dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Seventy-six full-term infants were included in the final statistical analysis. Participants were 38 full-term 9-month-olds (21 females, age range = 277-301 days), and 38 12-month-olds (17 females, age range = 351-387 days). Data were collected in Grenoble, (France). Infants were recruited using a local database and through contact at the regional maternity ward. Thirty-six additional infants participated in the study but were eliminated from the analysis due to: technical problems (n = 2 for 12-month-olds), changing state during the test (e. g. infant became too agitated or started to cry; n = 2 for 9-month-olds, and n = 2 for 12-month-olds), failure to compare both stimuli (n = 15 for 9-month-olds, and n = 12 for 12-month-olds), or identification as outliers based on median absolute deviation (MAD,  Leys et al.
Stimuli The stimuli presented to infants were 43 colour photographs of adult Chinese male (n = 23) and female (n = 20) faces, collected in China. All faces were presented against a red background (see Figure  1 ). Faces were presented in frontal orientation with neutral expressions or slightly positive expressions. Stimulus size and brightness were kept uniform using Adobe Photoshop. When projected onto the 52 × 32. 50 cm screen, each picture was 26-cm high and 19. 50-cm wide, and separated by a 12. 40-cm gap (subtending approximately 18. 46° × 24. 45° of visual angle at 60 cm viewing distance). Schematic display of the procedure and examples of the stimuli can be found in Figure  1. To provide an estimate of the within-category variability of the male and female sets (i. e. distance between faces within the male or female face set), a similarity measure was computed for all the faces used in the current study with a state-of-the-art deep convolutional neural network (DCNN), SENet50, pre-trained on the VGGface2 dataset (i.
e. 3. 31 million pictures from 9131 different individuals from various ethnicities, including Chinese,  Cao et al. , 2018). This model achieves close or even better performance than human observers in face recognition  (Cao et al. , 2018; Parkhi et al. , 2015). The model was implemented on python using an adapted version of VGGface on tensorflow, with the keras-vggface library and the face detector MTCNN (TensorFlow v2. 0, VGGFace v0. 6 and MTCNN v0. 1. 0). Source code for the DCNN model can be found here: https://github. com/ox-vgg/vgg_face2, and the python script developed to measure the distance between face representations extracted from the model is provided in the Appendix S1. The measure representing face distance was cosine similarity  (Cao et al. , 2018) , with minimum-maximum distance values between 0 and 1.
Damon et al. , 2016). During the test phase, infants were presented with one novel face from the familiarized gender category and one novel face from the novel gender category (randomly selected from the pool of faces not used in familiarization). For both test trials, images were presented during 5 s after the infant's first look at one of the faces. Left/right positioning of the male/female faces on the first test trial was counterbalanced across infants, and reversed on the second trial. The same pair of faces was used in both test trials. Before each trial, an attention getter (i. e. a soundless, moving, abstract geometric form located at the middle of the screen) was presented until the infant looked at the middle of the screen. Image presentation was controlled using E-prime 2. 0 software (Psychology Software Tools, Pittsburgh, PA).
Results All analyses were performed with an alpha threshold defined at α =. 05, using R (R Development Core  Team, 2008)  and Jamovi (The Jamovi Project, 2021; Version 2. 0. 0). Graphical illustration was realized using the ggstatplot R package  (Patil, 2021). Effect sizes were Cohen's d and partial η 2. We supplemented frequentist statistical analysis with Bayes Factors, such that both accepting and rejecting the null hypotheses were possible (see, e. g. Dienes, 2014, advocating for this approach).
Participants Forty-eight infants were included in the final statistical analysis. Participants were 23 full-term 9-month-olds (14 females, age range = 249-304 days), and 25 12-month-olds (17 females, age range = 354-388 days). Data were collected in Grenoble (France) Infants were recruited using a local database and through contact at the regional maternity ward. Six additional infants participated in the study but were eliminated from the analysis due to: technical problems (n = 2 for 9-month-olds), or failure to compare both stimuli (n = 2 for 9-month-olds, and n = 2 for 12-month-olds). All infants included in the experiment had no experience with Asian faces (except for faces glimpsed in the street). All infants were White, and parents reported a female own-race face as the primary caregiver.
