dataesr/scientific-paragraphs-categorization huggingface dataset:
Likewise for the feedback connections, a greater hierarchical distance between the areas implies that the higher area sends a greater proportion of it projections from the infragranular layers. This implies that the fraction of neurons coming from the supragranular layers in a given connection gives an estimate of the relative hierarchical position of two connected areas  (Barone et al. , 2000; Markov et al. , 2014a). Here, following  (Markov et al. , 2014a) , we estimate a set of hierarchical levels (one per area) that best predicts the SLN values for all connections in the dataset. The model to estimate the hierarchy has the form  4 ) g(E(SLN)) = Xβ (Equation where g is a function that links the SLN of the connection between areas to the hierarchical distance between them. β is a column vector of length n areas,SLN , containing the hierarchy values to be estimated.
X is an incidence matrix of shape n conns ×n areas,SLN , where n conns ( = 2619) is the number of observed (non-zero) connections between cortical areas in the remaining dataset. Each row in X represents a connection, and each column represents a cortical area. All entries in each row equal 0 except for the column corresponding to the source area, which has a value of -1, and the target (recipient) area, which has a value of 1  (Strang, 1993). The hierarchical values can be estimated with maximum likelihood regression. However, the model is singular (the rows sum to zero). In order to make the model identifiable, we therefore removed one column from X. We chose to remove the column corresponding to area V1, which is therefore forced to have a hierarchical value of 0. However, the choice of column is unimportant, as it is possible to estimate negative hierarchical values (in the case that other areas are lower than V1 in the hierarchy).
We fit the model using μ = Φ(Xβ), where Φ is the cumulative Gaussian, as it maps the real numbers to the (0,1) range. Φ -1 = g in Equation  4 is the probit link function. The hierarchy is estimated by minimizing the log-likelihood. For more details see  Markov et al. (2014a). We then rescaled the hierarchy so that the maximum hierarchial value within the 40 region complete subgraph (containing all injected areas) equaled 1: ℎ [k] = β [k] max β subgrapℎ (Equation  7 ) for all cortical areas k in the complete 40-area subgraph. For the circular embedding of the connectivity data, we estimate angles θ i,j between areas A i and A j so that a smaller angular distance between areas corresponds to a higher connectivity strength  (Chaudhuri et al. , 2015). The dissimilarity d(A i , A j ) is defined as d(A i , A j ) = -log 10 (F LN(A i , A j )) for F LN(A i , A j ) ≥ 0 -log 10 (F LN min ) for F LN(A i , A j ) = 0 where FLN min = 10 -7 , a value smaller than any FLN in the dataset.
The angles θ i are assigned to each area such that d(A i , A j ) ≈ min( | θ i -θ j | , 2π -| θ i -θ j | ) The estimated angles θ i are constrained to lie within the range [0, 1] and then mapped onto [0, 2π]. The radial distance from the center of the circle is r i = 1 -ℎ i , where h i is the hierarchical value of the area, as defined above. Integration of anatomical datasets-All anatomical data was mapped to the appropriate parcellations on the Yerkes19 surface. For the present study, we mapped all data to the 40 area Lyon subgraph  (Markov et al. , 2014b) , as the areas in this parcellation were generally larger than those in the Julich Macaque Brain Atlas  (Impieri et al. , 2019; Niu et al. , 2020; Rapan et al. , 2021;  this paper) and the Queensland (spine count) injection sites  (Elston, 2007) , and closer to standard areal descriptions than the Vanderbilt (neuronal density)  (Collins et al.
, 2010)  sections. The receptor densities were quantified in 109 cortical regions defined by cyto-and receptor architecture. The method for the delineation of cortical region borders is described in  (Impieri et al. , 2019; Niu et al. , 2020; Rapan et al. , 2021). Using the same method, anatomists (NPG, MN, LR) identified cortical areas on the basis of the receptor and cyto architecture. See Figure  1  for the definition of the areas. Anatomists carefully drew (NPG, MN, LR) and independently revised (NPG, MN, LR, SFW) defined borders on the Yerkes19 cortical surface  (Donahue et al. , 2016)  to enable comparison with other data types. The D1 receptor data was mapped to the Lyon atlas as follows. For each area in the Lyon atlas, we searched for overlaps with areas in the Julich Macaque Brain Atlas.
