefèvre et al. 2016; Roux et al. 2006). Inspiration was considered to start at the zero-crossing point of the falling phase and ends at the zerocrossing point of the rising phase, whereas the expiratory phase began at the zero-crossing point of the rising phase and ends at the zero-crossing point of the falling phase. In order to reduce noise and to eliminate potential artifacts, raw data were visually inspected, and an automatic Python script was further used to remove artifacts from the dataset.
Respiratory signals in different behavioral states The association between animal behavior and their specific respiratory outcomes has been described in several studies  (Dupin et al. 2020; Girin et al. 2021; Hegoburu et al. 2011; Janke et al. 2022). Therefore, during the passive exposure to the experimental cage, we decided to inspect the rats' respiratory signal across three vigilance states: sleep, quiet waking and exploration  (Girin et al. 2021). The video recordings of each session were manually encoded using the ephyviewer (https://github. com/NeuralEnsemble/ephyviewer. git). Sleeping was encoded when the rat had his eyes closed, with his body lying on the cage floor or in a curled-up position (Fig. 2B ). The quiet waking state was encoded when the rat's eyes were open and when he was either immobile or slightly moving.
Melatonin suppression ~ 𝑎 exp[𝐴 (𝑎-𝑥)]+ exp[𝐵 (𝑏-𝑥)]+ exp[𝐶 (𝑐-𝑥)]+ 𝐷 … (Eq. 3) where 𝑥 = 480/wavelength. Other parameter values were A = 69. 7, a = 0. 88, B = 28, b = 0. 922, C = -14. 9, c = 1. 104, D = 0. 674 as in the  Govardovskii et al. , (2000). The amplitude, a, was not constrained to the value of 1 as in  Govardovskii et al. , (2000) , but allowed to be optimally determined by the nonlinear least-squares curve-fitting procedure. The peak sensitivity of melanopsin was fixed at 480 nm  (CIE, 2018). Given that the dataset used is identical to the previously published data in  Najjar et al. , (2014) , the lens filtering spectra obtained for the young and the older in that study was applied so as to account for the lens transmittance and its change with aging. Each fitted function in the young and older participants was accounted for pre-receptoral filtering by multiplying the function with its respective ocular lens transmittance spectra, published in  Najjar et al.
Definition of anatomical regions of interest Anatomical labeling of bipolar derivations was performed using the IntrAnat software  (Deman et al. , 2018). The 3D T1 pre-implantation MRI gray/white matter was segmented and spatially normalized to obtain a series of cortical parcels using MarsAtlas  (Auzias et al. , 2016)  and the Destrieux atlas  (Destrieux et al. , 2010). 3D coordinates of electrodes contacts were then coregistered on post-implantation images (MRI or CT). Each recording site (i. e. , bipolar derivation) was labeled according to its position in a parcellation scheme in the participant's native space. Thus, the analyzed dataset only included electrodes identified to be in the gray matter. Four regions of interest (ROIs) were defined for further analysis: 1) the ventromedial prefrontal cortex (vmPFC) ROI was created by merging six (three per hemisphere) parcels in MarsAlas (labeled PFCvm, OFCv and OFCvm in MarsAtlas) corresponding to the ventromedial prefrontal cortex and fronto-medial part of the orbitofrontal cortex, respectively; 2) the lateral orbitofrontal cortex (lOFC) ROI included four (two per hemisphere) MarsAtlas parcels (MarsAtlas labels: OFCvl and the OFCv); 3) the dorsolateral prefrontal cortex (dlPFC) ROI was defined as the inferior and superior bilateral dorsal prefrontal cortex (MarsAtlas labels: PFrdli and PFrdls); 4) the anterior insula (aINS) ROI was defined as the bilateral anterior part of the insula (Destrieux atlas labels: Short insular gyri, anterior circular insular sulcus and anterior portion of the superior circular insular sulcus).
, 2020; Michelmann et al. , 2021; Ten Oever et al. , 2021). Mathematically, the GCMI is a lower-bound estimation of the true MI and it does not depend on the marginal distributions of the variables, but only on the copula function that encapsulates their dependence. The rank-based copula-normalization preserves the relationship between variables as long as this relation is strictly increasing or decreasing. As a consequence, the GCMI can only detect monotonic relationships. Nevertheless, the GCMI is of practical importance for brain signal analysis for several reasons. It allows to estimate the MI on a limited number of samples and it contains a parametric bias correction to compensate for the bias due to the estimation on smaller datasets. It allows to compute the MI on uni-and multivariate variables that can either be continuous or discrete (see Table  I in   (Ince et al.
