dataesr/scientific-paragraphs-categorization huggingface dataset:
Following this procedure, for each patient dataset we obtained a map of region-specific iEEG responses to each stimulation event. Second, we averaged responses across stimulation events delivered to the same AAL atlas region at different stimulation intensities within each patient. This resulted in an accurate region-and patient-response estimate to single pulses of electrical stimulation. Third, we gathered in normalized brain atlas space, local regional-and intensity averaged-iEEG responses to electrical pulses delivered from all patients. As a result, we To analyze iEEG responses to stimulation in the frequency domain, we convoluted EEG datasets obtained from each AAL atlas region to a complex Morlet wavelet (see  16  for details on the procedure and its validation). The analyzed frequencies ranged from 5 to 80 Hz, with a linear increase in 1 Hz in steps.
Since due to the need to remove electrical pulse artifacts we interpolated 12 ms intervals of post-stimulus data, we had to exclude unreliable analyses of frequencies higher than 80 Hz. The time-varying energy maps, measured as the square of the absolute value of the Morlet coefficient (a. k. a. power), were computed for each trace from each region-specific iEEG dataset. We then calculated for each 1 Hz frequency bin of the frequential spectrum the percent change in power with respect to a baseline epoch, prior to the onset of the electrical pulse. The baseline period considered for spectral analyses was adapted to each frequency, hence set to a duration of 2 cycles (2 periods of oscillation) for each 1 Hz frequency bin. Moreover, since iEEG responses were recorded from different subjects and obtained under different stimulation intensities, time-varying power increases at each frequency bin were transformed to z-scores, obtaining a single time-frequency chart characterizing power increases in response to single electrical pulses for each AAL sampled region (See Figure  3B ).
Relationship between increase of power at the first cycle and on-going oscillations Finally, we tested whether absolute increases of power at the 'natural' frequency induced by individual electrical pulses were influenced by the phase of the ongoing oscillation at the time of the stimulation pulse. This was accomplished by measuring time differences between the latter and the 'up-state' (π/2 radians) phase of the oscillation (Figure  9A ). A significant correlation was found between absolute increases of power and the difference between the instantaneous phase and 'upstates' at electrical pulse onset (ρ = -0. 186; p <0. 0001). In order to confirm the statistical significance of this observation, we recalculated the correlation by bootstrapping 100000 samples (with replacement from the original dataset (n=1035)).
e. , imposed by pulsed electrical perturbations) and internal brain rhythms 1. In our study, we mapped the intrinsic 'natural' frequencies of specific cortical human brain areas using iEEG responses to single electrical pulses. Datasets were obtained from a cohort of implanted epilepsy patients during a clinically-guided mapping quest to identify seizing foci and carried out prior to considering neurosurgical removal of epileptogenic regions. Existing evidence on extrinsically driven modulation of brain oscillations in humans  10, 14  supports the notion that brief local perturbations causes the phase-resetting of ongoing EEG rhythms generated by local neural populations, acting as local rhythm generators or oscillators. Because of a progressive alignment in phase, the power of the predominant rhythms generated by such oscillators (the so called 'natural' rhythm) is increased.
We here hypothesized that such frequency-specific increases of power could serve as proxies of the oscillatory properties operating 'naturally' in a locally stimulated brain region. The results of our study support our ability to identify, map, and characterize the spatial distribution of site-specific spectral fingerprints associated to 'natural' oscillatory activity in highly discrete regions of the human brain. Moreover, this evidence could be likely used to infer information about the structural and neurophysiological features of the local circuits (i. e. , oscillators) generating such patterns and better understand their contributions to cerebral dynamics and cognitive processing. Equally important, our datasets compiled in an atlas of 'natural' spectral fingerprints may provide useful information to optimize the entrainment of local oscillatory activity with invasive (intracranial electrical stimulation) and noninvasive (TMS/tACS) neurostimulation technologies in exploratory, diagnostic or therapeutic applications.