Areas 10, 11 and 12 were described with reference to  Preuss and Goldman-Rakic (1991). The injection in area TEa used the maps in  Seltzer and Pandya (1978)  for area definition. We used these maps to approximate the injection location. Area STP was identified with the corresponding region STPp in the atlas of  Felleman and Van Essen (1991). Area FEF was identified as lying on the anterior bank of the medial aspect of the arcuate sulcus, as described by  Elston (2007). All identified injection sites on the cortical surface were independently verified by MN, LR and NPG. Spine count data was expressed according to injection sites, rather than entire cortical areas. As such, we found the number of vertices from each injection site overlapping with each area in the Lyon atlas. For each Lyon area, the spine count was an average of the spine counts for all the injection sites overlapping with the area, weighted by the number of vertices of each injection site contained within the area.
In this way we estimated the spine counts on pyramidal cells in 24 of the 40 regions in the Lyon atlas. Based on the strong positive correlation between spine count and cortical hierarchy (r = 0. 61, p = 0. 001), and following previous work  (Chaudhuri et al. , 2015; Mejias and Wang 2021) , we inferred the spine count for the remaining regions based on the hierarchy using linear regression. Neuroanatomists (NPG, LR, MN) classified each of the 109 cortical areas for which D1 receptor data is available as being either granular, or agranular, and according to the ratio of cell body size between layers III and V. Delineations of the areal borders for each atlas, and the anatomical data in the Yerkes19 space are available on the BALSA database. Overview of dynamical models-We first describe the connectivity structure of our local circuit model, and how dopamine modulates the efficacy of these connections.
Parts of this dataset of been included in previous publications  (Markov et al. , 2013 (Markov et al. , , 2014a (Markov et al. , , 2014b)). The long-range connectivity matrices are built from the FLN matrix. However, as noted in  Mejias et al. (2016) , the FLN matrix spans 5 orders of magnitude. The relationship between anatomical and physiological connectivity strengths is not clear, but if we were to use the raw FLN values in the large-scale model, many of the weaker connections would become irrelevant. To deal with this, we rescale the FLN matrix in order to increase the influence of smaller connections while maintaining the topological structure  (Mejias et al. , 2016; Mejias and Wang, 2021). w [k, l] = F LN [k, l] b 1 ∑ l = 1 n sub F LN [k, l] b 1 (Equation  25 ) Here we restrict calculations to the injected cortical areas i, j, which allows us to simulate the complete bidirectional connectivity structure within the subgraph (n sub = 40).
ACKNOWLEDGMENTS We thank  Camille Lamy  and  Pierre Misery  for their work in histology,  Jorge Mejias  for providing an early version of his code for a related model (Mejias and Wang, 2021), and the  Wang lab  for helpful discussions. This project was funded by  NIH/BMBF   CRCNS grants  ( R01MH122024  and  01GQ1902  to N. P. -G. and X. -J. W. );  NIH  grant  R01MH062349 ,  NSF   NeuroNex grant   2015276 ,  ONR  grant  N00014 , and  James Simons Foundation  grant  543057SPI  (to X. -J. W. ); the  European Union   Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements 785907  ( Human Brain  Project  SGA2 ) and 945539 ( Human Brain  Project  SGA3 ) (to K. Z. and N. P. -G. ); ANR  grant  DUAL_STREAMS ANR-19-CE37-0025  (to K. K. ); grants  LABEX CORTEX ANR-11-LABX-0042 ,  Université de Lyon   ANR-11-IDEX-0007 ,  A2P2MC ANR-17-NENC-0004 ,  CORTICITY ANR-17-HBPR-0003 ,  REGION AUVERGNE-RHON-ALPES SCUSI 1700933701 , and  Chinese Academy of Sciences President's International Fellowship Initiative  Grant  2018VBA0011  (to H.
Participants Fifteen right-handed French-speaking volunteers were recruited from a pool of participants at the University of Louvain (Belgium). One participant's data were excluded from the analyses because of neurological history, leading to a final sample of 14 participants (8 females, mean age = 23. 28, SD = 2. 29). The sample size was based on previous FPVS of word and face categorisation in human adults (e. g. ,  Lochy et al. , 2015 , N = 10; Rossion et al. , 2015 , N = 12, respectively) , considering that the FPVS approach is characterized by a very high signal-tonoise ratio (SNR)  ( Regan, 1989 ; Norcia et al. , 2015 ). This high SNR is partly due to the large number of discrimination trials (i. e. , here, wordsemantic contrasts) recorded in a short amount of time for every individual tested (i.
