dataesr/scientific-paragraphs-categorization huggingface dataset:
The idea that mixed features, suicidality, and treatment response may be markers of latestage depression is worth pursuing. Limitations. The main limitation of this study was that the choice of the more than three MDEs cut-off criterion we adopted to define recurrence was literature-based, rather than obtained through the use of ROC (receiver operator characteristic). Furthermore, its cross-sectional nature prevents us from being able to assess conversion from MDD to BD. Moreover, clinical variables and treatment response were collected retrospectively, hence are susceptible to recall bias. However, our study has also remarkable strengths, represented by the large sample and the use of a MDD only population. Further research is needed focusing on recurrence rather than on polarity; other than clinical, genetic, neuroimaging, neuropsychological, neurobiological, and treatment response data are required to better characterise this issue.
Acknowledgments. We gratefully acknowledge the contribution of the Librarians of the  School of Medicine and Psychology of Sapienza University, Ms. Mimma Ariano ,  Ms. Felicia Proietti ,  Ms. Ales Casciaro ,  Ms. Teresa Prioreschi , and  Ms. Susanna Rospo  for rendering precious bibliographical material accessible, as well as our  Secretary Lucilla Martinelli  for her assistance during the writing of this manuscript.
Reversal Leaning Task The RL task utilized in the present study was implemented by using the Psychotoolbox software 1. Stimuli consisted of pairs of Hiragana characters that were presented on a 19 computer monitor placed 50 cm apart from the participant. The RL task included two types of trials, e. g. , trials with and without metacognitive assessment, which we termed as metacognitive and standard trials, respectively. The order of trial presentation was randomized. For standard trials, subjects selected one of two symbols displayed in the screen by pressing a key on a keyboard. Stimuli remained on the screen till subjects responded. Feedback (happy or sad smiley) was presented for 500 ms after participant's response. A new stimulus was presented at a random time between 750 and 1250 ms (Figure  1A ).
Data Analysis Behavioral Data For analysis of the behavioral data, we considered the following RL parameters: (1) mean number of trials to reach reversal; (2) number of consecutive errors after reversal; (3) random switches, which refers to trials in which subjects randomly switched their choice in spite of getting positive feedback; (4) strategy change after probabilistic error (PE), which quantifies trials with change in decision occurring after PE. Focusing on performance, the following parameters were considered: (5) response time (RT), and (  6 ) accuracy. For metacognition, we included: (7) mean confidence level.
Fitting of the RWLM Model In order to determine values of parameters α and β in the RWLM model we made use of a grid search approach that maximized the probability of the model to capture the observed data. Thus, initial interval values for the grid were selected so that maximum probability was attained. Analysis of behavioral data as well as calculation of RWLM parameters was performed by using MATLAB scripts (MathWorks, Natick, MA, United States).
Comparison of correlation coefficients between conditions was performed by means of the cocor package  (Diedenhofen and Musch, 2015). Statistical analyses were performed by using the software IBM SPSS Statistics (Version 24, IBM Software, Business and analytics, Armonk, NY, United States). The significance level for all two-tailed statistical tests was set up at 0. 05. In order to cope with the issue of multiple comparisons, we applied the Holm correction (By considering m = 30) p-values ordered from smallest to largest, p * is the smallest p-value that satisfies the condition p k > α m+1-k , where k is the p-value index), thus any p-value <p * = 0. 041 was considered significant.
Acknowledgements The authors would like to thank  Balthazar Ntab  of the  Department of Medical Information, Saint-Laurent-du-Maroni Hospital, Catherine Caillaud of the Department of Biochemistry, Necker-Enfants-Malades Hospital, Paris ,  Roseline Froissart  of the  Department of Biochemistry, University Hospital, Lyon , and  Hélène Ogier de Baulny  of the  Department of Metabolic Disorders, Robert Debré University Hospital, Paris , for data collection.
subject's treatment status (i. e. before or after implantation) and the timepoint of the measurement. The FOG data of 38 subjects (of 40 implanted) were analyzed (two subjects did not complete the FOGQ at baseline due to incomplete data collection). At the group level, the FOGQ showed an overall improvement (p < 0. 0001) at week 26 compared to baseline. Separating subjects into subjective freezers or non-freezers at baseline (according to item 3 of the FOGQ), 10 freezers remained freezers, 15 freezers became non-freezers, 10 non-freezers remained non-freezers, and one non-freezer became a freezer. Two additional subjects dropped out at week 26, as the FOGQ was not completed at that timepoint (see Supplementary Fig. 1 ). The effect of STN-DBS on gait-specific questionnaires and subscores at baseline, and postoperatively can be found in the Supplementary Table  1.
