dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, 551 (sd = 81) correct trials on average per subject were kept for behavioral analyses due to the removal of errors, absence of response within 1000 ms or remaining artifacts. RT were log-transformed for increased compliance with the model's assumptions (homogeneity of variance and normal distribution of residuals model). Fixed effects significance were computed through the Anova function of the {car} package  70  that calculates type II Wald chi-square tests. Marginal (mR 2 ) and conditional (cR 2 ) were calculated using the {MuMin} package. 71  These models offered the possibility to exploit the whole dataset, avoiding the loss of statistical power due to averaging data, and also enabled taking into account interindividual variability and unbalanced data. 72  Group effect on the average first bin of CAF and on the last slope values of delta plots were estimated with Welch's t-test, since only one averaged value exists for each subject, and are reported with effect size (Cohen's d).
Bioinformatics Bulk-RNA sequencing reads were first pre-processed to ensure data quality. Thus, fastq files quality was analyzed using the fastp software with default parameters  (Chen, 2023)  and only reads longer than 20bp were kept. Then, reads were aligned to the rat genome (Rnor_6. 0 genome assembly) using the splice-aware mapper STAR with default parameters  (Dobin et al. , 2013)  and gene counts were summarized using the GenCounts options of STAR. To determine differentially expressed genes, we used the DESeq2 algorithm in R with default parameters  (Love et al. , 2014). Gene expression data were displayed as volcano plots using the EnhancedVolcano package in R (https://github. com/kevinblighe/EnhancedVolcano. ). Table 1.
Acknowledgements Part of the experiments (sequencing of the RNA-seq experiment) were performed at the PGTB (doi:10. 15454/1. 5572396583599417E12) with the help of  Erwan Guichoux  and  Zoé Delporte. We thank the staff of the  PIVE-EXPE animal facility  for animal care. Microscopy was done in the  Bordeaux Imaging Center  a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure France BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 ). The help of  Magalie Mondin  is acknowledged and we thank  Mélodie Ambroset  for her assistance on image analysis.
We failed to observe a close relation between spectral and spatial dimensions, as indexed by the much less spatially structured pattern of the spectral gradient (Fig. 3c ). This dynamic reorganization in the form of a spectral gradient along the dorsal auditory pathways during music listening was confirmed by an individual level estimation of the quality of fit for the two datasets (rm-ANOVA: main effect of dataset: F(1,28) = 54. 4, p < 0. 001; Fig. 3d left ). Further analyses showed that such spectral gradient is a general characteristic of music listening, independent of the specific acoustic, melodic or cognitive attributes of the music. Indeed, we observed that the spectral gradient does not vary across conditions (low, medium, high), neither in shape (Fig. S2a ) nor in its quality of fits (Fig.
Acoustic stimuli. A professional musician composed in MIDI format 36 melodies lasting 8 seconds each (stimuli are available on github. com/DCP-INS/Groove). All had the same strictly periodic drum beat at 2 Hz combined with a specific bass melody. To vary the level of rhythmic predictability while minimizing other acoustic variations, three variants were derived from each of 12 original melodies, using an ascending degree of syncopation ((the amount of syncopation is inversely proportional to the rhythmic predictability). The bass melodies were maximally matched between the three variants of the same original melody, with the number of notes per measure, their pitch and their order being identical or closely matched. Other musical characteristics (volume, timbre, etc. ) were kept constant. This procedure resulted in the creation of three conditions, reflecting low (black), medium (grey) or high (light grey) degree of syncopation (Fig.
Spectral decomposition of the acoustic stimuli. To estimate the temporal envelope of each melody, the sound signal was decomposed into 32 narrow frequency bands using a cochlear model, and the absolute value of the Hilbert transform was computed for each of these narrowband signals. The broadband temporal envelope resulted from the summation of these absolute values and was used as the acoustic signal for all subsequent analyses. Using a fast experiment and resting-state datasets. We approximated the data with a five-order polynomial function, fitting the dominant frequency of each vertex to each spatial dimension (x,y,z) and projecting the results in the 3D space. Searchlight analysis on source-level MEG data. We conducted searchlight-based multivariate pattern analyses on the entire power signal of the reconstructed volume sources (vertices), for each participant and each regressor (degree of syncopation and groove ratings).