Preprocessing of EEG data EEG data were analysed using Matlab 2014 (The Mathworks) and the free software Letswave 5 ( https://github. com/NOCIONS/ Letswave5 ), following procedures validated in many FPVS-EEG studies (e. g. ,  Rossion et al. , 2020 )  but also detailed here. After importation of raw data files, continuous blocks of recording were aligned to the first block to correct for vertical jumps due to voltage drift during pauses. A bandpass filter was applied on EEG data at 0. 1 and 100 Hz, followed by a notch filter at 50 Hz (0. 5 Hz width). A downsampling to 256 Hz was computed to reduce file size and data processing time. Data files were then segmented on the basis of four stimulation start triggers (each trigger corresponding to one of the four contrasts), including two seconds before each trial.
To get an overview of electrophysiological responses independently of the four types of stimulations, we merged the data from these contrasts at the segmentation phase. Prior to channel interpolation, a single component accounting for blink artefacts detected on the electrode located below the right eye was removed for all participants, based on an independent component analysis (ICA) applied on the EEG data. Residual noisy electrodes (less than 5% per participant) were linearly interpolated. All electrodes were then re-referenced to the common average. Data files were segmented again using an integer number of 1 Hz cycles ( = full cycles of one alternate + three base stimuli, to remove incomplete cycle at the end of the sequence), corresponding to 17,666 bins in total, which excluded fadein and fade-out portions of the trial.
Frequency-domain analysis of EEG data A Fast Fourier Transform (FFT) was applied on these averaged epochs to perform frequency-domain analyses, and amplitude spectra were extracted for each individual. Three more transforms were computed on data files to (1) quantify the electrophysiological response in microvolts (baseline subtraction), (2) estimate the SNR (baseline division), and (3) determine whether significant responses were present at the conceptual categorisation and base frequencies (z-scores computation)  ( Retter and Rossion, 2016 ). The baseline subtraction was defined as the difference between the amplitude of the frequency bin of interest and the average amplitude of the 20 surrounding bins (10 on each side), excluding the immediately adjacent bin in case of remaining spectral leakage, and the local maximum and minimum amplitude bins to avoid projecting the signal in the neighbouring bins containing noise.
SNR was computed in a similar way except that the amplitude of the bin of interest was divided by the average amplitude of the 20 surrounding bins (10 on each side). Z-scores were calculated by computing the difference between the amplitude of the bin of interest and the mean amplitude of the 22 surrounding bins (11 bins on either side, no exclusion of maximal or minimal bins) and dividing this value by the standard deviation of amplitudes in the 22 corresponding surrounding bins. FFT data were averaged across all 14 participants to compute topography maps and visualise the global response on the frequency spectrum. As for individual data, baseline-subtraction, SNR estimation and z-scores were computed on these grand-averaged data files. To quantify the response at the frequency of category change, the sum of harmonics of the frequency of conceptual change was computed by segmenting individual FFT epochs into successive chunks centred on the bin containing the conceptual categorisation frequency (1 Hz, 2 Hz, 3 Hz, etc.
)  ( Retter and Rossion, 2016 ). Each chunk contained 25 bins (i. e. , a chunk length of 0. 362 Hz), with the bin in the middle (the 13th bin, with 12 bins on each side) corresponding to the conceptual categorisation frequency. Based on the grand-averaged significant responses across electrodes ( z > 1. 65) for all contrasts averaged together, we extracted 7 chunks, corresponding to the first six harmonics of the conceptual categorisation frequency (i. e. , 1, 2, 3, 5, 6 and 7 Hz), removing the 4th chunk corresponding to the base frequency (4 Hz). Resulting epochs were then summed. Individual results and grand-averaged data for each electrode across participants were examined. Baselinesubtraction, SNR estimation and z-scores were also calculated on the sum of harmonics. All amplitude values reported in the results section are baseline-corrected amplitudes.
Acknowledgments AV was supported by the  Université de Lorraine  ( MESRI doctoral grant and DrEAM/LUE grant ) and by the  University of Louvain  (postdoctoral grant). We thank  Corentin Jacques  and  Xiaoqian Yan  for their advice on data analysis for the revised version of the article, and three anonymous reviewers for their helpful comments on a previous version of this manuscript.