Of the 38 subjects, 20 performed the walking test at all time points during the medication-off state, and their data were taken for sub-analysis. There were no significant differences between the subgroup (n ¼ 20) and the total subject group (n ¼ 38) for baseline demographics and clinical characteristics. Importantly, FOG severity according to the FOGQ did not differ between the two groups. For the subgroup (n ¼ 20), FOG at baseline responded to L-dopa (mean percentage time frozen: 0. 21 ± 0. 32 for medication-off and 0. 02 ± 0. 07 for medication-on; p ¼ 0. 0012). Total walking time (Fig. 1A ), the percentage of time frozen (Fig. 1B ), and number of FOG episodes (Fig. 1C ) were reduced at weeks 12, 26, and 52 compared to baseline during medication-off. The different FOG subtypes (Fig. 1 D ) and patterns (Fig.
1 E ) all responded to STN-DBS. As seen in Supplementary Table  2 , the additional application of L-dopa improved FOG even further compared to DBS alone in most cases. Our results show a profound ameliorating effect of STN-DBS on FOG in this cohort of subjects with PD within the first year of treatment. Our present findings are comparable with the overall trend observed in prior work, i. e. , STN-DBS reduces FOG severity within the first year of treatment. However, our study adds valuable new insights, because we collected subjective information (questionnaires) and objective data (blindly analyzed by an independent rater). Our study is the first to prospectively demonstrate a direct effect of STN-DBS on FOG patterns and subtypes. We found that STN-DBS was effective for all three patterns and for all provoking circumstances.
Declaration of interest Michael T. Barbe  received speakers' honoraria from  UCB, Bial, St. Jude, Medtronic. and research funding from  Medtronic Inc. and  Boston Scientific. Claudia Barthel  received research funding from  Boston Scientific  and from a  European Community 's  Seventh Framework Programme   FP7/2012  under grant agreement No. 316639. Nic  Van Dyck  and  Lilly Chen  are fully employed by  Boston Scientific. Gereon R. Fink  received speaker's honoraria from  Bayer  and royalties from  Thieme Publishers  and  Hogrefe Publishers. Bastiaan R. Bloem  currently serves as Associate Editor for the Journal of Parkinson's disease, has received honoraria from serving on the scientific advisory board for Zambon and  Kyowa Kirin , has received fees for speaking at conferences from  AbbVie, Zambon and Bial , and has received research support from the  Netherlands Organisation for Scientific Research , the  Michael J Fox Foundation ,  UCB , the  Stichting ParkinsonFonds , the  Hersenstichting Nederland ,.
Current study Our study aimed to identify patterns of intrinsic connectivity associated with different patterns of unconstrained states and examines their neuro-cognitive features from the perspectives outlined above. We used a large publicly available dataset, containing measures of resting-state functional magnetic resonance imaging (fMRI), and an accompanying selfreport instrument describing cognition experienced during the resting-state  (Gorgolewski et al. , 2014; Nooner et al. , 2012). We previously explored the relationships between patterns of ongoing thought and measures of neural activity, such as the fractional amplitude of low frequency oscillations, as well as the regional homogeneity of neural activity, in a sub sample of this data set  (Gorgolewski et al. , 2014). In this study we focused on connectivity, we applied sparse canonical correlation analysis (SCCA) to obtain a conjoined decomposition of selfreports of experience with matrices of whole brain connectivity data.
Participant We analysed 258 participants (females = 162; age range 18 -55, M = 34. 97, SD = 12. 24) obtained from the enhanced Nathan Kline Institute-Rockland sample (NKI-RS; http://fcon_1000. projects. nitrc. org/indi/enhanced/). Full details of the acquisition of this sample can be found in  Nooner et al. , 2012. We selected participants between 18 and 55 years old as our sample, this choice allowed us to maximise the cohesive nature of our sample. All the participants have the MRI data and less than 5 missing data points among the selected assessments.
