dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Authors would like to acknowledge the help of the following  Institut Pasteur  facilities:  Zebrafish Projects Hub ,  Photonic BioImaging platfrom, Biomaterials and Microfluidics platform , Image Analysis Hub, Bioinformatics and Biostatistics Hub. Authors thank  Frédéric Rosa  for his kind advice and help with the assembly of pXT7-lyn-mKATE-E2Aphb-EGFP plasmid. Authors would like to thank all members of the  Zurzolo group  for fruitful discussions and  Reine Bouyssie , a member of the administrative staff of the  Membrane Traffic and Pathogenesis Unit , for her continuous support. Authors would like to thank  Janelia Research Campus Advanced Imaging Center  members  Teng-Leong Chew  and  Chad Hobson  for valuable insights on the project. This work was supported by grants  Equipe FRM  # EQU202103012692  and  ANR-20-CE13-0032-01  to  Chiara Zurzolo.
Additionally, mean field variational inference assumes that model parameters are independent, preventing the ability to capture biologically meaningful parameter interactions. In recent years, simulation based inference (SBI) has been proposed as an alternative Bayesian inference framework to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function by leveraging advances in deep learning to perform density estimation  [16] [17] [18]. An advantage of SBI is that it only relies on a dataset of simulations from the model being investigated, rather than requiring knowledge of a parameter likelihood function, which is typically not accessible in large-scale biophysically detailed models. From this dataset, a neural density estimator (i.
g. time series waveform). In mathematical terms, this goal is stated as follows. Given an observation x and model with parameters θ, SBI seeks to create an approximation Φ(θ | x) of a posterior parameter distribution p(θ | x) such that Φ(θ | x) ≈ p(θ | x) ∝ p(x | θ) p(θ). (4) Bayes' rule specifies a closed form for the desired posterior distribution p(θ | x) as being proportional to the likelihood p(x | θ) multiplied with the prior p(θ)  [37]. Unfortunately, in detailed biophysical models the likelihood function p(x | θ), which encodes the relationship between model parameters θ and outputs x, is often analytically intractable but can be approximated from a large number of simulations. The novelty of SBI is that it circumvents likelihood evaluations altogether, and instead approximates the posterior distribution directly from a simulated dataset of model outputs x i ∼ p(x | θ i ) with parameter values sampled from a user defined prior distribution θ i ∼ p(θ).
This constitutes the prior distribution p(θ) over model parameters θ to be inferred. The importance of a well-constructed prior cannot be understated, as it encodes the assumptions and hypotheses of the inference problem considered, and strongly impacts any resulting predictions. Creating a good prior distribution requires domain expertise to choose meaningful parameters, and a biologically realistic range of values. That is not to say that the predictions are predetermined by the prior, as uncertainty can be encoded using flat/uninformative priors where the probability mass is evenly spread over the desired parameter range. Nevertheless this aspect is highly important for detailed neural models where the inferred parameters represent a small subset of the total set of parameters. With the prior constructed, a simulated dataset of observations x 1:N is generated by simulating a large number of N time series using model parameters θ 1:N drawn from the prior parameter distribution.
Steps 3-4: Training neural network based on chosen summary statistics to describe the time series waveform. With the simulated dataset, a specialized deep learning architecture known as a conditional neural density estimator Φ is trained to approximate the posterior distribution p(θ|x) that can account for chosen summary statistics for any observation x. The output is a distribution of parameters which can generate simulations close to the summary statistics of the conditioning observation x 0. The neural density estimator Φ can be trained directly from the entire time series x, or from summary statistics s = S(x) which constitute a lower dimensional vector of values. The choice of s should aim to obtain Φ(θ | s) ≈ p(θ | s) ≈ p(θ | x), meaning that the posterior estimates are well enough approximated from s alone.
This work does not aim to be an exhaustive guide to inference in HNN, nor to focus on specific neuroscientific questions, but instead to provide useful examples and methods to highlight critical decisions in the inference pipeline. There are several major takeaways from our study. First, highly non-linear biophysically detailed neural models like HNN are not suitable for Bayesian estimation methods that require access to a likelihood function (e. g. variational inference) or that approximate posterior distributions with independent Gaussians (a. k. a Laplace approximation). Rather they necessitate a method that can estimate complex posterior distributions and parameter interactions from a simulated dataset (e. g. SBI with masked autoregressive flows). Second, an important initial step in the SBI process is to identify a limited set of parameters and a range of values for those parameters that are assumed to be able to generate the waveform of interest and variation around it (i.