Participants Using a cross-sectional study design, data were collected at three different sites using uniform procedures: the university psychiatric hospitals of Münster (Germany), München (Germany), and Leuven (Belgium). Samples of 140 MDD patients and 120 age-and sex-matched healthy controls (HC) were analyzed for gene expressions. Adult men and women patients (ages 18-65 years) were included if they were diagnosed with MDD and were free of the following diseases: clinical inflammation-related symptoms (including fever), current or recent inflammatory or infectious disease, uncontrolled systemic disease, uncontrolled metabolic disease, other uncontrolled somatic disorder affecting mood. Participants were excluded if they used somatic medication that affects mood or the immune system, e. g.
Pvalues of single gene group comparisons were calculated using Wilcoxon rank sum test (Benjamini-Hochberg-corrected). Visual data presentation follows the order resulting from hierarchical cluster analysis. For multivariate analyses, missing data were imputed by multiple imputation using chained equations (mice package). Five imputed datasets were created and analyses were run on each dataset. P-values were calculated based on pooled F-values from all datasets. A clusterwise multivariate analysis of co-variance (Mancova) was performed to obtain and verify pooled differences of gene expression profiles as were observed after clusterwise depiction of single gene expressions. BMI and age were included as covariates. In a second step, the effect of sex, duration of illness, and medication (binary variable anti-depressants yes/no) were also studied and added as covariates, respectively.
AstraZeneca,  Eli Lilly ,  Janssen-Cilag ,  Lundbeck ,  Otsuka ,  Sanofi ,  Servier , and  Trommsdorff. He  received grants from the  German Ministry of Science and Education , the  Münster Interdisciplinary Center of Clinical Research , and from the  European Union. NM has given presentations for Janssen-Cilag during the last 6 months and was supported by the foundation " Immunität und Seele ". HAD is the coordinator of the project funded by the  EU  as indicated in the funding section. He has received further grants from the  Netherlands Organization for Health Research and Development , the  Stanley Medical Research Institute , the  Dutch Diabetic Foundation  and the  JDRF ; he has received speaker's fees from  AstraZeneca  and he serves/ has served on advisory boards of the  Netherlands Organization for Health Research and Development , the  European Union  and the  JDRF.
Funding This work was supported by the  European Commission :  EU   7th Framework program  (grant number  EU-FP7-CP-IP-2008-222963 ) and  Horizon 2020  (grant number  H2020-SC1-2016-2017/H2020-SC1-2017 - Two-Stage-RTD ) grants were received by  HAD ,  Erasmus Medical Center Rotterdam. The funder had no role in the study design, data collection, analysis, and interpretation, writing of the report, and decision to publish.
In the present study, principal components that together account for 75% of the total explained variance were highlighted. Here, the original datasets of each individual rat, containing 20 variables, including 7 parameters related to behaviour (social investigation, social play, social anxiety, locomotor activity, latency to eat in a novel environment and in their home cage, sucrose consumption), 3 variables related to neuroendocrine stress response (CORT, hypothalamic and accumbal CRH), 4 variables related to pre-and postsynaptic DA signaling (TH, DAT, D1R, D2R), 6 markers of postsynaptic MSNs signaling (PSD95, HOM1, HOM2, Arc, LIMK, FOXP1) were standardized using z-scores and analysed to obtain their correlation matrix and PCAs as previously described  [58]. Statistical analysis was performed using Prism v.
982, p = 0. 4177) (Figure  1D ). When rats were assessed in the NSFT, data analysis of their latency to eat, in the novel arena and their home cage, indicates a significant main effect of binge-like alcohol exposure during adolescence (F(3,48) = 5. 75, p = 0. 0019), of novel environment (F(1,48) = 83. 2, p < 0. 001) and of the interaction between the two statistical factors (F(3,48) = 4. 65, p = 0. 0062). Bonferroni post hoc tests show that latency to eat in the novel arena, at BD, was not different in comparison with CTRL rats (t = 2. 64, df = 48, p = 0. 0676). However, a significant increase was observed at WD1 (t = 3. 11, df = 48, p = 0. 0187) and WD10 (t =5. 56, df = 48, p < 0. 001) with respect to CTRL rats. Moreover, rats at WD10 displayed increased latency to eat in the novel arena with respect to BD (t = 2.
