dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlation analysis of electrophysiology data Raster plots were created by detecting threshold crossings using Neuromatic in Igor Pro. Inter-spike intervals (ISIs) were calculated by differentiating the spike timings of the rasters. We considered the median as the parameter which best reflected the central tendency of the ISI distributions and used it to describe the most regular spiking behavior of the cells. Then, we defined pauses as quiescent times which lasted more than 3 times the median (in other words, missing at least two regular action potentials), in order to avoid detecting irregular spiking patterns caused by jittered or misfired spikes. Windows of one median ISI were then attached to the beginning and end of pauses in individual recordings (Fig. 5F  upper inset) and when these windows overlapped in simultaneous recordings they were considered to be synchrounous transitions (purple in Fig.
Acknowledgements This work was funded by the  CNRS. We would like to thank the members of the  Brain Physiology Lab  ( CNRS unit UMR8118 ) for their comments on the manuscript and/or helpful discussions throughout the project, in particular Michael Graupner who translated some of our fitting routines from  Igor into Python. We would also like to thank  Boris Barbour  for suggesting the method used for determining significance of correlations between Purkinje cells, for comments on the manuscript, and discussions throughout the project.
The space between the edge of the Pétri dish and the agar was filled with ddH 2 O to avoid escaping larvae. A single larva was placed on the middle of the agar. Recordings and analysis were performed automatically using EthoVision XT software (Noldus information technology, Wageningen, Netherlands). Dynamic subtraction function was chosen to distinguish the larva from the background. Data collection was started two minutes after the larva was placed on the agar plate. A ten-minute movie was recorded for each individual. Speed and meander behaviors were calculated using the same software, since these parameters reflect most of the locomotor activity characteristics of larvae in our behavioral set up. Lethality and Developmental time. Flies were allowed to lay eggs for 20 h. Approximately 3 × 100 embryos were transferred to fresh vials.
Acknowledgements We thank  Pr. Bernhard Hovemann ,  Dr. Johannes Bischof , and  Dr. Hermann Abele  for providing fly stocks. We thank  Serge Loquin  and  José Solonot  for their technical help. We also thank  Ingrid Adjovi  and  Isabelle Chauvel  for conducting preliminary experiments concerning larval locomotor activity and immunostaining in Y. G. 's laboratory. Research in Y. G. 's laboratory is supported by the  Centre National de la Recherche Scientifique , the  European Union  ( ERC Starting Grant ,  GliSFCo-311403 ), the  Agence Nationale de la Recherche  ( ANR-JCJC: GGCB-2010 ), the  Conseil Régional de Bourgogne (Faber) , and the  Université de Bourgogne Franche-Comté. Research in D. E. F. 's laboratory was supported by the  Muscular Dystrophy Association , and by the  National Institutes of Health, National Institute of Neurological Disorders and Stroke  (Grant Nr.
However, such data do not seem to exist. In neurophysiology experiments, behavior is generally shaped according to the hypothesized specialization of the tissue under study and the interests of the investigators. In the data analyzed here, the neuronal activity was recorded while the individual subjects were awake and actively engaged in behavioral tasks. We characterized these spike trains using the firing rate, firing regularity, and serial correlation of interspike intervals (ISIs). Despite the differences in behavior, the firing regularity (defined as the variability of ISIs) differed systematically across different cortical regions, and was related across species.
Neuronal spike data Spike trains were recorded from motor, visual, and prefrontal cortices, as well as hippocampus from mice, rats, cats, and monkeys of either sex. To see the degree to which firing characteristics depend on the recording methods and behavioral tasks, for each brain region, we grouped spike trains according to the experimental laboratories that obtained them. This provided 36 data sets: cortical motor areas of mice (1 data set), rats (2 data sets), and monkeys (11 data sets); visual areas of cats (1 data set) and monkeys (9 data sets); sensory (barrel) cortex of rats (1 data set); medial prefrontal cortex (mPFC) of rats (2 data sets) and monkeys (2 data sets); and hippocampus of mice (1 data set), rats (4 data sets), and monkeys (2 data sets; Table  1 ). For the data in which spike sorting was involved, we accepted the criteria for isolating spikes of a single neuron that have been used in respective experimental groups.