Local spectral fingerprint features: diversity and level of complexity A final crucial aspect that deserves attention concerns the complexity and diversity of the local spectral fingerprints revealed by intracranial stimulation across cortical regions. Indeed, following a focal single pulse electrical perturbation, some regions (parcellated according to the AAL atlas) showed during the 1 st oscillation cycle a single unimodal spectral distribution of power enhancement, similar to those previously characterized non-invasively  13. Nonetheless other cortical areas revealed spectral signatures of higher complexity, made of several power peaks, often spanning across different frequency bands (Figure  5 ). The constrained scope of our study, based on clinically-guided mapping datasets from implanted patients limits at this time a more profound interpretation of site-specific structural and physiological substrates subtending these spectral fingerprints.
General methodological considerations We here benefited from the unique stimulation focality and signal-to-noise ratio of intracranial approaches to perturb and record rhythmic brain activity. Nonetheless, the use of iEEG data from epilepsy patients suffers some limitations. First, multielectrode implantation schemes and contact position differ greatly across individuals, hence a comprehensive map of 'natural' frequencies requires the integration of data from large cohorts of individually implanted patients. In our study, the combined implantation schemes of n=19 epilepsy patients achieved quite widespread anatomical coverage. Nonetheless due to the exquisite focality of intracranial stimulation and recording techniques (in the order of 5-10 mm), our current atlas of 'natural' frequencies remains sparse and needs to be further enriched by additional datasets, particularly from patients implanted in under-sampled non-temporal brain regions.
Second, to keep clinical mapping sessions within a reasonable duration and minimize the number of pulses and the total cumulated electrical charge undergone by patients, neurologists retest the impact of electrical stimulation using identical parameters (i. e. , same site, same pulse intensity and same burst frequency) at best, a few times for each pair of adjacent contacts. The scarce number of repetitions requires analyses at the single trial level and the averaging of datasets from the same or different patients, across stimulation intensities, and multi-electrode contacts hosted in different sites but laying within the same AAL regions (which in some cases can be very large). These approaches have been shown effective to cancel out spurious non-consistent spectral effects. Nonetheless, they also reduce the spatial resolution of the mapping effort and increase the risk of biases caused by high inter-individual variability, non-linear responses to increasing levels of electrical stimulation or architectural anisotropies within AAL atlas sites from which data end up clustered together.
The methodological reliability of this method, initially developed to process concurrent TMS-scalp EEG recordings  14  has been extended and validated for intracranial stimulation studies on "artificially" artifacted real and/or modeled EEG datasets, by showing that the oscillation power remains identical following artifact cleaning  16. Nonetheless, the removal of 12 ms of post-stimulus electrically artifacted data precludes reliable analyses of power increases at frequencies equal or higher than 80 Hz, since a complete cycle of real iEEG is eliminated by artifact cleaning procedures. Consequently, we can neither confirm nor rule out the presence of gamma components higher than 80 Hz in our spectral fingerprints.
Concluding remarks and future directions We here developed and tested a procedure based on direct electrical brain stimulation combined with intracranial EEG recordings of the human brain. We used such datasets to characterize and map the topographic distribution of local 'natural' rhythmic activity in the human brain. Our measurements could have likely capitalized on previously reported phase-resetting properties of focal single electrical perturbations, revealing the frequencies at which brain regions might most likely synchronize spontaneously. From a fundamental research perspective, by characterizing the complexity of site-specific 'natural' spectral fingerprints, our study has generated testable hypothesis on the anatomical and neurophysiological properties of local oscillators in the human brain.
Nonetheless, future intracranial datasets and analyses are needed to further enrich our understanding on why specific sets of local or distant brain regions might be more likely to get synchronized, why this might happen at specific frequency bands, and determine which sites share most similar or compatible spectral fingerprints, hence could be more likely to get coupled in phase or frequency. Moreover, by deepening our knowledge on how local oscillatory properties influence coding locally and across large-scale interregional systems, we will also be able to better understand the mechanism by which local and interregional synchrony enables specific cognitive processes and behaviors in the human brain. FIGURES Figure 1.
obtained 33 different datasets (one per each AAL atlas region for which we had stimulation data provided by at least a patient), corresponding to the local average of iEEG responses to each pulse delivered across our cohort of 18 patients. The averaging of physiological responses to intracranial stimulation between and across individuals is justified by 3 main reasons: First, in order to keep stimulation sessions short, the number of pulses delivered through each pair of contacts into a given site for each individual patients was rather scarce (5 single pulses for each stimulation event (i. e. contact pair, brain site and current intensity), determined by a standardized clinical protocol). Therefore, grouping datasets recorded by electrodes located within the same AAL region and considering each local iEEG response to individual stimulation pulses an independent event proved absolutely necessary to optimize signal-to-noise ratio.