Participants Thirty-two infants were included in the final statistical analysis. Participants were 18 full-term 9-month-olds (8 females, age range = 265-293 days), and 14 12-month-olds (3 females, age range = 350-408 days). Data were collected in Grenoble and Dijon (France). Infants were recruited using a local database and through contact at the regional maternity ward. Seven additional infants participated in the study but were eliminated from the analysis due to failure to compare both stimuli (n = 3 for 9-month-olds, and n = 4 for 12-month-olds). All infants included in the experiment had no experience with Asian faces (except for faces glimpsed in the neighbourhood). All infants were White, and parents reported a female own-race face as the primary caregiver.
AC K NOW L E DGE M E N T S This research was supported by the French 'Investissements d'Avenir' program, project ISITE-BFC (contract ANR-15-IDEX-0003) attributed to FD. PQ's participation in the project benefited from support from the National Science Foundation (NSF BCS-2141326). The authors are grateful to the families that took part in the study, and thank Marie Sarremejeanne, Logan Magnier, and Florenn Gallian for help with data collection.
It also enabled us to use a handheld VIVE controller that participants positioned in space to indicate sound localization (during evaluation sessions), and to track the 3D position of the loudspeaker using a second VIVE device (see  Coudert et al. 2021  for more details). All training and evaluation sessions were performed in a quiet environment (i. e. , background noise at 33. 7 dB SPL). Tested positions were predetermined and controlled using custom-made software (Unity, 2017. 4. 10f1). To avoid any transfer of sensorimotor learning from the training to the evaluation sessions, we used different stimuli and responses. During training, stimuli were spoken words (see details below) and participants responded by orienting their head to the direction of the sound. During evaluation, stimuli were 3-second white noise bursts and participants indicated sound sources using a handheld pointer.
Data Analysis To assess the impact of the training sessions on auditory performance and perceived quality of life, for each participant we analyzed performance during each of the four evaluation sessions (E0 to E4). For the sound localization test, we calculated two separate values: (1) the percentage of trials on which they made front-back confusions in azimuth and (2) the percentage of trials on which they confused up and down sound sources in elevation. Performance at chance level was defined as a score of >50% of confusions as in this task the participant's response was either in the correct hemi-field or not. For the matrix test, we averaged the two SRT values (one per 20-sentence block) to obtain a mean SRT score. For the SSQ questionnaire, for each patient we calculated the mean SSQ score per subscale (speech perception, spatial hearing, and other qualities of hearing), as well as the mean total SSQ score across the three subscales.
Auditory Performance with Audiovisual or Visual-only Feedback During Training Since our main goal was to examine the feasibility of this novel rehabilitation protocol and to obtain preliminary data concerning its potential benefits, the data presented above include all participants, regardless of the type of feedback they received. Despite the small number of participants in each group (n = 6), we were also interested to know whether there was an added benefit of training for those participants who received feedback in two sensory modalities. To assess this, we first ensured that performance in the two groups was similar before training. A Mann-Whitney test revealed no significant differences between groups for the sound localization test (V = 13, p = 0. 466), the matrix test (V = 22, p = 0.
046, p = 0. 0003), without changes in the number of GAD 67 -positive cells compared to the contralateral side as well as to vehicle-injected mice (Figure  2a, b ). Furthermore, our data also show that the brain volume transduced by the AAV10 serotype was extensive, leaving different populations of cells outside the SNc/VTA expressing the γ-Syn gene, although we did not identify the cell types (Figure  1a ). To rule out any possible effect of the viral vector itself, we performed an experiment with an AAV-EV using exactly the same protocol that we followed for the AAV10 mice. The injection of the AAV-EV did not alter the γ-Syn expression, nor did it affect behavioral performance (Supplementary Materials Figure  S3 ). Next, we performed immunoblot analysis of microdissected mouse SNc/VTA extracted only at 4 weeks after AAV10 injection.