Possible origin of the difference in firing regularity across brain regions The fast-spiking interneurons are known to exhibit high firing rate rather regularly  (Connors and Gutnick, 1990) , and the difference in firing patterns across brain regions might be due to the difference in the population of the cell types. However, pyramidal neurons, the majority type in the cortex  (Hendry et al. , 1987; Abeles, 1991) , can also exhibit different firing patterns due to underlying network properties. Here we attempt to explore the origin of the difference in firing regularity across brain regions. To see the difference in firing characteristics between different cell types, we analyzed spike trains recorded from the hippocampus of rats (data set 7). In this data set, neurons were classified into putative interneurons and pyramidal neurons according to whether the duration of action potential is short or long  (Sirota et al.
Nevertheless, pyramidal neurons alone may exhibit different firing patterns in different cortical areas; we analyzed spike trains recorded from rat motor cortex (data set 1) and somatosensory (barrel) cortex (data set 5) recorded using a juxtacellular labeling technique, with which the recorded cells can be identified as pyramidal with morphological information. Firing regularity turned out to be significantly different between these cortical areas (t test, t (60) ϭ 7. 03, p Ͻ 10 Ϫ8 ; Fig. 8 B, C ). Firing patterns of pyramidal neurons in hippocampus, somatosensory cortex, and motor cortex are compared in Figure  8D , exhibiting systematic difference in firing regularity. This implies that the mean firing regularity in each brain region is mainly due to pyramidal neurons.
However, we were able to compare neuronal firings recorded from two brain regions of the same monkeys performing the same task. Overall, however, we collected data obtained under a variety of conditions from several different laboratories. Our single requirement was that the data be recordings taken from experimental groups using awake animals. There is long-standing evidence that there are systematic patterns of activity that characterize the waking state  (Klimesch, 1999). There is a concern that data taken from different laboratories might differ because of systematic differences in techniques across laboratories. These differences include the criteria for isolating spikes from single neurons and biases in the sampling of neurons, e. g. , sampling large (likely) versus small (less likely) neurons.
Figure3. Regionalspecificityofthreefiringcharacteristics(ratsandmonkeys). A,C,Distributionsofthefiringcharacteristicsofdifferentdatasets. Eachcirclerepresentsthemeananddispersion(asrepresentedbytheSDofthesampledpopulation)ofeachfiringmetricforaneuronalpopulation collectedbyanindividualexperimentalgroup,andthesurroundingellipserepresentsthetypicalstatisticalfluctuationduetorandomsamplingof10 neurons (75% confidence region). B, D, Hierarchical clustering of the data sets applied to the mean and dispersion of firing characteristics (see MaterialsandMethods). ThenumbersintheparenthesesrefertothedatasetsinTable1.
Figure3. Figure3. Regionalspecificityofthreefiringcharacteristics(ratsandmonkeys). A,C,Distributionsofthefiringcharacteristicsofdifferentdatasets. Eachcirclerepresentsthemeananddispersion(asrepresentedbytheSDofthesampledpopulation)ofeachfiringmetricforaneuronalpopulation collectedbyanindividualexperimentalgroup,andthesurroundingellipserepresentsthetypicalstatisticalfluctuationduetorandomsamplingof10 neurons (75% confidence region). B, D, Hierarchical clustering of the data sets applied to the mean and dispersion of firing characteristics (see MaterialsandMethods). ThenumbersintheparenthesesrefertothedatasetsinTable1.
Data Analysis The number of PH3-labeled mitotic cells was manually measured by the cell counter feature in Adobe Photoshop CS6. VZ cells lining the ventricular surface were counted within 1 mm. Apical and basal cleaved caspase-3-labeled cells were manually counted in the VZ lining the ventricular surface and in a region of interest (400 × 400 µm) drawn throughout the VZ/SVZ, respectively. The integrated density of labeling from BrdU, Ki67, and Dlx2 was measured in ImageJ 1. 47v software (NIH, Bethesda, MD, USA). Analyses of cell cycle parameters were performed according to  Chenn and Walsh (2002). For cell cycle length analysis, E12. 5 NKCC1 heterozygous mice were treated with BrdU for 30 min, and followed by co-labeling of BrdU together with Ki67 in brain sections. Percentage of S-phase cells labeled with BrdU, defined as the cell cycle length index, were determined by the integrated density of BrdU-labeling divided by the integrated density of Ki67-labeling (BrdUpositive/Ki67-positive labeling) in the LGE.