Second, electrode implantation schemes were determined by a multidisciplinary team of clinical experts working in the field of epilepsy (neurologists, clinical neurophysiologists, neuroanatomist, and neurosurgeons), guided by clinical requirements considering the unique needs of each patient. Clustering datasets from contacts across different patients on the basis of AAL atlas parcellations allowed us to associate responses from different individuals to specific brain regions. Third, for ethical reasons, the selection of stimulation intensities used on a given patient, on a given site or in a given trial relied exclusively on clinical and neurophysiological criteria applied on a case-by-case basis by an expert epileptologist in charge of stimulation sessions, ignoring any non-clinical consideration.
ACKNOWLEDGEMENTS This article is dedicated to  Lena Amengual Rocamonde , who came to this world in the middle of the production of this article. Authors thank  Prof. V. Navarro ,  Dr. K. Lehongre ,  V. Lambrecq ,  Dr. S. Withmarsh ,  Dr. V. Frazzini  and the CENIR EPIMICRO platform for conceptual, logistic an analytical assistance and for critical comments at different phases during the development of this project. Special thanks also for  Dr. Mario Valderrama PhD , for allowing the use of custom-developed software to display electrode contact location data reported in this manuscript. We finally also thank  Naturalia & Biologia Foundation  for financial assistance for traveling and attendance to meetings. The activities of  Dr. Valero-Cabré  laboratory are supported by research grants  IHU-A-ICM-Translationnel ,  Agence National de la Recherche (ANR), projet Générique "OSCILOSCOPUS" , the  PHRC Regional "NEGLECT"  and the  PHRC Regional "STIM-SD " and eraNET-JTC-HBM CAUSALTOMICS.
nd , 3 rd , and 4 th ) oscillation cycles. Electrical stimulation induced power increases that achieved their maximum during the 1st cycle [t(32) > 9. 8, p<0. 001 for all the comparisons], and decayed progressively thereafter, extinguishing when oscillations reached the 3 rd cycle post electrical pulse [2 nd cycle vs. 3 rd cycle, t(32) = 10. 22, p<0. 001; 3 rd cycle vs. 4 th cycle, t(32) = 2. 9, p=0. 069] (Figure 8A). No significant interaction between factors 'Time interval' and 'Cerebral lobe' [F (4, 128) = 1. 73, p = 0. 16, ε = 0. 33] was found in our dataset, indicating that site-specific temporal dynamics of 'natural' frequency power enhancement was not determined by the brain lobe in which these phenomena were being probed. However, factors 'Frequency band' and 'Time interval' [F (4,128) = 1.
Third, we included a long post-adaptation phase, in which the target no longer undergoes an adaptation step. Indeed, it is still unclear whether the course of recovery matches the one of the Running head: Inter-individual variability and consistency of saccade adaptation in oblique saccades 8 original adaptation. Fourth, all data were collected by a single experimenter (one of the authors, SR) to limit a possible experimenter effect which might affect human behavior. The data collected in the present experiments permitted us to measure the extent of adaptation in naïve participants and quantify the inter-individual variability in the changes in saccade gain. We asked whether the individual extent of adaptation or the adaptation rate might be predicted based on the individual baseline saccade characteristics and their variability.
Subjects One hundred and thirteen subjects participated in the experiment but thirteen were discarded due to the low number of correct trials (see below). For each of our four experimental groups, which differed only by the orientation of the ISS during adaptation trials (see below), we randomly assigned 25 participants. Based on a review of the literature and using our own data previously collected in pilot experiments, we estimated that one could conservatively expect an effect size (Cohen d) of 1. 4, leading to a minimum sample size of 12 to reach a statistical power of 1. We chose to have 25 participants per experiments to provide a solid estimate of the extent of adaptation at the group level. As a comparison, the median number of participants is 7 in the 28 relevant papers cited in this article.
