dataesr/scientific-paragraphs-categorization huggingface dataset:
The quality of the normalization was assessed visually. From each subject's whole-brain tractography and corresponding GM ROI set, the number of streamlines connecting each couple of GM ROIs and the corresponding mean tract length was calculated using an in-house software written in Interactive Data Language (IDL, Harris Geospatial Solutions, Inc, Broomfield, CO). Connectomes in the replication dataset were constructed using an alternative mapping pipeline and diffusion MRI data from the HCP. Deterministic tractography was performed using MRtrix3  (Tournier et al. , 2019)  under the following parameters: FACT algorithm, 5 million streamlines, 0. 5 mm propagation step size, 400 mm maximum propagation length, and 0. 1 FA threshold for the termination of streamlines  (Seguin et al. , 2019). The number of streamlines connecting any couple of regions was normalized by the combined volume of the two regions.
To estimate the suitable time bin length, for each subject, each neuronal avalanches, and each time bin duration, the branching parameter s was estimated  (Haldeman and Beggs, 2005; Harris, 1964). In fact, systems operating at criticality typically display a branching ratio ~1. The branching ratio is calculated as the geometrically averaged (over all the time bins) ratio of the number of events (activations) between the subsequent time bin (descendants) and that in the current time bin (ancestors) and then averaging it over all the avalanches  (Bak et al. , 1987). More specifically: s i ¼ 1 N bin À 1 Y NbinÀ1 j¼1 n events j þ 1 ð Þ n events j ð Þ 1 N bin À1 (1) s ¼ 1 N aval Y Naval i¼1 s i ð Þ 1 N aval (2) where s i is the branching parameter of the ith avalanche in the dataset, N bin is the total amount of bins in the ith avalanche, n events j ð Þ is the total number of events active in the jth bin, and N aval is the total number of avalanche in the dataset.
Perturbations were separated by at least 200 samples (no overlap was allowed, i. e. , the perturbations could only occur in one region at a time), their length was randomly selected among 5, 10, or 100 samples, their amplitude between 50 and 400. This procedure was carried out 47 times to obtain an independent surrogate dataset for each one of the 47 participants, which will be referred to as the 'uncoupled' dataset. The uncoupled dataset was then transformed using the subject-specific leadfield matrix, yielding new surrogate sensor-level time series, where each sensor is a weighted sum of all the sources, according to the same leadfield matrix that was used to reconstruct the real data. Noise, correlated as 1/distance among sensors, was then added to the sensor-level time series, with an SNR = 4.
Data availability The MEG data and the reconstructed avalanches are available upon request to the corresponding author (Pierpaolo Sorrentino), conditional on appropriate ethics approval at the local site. The availability of the data was not previously included in the ethical approval, and therefore data cannot be shared directly. In case data are requested, the corresponding author will request an amendment to the local ethical committee. Conditional to approval, the data will be made available. The Matlab code is available at https://github. com/pierpaolosorrentino/Transition-Matrices-(copy archived at https://archive. softwareheritage. org/swh:1:rev:3846bb80457ddd12cf8c29f36d714b5b8f64eefc).
Cell filtering To retain only high quality cells, we applied filters on unique molecular identifier (UMI), gene and mitochondrial counts per cell. We first filtered cells based on the percentage of UMI associated with mitochondrial transcripts and number of detected genes and retained cells within a 3 median absolute deviation (MAD) around the population median for these metrics. We further excluded cells having a number of UMI above 3 MADs of the population median. Potential doublets were removed using Scrublet  (Wolock et al. , 2019). Finally, we excluded genes detected in less than 3 cells. Dimensionality reduction was performed using the SPRING tool  (Weinreb et al. , 2018). After dimensionality reduction, 11 cells sharing neighbours between excitatory and inhibitory neurons, and therefore likely corresponding to undetected doublets, were removed manually.
Neuronal populations clustering To explore neuronal transcriptional diversity we first applied iterative graph-based clustering using scrattch. hicat (v0. 0. 16)  (Tasic et al. , 2016)  on glutamatergic and GABAergic neurons separately after removing cell cycle, ribosomal or mitochondrial associated genes form the input count matrix. At each clustering iteration step we further excluded principal components (PCs) highly correlating with sequencing depth, percentage of ribosomal or mitochondrial transcripts detected per cells. Because variation among some glutamatergic neuron clusters were obviously associated with different maturation states we further excluded those with a LN score < 1. Hierarchical grouping of the remaining clusters was performed with the scrattch. hicat function 'build_dend' using the mean gene expression profile across clusters as input.