Decreased cell cycle length index suggests lengthened cell cycle. For cell cycle reentry analysis, E12. 5 NKCC1 heterozygous mice were treated with BrdU for 24 h, and followed by co-labeling of BrdU together with Ki67 in brain sections. Cells that left the cell cycle were identified as BrdU-positive and Ki67-negative, and cells that reentered the cell cycle were identified as BrdU-positive and Ki67-positive. Percentage of cells that reentered the cell cycle, defined as the cell cycle reentry index, was determined by the integrated density of Ki67-labeling divided by the integrated density of BrdU-labeling (Ki67-positive/BrdU-positive labeling) in the LGE. Data are presented as mean fold change ± standard error mean (SEM) relative to WT embryos. The length of labeling from TuJ1, Olig2, and Sp8 was measured in ImageJ 1.
47v. The length of the region containing either TuJ1-or Olig2-labeling was measured along a line perpendicular to the middle of the ventricular surface in the LGE of NKCC1 KO and WT mice. The length of the region containing Sp8-labeling was measured along a line extending from the SVZ of dorsal LGE of NKCC1 KO and WT mice. Data are presented as mean ± SEM relative to WT embryos. Statistical analyses were performed using the two-tailed Student's t-test with a value of p < 0. 05 considered as significant, in SigmaPlot software version 11. 0 (Systat Software, Erkrath, Germany).
The reduced morphology of the LGE was evaluated quantitatively by measuring the length of the total size of the LGE in nestin-labeled sections, and the mantle region containing TuJ1labeled cells. This analysis showed a significant reduction in the LGE (Figure  2C ), and in the mantle region of NKCC1 KO relative to WT mice (Figure  2F ). These data indicate that mice lacking NKCC1 exhibit smaller LGE than of the WT mice at E12. 5.
ACKNOWLEDGMENTS We thank  Dr. Guillaume Huet  for critically reading the manuscript,  Dr. Robert James Turner  and  Dr. Kazuaki Yoshikawa  for kindly providing α-wNT NKCC1 and Dlx2 antibodies, respectively. This work was supported by  doctoral program Brain and Mind (ACM) ,  University of Helsinki (CR) ,  Academy of Finland  ( SA 259799 , CR),  Sigrid Juselius Foundation (CR) , and  Agence Nationale de la Recherche (ANR)  ( TRAUMEP ANR 13-BSV4-0012-02 , CR). Part of this work appeared in a dissertation thesis by  ACM.
Quality assessment The risks of biases, ie, selection bias (random sequence generation and allocation concealment), performance bias (blinding of participants and personnel), detection bias (blinding of outcome assessment), attrition bias (incomplete outcome data addressed), and reporting bias (selective reporting), were assessed for each study according to the Cochrane Handbook. 22 he overall quality of evidence was assessed according to the Grading of Recommendations Assessment, Develop ment and Evaluation (GRADE) approach  23, 24  based on several factors: study design, study quality, consistency, directness, and reporting bias. Four levels of quality of evidence were defined according to the GRADE system (Table  2 ). 24 he publication biases were assessed by extensive search of unpublished data in the clinical trial register (funnel plot or Egger's tests were not performed due to the limited number of included studies).
Nonetheless, based on the previous limitations, this meta-analysis is unlikely to provide definitive answers to the use of pioglitazone as either an add-on to standard medications or a stand-alone treatment. Two other RCTs of pioglitazone are currently ongoing to assess pioglitazone for the treatment of bipolar depression, alone (NCT01717040, ClinicalTrials. gov) and as add-on therapy (2014-003803-31, clinicaltrialsregisters. ue). In these two ongoing registered double-blind RCTs, several biomarkers of insulin resistance and inflammation, BDNF levels and cognitive functioning will be assessed. These studies will enable to assess in larger samples the benefits of pioglitazone for MDE and to explore its mechanisms of action in patients with mood disorders.
search was conducted by two investigators (DDL and RC) on PubMed, Cochrane Central Register of Controlled Trials, ClinicalTrials. gov, and EU Clinical Trials Register with the following keywords: (pioglitazone) OR (thiazolidinedione) OR (PPAR-γ) [Title/Abstract] AND (depress*) [Title/Abstract] OR (bipolar) [Title/Abstract]. The search period comprised between January 1990 and August 2016.