In Situ Hybridization Mice were euthanized by pentobarbital overdose and their brains were rapidly removed, frozen on dry ice and stored at -80 • C. Coronal brain sections containing SNc, VTA, RN, and LC (14 µm-thick) were obtained and processed, as described elsewhere  [21, 31, 34]. The oligodeoxyribonucleotide probes were complementary to bases: mouse α-Syn (mα-Syn, 411-447 sequence, GenBank accession NM_001042451), mouse γ-Syn (m-γ-Syn, 366-416 sequence, GenBank accession NM_011430), and glutamic acid decarboxylase (GAD 67 , 1600-1653 sequence, GeneBank accession NM_017007), respectively (IBIAN Technology, Zaragoza, Spain). For the α-Syn and γ-Syn assessment, each oligonucleotide was individually labeled (2 pmol) at the 3 -end with [ 33 P]-dATP (>2500 Ci/mmol; DuPont-NEN) using terminal deoxynucleotidyl-transferase (TdT, Calbiochem)  [21, 34, 35].
Primary anti-TH was incubated for 48 h at 4 • C, followed by incubation with the corresponding biotinylated goat anti-rabbit (1:500; ref. : BA-1000, Vector Laboratories, Burlingame, CA, USA). The color reaction was performed by incubation with diaminobenzidine tetrahydrochloride solution (DAB, ref. : 5905-50TAB, Sigma-Aldrich, Madrid, Spain). Sections were mounted and embedded in Mowiol 4-88 (Sigma-Aldrich, Madrid, Spain). Microphotographs from SNc/VTA sections were acquired using a Nikon Eclipse E100 microscope (Nikon, Tokyo, Japan). The number of m-γ-Syn mRNA labelling TH-positive neurons and their intracellular density were assessed in SNc/VTA sections corresponding to different antero-posterior levels -2. 46 to -3. 64 mm from bregma using ImageJ (v1. 51s, NIH, Bethesda, MD, USA) software.
All labelled cells with their nuclei within the counting frame were counted in five consecutive sections and three different microscope fields were analyzed in each section. For film autoradiography, some hybridized sections were exposed to Biomax-MR (Kodak, Sigma-Aldrich, Madrid, Spain) films for 24-72 h at -70 • C with intensifying screens. For specificity control, adjacent sections were incubated with an excess (50×) of unlabeled probes. Films were analyzed and relative optical densities were evaluated in three adjacent sections by duplicate of each mouse, and averaged to obtain individual values using a computer-assisted image analyzer (MCID, Mering, Germany). Figures were prepared for publication using Adobe Photoshop software (Adobe Software, San José, CA, USA). Contrast and brightness of images were the only variables we adjusted digitally.
Four visual clues were placed on the walls of the tank (N, E, S, and W). Non-toxic, white latex paint was added to make the water opaque, and a white escape platform was submerged 1 cm below the water level. The animals' swimming paths were recorded by a video camera mounted above the center of the pool, and data were analyzed with SMART version 3. 0 software (Panlab, Cornellà de LLobregat, Barcelona, Spain). The learning phase consisted of 4 days of trials for each mouse. The animals were submitted to four trials each day which were averaged, starting from the positions set (in random order) and without a resting phase between each trial and the subsequent one. At each trial, the mouse was placed gently into the water, facing the wall of the pool, and allowed to swim for 60 s. If not able to locate the platform in this period, the mouse was guided to the platform by the investigator.
Statistical Analysis All values are expressed as the mean ± standard error of the mean (SEM). Statistical comparisons were performed using GraphPad Prism 8. 01 (GraphPad software, Inc. , San Diego, CA, USA) using the appropriate statistical tests, as indicated in each figure legend. Outlier values were identified by the Grubbs' test (i. e. , Extreme Student zed Deviate, ESD, method) using GraphPad Prism software and excluded from the analysis when applicable. Differences among means were analyzed by either one-or two-way analysis of variance (ANOVA) or the two-tailed Student's t-test, as appropriate. When ANOVA showed significant differences, pairwise comparisons between means were subjected to Tukey's post-hoc test or Sidak's multiple comparisons test, as appropriate. Differences were considered significant at p < 0.
13 and the other at -0. 14 (Fig. 3 ). Both these thresholds allowed perfect distinction between the group A and group C on our dataset (Fig. 2B ). We thereby used the averaged threshold value of ΔE at -0. 135 for our analysis. Values in Group B were varying around the threshold (that was optimized for classi cation of C versus A) and showed larger distribution suggesting that this group is more heterogeneous.
