dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the number of neuroscientists in the field and the amount of papers in the database that deal with the neuroscience of decision-making and moral reasoning, this is, however, extremely unlikely. Only about 8% of all articles relate to questions of moral theory and neuroscience (i. e. , the subject-category moral theory), whereas about 56% of all top researchers in neuroethics have a background in neuroscience, psychology and medicine. Hence, it is much more plausible to assume that many neuroscientists have actively contributed to research on the ethical implications of neurotechnology and neuroscience research. At least to a certain degree this implies that the ethics of neuroscience is profoundly shaped by the knowledge and research agendas of neuroscientists themselves. This need not necessarily be regarded as a problematic dilettantism of the neuroethics community, but may be an acknowledgment of the fact that ethical assessments of issues associated with neurotechnologies and clinical neurology structurally rely on knowledge of specialists in the field.
It is further reflected by the foundation of two journals (Neuroethics, AJOB-N) with the explicit aim of providing a platform for neuroethics research. Our analysis indicates that these journals significantly contribute to discussions in the field but that nevertheless only about a quarter of all relevant papers in the last quarter century has been published in these journals. In the period between 2008 and 2012 only about 374 contributions were published in these two journals. Based on the corpus of the MND, this makes up about 23. 4% of all publications relevant to neuroethics in this period. This is of course still a very high amount considering the fact that both journals together comprise only 2. 1% of the journals and book chapters that contributed to the MND. Even though this finding underlines the great importance of these two journals for the neuroethics community, our data indicate that the dominance of these journals should not be overstated.
Limitations It could be argued that our study is limited in two important regards. First, the quality of the database limits the validity of our results, and second, one might object that our analysis of topics and networks in neuroethics crucially depends on the reliability of our system of subject-categories. We believe, we can meet the first objection with reference to the fact that neuroethicists themselves know best, what their research is about. The MND was built as a resource for neuroethcists by neuroethicists. Hence, the versatile collection of material making up the MND should be more representative of neuroethics than the content of other, alternative databases. Second, this open, participatory structure prohibited that the regular scans of new publications turn out all too subjective.
Experimental Design The taste delivery system is described in Figure  1. Water and taste solutions were driven through the system by compressed air (controlled through a manometer). Two parallel silicone tubes were used: one for water and the other for a taste solution. Water and taste solutions were switched from one to the other by two electromagnetic valves controlled by an electronic device. This electronic device (stimulator) sent a signal to computer software (SystemPLUS EVOLUTION, 2007 Micromed S. p. A, Italy) when a taste solution was administered (with 1 ms precision), to obtain a precise time recording of GEP. Each subject put the two parallel tubes on the middle of his/her tongue in his/her mouth. The extremity of the semi-rigid tubes (silicone tubing, P/N 10025-02S, Bio-Chem valve) was placed at 1.
GEP Recording and Data Analysis Electroencephalographic (EEG) signals were recorded according to the international 10-20 system, using a conventional EEG recording. Nine sites were recorded for measurements in the primary gustatory cortex (Pz, Cz, C3, C4, Fz), and the secondary gustatory cortex (F3, F4, Fp1, Fp2; Figure  3 ). The primary gustatory cortex correspond to the insula and the operculum cortex, and activity was recorded at positions Pz, Cz, C3, C4, and Fz in previous studies  (Hummel et al. , 2010; Singh et al. , 2011). As for other cortical evoked potentials, the midline electrodes, the vertex electrode in particular, were the site of the most pronounced amplitudes of GEPs  (Kobal, 1985). The secondary cortical taste area is located in the orbito-frontal and prefrontal cortices, and activity there was recorded by frontal electrodes, FIGURE 2 | Design of the experiments.
Statistical Analysis Initial latency, amplitude and duration of GEPs (located in Pz, Cz, Fz, Fp1, and Fp2 according to the 10-20 system) were analyzed using means and standard errors of the mean (SEM). Mean differences in GEP latency, amplitude, and duration between data obtained in the morning and afternoon recordings, the results from the visual analog scales (hunger sensation, wanting, liking, solution intensity) and sucrose-gustatory thresholds were compared between the feeding and fasting sessions. The analyses were performed using a repeated measures ANOVA (with a single factor: feeding or fasting session). A p-value below 0. 01 was considered statistically significant (Bonferroni correction for five analyses). SAS 9. 2 (SAS Institute Inc, Cary, North Carolina) was used for all analyses.