, 2016). Participants rated each question on a scale of 1 (Completely did not characterise my experience) to 9 (Completely did characterise my experience). In the current study we analysed the two sections together to provide single solutions that combined information on both the content form of experience. The full list of questions and the corresponding labels are presented in Table  1. The questionnaire was administrated once after the resting-state scan in order to assess experiences during the scanning session. For the full details of the NYC-Q, please refer to  Gorgolewski et al. , 2014. We have placed the questionnaire measure used in this study along with an example self-report collection task on GitHub at the following address: https://github. com/htwangtw/restingstate_thoughtreports.
We used resting-state fMRI to describe the general functional organisation of the brain. We selected resting-state multiband functional magnetic resonance imaging (R-mfMRI; TR = 1400msec; voxel size = 2mm isotropic; duration = 10 minutes) for our analysis. Functional and structural data were pre-processed using Configurable Pipeline for the Analysis of Connectomes (C-PAC; https://fcp-indi. github. io/) to interface with FMRIB's Software Library (FSL version 5. 0, www. fmrib. ox. ac. uk/fsl). Individual FLAIR and T1.
Connectivity matrices To describe the functional architecture of the whole brain, we transformed the restingstate BOLD time series into connection strength values of the different networks for each participant. The whole brain parcellation was obtained from connectivity-based functional parcellation created by Yeo and collegues  (Yeo et al. , 2011). The 7 network parcellation was used in the current study. We split the networks into two hemispheres and extracted clusters. Two voxels are considered connected only if they are adjacent within the same x, y, or z direction. This yielded 57 clusters from the Yeo 7 networks parcellation. The implementation of spatial clusters extraction was retrieved from python library Nilearn  (Abraham et al. , 2014;  http://nilearn. github. io/, version 0. 3.
L1 regularisation conducted (i) feature selection (i. e. , select only relevant components) and (ii) model estimation (i. e. , determine what combination of components best disentangles the neuro-cognitive relationship) in an identical process. This way we handle adverse behaviours of classical linear models in high-dimensional data. A reliable and robust open-source implementation of the SCCA method was retrieved as R package from CRAN (PMA, penalized multivariate analysis, version 1. 0. 9, Witten, Tibshirani, & Hastie, 2009). The amount of L1 penalty for the functional connectomes and the NYC-Q reports were chosed by cross-validation. The procedure is described below.
We performed confound removal on functional connectomes and the NYC-Q reports as suggested by prior studies  (Smith et al. , 2015). We removed the effects of nuisance variables from the dataset. These confound variables were sex, age, and head motion indicated by Jenkinson's mean FD  (Jenkinson, Bannister, Brady, & Smith, 2002). The removal steps was performed on the training set in each CV fold. We standardized the confound by calculating the z-score, and also squared the three confound measures to account for potentially nonlinear effects of these confounds. The 6 resulting confounds were regressed out of both data matrices. The implementation of the confound removal method  (Friston et al. , 1994)  was retrieved from python library Nilearn  (Abraham et al. , 2014 ; http://nilearn. github.
io/, version 0. 3. 1). The number of latent components was determined by a preliminary analysis with no sparsity and calculated the explained variances for the two datasets (i. e. , brain network correlations and questionnaire ratings). The explained variance increased with the number of components and growth stablised at 10 components. We selected the number of components based on the point where the tangent stablised. This led to a model of 4 components, and it accounted for a total of 78% of the variance in connection strength and 29% of the variance in the self-report data. Next, we determind the two coefficients for the L1 penalty terms that was associated with the best model performance with 4 latent components. We searched for the best L1 penalty values between 0. 1 and 0. 9 in 0. 1 increments, which resulted in 81 set of.