Acknowledgments Simulations were implemented using HNN-core, a command line interface to the HNN model: https://github. com/jonescompneurolab/hnn-core Experiments were made possible thanks to the Python scientific ecosystem: Python [55], SBI [56], PyTorch [57], NumPy [58], SciPy [59], Matplotlib [60], Seaborn  [61], and Dask [62]. This work was supported by the  Chateaubriand Fellowship , computing resources offered by the  Neuroscience Gateway , and the following grants:  ANR-20-CHIA-0016  from  Agence nationale de la recherche  (ANR) https://anr. fr/ awarded to A. G. ;  R01AG076227 ,  RF1MH130415 ,  U24EB029005 , from the  National Institute of Health (NIH)  https://www. nih. gov/ awarded to S. R. J. (first two) and the NSG team;  1935749  and  1935771  from the  National Science Foundation (NSF)  https://www.
We decoded population responses to the same-azimuth trials by fitting models with complete datasets covering all azimuths, and compared the median cross-validated prediction errors across mice as a function of azimuth (Suppl. Fig. 2B, C ). We observed that, under the experimental design used by us and others  (Day and Delgutte, 2013, 2016; Panniello et al. , 2018)  , the decoding showed different error distributions as a function of stimulus azimuth, as predicted by chance (Suppl. Fig. 2B ), with a minimum median error for 0º azimuth trials (Suppl. Fig. 2C ), which is also the reference azimuth used in the binary discrimination study  (Lauer et al. , 2011). Considering this observation, we compared the decoding performances of MLD and MCD models for 0º trials to determine the minimum decoding error achievable with these models, by fitting them to complete datasets covering all azimuths, respectively.
2 dB R. M. S. SPL and a 20 kHz pure tone at 77. 2 or 94. 9 dB R. M. S. SPL, 500ms in total duration including 10 ms up and down ramps. Sound stimuli were delivered through an electrostatic speaker (Tucker Davis Technologies, ES1 coupled to a ED1 speaker driver) mounted on an arm at an 8. 5 cm distance away from the head of the mouse, which was mounted on a NEMA17 bipolar stepper motor (26 Ncm, 1. 8 deg/step) placed under the head of the mouse to position the speaker around the frontal hemifield. The motor was controlled using an Arduino UNO microcontroller, running GRBL v0. 9 (G-code interpreter, https://github. com/grbl/grbl) and coupled with the Arduino CNC Shield V3. 10 (https://blog. protoneer. co. nz/arduino-cnc-controller/) running A4988 motor drivers. To control and program speaker positioning, we used the universal G-code sender (https://winder.
github. io/ugs_website/). Synchronisation of speaker positioning and sound stimulus delivery to data acquisition was done through a second Arduino UNO board receiving a scan start TTL from the microscope to trigger stimulation programs. 500 ms audio stimuli (including 10 ms up and down ramps) were synthesised using MATLAB (0. 5 MHz sampling rate). Sound delivery was performed using a DAQ board (National Instruments, PXIe-6341) interfaced to MATLAB using the DAQmx wrapper from scanimage (https://vidriotechnologies. com/scanimage/). A typical stimulation protocol consisted of a series of presentations of a sound stimulus in which azimuth angles varied randomly from 13 different frontal positions (frontal 180°split into 15°steps). Stimuli were presented every 5 seconds and each azimuth angle was presented on average 14 times per session.
Minimum number of same-azimuth trials collected was 8. -Ca 2+ -imaging signal analysis: Volumetric imaging data was visualised and rendered using FIJI  (Schindelin et al. , 2012). Motion correction was performed using NoRMCorre  (Pnevmatikakis and Giovannucci, 2017)  on individual imaging planes from the volumetric datasets. jRGECO1a signal was extracted, denoised and neuropil corrected from each individual imaging plane using CaImAn  (Giovannucci et al. , 2019). Spike probabilities were estimated using MLSpike  (Deneux et al. , 2016). -Statistical analyses: Statistical analyses were performed using MATLAB functions and custom scripts. For hypothesis testing, a p value < 0. 05 was considered significant. nS/N ratios were evaluated as the inverse of the coefficient of variation of the recordings.