47, df = 24, p < 0. 001), with no difference between WD10-CBD and CTRL-CBD levels (t = 2. 113, df = 24, p = 0. 2713) (Figure  6E, G ). As to PSD95 immunofluorescence, two-way ANOVA indicates a significant effect of alcohol binging (F(1,24) = 13. 65, p = 0. 0011), CBD (F(1,24) = 13. 07, p = 0. 0014) and their interaction (F(1,24) = 8. 259, p = 0. 0084). Again, CBD treatment coun-teracted the increase in PSD95 levels observed at WD10 (WD10 vs CTRL: t = 4. 645, df = 24, p = 0. 0006; WD10 vs WD10 + CBD: t= 4. 589, df = 24, p = 0. 0007) (Figure  6F, G ). The effects of sub-chronic CBD administration during alcohol withdrawal were also evaluated on the expression of genes related to DA and glutamate signaling in the NAc. Data analysis shows that CBD increased the expression of D1R (F(1,24) = 4. 481, p = 0.
On the other hand, CBD is reported to modulate reward processing in the mesolimbic system  [122] , interfering with DA synthesis and postsynaptic responses  [123, 124]. In accordance with CBD-induced reduction in TH gene expression in the VTA of alcohol-drinking rats  [47] , our data show lower TH activity, accompanied by decremental levels of DAT immunoreactivity in the NAc, independently of alcohol exposure. At the same time, a significant increase in D1 and D2R expression was recorded after CBD treatment, in both WD10 and control rats, suggesting that the changes at the post-synaptic level aim at restoring DA signaling in the NAc. Notably, CBD-induced modulation of VTA DA activity may also occur through regulatory action on glutamatergic projections in the NAc  [125]. Consistently, we observed a down regulation in the expression of the glutamate-associated proteins here analysed, which suggests a functional remodeling of the postsynaptic side.
Next, we sought to test whether the associations were weaker for randomized TMs computed after randomizing the times of each avalanche while keeping the spatial structure unchanged. Randomized TMs resulted in markedly weaker associations with structural connectivity compared to the actual TMs (AAL atlas, z-score = 3: mean r = 0. 26, observed r = 0. 40, p<0. 001). Note that the mean correlation coefficient was greater than zeros for the randomized data because the randomization process preserved basic spatial attributes in the data. We also found that the findings remained significant after excluding subcortical regions (with lower signal-to-noise ratios). Finally, we replicated these findings for a group-level connectome derived using diffusion MRI acquired from 200 healthy adults in the Human Connectome Project (r = 0.
While we replicated our findings using alternative datasets (i. e. , Human Connectome Project [HCP]) and parcellations, further replication using alternative connectome mapping pipelines is warranted. In conclusion, using MEG to study fast neuronal dynamics and diffusion MRI tractography to map connectomes, we found that the connectome significantly constrains the spatial spread of neuronal avalanches to axonal connections. Our results suggest that large-scale structure-function coupling is dynamic and peaks during avalanche events.
MRI acquisition 3D T1-weighted brain volumes were acquired at 1. 5 Tesla (Signa, GE Healthcare) using a 3D Magnetization-Prepared Gradient-Echo BRAVO sequence (TR/TE/TI 8. 2/3. 1/450 ms, voxel 1 Â 1 Â 1 mm 3 , 50% partition overlap, 324 sagittal slices covering the whole brain), and diffusion MRI data for individual connectome reconstruction were obtained using the following parameters: Echo-Planar Imaging, TR/TE 12,000/95. 5 ms, voxel 0. 94 Â 0. 94 Â 2. 5 mm 3 , 32 diffusion-sensitizing directions (5 B0 volumes). The MRI scan was performed after the MEG recording. Pre-processing of the diffusion MRI data was carried out using the software modules provided in the FMRIB Software Library (FSL, http://fsl. fmrib. ox. ac. uk/fsl). All diffusion MRI datasets were corrected for head movements and eddy currents distortions using the 'eddy_correct' routine, rotating diffusion sensitizing gradient directions accordingly, and a brain mask was obtained from the B0 images using the Brain Extraction Tool routine.
A diffusion-tensor model was fitted at each voxel, and streamlines were generated over the whole brain by deterministic tractography using Diffusion Toolkit (FACT propagation algorithm, angle threshold 45˚, spline-filtered, masking by the Fractional Anisotropy (FA) maps thresholded at 0. 2). For tractographic analysis, the regions of interest (ROIs) of the AAL atlas and of an MNI space-defined volumetric version of the DKT ROI atlas were used, both masked by the gray matter (GM) tissue probability map available in Statistical Parametric Mapping Software -SPM (thresholded at 0. 2). To this end, for each participant, FA volumes were normalized to the MNI space using the FA template provided by FSL, using the spatial normalization routine available in SPM12, and the resulting normalization matrices were inverted and applied to the ROIs, to apply them onto each subject.