To defined clusters specific transcriptional signatures we further subset the clusters core cells as assigned more than 95/100 trials to the same cluster using the centroid classifier of the 'map_sampling' function. Top cluster marker genes were identified using a receiver operating characteristic (ROC) analysis implemented in the Seurat function 'FindMarkers'. Barplot representation of gene expression was built using the scrattch. vis package (v0. 0. 210). To identify modules of genes coexpressed across different neuronal clusters we implemented the WGCNA package (v1. 68) (Langfelder and Horvath, 2008). We used the normalised expression matrix of the first 1000 most variable genes defined with the Seurat function 'FindVariableGenes' as input and a softpower = 4 to compute the signed adjacency matrix.
Apical progenitors analysis We aimed at ordering APs by performing a new SPRING dimensionality reduction while excluding cell cycle associated genes. Cells were projected along the main axis of variation by fitting a principal curve using princurve (v2. 1. 4) as described previously  (Mayer et al. , 2018). Genes differentially expressed along the inferred DV axis were identified using the 'differentialGeneTest' function of the Monocle package (v2. 14) (Trapnell et al. , 2014). Progenitor cells were grouped in 7 clusters using partition around medoids on the smoothed expression profile of the significantly differentially expressed genes. Genes with similar expression dynamics were grouped by hierarchical clustering. To predict temporal maturation scores for apical progenitors we took advantage of the  Telley et al.
, (2019)  dataset. After retrieving the raw count matrix from the GEO repository (GSE118953) we extracted cells sorted 1h after FlashTag injection. Based on their expression profile, we excluded medial pallium APs as well as putative doublets with GABAergic interneurons to retain 725 high quality cells (E12: 178 cells, E13: 202 cells, E14: 124 cells, E15: 221 cells). We performed principal component analysis (PCA) dimensionality reduction using the Seurat function 'RunPCA' over all differentially expressed genes between the distinct sampling groups identified with the Wilcoxon rank sum test implemented in the 'FindAllMarkers' function. Cells were projected onto a pseudomaturation axis by fitting a principal curve over the first 6 PCs. We trained a random-forest regression model implemented in the Caret package (v6.
0-84) to predict the cells' pseudo-maturation score (position along the axis) using the scaled expression value of the top 1129 variable genes selected with the 'FindVariableGenes' function. The 100 most important features were set as input to reoptimise the regression model which was used to predict temporal maturation for APs in our dataset. To investigate the spatial and temporal components we first identified genes with variable expression in pallial apical progenitors along the DV axis using the 'differentialGeneTest' function as described above, and those differentially expressed between E12 and E15 1h after FlashTag injection in the Telley et al. dataset. The "temporal module" was defined as the intersection between the two sets of differentially expressed genes. We then computed the Pearson's correlation coefficient between the smoothed expression matrices over 200 points of temporal genes in our dataset and that of Telley et al.
Immunostaining The following primary antibodies were used: chick anti-GFP (Aves Labs GFP-1020, 1:1000), chick anti-Gal (Abcam ab9361, 1:1000), rabbit anti-Nfib (Atlas Antibodies HPA003956, 1:1000), rabbit anti-Foxp2 (Abcam ab16046, 1:1000), goat anti-Nurr1 (R&D systems AF2156, 1:200), mouse anti-Meis2 (Sigma WH0004212M1, 1:1000) and guinea pig anti-Bhlhb5  (Skaggs et al. , 2011, 1:100). The following secondary antibodies were obtained from Jackson ImmunoResearch: donkey anti-chick Alexa-488 (1:1000), donkey anti-mouse Cy5 (1:500), donkey anti-guinea pig Cy5 (1:500), donkey anti-rabbit Cy3 (1:700), donkey anti-rabbit Cy5 (1:500), donkey anti-goat Cy3 (1:700) and donkey anti-goat Cy5 (1:500). DAPI (1µg/mL) was used for nuclear staining. Slides were mounted in Vectashield (Vector).