DISCUSSION This study highlighted the change in the primary gustatory area 1 h after food intake compared with the fasting state: GEP latencies in response to sucrose stimulation lengthened after lunch even though the meal did not contain sweet food. A similar, though non-significant trend, was present FIGURE 6 | Comparisons of gustatory-evoked potential (GEP) latencies between the fasting and feeding sessions. GEPs were recorded in response to sucrose stimulation in the 15 healthy volunteers. Mean differences in GEP latency between data obtained in the morning and afternoon recordings were compared between the feeding and fasting sessions. The analyses were performed using repeated measures ANOVA (with a single factor: feeding or fasting session). A p-value below 0. 01 was considered statistically significant (Bonferroni correction).
Pz, Cz, Fz, Fp1, and Fp2 are the locations of the electrodes on the scalp where GEPs were recorded. Pz, Cz, and Fz correspond to the primary gustatory cortex; Fp1 and Fp2 correspond to the secondary gustatory cortex. Results are expressed as the mean ± SEM. **p < 0. 01; ***p < 0. 001. in the secondary taste cortex. In contrast, no decrease in GEP amplitudes was noted according to the prandial state. Some discrepancies should be noted in our findings on cortical changes in the primary gustatory cortex after ad libitum food intake when compare with data in the literature. In fact, neurophysiological recordings in monkeys and rodents and functional neuroimaging studies in humans showed decreased activation of the secondary taste cortex. The neuronal cortical activity was found to be modified in the orbitofrontal cortex  (Critchley and Rolls, 1996)  and not in the primary gustatory area  (Kringelbach et al.
Acknowledgements Funding was provided by  ANR  ( NATACS NR-07-NEUR-025-01 ,  SMIC ANR-12-BSV4-0027  and  NEUROWHISK ANR-14-CE24-0019 ) and by the  European Union   seventh Framework programme  under grant agreement no. 269921  (BrainScaleS). This work has also received support under the  programme Investissements d'Avenir , with the references  ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02   PSL* Research University. L. E. was partially funded by the  Humboldt foundation. We are grateful to  Valerie Ego-Stengel  for her careful reading of the manuscript.
Acknowledgements This work was supported by the  MRC  ( U117584268  to JPV), the  European Union  ( ERC grant  WNTEXPORT; 294523 to JPV), the  Swedish Cancer Society  ( 15/391  to RHP), the  Swedish Children Cancer Foundation  ( 2015-0096  to RHP), the  Swedish Research Council  ( 2015-04466  to RHP) and the  SSF Programme Grant  ( RB13-0204  to RHP). We thank the  Centre for Cellular Imaging (CCI) at the University of Gothenburg  for providing confocal imaging support. We are indebted to  Luis-Alberto Baena-Lopez  for devising the idea of tag switching, to  Cahir O'Kane  for providing the syb 144 allele, to  Hisashi Nojima  for help with generating Fig. 5 c and Supplementary Fig. 5 and  Yohanns Bellaiche  for discussion. We are also grateful to SWEDBO for organising the conference where our collaboration began.
Data analysis and statistical analysis To compare in vivo basal firing rates of neurons between groups, each cell was recorded for Ն120 s. In addition, for VTA DA neurons, burst analysis was performed. The onset of a burst was defined as the occurrence of two spikes with an interspike interval of Ͻ80 ms  (Grace and Bunney, 1984). We evaluated the amount of bursting activity by calculating the bursting rate (number of burst events per second) and the mean spike per burst. To analyze the effect of apamin on VTA dopaminergic activity, we compared the firing rate and the bursting rate of recorded VTA DA neurons 60 s before injection to 60 s after injection. A maximum of one infusion of apamin was performed in each hemisphere. For ex vivo firing rate comparisons, cells were recorded for Ն10 min to determine a stable firing rate.