A search was conducted by two investigators (DDL and RC) on PubMed, Cochrane Central Register of Controlled Trials, ClinicalTrials. gov, and EU Clinical Trials Register with the following keywords: (pioglitazone) OR (thiazolidinedione) OR (PPAR-γ) [Title/Abstract] AND (depress*) [Title/Abstract] OR (bipolar) [Title/Abstract]. The search period comprised between January 1990 and August 2016.
From the wavelet decomposition it is then possible to determine how the energy in the signal is spread over the different brain rhythms, and how this distribution of energy evolves over time. Precisely, this information is captured by modelling the signals associated each each brain rhythm with a GGD statistical model, whose parameters A and B are estimated from the observed data, see equation  (1). The shape parameter A is closely related to the variability of the brain activity and is therefore a good descriptor for performing SOD detection. We evaluated the performance of the proposed seizure onset detector and spread estimation algorithms by using the Children's Hospital Boston database  [45] , which consists of 36 EEG recordings from paediatric subjects with intractable seizures. In this work we used 30 crisis from 11 subjects.
Results The performance of the algorithm proposed for seizure onset detector and spread estimation was assessed using the Children's Hospital Boston database described above, and by comparing results with those obtained by visual inspection by an experimented neurologist relying on EEG and MRI data. For illustration figure  (8)  shows the EEG and MRI image for one patient; this information allows the reconstruction of a model of the brain and the determination of the location where the seizure was originated, as well as how is spread throughout the brain, which allows is the information that our algorithm seeks to estimate directly from the EEG. In this SOD example, we can see the A parameter in the x-axis, se Figures (1) to  (6) , which correspond to right temporal brain area. Our algorithm shows that the seizure starts in channel F8 and then spreads across the channels F8-T8-P8, F4-C4-P4, as is shown the data in table  (1)  and Figure  (7).
Acknowledgements Part of this work was funded by the  SuSTaIN program -EPSRC  grant  EP/D063485/1  -at the  Department of Mathematics, University of Bristol. Marcelo Pereyra  holds a  Marie Curie Intra-European Fellowship for Career Development. The authors acknowledges to  Gabriela Ugarnes , from  Fundación contra las Enfermedades Neurológicas Infantiles (FLENI) ,  Argentina.
, 2001). Parameter sets reproducing the cortical data were provided in the original publications of those models  (Pfister and Gerstner, 2006; Graupner and Brunel, 2012). However, the changes here, which consisted of including soft boundaries in the triplet-model and removing the bistable term in the calcium-based model, made refitting necessary. Our fits furthermore included the plasticity dataset obtained through jittered spike-pairs  (Sjo ¨stro ¨m et al. , 2001, their Fig. 8). The stimulation protocols used by Sjo ¨stro ¨m et al. (  2001 ) combine spike timing and firing rate components by varying the presentation frequency of regular and jittered spike-pairs. When spike-pairs are presented regularly (with fixed interpair intervals) with fixed ⌬, they find that pre-post pairs (⌬ ϭ 10 ms) induce no change at low presentations frequencies, whereas post-pre pairs (⌬ ϭ Ϫ10 ms) evoke LTD.
Data were collected from area MT of awake, actively fixating macaque monkeys that were seated 57 cm from the screen using single tungsten microelectrodes with an impedance of 0. 5-2 M⍀. Signals were sampled at 40 kHz, amplified, and processed using a Plexon MAP box. Visual stimulation during data collec- Calcium amplitudes and thresholds are scaled by d without loss of generality. The nonlinearity factors are 1 for the linear implementation (i. e. , linear summation of presynaptically and postsynaptically evoked calcium transients) and 2 for the nonlinear implementation  (Nevian and Sakmann, 2006). tion consisted of random dot kinematograms (RDKs) presented at full Michelson contrast (dot luminance was 42 cd/m 2 ) against a dark background (0. 1 cd/m 2 ). Dot density of the RDKs was 3 dots/degree 2.
RDKs were presented at a size of 10 degrees to fill the parafoveal receptive fields of the MT neurons with a dot lifetime of 200 ms and a diameter of 0. 1°for individual dots. RDKs moving in different directions around the circle were presented continuously, for 500 ms each, to determine the direction-tuning of the recorded neurons (for more details on animal training and physiological recording, see  Jazayeri et al. , 2012). Spikes were sorted into distinct clusters based on the information contained in their waveform shape and spike timing, along the principles outlined by  Lewicki (1998)  using the Plexon Offline Sorter 2. 8. 0 software. Doing so, we managed to distinguish signals from individual neurons from each other and from multiunit activity. Although it is widely recognized that there can be considerable drift in waveform shape, presumably because of physical movement between the electrode tip and the neurons, this drift usually occurs on long timescales.