Image acquisition In situ hybridisation images were obtained using a Hamamatsu Nanozoomer 2. 0 slide scanner. Immunofluorescence images were acquired using a Leica SP8 confocal microscope with a 40X objective. Quantifications were achieved by counting the proportion of GFP + cells among the markerpositive population using the ImageJ software. Data are represented as mean ± sd, individual values are plotted.
Sampling cellular diversity around the developing ventral pallium Using a droplet-based approach (10x Genomics Chromium V2 platform), we performed unbiased scRNAseq on dissected explants containing the PSB and adjacent tissue, obtained from four wildtype E12. 5 embryos (Fig. 1A ). After quality control and filtering, we obtained 4225 cells sequenced at a median depth of 11,665 UMI and a median detection of 3,571 genes per cell. Visualisation of transcriptional heterogeneity on a SPRING plot -a 2D projection using forcedirected layout of the k-nearest-neighbor  (Weinreb et al. , 2018)  -revealed that the dataset is organised in a "crab-shaped" topology consisting in a large group of cycling cells from which stemmed two branches of postmitotic (G0/G1 phase) cells (Fig. 1B ). Expression of pallial (Tbr1), excitatory neurons (Slc17a6/Vglut2) and inhibitory neurons markers (Dlx5 and Gad2/GAD65) revealed that the two branches correspond to pallial excitatory and subpallial inhibitory differentiation trajectories, respectively (Fig.
We therefore concluded that, at least at E12, progenitors undergoing direct neurogenesis transit through a BP-like state Eomes + (Tbr2), Btg2 + (Tis21) and Neurog2 + without replicating their DNA or dividing. Taken together, these results indicate that the two main axes in the low-dimensional manifold of our dataset reflect (i) the differentiation status, from cycling progenitor to postmitotic mature neuron and (ii) the pallial or subpallial origin of the cells (Fig. 1E ). Thus our data recapitulate global tissue organization around the PSB at E12. 5.
Spatial identity of neuronal subtypes Pbx3 + and Neurod6 + populations occupy distinct and complementary pallial domains. This could be imposed by the different lineages they belong to and/or by the position cells eventually occupy. As a first step in distinguishing between these possibilities, we reasoned that if regionally imposed identity exists it should be shared by neurons from very different identities but occupying the same histological location. This implies that we should be able to find gene modules common to GABAergic subtypes and excitatory neurons localised nearby the PSB. We investigated this hypothesis using weighted gene correlation network analysis (WGCNA)  (Langfelder and Horvath, 2008)  to identify co-expressed gene modules. By applying this analysis to all late neurons present in our dataset, we identified 6 co-expression modules (Fig.
Early spatial identity within pallial APs reflects temporal dorsal-pallial maturation The cerebral cortex displays a well-known neurogenic gradient in which neurogenesis is initiated in ventral regions and progressively extends to dorsal territories  (Bayer and Altman, 1991). It was recently shown that neuronal diversity in the developing neocortex arises mostly from the temporal progression of APs  (Okamoto et al. , 2016; Telley et al. , 2019). We therefore queried in which extent the distinction between LP, VP and DP progenitors could mirror that of young vs old dorsal cortex APs. To test such a possibility, we used the time-series dataset from  Telley et al. (2019) , which describes the transcriptomic signature of E12 to E15 APs from the dorsal pallium, and trained a regression model to predict a maturation score for each AP present in our dataset (Fig.
6A ). We found clear difference in the maturation estimate between clusters as the VP displayed the highest predicted score whereas the DP had the lowest score (Fig. 6B ). In other words, VP progenitors display a more mature transcriptomic profile than their DP counterparts. This also indicates that part of the DV axis transcriptional differences can be explained by the expression of genes responsible for the temporal maturation of dorsal cortex APs. We found that among the 392 pallial variable genes in our dataset, 101 also vary during dorsal cortex AP temporal maturation, therefore representing a "temporal module" of pallial AP identity. Conversely, the remaining 291 genes form a "spatial module" that does not show such variation in time during neocortical development (Fig. 6C , Tables  S5, S6 ).
We used genes of the temporal module to investigate whether the temporal-associated signature changes continuously along the spatial and temporal axes. To this aim we performed correlation between the smoothed gene expression profiles computed in the two datasets and found two blocks of strong correlation between VP/LP. 1 and E15 APs on the one hand, and LP. 2/DP and E12 APs on the other hand (Fig. 6D ). The distinction between LP. 1 and LP. 2 appears very sharp when considering genes from the temporal module, i. e. genes whose expression is turned on or off when dorsal cortex APs switch from the production of deeper layer to upper layer neurons. We found that within the temporal module, 66 genes are enriched in the E12. 5 VP and upregulated at E15 in the DP as exemplified by the transcription factor Zbtb20 (Fig.