Acknowledgments: We warmly thank  Aurélie Ponz  and  Sophie Chen  for their contribution to MEG data acquisition and  Christian Bénar  for discussions on the paradigm. Research supported by grants  ANR-20-CE28-0007  (BM),  ERC-CoG-101043344  (BM),  ANR-21-CE28-0010  (DS),  ANR-16-CONV-0002  (ILCB) and the  Excellence Initiative of Aix-Marseille University  (A* MIDEX ).
Funding:  ANR-20-CE28-0007  (BM)  ERC-CoG-101043344  (BM)  ANR-21-CE28-0010  ( DS )  ANR-16-CONV-0002  (ILCB) and the  Excellence Initiative of Aix-Marseille University  (A* MIDEX). SPARK Award  from  University of Connecticut (EWL).
Interneuron activity is linked to pyramidal cell assemblies Hence, in addition to the specific functional connectivity patterns of CA1, our simulations indicate that the presence of subnetwork structure in the form of mixed cell assemblies, comprising interneurons and pyramidal cells, is essential to explain our experimental observations. Thus, we next analyzed the contribution of interneurons to cell assemblies in our experimental dataset. First, we detected the cell assemblies nested in SCEs using a k-means-based method  27. We detected significant cell assemblies in 7/11 FOVs from 5 mice (2±2 assemblies per FOV, mean ± standard deviation, range: 1-6 assemblies, Fig. 4a ). Consistent with a proportional representation of interneurons in SCEs, we found that the proportion of pyramidal cells and interneurons forming cell assemblies was similar (p=0.
Acknowledgements We thank all the members of the  Cossart lab  for helpful discussions and constructive feedback. We thank  INMED's animal facility  and PBMC technological platform for excellent technical support. This work was supported by the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 646925  and  951330 ), by the  Fondation Bettencourt Schueller , the  Fondation Roger de Spoelberch , and by the  French National Research Agency  (grant agreement no  ANR-14-CE13-0016 ); A. V. was supported by the  ITN Marie Curie  ( H2020-MSCA-ITN-2017 , project.
Neuronal avalanches and functional repertoire In order to quantify spatio-temporal uctuations of brain activity, we estimated neuronal avalanches, de ned as events starting when at least one brain region deviates from its baseline activity and ending when all areas are back to normal activity  [35]. Each of the 90 source-reconstructed signals were ztransformed and thresholded according to a cut-off of 2. 5 standard deviations  [19]. We repeated the analyses setting the threshold to z > |2. 25| and z > |2. 75| to con rm that results do not depend on the choice of a given threshold. To select a suitable bin length, we computed the branching ratio σ  [13]. For each subject, for each time bin, and for each avalanche, the geometrically averaged ratio of the number of events (activations) between the subsequent time bin and the current time bin was calculated as 1 2 where σ i is the branching parameter of the i-th avalanche in the dataset, N bin is the total amount of bins in the i-th avalanche, n events (j) is the total number of events active in the j-th bin, and N aval is the total number of avalanches in each participant's recording.
Acknowledgments This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 945539  (SGA3);  Human Brain Project ,  Virtual Brain Cloud  No. 826421 ;  Ministero Sviluppo Economico ( Contratto di sviluppo industriale "Farmaceutica e Diagnostica " [ CDS 000606 ]);  European Union "NextGenerationEU"  ( Investimento 3. 1. M4. C2 ), project  IR0000011 ,  EBRAINS-Italy  of  PNRR. Equal Author Contributions  Arianna  and  Emahnuel Troisi Lopez  contributed equally to this work as rst authors.
The preprocessing steps for each participant were as follows: for each session, participants' five blocks in uncrossed posture, followed by their five blocks in crossed posture, were appended into a single dataset. The signal was resampled at 500 Hz and high-pass filtered at 0. 1 Hz. Faulty channels were interpolated using a spherical spline. We then epoched data into a time window of 3. 5 seconds, 1 second before and 2. 5 seconds after the cue and baseline corrected using the period from -500 ms to -100 ms before the cue as baseline. Next, we removed signal artifacts with two steps: first, we removed eye blinks and horizontal eye movements from the signal using independent components analysis (ICA  (Delorme and Makeig, 2004 )) and a semi-automated algorithm called SASICA  (Chaumon et al. , 2015).