F. Tuning curves in the population rate We define the tuning curve of neuron i in the population rate as the conditional probability of neuron i to spike given the summed activity of all neurons but i, K \i = j =i σ j. It is equal to: P (σ i =1|K \i ) = P (σ i =1, K \i ) P (σ i =0, K \i ) + P (σ i =1, K \i ) (7) where we can use P (σ i = 1, K \i ) = P (σ i = 1, K = K \i + 1) and P (σ i = 0, K \i ) = P (σ i = 1, K = K \i ). Each of these quantities can be computed using the solution to the direct problem (see Mathematical derivations). We then tested for each neuron if its tuning curve had significant local maxima. We first identified the set of K \i for which P (σ i = 1|K \i ) was significantly larger than points below and above K \i. To assess significance, we measured the standard deviation of the difference across 100 training sets consisting in random halves of the dataset.
G. Quality of the model Pairwise correlations. In order to measure the quality of the predictions of correlations between pairs of neurons σ i and σ j , we used cross-validation. We randomly divided the dataset into 100 training and testing sets half the size of data, and learned the model on the training sets. The correlations of each testing set, c test, ij were then predicted with the model c model, ij. The quality of the model prediction was measured by a goodness-of-fit index quantifying the amount of correlations predicted by the model. We define it as: C = i<j c 2 test,ij -i<j (c test,ij -c model, ij ) 2 i<j c 2 test,ij -i<j (c test,ij -c train,ij ) 2 , (9) where c train, ij is the correlation in the corresponding training set. The numerator of Eq. (  9 ) is the part of the correlations in the testing set that is predicted by the model, and the lower one is a normalization correcting for sampling noise.
45. Both quantities can readily be computed as derivatives of the normalization factor Z. For time efficiency, we only updated the Hessian every 100 iterations of the algorithm. We stopped the algorithm when the fitting error reached 10 -6. The fitting error was defined as the maximum error on P (K) and P (σ i ) for the minimal model, on P (K), P (σ i ) and Kσ i for the linear-coupling model, and on P (K) and P (σ i |K) for the complete coupling model. The code for the models inference is available at https://github. com/ChrisGll/MaxEnt_Model_ Population_Coupling.
The use and the benefit of finite-element analysis (FEA) is to be able to study stress and strain and to be able to take into contact interactions. One of the objectives of this study is to achieve a first FE simulation of grasping of deformable objects and the second one is to validate this model. A study of the literature provides experimental results. [7]  To compare our numerical results with experimental ones, the object is a rigid cylinder of 64 mm diameter. This process is illustrated as a flowchart in Figure  1. In this paper, the FEA is managed by an explicit scheme to cut down computational cost by using the software Radioss, Altair Hyperworks 11 #. This choice is justified by the highly nonlinear feature of the case, as it deals with the dynamic contact. The explicit resolution of contact problem is much more efficient than the implicit one because it is well adapted to speed and acceleration discontinuities when a sudden contact status change occurs.
As shown in Figure  1 , the starting point in the development of a biomechanical FE model is the three-dimensional reconstruction of the geometry. This geometrical representation is based on a three-dimensional reconstruction of CT scan images. A tomodensitometry examination of the hand was performed on an adult subject for medical purposes with no relation with the present study. No abnormalities of the hand were found by the medical staff. Using Scan2Mesh software (Altair HyperWorks #), a tool dedicated to the generation of a mesh from medical data, 300 slices separated by a thickness of 0. 7 mm were processed using an automatic detection method based on thresholding. Once the contours of the hand and of its components were detected, a reconstruction method leads three-dimensional surface meshed model.
Then, this mesh was imported in Hypermesh software (Altair #) for a step of surface reconstructions and converted into solid meshes. An anatomically detailed three-dimensional FE mesh of hand was developed, involving bones of the different phalanges of every fingers and wrist bones. Considering the anatomy of the hand, the mechanical joints (Synovial Capsule) were modelled at the junction of each phalanges. Finally, the meshed model of the hand consists of 20,000 8-nodes brick elements for the bone components (including carpal, meta-carpal and phalanges), 18,000 8-nodes brick elements for flesh around bone components, and 9000 4-nodes shell elements with 2 mm thickness for the skin, and 3400 4-nodes shell elements were also used to simulate the synovial capsule between phalanges, and between wrist bones.