Differentiation trajectories can be inferred from AP populations to mature neurons We then attempted to reconstruct developmental trajectories from AP to LN, assuming that most of the glutamatergic neurons we characterised were collected together with their progenitor of origin. Because CRs are known to migrate tangentially over long distances, our dissections must have not only excluded some VP-derived CRs that migrated away, but also included CR derived from progenitors located outside of the dissected area (hence absent from the dataset). We therefore excluded CR cells from our lineage analyses. In order to link neuronal populations to their progenitor pools of origin and identify their lineagespecific intermediate states we used FateID  (Herman et al. , 2018). Briefly, FateID algorithm starts from the most differentiated clusters (as defined in Fig.
, 2006; Kurrasch et al. , 2007). This population would represent an early-born LP contribution to the amygdala, as previously postulated  (Cocas et al. , 2009; Gorski et al. , 2002). Our findings therefore point to the idea that LP and VP derived structures are perhaps not as sharply distinguished as initially anticipated  (Puelles et al. , 2000). We captured distinct CR subtypes in our dataset. Because CRs originate from at least 4 regions of the developing forebrain: the septum, hem, VP and thalamic eminence  (Bielle et al. , 2005; Ruiz-Reig et al. , 2017; Takiguchi-Hayashi, 2004)  and because these regions are distant from each other and consist in vastly different micro-environments, the question of CR diversity has long been standing. Although a hallmark of CRs is their absence of Foxg1 expression, we were unable to identify a single gene specifically expressed by all CR subtypes, as exemplified by Reln and Calb2 (Calretinin) which are found in the dorsal cortex preplate as well as in some Foxp2 populations.
We found that APs occupying the most extreme position in the gradient (VP and DP) display the strongest spatial signature and bias towards specific terminal fates. By contrast, APs localised at intermediate positions along the DV axis do not show such a strong transcriptional identity that could be transmitted to BPs, explaining why our lineages reconstruction were only effective when comparing the most distant terminal fates in our dataset. However, the near-perfect tangential segregation of Ppp1r14c + and Fezf1 + neurons argues in favor of an equivalent spatial segregation of their respective progenitors in the LP. Therefore, we propose that at the cell population level, bias towards a specific lineage progressively decreases, as progenitors are located closer to a transition zone between two domains while the opposite probability towards generating the neighbouring cell type would increase (Fig.
, 2014; He et al. , 2012; Llorca et al. , 2019)  and we expect future studies taking advantage of such approaches to clarify the link between the behaviour of individual progenitors and their position on the DV axis. The continuity between APs contrasts with the discrete nature of LN, raising the question of the time point at which cell fate is assigned. In the neocortex, graft experiments of DP progenitors indicated that APs display greater plasticity than BPs which are already committed to specific neuronal fates  (Oberst et al. , 2019). In our dataset, the bias for Bhlhe22 + and Nr4a2 + lineages is fairly constant from AP to LN and does not differ between AP and BP, strongly supporting an early commitment taking place at the AP level for these lineages. Yet, the fact that not all trajectories were reconstructed back to APs could also indicate that fate restriction occurs at distinct steps in each lineage, although we cannot formally exclude that commitment indeed occurs at the AP level but remains hidden at the transcriptomic level as shown in other developmental systems  (Weinreb et al.
5 DP and decrease with time. F. Comparison of representative gene expression trends according to the pseudo DV score in our dataset (top) and maturation score in Telley's dataset (bottom). G. Heatmap showing the smoothed expression of the 291 genes belonging to the spatial module along the pseudo DV axis. H. Representative genes expression profi le according to the pseudo DV score in our dataset (top) and maturation score in Telley's dataset (bottom) illustrating time-invariant genes enriched either in the VP or the DP.