The final corrected p-values were inferred as the proportion of permutations exceeding the t-values. To generate the distributions of II and TE reachable by chance, we respectively shuffled the PE variable across trials for the II and the gamma power across trials of the source for the TE  (Vicente et al. , 2011). The rest of the significance testing procedure at the network level is similar to the local level, except that it is not applied within brain regions but within pairs of brain regions. Finally, for information-theoretic metrics and group-level statistics, we used a homemade Python software called Frites  (Combrisson et al. , 2022b). A*Midex (AMX-19-IET-004) and ANR (ANR-17-EURE-0029) funding. JB was supported by ANR-17-CE37-0018 and ANR-18-CE28-0016. Centre de Calcul Intensif d'Aix-Marseille is acknowledged for granting access to its high-performance computing resources.
Acknowledgments EC, RB, JB and AB were supported by the  PRC  project " CausaL"  ( ANR-18-CE28-0016 ). This project/research has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ). AB were supported by  FLAG ERA II "Joint Transnational Call 2017" -HBP -Basic and Applied Research 2, Brainsynch-Hit  ( ANR-17-HBPR-0001 ). RB acknowledges support through a  PhD Scholarship  awarded by the  Neuroschool. This work has received support from the  French government  under the  Programme Investissements d'Avenir, Initiative d'Excellence d'Aix-Marseille Université  via.
Similarly, we define the normalized mean strength (nMS), to quantify the mean functional connectivity (cross-correlation) of every contact with the rest of the signals in a frequency band and time window of interest. Results: The biomarker comparison was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that power-based biomarkers generally perform better for the detection of SOZ than connectivity-based ones. However, a similar performance level can be achieved when both biomarkers are independently optimized over frequency bands and time windows. Optimal performance was achieved in broadband or in the beta range for power-based biomarkers, and in the lower-gamma range for connectivity. Both biomarkers maximized their performance when using a 20-30 s period after seizure onset.
For the sake of comparison, we defined an analogous connectivity-based measure based on the cross-correlation  (Mierlo et al. , 2014;  1  Khambhati et al. , 2017). The normalized mean strength (nMS) corresponds to the mean functional connectivity (cross-correlation) of every contact with the rest of signals in a frequency band and time window of interest. Optimal frequency bands and time windows to identify the SOZ may be different for both types of measures. To account for such potential differences, we assessed the discrimination power of both metrics across various frequency bands and time windows of interest and selected the combination that maximized the observed effect in each case independently. This method was tested with a dataset of 67 seizures from 10 patients diagnosed with pharmacoresistant temporal lobe epilepsy that underwent stereo-electroencephalography (SEEG), a particular type of intracranial EEG technique  (Talairach et al.
This, together with the increased inter-seizure nMS similarity (Fig. S1 ), indicates that the results might generalize better for nMS than nMA for larger 7 datasets. The best time window size was also found to be patient-specific, although some consistency was found within each subject across measures (best time windows were similar for nMA and nMS). In general, the results of the classification accuracy under each criteria showed that the nMA outperformed nMS for most of the TOI-FOI combinations. However, when selecting the optimal combination for each variable, we did not find significant differences in AUC between nMA and NMS in our dataset (n=10, P>0. 1) regardless of the employed criterion. More precisely, under optimized conditions, we could differentiate patients in which the classification performance remained approximately the same, patients in which there was a moderate performance variation (AUC difference <0.
05) and patients in which there was a notable performance decrease from nMA to nMS (AUC difference >0. 05). In conclusion, the application of the proposed methodology to our available dataset suggests that power-based variables might yield slightly better SOZ identification performance than (single-site) functional connectivity variables such as the connectivity strength. Yet, larger datasets will be needed to characterize such comparison more accurately and to investigate whether the combination of each type of variable can also yield performance improvements. In any event, our methodology is aimed to provide an in-depth and fair comparison analysis between any SOZ discrimination biomarker candidate defined at a single-site level with respect to standard power-based variables to identify novel intracranial EEG features that could strengthen epilepsy presurgical diagnosis.
We illustrated the application of our method to analyze the power-connectivity comparison in a dataset of 10 patients with pharmacoresistant temporal lobe epilepsy that accounted for a total of 67 seizures and whose SOZ was clinically validated by epileptologists. The outcomes of our analysis suggest that power-based biomarkers generally perform better than connectivity-based ones. Yet, our results also show that both biomarkers may achieve similar performance levels when appropriately optimized over frequency bands and time windows.