Acknowledgements We thank  David Dahmen  for his support with the dimensionality analysis and modelling. We thank  Simon Essink  for useful feedback. This project received funding from the  DFG Priority Program  ( SPP 2041 "Computational Connectomics" ) [S. J. van Albada:  AL 2041/1-1 ]; the  EU 's  Horizon 2020 Framework Grant  Agreement No. 945539  ( Human Brain Project SGA3 ); the  DFG  ( RTG 2416  " MultiSenses-MultiScales "); and the  CNRS Multidisciplinary Exploratory Projects initiative.
In this study, we aimed to evaluate in Italian families whether MTNR1B variants are in linkage to and/or linkage disequilibrium (LD, i. e. , association) with familial MDD, T2D, and/or MDD-T2D comorbidity. We accessed the fully deidentified data of 212 families, descended from at least three Italian generations with T2D and family history of T2D. For T2D diagnosis, we used the National Diabetes Data Group Criteria, for which T2D can be diagnosed by the presence of the classical signs and/or symptoms of diabetes with unequivocally elevated hyperglycemia, or by fasting plasma glucose !140 mg/dL. Upon subsequentially updating the T2D status of initially unaffected subjects, we used the new American Diabetes Association criteria with the cut-off of at least two measurements of fasting glycemia at 126 mg/dL or higher, or random glycemia of at least 200 mg/dL or higher with symptoms, or at least 200 mg/dL or higher 2 h after an oral glucose tolerance test of 75 mg.
The EZ might comprise of multiple functional components and not necessarily all of them would engage in seizures  [14]. Hence, we asked whether the current dataset contained brain areas bearing the pathological features similar to EpiNet but did not engage in seizures, i. e. , the pathological-like NonEpiNet. To answer this question, we adopted an unsupervised classification approach. We first estimated all-to-all feature similarity between contacts of the whole cohort and then conducted clustering analyses of this contact similarity matrix (Supplementary methods). Thereby, contacts belonging to the same cluster are more similar with each other than with the contacts from other clusters. We examined whether there were well-defined clusters in the similarity matrix and found that through a range of partitioning resolutions, three clusters remained stable in their constituent contacts and were representative of the whole cohort (Supplementary Fig  8 a-b ).
Acknowledgements 7 Funding This project was funded by: the Academy of Finland grant (SA 253130 and 296304) awarded to JMP; Sigrid Jusélius Foundation grant awarded to SP and JMP; NEXTGENERATIONEU (NGEU) and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) -A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11. 10. 2022) awarded to LN; the theoretical work was partially funded the Ella and Georg Ehrnrooth Foundation awarded to SHW (14-10553-2); the analytical tools development, data analyses, and publication preparation have been been supported by the Finnish Cultural Foundation postdoc fellowship (00220071) and the Sigrid Jusélius Foundation fellowship (210527) granted to SHW.
We demonstrated, using two independent datasets, that EEG and fMRI connectivity is correlated for both healthy subjects and patients. The increased correlation of EEG and fMRI connectivity in rTLE patients vs. controls and decreased correlation in lTLE patients vs. controls suggests a differential organization of mono-lateral focal epilepsy of the same type, which needs to be considered when comparing fMRI to EEG connectivity. It also demonstrates that each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy. The observed property of distinct topological patterns depending on the lateralization of the epilepsy could be taken into account when clinically defining the epileptic focus of patients.
Participants and EEG-fMRI data acquisition We included patients with drug resistant focal temporal lobe epilepsy with clear unilateral epileptic focus (clinically defined by combined information from imaging, interictal epileptiform discharges (IEDs) and seizure onset) alongside with healthy controls. To do so, we retrospectively used data from two independent centers using a 256-channel EEG setup in a 3T scanner (dataset will be referenced as 256Ch-3T) and a 64-channel EEG setup in a 3T scanner (dataset 64Ch-3T). We included data of restingstate concurrent EEG-fMRI acquisitions in a total of 35 healthy controls (64-3T: 14 and 256-3T: 21) and a total 34 patients diagnosed with drug-resistant epilepsy of the temporal lobe (TLE, 64-3T: n=11 and 256-3T n=23 / distribution of left and right TLE: rTLE n=18 and lTLE n=16).