Acknowledgements The authors wish to acknowledge  Aurelia Dujardin ,  Camelia Piat  and the Animalliance platform of the  Imagine Institute  for animal care; the  SFR Necker imaging  and histology platforms for help with acquisition;  Fanny Coulpier  from the IBENS genomics platform for sequencing;  Christine Bole  and  Aurore Pouliet  from the  Imagine Institute genomics platform  for helpful discussions;  Frédéric Tores ,  Jean-Marc Plaza  and  Patrick Nitschke  from the  Imagine Institute bioinformatics platform  for implementing the offline version of SPRING Viewer; 10X Genomics and  Miltenyi Biotec  technical support for their help in the implementation of scRNAseq. We thank all members of the  Pierani lab ,  Marine Luka  and members of the Ménager lab as well as members of the  Rausell lab  for helpful discussions.
We are grateful to  Anne Teissier  and  Oriane Blanquie  for critical reading of the manuscript. MXM is the recipient of an  Allocation Spécifique PhD fellowship  from the  École Normale Supérieure , FC is an Inserm researcher, AP is a CNRS investigator. This work was supported by grants from  Agence Nationale de la Recherche  ( ANR-15-CE16-0003-01 ) and  Fondation pour la recherche médicale (Équipe FRM   DEQ20130326521 ) to AP and State funding from the  Agence Nationale de la Recherche  under " Investissements d'avenir" program  ( ANR-10-IAHU-01 ) to the  Imagine Institute.
5 VP and increase in the DP with time, 35 genes are enriched in the E12. 5 DP and decrease with time. F. Comparison of representative gene expression trends according to the pseudo DV score in our dataset (top) and maturation score in Telley's dataset (bottom). G. Heatmap showing the smoothed expression of the 291 genes belonging to the spatial module along the pseudo DV axis. H. Representative genes expression profi le according to the pseudo DV score in our dataset (top) and maturation score in Telley's dataset (bottom) illustrating time-invariant genes enriched either in the VP or the DP.
Participant-and trial-inclusion criteria ***** INSERT FIGURE  2  ABOUT HERE ***** In the post-experiment questionnaire, some participants reported frustration during the game part that resulted in loss of interest in the entire experiment, because they were generally losing and unable to beat the computer. We wanted to only analyze the data of participants who remained engaged with the experiment and continued to make an effort to win throughout the game part and further made an effort to be random during the RSG post-game part. We therefore included only participants who fulfilled both of the following inclusions criteria. (1) Deviation from an even (i. e. , 1/3) R-P-S split was no more than 0. 15 in any part of the experiment (Figure  2 ). Of the 153 participants, 32 (20. 9%) did not meet both these inclusion criteria and were excluded from further analysis.
Although these perspectives offer valuable accounts of the data connecting DMN activity to function within a given psychological domain, they also raise questions about the nature of the more general contribution that the DMN makes to cognition. Supplementary Fig. 1  shows the results of a meta-analysis of the functional profile of each region within the DMN, based on data available in the Neurosynth database  63  (https://neurosynth. org/) (see also Supplementary Methods). This analysis suggests that regions of the DMN are engaged across multiple, apparently distinct, psychological domains (episodic, linguistic, social and emotional). Two DMN regionsthe right angular gyrus and the left middle temporal cortex -appear to exhibit the most functionally specific profiles as they are implicated in only two of these domains.
Acknowledgements The authors thank  N. Ho  for help in the preparation of Supplementary Fig. 1 and  R. Braga ,  L. Lanzoni  and  D. Vatansever  for providing data for use in preparing the figures. The work in this Perspective was supported by consolidator grants from the  European Research Council (ERC)  (Award  646-927  WANDERINGMINDS to J. S. , Award 866533-CORTIGRAD to D. S. M. and Award 771863-FLEXSEM to E. J. ). D. B. was supported by the  Canada Institute for Advanced Research (CIFAR)  Artificial Intelligence Chairs programme, Google and  National Institutes of Health (NIH)  grant  R01AG068563A. R. L. was supported by  Wellcome/ Engineering and Physical Sciences Research Council (EPSRC) Centre for Medical Engineering  (Ref:  WT 203148/Z/16 /). B. B. received support from the  Natural Sciences and Engineering Research Council of Canada (NSERC)  ( Discovery-1304413 ), the  Canadian Institutes of Health Research  ( CIHR FDN-154298 ,  PJT-174995 ),  SickKids Foundation  ( NI17-039 ),  BrainCanada (Azrieli Future Leaders)  and the Tier-2  Canada  Research Chairs programme.