We excluded every trial when participants made a response (whether it was a correct or an incorrect answer) or accidentally released the foot pedal, so that we only kept trials free of any motor activity. We re-epoched the data around the tactile stimulation (time zero), from -1. 5 second before to 1 second after, leading to 2. 5s epochs, and used the period from -500 ms to-100 ms before the hit for baseline correction. We then manually rejected trials that were contaminated by muscle artefacts or other forms of signal noise. In total, this led to a mean exclusion of 52. 6 trials per participant (range: 2-205). Next, we used the EEGLab function pop_reref to add FCz (the online reference) back into the dataset and re-referenced the data to the average voltage across the scalp. Finally, for a better signalto-noise ratio, we swapped the electrodes order of all the trials where the left hand or tool has been touched, so that all trials would now be in reference to the right hand or tool being touched  (Buchholz et al.
This work was supported by the  Agence Nationale de la Recherche  [ ANR-16-CE28-0015 ]  Developmental Tool Mastery  to A. F. & [ ANR-19-CE37-0005 ] BLIND_TOUCH to A. F. and L. E. M;  Fondation pour la Recherche Medicale   Post- doctoral Fellowship  SPF20160936329  to L. E. M. and by a  Foundation Berthe Fouassier Doctoral fellowship  to C. F. under the umbrella of the  Fondation de France. All work was performed within the frame work of  LABEX CORTEX  [ ANR-11-LABX-0042 ] of  Université de Lyon. We thank  Frederic Volland  for his help constructing the experimental setup.
Acknowledgments We thank  Dr Serge Luquet ,  Giuseppe Gangarossa  and  Claire Martin  for their advice and helpful discussions. The help of  Zahra Boudra  is also acknowledged. We thank the technical platform Functional and Physiological Exploration platform (FPE) of the  Université de Paris (BFA-UMR  8251), the animal core facility  Buffon  of the  Université de Paris/Institut Jacques Monod  for animal experiments. We also thank the core imaging facility of  Institut Jacques Monod (ImagoSeine facility)  for help in image acquisition and processing. The microscopy for LH cFos analysis was done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure France BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
The help of  Sébastien Marais  is acknowledged. This work was supported by  Research grant  of the  French society for study of diabetes  ( SFD-017-2019  to CM and HG, the  Medical Research Council UK  ( MR/N02589X/1  to ST), a  British Heart Foundation   Postdoctoral Fellowship  ( FS/IPBSRF/20/27001  to MKH) and a grant from the  European Foundation for the Study of Diabetes Germany  ( Merck Sharpe Dohme  grant to ST). Additional support was obtained from the  Cities Partnership program   UCL  to ST, CM and HG. MM is supported by a " Université Paris Cité IdEx " PhD fellowship and a EUR GENE, G. E. N. E. Graduate School  fellowship. WJ is supported by a  UCL ORS  scholarship and a CSC scholarship from the  Chinese Government. We also acknowledge INSERM (to D. C. ) and  Agence Nationale de la Recherche  ( ANR-21-CE14-0018  to D.
Read processing, mapping, and quantification Raw sequence reads for diabetic and healthy control retinas were initially inspected for quality using FastQC  (Andrews, 2010). Raw sequence reads were mapped to the Mus musculus reference genome (GRCm38. p5) and annotated using Gencode (M15) with the STAR aligner program (v2. 7. 4a)  (Dobin et al. , 2013). 1)  (Anders et al. , 2015)  was used to generate the raw counts for each sample, and the resultant read count matrix was normalized together using DESeq2 (v1. 26. 0)  (Love et al. , 2014). To increase statistical power for identifying truly cycling gene features with extremely low read counts  (Hughes et al. , 2017) , following normalization and visual inspection of the data, genes with a normalized median read count > across all samples were considered expressed and included for further analysis.
Identification of cycling genes Genes displaying an approximately 24-hour rhythm were identified using the nonparametric rhythmicity detection algorithm empirical JTK_CYCLE with asymmetry search (empJTK)  (Hutchison et al. , 2015). Briefly, empJTK is an advancement on the well-accepted JTK_CYCLE rhythmicity detection method, which allows the identification of transcripts displaying asymmetric waveforms and is considered one of the most robust methods for identifying periodic patterns in gene expression data  (Laloum & Robinson-Rechavi, 2020). Normalized diabetic and control read count matrices were formatted as a single cycle with 4-5 biological replicates for each timepoint, and both phase and asymmetry searches were set to 4-hour intervals ranging from ZT0 to ZT20. Significantly cycling genes were defined as those with an empJTK empirically calculated p-value (empP) < 0.