256Ch-3T: 21 healthy subjects (7 females, mean age: 32, age range 24-47) with no history of neurological or psychiatric illness and 23 TLE patients (14 females, mean age: 34, age range 18-60, 13 lTLE and 10 rTLE) were recorded. Ethical approval was given by the local Research Ethics Committee (Commission Cantonale d'Ethique, Genève) and informed consent was obtained from all subjects. The control group has been previously analyzed in  (Wirsich et al. , 2021). For patient and control group a variable time period of resting-state simultaneous EEG-fMRI data were acquired. In order to have a consistent recording length within the dataset we only analyzed the first 4min58s of each dataset (see SI Table  1 ). Subjects were asked not to move, to remain awake and keep their eyes closed during the resting-state scan.
3-70 Hz. Both datasets: A trained neurologist (L. S. ) visually inspected all EEG data to mark interictal epileptiform discharges (IEDs), IED segments were not removed but were used as a covariable in our analysis. Cleaned EEG data was imported and analyzed with Brainstorm software  (Tadel et al. , 2011)  256Ch-3T (the following steps where already carried out in the Brain Vision Analyzer software for 256Ch-3T data): Data was bandpass-filtered at 0. 3-70 Hz. Data was segmented according to one TR of the fMRI acquisition (TR=1980-2000ms, see SI Table  1 ). In order to minimize effect of head motion EEG epochs containing motion were automatically detected if the signal in any channel exceeded the mean channel timecourse by 4 standard deviations. Then the whole timecourse was also visually inspected to exclude all motion segments from further analysis  (Wirsich et al.
, 2021). Both datasets: Channels that remained artefactual were removed from the analysis (without interpolation). Electrode positions and T1 were coregistered by manually aligning the electrode positions onto the electrode artifacts visible in the T1 image. A forward model of the skull was calculated based on the individual T1 image of each subject using the OpenMEEG BEM model,  (Gramfort et al. , 2010; Kybic et al. , 2005). The EEG signal was re-referenced to the global average and projected into source space (15,000 solution points on the cortical surface) using the Tikhonov-regularized minimum norm  (Baillet et al. , 2001)  with the Tikhonov parameter set to 10% (Brainstorm 2018 implementation, with default parameters: assumed SNR ratio 3. 0, using current density maps, constrained sources normal to cortex with signs flipped into one direction, depth weighting 0.
Connectivity Analysis Split-half and cross-dataset spatial correlation Spatial similarity of monomodal FC was assessed by correlating the split-half averages of the upper triangular of the connectivity matrix of each dataset and group. To do so, each group was randomly split into two equally sized datasets and the correlation between the two split-averaged matrices was calculated for multiple iterations. We report the correlation averaged over each split-half iteration (5000 iterations or in the case of group sizes n<16 we calculated all possible combinations to split the dataset into two parts). As those split-half correlations depend on the group size, the results should be only used to qualitatively assess the data and compare them to the results of  Wirsich et al. (2021) , but not to assess differences between controls and patients.
Network based statistics of monomodal measures With the goal to better understand if the EEG and fMRI connectomes are altered across groups due to local and monomodal shifts of connectivity we used network-based statistics  (Zalesky et al. , 2010)  on each modality. For FC EEG this was done for each frequency band. In detail we built 6 linear models with FC fMRI (Fisher z-transformed), FC EEG-δ , FC EEG-θ , FC EEG-α , FC EEG-β and FC EEG-γ as response variables, group label as regressor of interest and age, sex and dataset site as regressor of non-interest. We tested for monomodal network changes between controls and patients by applying the following contrasts: controls>rTLE, controls>lTLE, controls<rTLE and controls<lTLE (one-sided t-test, connection level threshold T=2, NBS-corrected threshold adapted to 6 models p<0.
Crossmodal spatial EEG-fMRI connectivity correlation Crossmodal spatial correlations between FC EEG and FC fMRI of each group-averaged connectivity matrices were calculated. To test if the crossmodal correlation of rTLE and lTLE patients was different to the one of healthy controls, we built a distribution of 5000 averaged matrices by randomly switching the group labels  (Wirsich et al. , 2016). Previously, we demonstrated in healthy controls that the spatial relationship of EEG-fMRI connectivity can be robustly extracted when averaging around 7-12 subjects  (Wirsich et al. , 2021). This excellent reproducibility of averaged resting state recordings was also recently demonstrated on large fMRI datasets (n>1000, r>0. 9 for average connectomes with n>10, see supplementary Figure  17  within  (Marek et al.