Patients' selection Patients were retrospectively selected from our database of focal pharmaco-resistant epilepsy cases undergoing pre-surgical evaluation within the Epilepsy Unit at the Timone Hospital in Marseille using SEEG recording between October 2015 and December 2018. A systematic patient examination and interview has been conducted by nurses or physicians during seizures to assess the level of interaction as well as language impairment  22, 23. The study was approved by the institutional review board (Assistance Publique Hôpitaux de Marseille APHM 2021 - 179) and informed consent was obtained from each subject at the time of SEEG recording. All experiments were performed in accordance with relevant guidelines and regulations.
Statistical analysis: We computed the linear correlation between consciousness score and delta entropy using Pearson method. Then a linear mixed-effect model (LMM) using the "lme4" package (version 1. 1-26  27  , of the statistical software R version 4. 0. 3 (R Core Team, 2020) was used to study the effect of the group (A, b or C) on the ΔE value, with subject as random effect. Restricted Maximum Likelihood (REML) was used to t the LMM and Satterthwaite's method was used to estimate the degrees of freedom. Post-hoc pairwise comparisons were performed with the Least Square Means (LSM) method. Importantly, the use of a mixed model allows to take into account variability across patients. Concerning the different anatomical regions, due to the above-mentioned limitations, it was impossible to perform a statistically powerful analysis of the ΔE per region to see the effect of the group, neither a statistical comparison between the regions and the seizure groups.
Limitations A limitation to our study is the small sample size: In fact, more data is needed to properly apply the threshold entropy marker and its usage as a predicting tool in terms of the extent of alteration of consciousness. Another limitation is the lack of su cient electrode sampling of some regions: in the setting of temporal lobe epilepsy, considered as the main hypothesis during the pre-surgical evaluation, some cases did not have enough sampling of the extratemporal, particularly in the parietal regions. Additionally, not all patients had subcortical contacts in the thalamus. Thus, the extent of involvement of the thalamus and parietal regions remains not clearly de ned by the current data. Furthermore, the calculation of the threshold was determined on the same dataset used for the analysis; this method needs to be applied on another dataset to con rm the threshold value.
Subclinical depressive symptoms are associated with increased risk of Alzheimer's disease (AD), but the brain mechanisms underlying this relationship are still unclear. We aimed to provide a comprehensive overview of the brain substrates of subclinical depressive symptoms in cognitively unimpaired older adults using complementary multimodal neuroimaging data. We included cognitively unimpaired older adults from the baseline data of the primary cohort Age-Well (n = 135), and from the replication cohort ADNI (n = 252). In both cohorts, subclinical depressive symptoms were assessed using the 15-item version of the Geriatric Depression Scale; based on this scale, participants were classified as having depressive symptoms (>0) or not (0). Voxel-wise between-group comparisons were performed to highlight differences in gray matter volume, glucose metabolism and amyloid deposition; as well as white matter integrity (only available in Age-Well).
Notably, the absence of major depression was assessed using a clinician-administered questionnaire, the Montgomery-Åsberg Depression Rating Scale (MADRS)  [28] , with a cut-off value of 6 (participants with MADRS > 6 were excluded). All participants gave their written informed consent prior to the examinations, and the Age-Well randomized clinical trial was approved by the ethics committee (Comité de Protection des Personnes Nord-Ouest III, Caen, France; trial registration number: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials. gov Identifier: NCT02977819). ADNI cohort. Two hundred fifty-two CU older adults from ADNI were included in our study as a replication cohort. The main exclusion criteria included the presence of psychiatric illness (major depression or bipolar disorder) or neurological disease (see  [29]  for details).
The absence of major depression was assessed using the self-reported Geriatric Depression Scale (GDS)  [30] , and the investigators indicated that "although differing sensitivities and specificities have been obtained across studies, a score >5 was considered suggestive of depression and warranted a followup interview" (in our study, only one ADNI participant was concerned with a GDS score = 6) and "scores >10 are almost always depression". Details regarding the cohort recruitment and all data collection methods is available online (http://adni. loni. usc. edu/). Only participants with an available score of depressive symptoms and whose multimodal neuroimaging scans were acquired no more than 3 months after the assessment of depressive symptoms were selected. The ADNI study was approved by the institutional review boards of all of the participating institutions.