Acrophase prediction Since empJTK is only effective at predicting acrophases no more densely than the resolution of the data, harmonic regression was subsequently used to accurately predict the acrophases of significantly cycling genes using the Harmonic Regression R package (v. 1. 0) with a fixed period of 24 hr and normalization set to false. Radian acrophases (φ) were then converted to hours (φ′) using the equation φ′ = (-P/2π)φ, where P is the pre-set period of rhythmicity (24hr)  (Refinetti et al. , 2007). Acrophases for all rhythmic genes are listed in S. Table  3.
Functional annotation and phase set enrichment analysis To analyze the functions of cycling genes while considering the acrophase of the transcript, a DO and KEGG phase set enrichment analysis (PSEA)  (Zhang et al. , 2016)  was carried out using gene sets from the Molecular Signatures Database (MSigDB)  (Subramanian et al. , 2005). Any GO or KEGG term with a BH-corrected p-value < 0. 01 was significantly enriched. Data is shown in S. Table  4. Harmonic regression predicted acrophases were rounded to the nearest hour. Testing was conducted on a uniform background using the Kuiper test. If sets contained less than ten transcripts, they were excluded from the analysis.
Differential rhythmicity detection Differential gene expression at each time points and over all time points was performed using DESeq2  (Love et al. , 2014). Differentially expressed transcripts were defined as those with a Benjamini-Hochberg adjusted p-value < 0. 05. Data are shown in S. Table  5. To test for rhythmicity changes in amplitude and phase of genes that had been identified as significantly cycling in either diabetic or control retinas, the DODR R package (v0. 99. 2) was used,  (Thaben & Westermark, 2016). Normalized read counts were used with the robust DODR method with a fixed period of 24 hrs. The resultant DODR p-values were Benjamini-Hochberg (BH) adjusted for multiple testing, and an adjusted p-value < 0. 05 was used to define differential rhythmicity. Differentially rhythmic transcripts were then further subdivided into those displaying a phase shift when the difference in acrophase between diabetic and control > 1 h and those displaying a substantially altered amplitude with absolute log 2 amplitude fold-change > 0.
Global daily rhythmic transcriptome in the diabetic retina Next, we compared the global rhythmic output in control and diabetic mice. 4,700 transcripts in control mice and 5,177 transcripts in diabetic mice were identified as rhythmic using empJTK (S. Table  3 ). When compared to the total number of transcripts identified as expressed (>4 read counts) in the control  (Boden et al. , 1996)  and diabetic retinas  (Boden et al. , 1996) , 25 % of expressed transcripts in control and 27 % in diabetic retinas displayed rhythmic expression. In contrast to the subtle effects identified for the clock genes, the level of disruption by diabetes on the global rhythmic transcriptome was significantly greater. Of all rhythmic genes, only 36. 7 %  (Veenstra et al. , 2015)  transcripts were shared between control and diabetic retinas, while 28.
Amplitude and phase changes in the rhythmic transcriptome in the diabetic retina We first aimed to identify whether diabetes resulted in differential expression at individual timepoints  (ZT1,  or among all samples in control and diabetic groups independent of the time, but we only identified few genes been significant different with these approaches (Sup. Table  5 ). The very low numbers of differentially expressed transcripts perhaps indicates there were no differences in the relative expression of each transcript, although they may display changes in rhythmicity patterns. To further examine the differential rhythmic expression of genes between control and diabetic retinas, we used the differential rhythmicity analysis, DODR  (Thaben & Westermark, 2016) , and identified a total of 478 genes as differentially rhythmic (S.
5B ) were identified as the major upstream regulators for the amplitude changes observed in the diabetic retina. We then explored some of the pathways that were identified to be driving the phase shifts. Among the genes that were differentially Fig. 4. Phase and amplitude changes of differentially rhythmic transcripts between controls and diabetic retinas. DORD analysis was performed to identify differential rhythmic transcripts based on amplitude and acrophase. (A) All transcripts that showed a statistically significant difference in phase are highlighted in green with the distribution of the number shown on the top. (B) All transcripts with differences in amplitude are shown in blue. A distribution of the number of genes shown on the right. (C) Differentially rhythmic genes were further subdivided into those that displayed advanced or delayed phase and Metascape analysis was performed for pathway and enrichment analysis; (D) Metascape analysis for genes that had increased or decreased amplitude in diabetes.