S1 ). Assessing seizure homogeneity was key in our method to extract a median value per contact across seizures, which could provide a sufficiently good representation of the patient's ictal activity for each measure. This step was therefore instrumental in performing statistical analysis comparing the activity (power/connectivity) between SOZ and non SOZ contacts per patient (Fig. 3 ). However, it is due stating that the reported seizure homogeneity of our dataset was possibly favored by the fact that all patients had temporal lobe epilepsy with a clear diagnosed focus. It remained out of the scope of this study to investigate to which degree seizure homogeneity still holds in patients with extra-temporal lobe epilepsy or patients with heterogeneous seizures exhibit foci across different brain areas.
Limitations and other considerations In this study, we performed a comparison between a power-and a connectivity-based measure to infer the SOZ from iEEG signals. As a result of our analyses, we could establish the optimal parameters for each type of measure in our dataset, but we did not build any joint biomarker that combines both power and connectivity. Further research should investigate how to combine both sources of information, considering that this might vary depending on various factors, such as the epilepsy type, the spectral 10 features of the seizure onset patterns or the spatial extent of the implantation. In addition, it should be noted that our results are based on the assessment of a very specific connectivity measure derived from cross-correlation. Cross-correlation is defined to capture linear relationships between signals with possible time delays.
5 Acknowledgements M. V. and R. Z. were funded by the  European Regional Development Funds , Grant/Award Number:  CECH/001-P-001682. A. T. C. and M. V. were funded by the  Spanish Ministry of Science, Innovation, and Universities (MCIU) , Grant/Award Number:  PID2020-119072RA-I00/ AEI/10. 13039/501100011033. G. D. was supported by the  AGAUR research  support grant (ref. 2021 SGR 00917 ) funded by the  Department of Research and Universities of the Generalitat of Catalunya , the  NODYN  Project  PID2022-136216NB-I00  financed by the  MCIN/AEI/10. 13039/501100011033/FEDER , UE. , the  Ministry of Science and Innovation , the  State Research Agency  and the  European Regional Development Fund  and the project  NEurological MEchanismS of Injury, and Sleep-like cellular dynamics (NEMESIS)  (ref.
Rats were given a 24 h washout period in between hormone injections. Rats were then surgically implanted subcutaneously with osmotic minipumps containing 1 mg/kg/day nicotine. Rats were allowed to establish dependence to nicotine for 2 weeks, and injections were repeated with minipump on board to maintain nicotine dependence. 200 μL blood was again collected at baseline and postinjection time points. Following blood collection, rats were placed into operant chambers, and food and water intake was recorded in 21 h sessions. All blood samples were kept on ice after collection, centrifuged at 3000 rpm, and plasma was extracted and stored at -80 • C until use. Food intake data were collected in 10 s bins throughout the 21 h operant session. Data were smoothed by taking the running average of the data 30 s before and after each data point, and the values between 6 and 9 min were chosen to focus on as this is the window in which we expect to see the increased feeding behavior following nicotine injection.
25 μL of each thawed plasma sample (diluted 1:2 in MSD Metabolic Assay Working Solution) were added in duplicate wells to the plate, and incubated for 2 h while shaking. Plates were then washed 3× with wash buffer (1X PBS + 0. 05 % Tween-20), and detection antibody solution was added to the wells and incubated for 1 h while shaking. Plates were washed 3× with wash buffer and read buffer was added to wells immediately following last wash. Plates were read using MESO QuickPlex SQ 120MM plate reader (Mesoscale Discovery). Analyte concentrations in samples were calculated using MSD Discovery Workbench software.
Statistical analysis All of the data was analyzed using Prism 9 software (GraphPad, San Diego, CA, USA). Outliers were removed prior to analysis using Grubb's outlier test. All data analyses combine males and females as we did not see a significant effect of sex (data not shown). Data were tested for normality using the D'Agostino & Pearson test, and as data passed the test of normality, parametric analyses were subsequently used. For repeated measures analysis, sphericity was assumed as data sets between groups had equal variances. Food and water intake following nicotine injections were analyzed using 1-way repeated measures analysis of variance (RM ANOVA) with Tukey's multiple comparisons post hoc. Baseline hormone levels in nondependent vs. dependent rats, hormone levels following subcutaneous injection of nicotine or saline, and hormone levels following subcutaneous injection of hormone were analyzed using paired t-tests.