Permutation test We used permutation testing to assess the robustness of the components identified through our analysis. We constructed a null distribution for each canonical component by holding the functional connectivity data in place and randomising the row order of self-reports data. This permutation scheme broke the link of individual differences in the dataset, therefore testing the robustness of the components in the hypothetical population. By calculating the falsediscovery rate in the null distribution, we can conclude the possibility of discovering our components by chance with the given penalty coefficients. Hypotheses that are accepted with a 5% level of significance. In the current analyses we adopt the permutation test with the FWE-corrected p-value by  Smith and colleagues (2015)  with data argumentation to increase the size of the resampling datasets to 1000.
In our study, Components 1 and 3 were statistically significant in both cases and fitted well with contemporary accounts of ongoing cognition. Accordingly we place encourage readers to focus on these patterns from our data. There are alternative strategies that could help validate the robustness of patterns of brain-experience association. One approach could be to compare the relationship between multiple sessions within the same individual  (Poldrack et al. , 2015)  and to have a larger sample that would allow the reproducibility of these results through a formal split-half validation procedure. To achieve this latter aim for future studies, we have placed the questionnaire measure used in this study along with an example self-report collection task on GitHub at the following address: https://github.
In Vitro Electrophysiology-Stimulation and Recording Extracellular field potentials (FPs) were recorded from the stratum radiatum of cornu Ammonis 1 (CA1) area. The recording electrode consisted of a chlorinated silver wire inserted into a glass pipette filled with 154 mM NaCl (5-10 MΩ). Schaffer collateral axons received the orthodromic stimuli through a bipolar twisted electrode (Ni-Cr wire with 50 µm diameter tip) positioned in the stratum radiatum of the CA1 hippocampal subfield. All data were low-pass filtered at 3 kHz, digitized at 10 KHz and stored on computer disk using a CED 1401 interface for off-line analysis using Spike 2 v6. 09 (CED-1401, Cambridge, UK). Before starting the LTP protocol, input-output (I/O) relationships of stimulus intensity vs. field excitatory postsynaptic potentials (fEPSP) magnitude were performed in order to determine the maximal response after constant increments of stimulus intensity until no further increase in the fEPSP amplitude.
Statistical Analysis Electrophysiological data were analyzed using custom-made scripts (© Jan-Oliver Hollnagel, MATLAB R2013b;  Salar et al. , 2016). The fEPSP slope was measured between 20%-80% of its maximal amplitude. In order to verify the potentiation after TBS, field EPSP slopes were normalized relative to the averaged baseline response. Statistical analysis of behavioral and electrophysiological data was reported as mean ± standard error of the mean (SEM). Shapiro-Wilk test was used to determine normality. On behavioral tests, the statistical significance was assessed by non-parametric Mann-Whitney test, since less than 30 animals were used in the study, therefore the sample is not reliable to perform parametric approach  (Ghasemi and Zahediasl, 2012). Statistical analysis for LTP was performed using two-way ANOVA (fEPSP slope at different times) followed by Bonferroni post hoc test.
Proechimys's LTP potentiation decayed over time reaching baseline levels at 90 min after TBS, in contrast to Wistar rats in which potentiation could be observed throughout 3-h recording period. As shown in Figure  5C , fEPSP recorded in Proechimys's brain slices showed that TBS could only produce a rapid decaying LTP. The literature reports that even weak protocols of induction can usually induce LTP that can last several hours in rodents  (Lu et al. , 2011; Dong et al. , 2015). Theta-burst and tetanic stimulation have been the most frequently used experimental paradigms to induce LTP  (Cao and Harris, 2014). Maybe, theta stimulation used in our work could be a very weak stimulation for Proechimys. For this reason, we also assessed LTP using different tetanic stimulation protocols, even multiple tetani in quick succession, but with similar results as those found with TBS (data not shown).
5% nickel chloride (Sigma, Saint Quentin Fallavier, France) and dehydrated before mounting. Quantification of DARPP32 and GAD 67 positive fibres and cells. GAD67-positive neurons, DARPP32-positive neurons as well as NeuN-positive neurons were counted in the striatum at different time-points, from 0 until 30 days post-lesion, using Metamorph software from Molecular Devices (Roper Scientific, Evry, France). Briefly, the region of interest in the control slice (isotypic control) and the equivalent in the slice analysed were selected by taking as reference the surrounding structures (corpus callosum, hippocampus, etc. ). Then, the intensity of the staining in those areas was measured. To eliminate the background the intensity measured in the control slice was subtracted to the intensity measured in the slice analysed.