Monomodal split-half and cross-dataset correlation Intra-group monomodal consistency of the subject groups (split by dataset-site and control, lTLE and rTLE patient group) was accessed by randomly splitting the dataset into two equally sized parts (5000 iterations or all combinations in case the number of subjects in the group was n<16) and spatially correlating the FC EEG and FC fMRI matrices. The split-half correlation of FC fMRI ranged from r=0. 88 (controls, dataset 64Ch3T) to r=0. 62 (rTLE patients, dataset 256Ch3T). The FC EEG split-half correlation ranged from r=0. 82 (FC EEG-β , controls, dataset 256Ch3T) to r=0. 28 (FC EEG-γ , rTLE patients, dataset 64Ch3T, for all results see SI Table  2 ).
EEG-fMRI correlation In line with  Wirsich et al. (2021 Wirsich et al. ( , 2017) )  healthy controls moderately (r~0. 3-0. 4) correlated in the 256Ch-3T and 64Ch-3T dataset. EEG-fMRI correlation was also moderately correlated (r~0. 3-0. 4) in both patient groups (Fig 2, SI Table  3 ). When combining all subjects to a grand average (patient and control group) the multimodal correlation peaks at r~0. 40 (except γ: r=0. 33, see 1 st row of SI Table  3 ) as observed in  Wirsich et al. (2021). As we have previously demonstrated, adding more healthy controls will generally increases the correlation  (Wirsich et al. , 2021)  the higher correlation of rTLE (n=17) compared to all pooled subjects (n=35) makes the possibility that the result is driven by a random higher SNR of rTLE patients very unlikely (see S Table  3 ).
Bootstrapping the group averaged FC fMRI -FC EEG correlation Using bootstrapped group averages in a linear model in order to analyze how different resamplingiterations with replacements change the average EEG-fMRI correlation we observed that: 1) in a model including controls and rTLE patients (controlling for age, sex and dataset-site): FC fMRI -FC EEG-δ /-FC EEG-θ /-FC EEG-α /-FC EEG-β correlation was significantly increased for rTLE patients as compared to healthy controls (p<0. 05/5=0. 01, Bonferroni corrected). 2) in a model including controls and lTLE patients (controlling for age, sex and dataset-site): 3) in a model including lTLE and rTLE patients (controlling for age, sex and dataset-site, epilepsy duration, existence of hippocampal sclerosis and spikes/minute), we observed a significant increase of FC fMRI -FC EEG-δ /-FC EEG-θ /-FC EEG-α /-FC EEG-β correlation when comparing rTLE to lTLE patients (rTLE>lTLE, p<0.
Discussion This study based on simultaneously recorded EEG and fMRI functional connectivity in patients with temporal lobe epilepsy and healthy controls in two independent datasets, characterisedhow a wholebrain network approach in epilepsy relates between both modalities. We replicated the moderate relationship between whole-brain FC fMRI and FC EEG in healthy controls (Wirsich et al. , 2021) and we confirmed for the first time that this relationship also exists in patients with epilepsy. While networks of rTLE patients show a widespread change of the relationship for the lower EEG frequency bands, the networks of lTLE patients have a global relationship of EEG and fMRI connectivity more similar to controls. Nevertheless, alterations between lTLE patients and healthy controls were observed locally (in particular the DMN) and were linked to FC EEG-β.
Monomodal relationship For both datasets and in line with our previous research  (Wirsich et al. , 2021)  we showed that monomodal intragroup correlation was high (FC fMRI ) to moderate (FC EEG-γ ). When comparing connection- wise differences in networks between patients and controls we were unable to observe any significant differences between controls vs. lTLE patients and controls vs. rTLE patients. This is opposed to our previous findings in rTLE  (Wirsich et al. , 2016) , where we observed FC fMRI differences in rTLE patients vs. controls (though using a high resolution 512 regions atlas as opposed to the low-resolution atlas of 68 regions used in this study). The difficulty of identifying a consistent localized network across patients reflects the general heterogeneity of network neuroscience literature in epilepsy which can be very sensitive to individual methodological choices analyzing resting-state connectivity  (Centeno and Carmichael, 2014; Slinger et al.
Consequently, the relationship of FC fMRI and FC EEG might provide a potential additional clinical marker to determine lateralization  (Douw et al. , 2019; Sadaghiani and Wirsich, 2020). Our results are encouraging as they generalize across two datasets and future work should validate if the EEG-fMRI can be clinically used to determine if a patient has a lateralization of epilepsy in the left or right hemisphere.