Acknowledgments We would like to thank the  Genomics CTU at QUB  and colleagues that provided insightful conversations, and  Thomas Friedel  for his technical support. Finally, we would like to thank the funding sources: To EB: JDRF,  USA 1-FAC-2019-878-A-N ; To CEM:  NIH ,  USA  grants  R01 DK093954 ,  R01 DK127308 ,  U01DK127786 ,  UC4 DK 104166 ,  JDRF ,  USA ,  2-SRA-2019-834-S-B ,  JDRF 2-SRA-2018-493-A-B.
Previous work from some of the authors showed that it is possible to spatio-temporally demix uorescence scattering patterns (speckles) transmitted through a highly scattering media (e. g. , mouse skull) by using a Non-Negative Matrix factorization (NMF) algorithm  43, 44. The NMF algorithm relies on the premise that the input data matrix only contains non-negative values, and it has been used to decompose datasets into their representative parts or components  42, 43, [45] [46] [47] [48]. In addition, if a priori knowledge of the input dataset is known (e. g. , sparsity, calcium transient pro le, etc. ), it is possible to improve the performance of NMF by adding some constraints  [49] [50] [51] [52]. Therefore, the video data of uorescence speckles uctuating over time are a highly appropriate input matrix for NMF, because the superposition of these intensity signals is naturally positive, linear, and sparse due to the incoherent nature of the uorescence process (i.
Acknowledgments The authors are grateful to  Serena Bovetti ,  Stefano Zucca , and  Laurent Bourdieu  for productive discussions about the applications of the concept to in-vivo imaging. We are very thankful to  Walther Akemann  for the mouse brain extraction and xation. In addition, we are grateful to  Guillaume Dugué ,  Thomas Pujol ,  Arnaud Leclercq , and  Maurice Debray  for the ber-ferrule assembly discussions and technical support (Lab. Kastler Brossel  and  IBENS' FabLab  facilities, the latter which received support from the  Fédération pour la Recherche sur le Cerveau -Rotary International France  (2018)). The authors acknowledge the funding sources  HFSP ,  SMARTIES , and  France's Agence Nationale de la Recherche (ANR). The project is funded thanks to  HFSP  project  N°RGP0003/2020.
2E ), respectively], whereas 9% of GABA-neurons (896 cells) were positive for Napepld (Fig. 2D ). Interestingly, this pattern of Napepld expression mirrors the higher levels of NAEs observed in the VTA compared to the DS/NAc (Fig. 1C ). Moreover, the transcriptomic profiling of eCBs-and NAEs-producing enzymes was in line with the meta-analysis of bulk VTA transcriptomics in the murine (50) (Slc6a3-bacTRAP mice, GSE64526, Fig. 2F ) and human midbrains  (51)  (GSE114918, Fig. 2G ). Altogether these observations underline the potential role for NAPE-PLD in the midbrain VTA as a regulator of food-associated reward processes.
Acknowledgments: We thank  Olja Kacanski  for administrative support,  Isabelle Le Parco ,  Aurélie Djemat ,  Daniel Quintas ,  Magguy Boa ,  Ludovic Maingault  and  Angélique Dauvin  for animals' care, and  Florianne Michel  for genotyping. We acknowledge the technical platform Functional and Physiological Exploration platform (FPE) of the  Université Paris Cité ,  CNRS, Unité de Biologie Fonctionnelle et Adaptative, the viral production facility  of the  UMR INSERM  1089 and the animal core facility " Buffon " of the  Université Paris Cité/Institut Jacques Monod.
Funding: This work was supported by the  Nutricia Research Foundation  (# 2022-E7 ),  Agence Nationale de la Recherche  ( ANR-21-CE14-0021-01 ,  ANR-19-CE37-0020-02 ),  Fondation pour la Recherche Médicale  (Équipe FRM # EQU202003010155 ),  Fédération pour la Recherche sur le Cerveau and Association France Parkinson , the  Modern Diet and Physiology Research Center (MDPRC) ,  Université Paris Cité  and  CNRS. G. L. is supported by a  China Scholarship Council (CSC)  fellowship. O. O is supported by an  FRM  fellowship. We are grateful to  Dr. Sylvie Robin  for the gift of the Villin-Cre ERT2 mice. P. D. C. is recipient of grants from  FNRS  ( FRFS-WELBIO :  WELBIO-CR-2022A-02 ,  EOS : program no. 40007505 ) and  La Caixa (NeuroGut). A. E. is research associate at  FNRS  and the recipient of grants from  FNRS  ( FRFS-WELBIO :  WELBIO-CR-2019S-03R ,  FNRS: J.