Applying an independent Gaussian jitter of zero mean and SD of 80 ms eliminates short-time correlations but largely preserves firing rate correlations (see Figs. 2 H,J, 8B). A Gaussian jitter with a SD of 1 s abolishes short-time and greatly reduces firing rate correlations, in particular stimulus-induced firing rate variations (see Figs. 2 I,J, 8B). We generated 100 surrogate datasets per jitter and imposed them on the two neurons. The synaptic changes after jittering shown in Figure  8C -E are the averaged across 100 final synaptic weights using the jittered spike data. Also, the short-time-and firing rate correlation coefficients after jittering are the average coefficients from the 100 surrogate spike trains (Fig. 8B ). Synaptic plasticity dynamics for correlated neurons. The main aim of our study is to quantitatively compare the influence of firing rates and spike correlations on synaptic changes in irregularly firing neurons.
Natural firing patterns Our interest ultimately lies in understanding how naturally occurring firing patterns shape synaptic plasticity. We therefore used single-unit activities recorded simultaneously in cortical area MT of awake macaque monkeys to drive plasticity changes in models of synaptic plasticity. An important difference with the previous stimulation protocols is the nonstationary nature of the monkey data. Indeed, pairwise correlations occur on a range of time-scales in the data, and we investigated their respective influence on the change in synaptic strength. The spiking data are not recorded during a learning task per se, and we are not aware of any plasticity data from monkey area MT comparable with Sjo ¨stro ¨m et al. (2001) ; instead, we use the spiking as a proxy for highly dynamic activity potentially occurring during learning.
The calcium transient elicited by a presynaptic spike remains unchanged and has the amplitude C pre. To compare the results of the calcium-based model from the "nonlinear calcium dynamics" with the "linear calcium dynamics," we first fit the calcium-based model to the Sjo ¨stro ¨m et al. (  2001 ) datasets using the nonlinear calcium dynamics (Fig. 9 A, B ). Interestingly, the calcium-based model with the nonlinear calcium implementation provides the best fit of the regular-and jittered spike-pairs presented at different frequencies compared with the calcium-based model with linear calcium dynamics and the triplet-based model (compare Fig. 9  A, B with Fig. 1 A, B ). We then compared the STDP curves for regular-and irregular spike-pairs at different rates (Fig. 9D ). As before, we furthermore varied the firing rate for a given ⌬ and correlation between prespikes and postspikes to quantitatively compare the impact of firing rate changes and correlations on synaptic changes (Fig.
- 01A1 , and  E ´cole des Neurosciences Paris I ˆle-de-France post-doc Fellowship. This work was indirectly supportedbyNationalEyeInstituteGrantEY-13138toDavidC. Bradley,whichfundedthelaboratoryinwhichthevisualarea MT responses were collected. S. O. was supported by  ANR-11-0001-02   PSL ,  ANR-10-LABX-0087 , and the  City of Paris  through the program Emergences. We thank  Jesper Sjo  ¨stro ¨m for providing data;  Arnd Roth  for many helpful discussions (withM. G. )inanearlystageoftheproject; DavidC. Bradleyandhislaboratoryforgivingtheopportunity (toP. W.
The second genetic risk locus for all ischemic stroke (chr12q24. 12) was identified in a case-control dataset with over 17,000 ischemic stroke patients and found to be equally associated with all subtypes of ischemic stroke. The single nucleotide polymorphism (SNP) showing the most significant association is in linkage disequilibrium with a non-synonymous variant in SH2B3 and is associated with gene expression of ALDH2, pointing to a potential role of these two genes in the association  [33• ]. The same locus was previously associated with blood pressure and coronary artery disease  [47, 63, 64]. Two other recent studies have also described associations of variants near HABP2 and AQP9 to be associated with all ischemic stroke and all stroke, respectively  [40•, 41•]. HABP2, which encodes for an extracellular serine protease involved in coagulation, fibrinolysis, and inflammation, was identified through a young-onset stroke GWAS where only stroke cases with age <60 years were studied, and replicated in an independent dataset  [40• ].