Correlational analyses of hormone levels vs. body weight, hormone levels vs. baseline food intake, and food intake post-injection vs. hormone level post-injection were performed using Pearson's correlational analysis. Analysis of circulating hormone time course following injection was performed using 1-way RM ANOVA with Tukey's multiple comparisons post hoc. PSTH of food intake following injection of hormone and nicotine or saline was analyzed using 2-way RM ANOVA with Sidak's multiple comparisons post hoc. Effect sizes for all significant data are reported by η 2 (ANOVA) or Hedges' g (t-tests) as guided in the detailed report by Lakens  (Lakens, 2013). All data are expressed as mean ± SEM.
108; PYY p = 0. 0003, t = 5. 684, g = 2. 927) (Fig. 8A, C, E ). To assess the effect of hormone injection on feeding behavior, a within-subjects study was performed, so that all rats were given injections of each hormone prior to nicotine or saline injection (N = 6/ group). After nicotine or saline injection, rats were placed in operant chambers and food intake was recorded in 10 s bins for 20 min. Average food intake was analyzed using PSTH. Control data of food intake following nicotine or saline injection without additional hormone injection was also used for comparison, and we observed that acute nicotine injection did increase food intake (treatment p = 0. 05). 2-way RM ANOVA of food intake following active GLP-1 injection showed no significant effect of hormone treatment on food intake following nicotine injection (Fig.
Visual stimuli and virtual tunnel All visual stimuli for two-photon imaging were presented on a gamma-corrected full HD LED monitor using OpenGL and Psychophysics Toolbox 3 running on MATLAB (Mathworks). The monitor was positioned 15 cm from the mouse. For passive visual stimulation before and after training, we used full screen square gratings (0. 05 cpd) of different contrasts (0-0. 05-0. 1-0. 2-0. 4-0. 8-1) moving in one of eight different directions (45° apart, 1 cps). Stimuli were presented for 1 s with a random inter trial interval of 4-6 s and repeated a minimum of eight times each. To model visual RF selectivity using the CNN model, we used 3600 unique images from 720 classes taken from the THINGS database  (84). In order to test reliability across repetitions of the same stimuli and model cross-validated correlations, an additional 40 unique images were repeated 10 times.
Acknowledgments We thank all members of the  Levelt lab  for discussion and support. We thank staff of the animal facility and mechatronics department at the  Netherlands Institute for Neuroscience  for technical support. We thank  Dr. Seungho Lee  from  Pohang University of Science and Technology  for kindly providing viral vectors for rabies tracing experiments and  Dr. Hongkui Zeng  and the  Allen Institute for Brain Science  for providing the Vipr2-Cre mice. We thank  Dr. Maren Engelhardt  for providing the Rabbit-β-IV-Spectrin antibody,  Barbara Hobo  for help with tail vein injections,  Ulrike Schlegel  for help with QUINT software and  David van Oorschot  and  Dilara Ilhan  for help with experiments. We are grateful to  Dr. Jean-Charles Paterna  and  Dr. Melanie Rauch  from the  Viral Vector Facility VVF at the ETH Zurich and University of Zurich  for their expert advice, support and services regarding viral vector production.
This project received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement nos. 785907  ( HBP SGA2, CL & PR ) and  945539  ( HBP SGA3 , CL & PR), from the  Dutch Research Council  ( NWO-OCENW. KLEIN. 178 , CL & PR and  FlagEra SoundSight ,  680-91-320 , CL). The authors declare that they have no competing interests. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Model parameter values used in simulations 5 Acknowledgments We thank Romain Brette, Matthias Henning and Romain Veltz for helpful discussions about the model, and Matias Goldin and Brice Bathellier for critical reading of the manuscript. This work has been funded by a PhD fellowship from the Neuromod Institute, Université Côte d'Azur to S. E. , by an ERC grant (No 101045253, DEEPRETINA) to O. M. , ANR grants (DECORE, ANR-18-CE37-0011, and PerBaCo, ANR-22-CE37-0016-02) to O. M. , a grant from Retina France to O. M. , and ANR ShootingStar (ANR-20-CE37-0018-04) to O. M. and B. C. T. B. was funded by a PhD fellowship from ENS. E. , T. B. , O. M. and B. C. designed the study. T. B. and B. S. S. performed the experiments. S. E. and T. B. analyzed the data with help from O. M. and B. C. S. E. did the model with help from T.