RNA integrity analysis Identification of amplification products was performed using the Experion automated electrophoresis system (Bio-Rad Laboratories, Marnes-la-Coquette, France). All gel-based electrophoretic steps (such as sample separation, staining, de-staining, imaging, band detection and data analysis) were automatically performed to generate reproducible separation and quantitative results. For this study, DNA analysis was performed using the Experion DNA 1 K Analysis kit, which contains reagents, DNA ladder controls, microfluidic chips and other materials required to perform the DNA electrophoresis in a range varying between 25 and 1000 bp. The quantitative amount of the loaded DNA samples should range between 0. 1 and 50 ng/ml. DNA fragment separation depends on the nucleotide composition in bps.
RNA extraction and RT-qPCR The following experimental details were performed following the guidelines of the PACEM core facility (Plate-forme d'Analyse Cellulaire et Moleculaire, Angers, France). Design of primers specific for human genes and PCR were performed as described elsewhere  [34] [35] [36]. RT-qPCR was performed as previously described  [34]. A housekeeping gene was tested as reference and raw data were analysed.
Slice desiccation protocol To be able to isolate grafted cell from host tissue by LMD, slice desiccation needed to be performed first. Twenty-four hours after cells implantation, LMD was performed on organotypic slices to isolate grafted hNSCs. First, LMD was tested on fresh 400μm organotypic slices. The laser was ineffective due to the high-water content of the slice (data not shown). Water extraction from the organotypic slices was then performed to allow a clean cut of the graft. Water extraction based on water sublimation was first tested (Fig  6B ). After sublimation, slices could be cut with LMD, but they got distorted, particularly at their edges. Furthermore, still too much water was retained within the slices, which made the cut difficult to perform (.
Acknowledgments The authors thank  Prof. Martinez-Serrano  for kindly providing the NSC cell line, the  SCAHU (Service commune  d'animalerie hospital-universitaire), particularly  Pierre Legras  and  Jerome Roux  for animal care, the  SCIAM ("Service Commun d'Imageries et d'Analyses Microscopiques") of Angers for LMD as well as the PACeM (Plateforme d'Analyse Cellulaire et Moleculaire) of Angers  for the use of PCR facilities. We are also grateful to  Jeremy Riou  and  Yoan Fourcade  for their helpful hints about statistical analysis. This project is supported by the " Education Audiovisual " and the executive cultural agency of the  European Union  through the  NanoFar Erasmus Mundus joint Doctoral program , by  Angers Loire Metropole , by " Region des Pays de La Loire  through the  "Paris Scientifiques" grant  and by the  "Fondation de l'Avenir" & "Inserm", France.
Discussion: This study will assess the effects of antidepressants on neuroinflammation as well as its role in the treatment response. It will contribute to clarify the putative relationships between neuroinflammation quantified by brain neuroimaging techniques and peripheral markers of inflammation. Lastly, it is expected to open innovative and promising therapeutic perspectives based on anti-inflammatory strategies for the management of treatment-resistant forms of MDD commonly seen in clinical practice. Clinical trial registration (reference: NCT03314155): https://www. clinicaltrials. gov/ct2/show/NCT03314155?term=neuroinflammation& cond=depression&cntry=FR&rank=1.
Methods : Depressed patients will be recruited throughout 4 centers (Bordeaux, Montpellier, Tours, and Toulouse) of the French network from 13 expert centers for resistant depression. The patient population will be divided into 3 groups: (i) experimental group-patients with current MDD (n = 20), (ii) remitted depressed group-patients in remission but still being treated (n = 20); and, (iii) control group without any history of MDD (n = 20). The primary objective will be to compare PET data (i. e. , distribution pattern of neuroinflammation) between the currently depressed group and the control group. Secondary objectives will be to: (i) compare neuroinflammation across groups (currently depressed group vs. remitted depressed group vs. control group); (ii) correlate neuroinflammation with clinical severity across groups; (iii) correlate neuroinflammation with MRI parameters for structural and functional integrity across groups; (iv) correlate neuroinflammation and peripheral markers of inflammation across groups.