Shield1 stabilizes the probes, which are then imported into the mitochondrial matrix through the TOM and TIM complexes. Light emission in the cytoplasm is weak, due to the presence of the MTS, blocking the N-terminus of RGFP. Cleavage of the MTS by MPP allows interaction between the RGFP and RLuc modules, leading to a characteristic light emission due to resonance energy transfer and quantum yield enhancement in the presence of the Rluc substrate coelenterazine 400A (CLZ400A). The RGFP and Rluc protein modules (PDB ID: 2RH7  78  and PDB ID: 2PSD  78  ) were obtained from the Protein Data Bank 79 at the following URL: http://www. rcsb. org. and Supplementary Fig. 1D ). In all cases, the intensity of the signal was decreased by 40 to 60%, consistent with lower levels of import. Under these experimental conditions, the abundances of proteins representative of the various submitochondrial compartments remained similar, excluding more global effects on mitochondrial turnover (Supplementary Fig.
Fluorescence microscopy and bioluminescence assay. Images of probe expression and colocalization with mitochondrial dyes were obtained with an inverted spinning disk microscope from Till photonics fitted with an Olympus 100x oil immersion objective (N. A 1. 4). Probe induction was analyzed with ImageJ software, on fluorescence images from at least 15 cells per condition. For bioluminescence assays, cells were plated in black precoated 96-well plates (Greiner Bio-One), at a density of 25,000 (HEK293T) or 10,000 cells/well (primary fibroblasts). They were analyzed 48 h after transfection in Hank's Balanced Salt Solution (Invitrogen) containing 20 mM HEPES (Invitrogen), with an FDSS 7000 Functional Drug Screening System (Hamamatsu). A control condition with CCCP treatment was systematically included in each experiment.
Bioluminescence signals were collected every second for five minutes after the addition of CLZ400A to the cells, and normalized relative to the total RGFP fluorescence of the probe in each well, recorded at an excitation wavelength of 470 nm, and an emission wavelength of 510 nm. For Supplementary Fig. 2C , bioluminescence signals were also normalized relative to the intensity of MitoTracker Deep Red staining, captured with the Odyssey Imaging (Li-COR) system and quantified with Li-COR Image Studio. For Fig. 5D , bioluminescence signals were normalized relative to the intensity of anti-GFP immunostaining signal, measured with an Arrayscan XTi automated fluorescence microscope (ThermoScientific, Courtaboeuf, France). On graphical representations of bioluminescence peaks, each dot corresponds to the mean peak value for three to eight wells in a single independent experiment, determined as follows: for each well, the peak value was obtained as the mean of the bioluminescence signals recorded during the first 100 seconds normalized against fluorescence, corrected by subtracting the mean signal obtained for cells treated with CCCP (calculated from data for 3-8 wells).
Fluorescence/chemiluminescence signals were captured with the Odyssey Imaging (Li-COR) system, detected on a film (ECL, Amersham Hyperfilm) or with the Chemidoc touch Imaging System (Bio-Rad) and quantified with Li-COR Image Studio, Image Lab Software (Bio-Rad) or ImageJ software (NIH). The blots were cropped for improved clarity and full-length blots were included in the Supplementary Informations. Mitochondrial poly-ubiquitin capture and UB-AQUA/PRM proteomics. Mitochondrially-derived ubiquitylated proteins were purified using Halo-4xUBA UBQLN1 , as described  35. Briefly, crude mitochondrial extracts (100 μg) were incubated at 4 °C for 3 hours with 10 μl of Halo-4xUBA UBQLN1 beads (pack volume). Following 4 washes with lysis buffer containing 1 M NaCl and one final wash in 10 mM Tris pH 8.
Digests were acidified with an equal volume of 5% (vol/vol) formic acid (FA) to a pH of ~2 for 30 min, dried down, and resuspended in 1% (vol/vol) FA. Quantitative real-time RT-PCR. Total RNA was purified with the RNeasy plus Mini Kit (Qiagen) and reverse-transcribed (1 µg) to generate cDNA (iScript cDNA Synthesis, Bio-Rad). Real-time PCR was performed with the primers indicated in Supplementary Table  3 , SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) and the LightCycler 480 System (Roche Applied Science). Results were analyzed with LightCycler 480 sw 1. 5 quantification software. The data for each gene of interest were normalized relative to ACTB expression.