We combined the neuroanatomists' approach of defining a fascicle as all fibers passing through its compact stem with diffusion-weighted tractography to investigate the cortical terminations of two association tracts, the inferior fronto-occipital fasciculus (IFOF) and the uncinate fasciculus (UF), which have recently been implicated in the ventral language circuitry. The aim was to provide a detailed and quantitative description of their terminations in 60 healthy subjects and to do so to apply an anatomical stem-based virtual dissection, mimicking classical post-mortem dissection, to extract with minimal a priori the IFOF and UF from tractography datasets. In both tracts, we consistently observed more extensive termination territories than their conventional definitions, within the middle and superior frontal, superior parietal and angular gyri for the IFOF and the middle frontal gyrus and superior, middle and inferior temporal gyri beyond the temporal pole for the UF.
, 2012)  and cingulate gyrus  (Ebeling and von Cramon, 1992; Thiebaut de Schotten et al. , 2012)  posteriorly and to the superior and middle frontal gyri, anteriorly  (Dejerine and Dejerine-Klumpke, 1895; Kier et al. , 2004; Thiebaut de Schotten et al. , 2012). A common understanding of the anatomy of these tracts is needed. In order to study these open questions of whether a particular cortical region belongs to the IFOF or UF, we adopted the approach of neuroanatomists who first locate the stem, which is the point of passage where all fibers pass through, as an anatomical reference to identify the tract. We therefore performed an anatomical stem-based virtual dissection to extract these two bundles from whole-brain tractography datasets in a large cohort of 60 healthy subjects. Starting from widely accepted anatomical evidence regarding the locations of the IFOF and UF stems, we exposed their respective stems and manually delineated single regions of interest (ROI) around each of them.
Image Acquisition Diffusion-weighted images were previously acquired for 60 healthy right-handed (30 female, mean age = 30. 1, age range = 20-53) belonging to the BIL&GIN database (Brain Imaging of Lateralization by the Groupe d'Imagerie Fonctionnelle;  Mazoyer et al. , 2016). All the subjects gave written informed consent to participate in the study, which was approved by the local ethics committee (CCPRB Basse-Normandie). Imaging was performed on a Philips Achieva 3 Tesla MRI scanner using a single-shot spin-echo echo-planar sequence with 21 non-collinear diffusion gradient directions (b = 1000 s/mm 2 ). Seventy axial slices parallel to the AC-PC plane were acquired from the bottom of the cerebellum to the vertex. Imaging parameters were as follows: TR = 8500 ms, TE = 81 ms, angle = 90 • , SENSE reduction factor = 2.
Whole-Brain Tractography The raw diffusion images were corrected for eddy current distortion using the FMRIB Software Library  (Smith et al. , 2004)  and processed with the Diffusion Toolkit software package to obtain the local tensor orientation estimates and fractional anisotropy maps and perform fiber tracking (Figure  2 , top row). Deterministic whole-brain fiber tracking was performed in the native space of each subject using the Fiber Assignment by Continuous Tracking algorithm  (Mori et al. , 1999)  with stopping criteria of 0. 2 fractional anisotropy and a 45 • angle threshold. Tracking was initiated by seeding from all voxels in the volume to generate the streamlines. This produced a 3D reconstruction of streamlines in the whole brain, namely a tractogram, which can then be segmented into anatomically defined bundles.
, 2010 (Martino et al. , , 2011;; Sarubbo et al. , 2013) , which exposes the IFOF and UF stems, collecting all the fibers belonging respectively to the IFOF and UF, after meticulous removal of the cortical and U-shaped fibers. Once these two stems were isolated, we were able to perform a quantitative analysis of their cortical terminations with minimal anatomical a priori. The software TrackVis  (Wang et al. , 2007)  was used to perform the virtual dissection using the streamline filtering tools to select streamline groups and to display simultaneously the streamlines and FA maps. A combination of in-house Matlab and TrackVis command line tools were used to prepare the tractograms and extract the tracts. An overview of the complete virtual dissection is shown in Figure  3. In the first step, we removed from the whole-brain tractogram (Figure  3A ): the extraneous streamlines shorter than 10 mm, the superficial streamlines restricted to the cortical GM region and the U-shaped streamlines passing through the SWM region.