DISCUSSION The aim of this study was to provide a comprehensive overview of the brain substrates of subclinical depressive symptoms in CU older adults using complementary multimodal neuroimaging data in two independent cohorts. We showed that participants with subclinical depressive symptoms from both cohorts presented lower GM volume in the hippocampus. In ADNI, glucose hypometabolism was also found in the hippocampus and extended to the amygdala, precuneus/posterior cingulate cortex, medial and dorsolateral prefrontal cortex, insula, and temporoparietal cortex. In Age-Well, we also found lower WM integrity mainly in the fornix, posterior cingulum and corpus callosum and inferior longitudinal fasciculus. Furthermore, the presence of subclinical depressive symptoms was not associated with brain amyloid deposition in either cohort.
Strengths and limitations The main strength of our study was to provide an overview of the brain substrates of subclinical depressive symptoms in CU older adults using complementary multimodal neuroimaging data (i. e. , GM volume, WM integrity, glucose metabolism, and amyloid deposition). Furthermore, the results were partly replicated in two independent cohorts with large sample sizes (i. e. , Age-Well and ADNI)-using the same scale to assess depressive symptoms (i. e. , GDS). However, the cross-sectional design of our study is a limitation as it prevents us from assessing the links between baseline levels and changes over time in depressive symptoms and neuroimaging biomarkers. Moreover, the threshold we used to define "subclinical" depressive symptoms was not validated; further studies might allow researchers to select the specific threshold for "clinically relevant" subclinical symptoms.
ACKNOWLEDGEMENTS A. Cognet ,  C. Gaubert ,  T. Jorand ,  M. Botton ,  MSc, A. Joret Philippe, MSc, S. Egret, MSc, P. Lacheray, MSc ,  J. Lebahar, PhD ,  C. Tomadesso, PhD ,  H. Espérou, MD, PhD ,  E. Frison, MD, PhD , and the  Cyceron MRI-PET  staff members for their help with recruitment and data acquisition or administrative support. We also acknowledge the  Euclid team , the sponsor (INSERM) and all the participants of the study for their contribution.
Modeling efforts provide further evidence for the close relationship between the empirical data features and the properties of the structural network, local dynamics, coupling strength, neural gain  [4, 13, [26] [27] [28] [29] [30] [31]. The resting state dynamics can then be understood as noise-driven fluctuations of brain activity, operating near criticality and constrained by the brain connectivity  [2, 32]. However, none of the above qualifies as a description of a mechanism. Descriptions of mechanisms require formulation in terms of causal activities of their constituent entities and render the end stage, in our context the resting state dynamics, intelligible by showing how it is produced  [33]. To explain is thus not merely to redescribe one regularity (e. g. functional connectivity dynamics, or maximization of entropy) as a series of several (such as near-criticality, cascades, ignition).
Example of a cascade-coordinated increase in activity translating to a delayed correlated peak in the BOLD signal. Below we compare the network dynamics in and outside the working point, and the empirical data. In both empirical data and the working point (G = 0. 54), the BOLD co-activations follow the neuronal cascades with a latency (C), and show distinct spatial profiles which are recurrent in time (D): edge time series on the (top panel) captures the spatial profiles of the co-activations, the similarity across time is captured by the dF C e matrix (middle panel), and the distributions of correlation between co-activation events (CA) and non-events (nCA) is compared (bottom panel). 8.
To project the trajectories of the events observed at the slow time scale of the BOLD on the manifold, we have shifted the BOLD signal by the characteristic lag, and for each BOLD time point belonging to the cluster we selected the corresponding time points in r(t), and convolved the resulting data with a Gaussian kernel to smoothen out the noisy fluctuations (see Methods for details). We then spanned the subspace corresponding to the first two PCA components of the co-fluctuation trajectory and overlayed the smoothened trajectory over the density plot of the full r(t) time series. The density plots (shades of red, Figure  4C ) of the example events show a separation of the event subspace marked by the peak in the RSS (shown in yellow in Figure  4C ) on the smoothened trajectory from the rest of the manifold.