Analysis of the IFOF and UF Cortical Terminations Once the IFOF and UF streamlines were extracted, we can describe within each subject their cortical termination territories based on macro-anatomical landmarks. Each streamline ending in one of 28 cortical regions of the JHU template was tallied to produce a measure of tract termination density for this region. The tallies were obtained using the UCLA Multimodal Connectivity Software Package  (Brown et al. , 2012 ). An individual normalized termination density score (NTDS) was obtained for each region by dividing the total number of tract streamlines ending in it by the total number of left and right tract streamlines for IFOF and UF, respectively. A first assessment was done to consider a cortical region as a termination territory of a given tract.
Background: Cognitive impairments are a core feature in schizophrenia and are linked to poor social functioning. Numerous studies have shown that cognitive remediation can enhance cognitive and functional abilities in patients with this pathology. The underlying mechanism of these behavioral improvements seems to be related to structural and functional changes in the brain. However, studies on neural correlates of such enhancement remain scarce. Objectives: We explored the neural correlates of cognitive enhancement following cognitive remediation interventions in schizophrenia and the differential effect between cognitive training and other therapeutic interventions or patients' usual care. Method: We searched MEDLINE, PsycInfo, and ScienceDirect databases for studies on cognitive remediation therapy in schizophrenia that used neuroimaging techniques and a randomized design.
Method We conducted a systematic search of online databases from May 2015 until July 2015. Databases included MEDLINE, PsycInfo, and ScienceDirect. Additional studies were identified through a manual reference search. In order to conduct a systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines  (Moher, Liberati, Tetzlaff, Altman, & The PRISMA Group, 2009).
Search and selection methods Search terms were selected to target our population (schizophrenia), intervention (CR, cognitive training, rehabilitation), and assessment technique (magnetic resonance imaging, diffusion tensor imaging [DTI], positron emission tomography, near infrared spectroscopy, electroencephalography  [EEG] , magnetoencephalography  [MEG] ) of interest (see Appendix for search method for the MEDLINE database). After database searching, reports where scanned according to their authors, title, and Digital Object Identifier (DOI) if available, in order to remove duplicates. Reports were then screened by one of the authors (CI) on the basis of their title and abstract and were excluded if they clearly didn't meet the eligibility criteria cited above. Remaining reports were screened by the same author (CI) on the basis of their full text, using a form to identify precisely if they met eligibility criteria.
Data collection process For each included study, a form was completed to obtain information on study design, population, intervention, assessments, results, and risk of bias. Risk of bias was assessed by adapting Cochrane risk of bias tool  (Higgins, Altman, & Sterne, 2011)  and including it in our form. We contacted three authors for further information. All responded and provided details on inclusion criteria, study design, and risk of biases. Because of the heterogeneity of methodology, population, intervention, and assessment, we've chosen a qualitative description of the included studies rather than a quantitative synthesis.
Studies selection The search on MEDLINE, PsycInfo, and ScienceDirect databases provided 279 citations. Two hundred fifty-four reports remained after removing duplicate citations. We excluded 225 reports after screening their title and abstract because they clearly didn't meet our eligibility criteria: 32 reports had a different population, 161 of the remaining reports didn't propose a CR program, 21 of the remaining reports were not peer-reviewed or didn't present study results, and 11 of the remaining reports didn't use imaging techniques. Of the 29 remaining reports reviewed on the basis of their full text, 11 were excluded because they didn't meet our criteria for intervention (five reports), study design (five reports), or assessment (one report). An additional report was found through online manual search.
Conclusion Our objective was to assess the effect of CR programs on neural processes. We selected 15 reports including 19 randomized controlled studies on 455 adult patients suffering from a schizophrenia spectrum disorder. The main limitations of this systematic review are the discrepancy in control groups as well as the small sample in several studies. Other limitations include possible risks of bias due to a partial randomization for two studies; a partial or absent blinding in the majority of the studies; a selective reporting of correlation between neurobiological and behavioral results in five reports; data substitution in three reports; patients receiving financial compensation for participation in eight reports; and financial conflict of interest in four reports. Furthermore, reporting bias may have occurred during study selection.