Acknowledgements We thank  S. Forlani  and the DNA and  Cell Bank  ( Hôpital Pitié-Salpêtrière, Paris, France ) for human fibroblast collection and banking, and the ICM iVector facility for generating and producing lentiviral particles. We also thank  Y. Yarden ,  N. Matsuda  and  B. Bingol  for providing vectors, as indicated in the Methods section. This work has received support from  Institut national de la santé et de la recherche médicale, Association France Parkinson ,  Fondation de France  ( Engt 2012 00034508 ),  Fondation ICM , " Investissements d' avenir "  ANR-10-IAIHU-06 ,  Institut de Recherches Servier/Les Laboratoires Servier , the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  821522  ( PD-MitoQUANT ; this Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA and Parkinson's UK) ,  NIH  ( R37 NS083524  to J.
Study sample Fourteen patients with AD (seven prodromal AD, seven AD dementia), and nine cognitively unimpaired individuals (THK-controls; five young, 20-30 years old, four elderly, 58-71 years old) who had previously participated in baseline investigations were included  [11, 17]. Follow-up [ 18 F]THK5317 and [ 18 F]FDG PET data were acquired in ten AD patients (six prodromal, four dementia) after a median of 17 months (interquartile range, IQR, 15-18 months)  [18]. Baseline cross-sectional [ 18 F]THK5317 and [ 18 F]FDG PET studies were performed within 1. 5 months (IQR 1-2. 7 months) and follow-up studies within 0. 5 months (IQR 0-1 months). CSF samples were collected approximately 24 months before the baseline [ 18 F]THK5317 PET studies (IQR 14-34. 5 months). Owing to outlying CSF tau values, one patient with AD dementia was only included in regression analyses involving CSF ratio measurements.
PET data acquisition and analysis Dynamic [ 18 F]THK5317 PET studies were performed over 60 min following intravenous bolus injection of 217 ± 42 MBq. A static 15 min [ 18 F]FDG scan was performed 30 min after injection of 3 MBq/kg. Baseline and follow-up images were coregistered to their respective T1weighted MR images using PMOD v. 3. 5 (PMOD Technologies. , Zurich, Switzerland). In order to account for possible spillover effects from the white matter, MRIbased partial volume correction was applied to [ 18 F]THK5317 PET images, as implemented in PMOD  [23]. Distribution volume ratio (DVR) images were calculated for [ 18 F]THK5317 using the cerebellar cortex as the reference tissue  [12, 24]. For [ 18 F]FDG  [18] , standardized uptake value ratio (SUVR) was used as the outcome measure using the pons as the reference region.
10KIN1DAY One type of event that fits well with this grass-roots collaboration philosophy are short gatherings of scientists around a single theme, bringing together expertise and tools to jointly analyze existing neuroscience data. We organized 10Kin1day, an MRI connectome event, with the goal to bring together an international group of researchers in the field of neuroimaging and consistently analyze MRI connectivity data of the human cerebrum. We organized the event around five founding principles: • use existing neuroimaging data, available from many research groups around the world; we focused on diffusion MRI data and aimed to bring together 10,000+ datasets • analyze data from varying cohorts and imaging protocols, using a single, straightforward analysis strategy to encourage across-group collaborations and multisite studies • perform all processing during a short workshop, with only basic expertise of analysis needed • provide education on how to analyze resulting connectome data, so participants can continue to work on their projects after the event • each participant analyzes their own data and is free to decide what to do with their analyzed results.
OPEN DATA In line with the collaborative nature of the event, the 10K group discussed making the connectome maps available to the scientific community for non-commercial use, free of restrictions. We include herein the resulting individual connectome maps of 8,000+ connectome datasets across an age range of 0-90 years, with five different edge weights [number of traced streamlines (NOS), streamline density (SD), fiber length, fractional anisotropy (FA), and mean diffusivity (MD)] at three parcellation resolutions (80+ cortical and subcortical regions, 100+ and 200+ cortical regions, see section Materials and Methods for details). Connectome maps are presented anonymously and blinded for participation site, together with basic demographics (age in bins of 5 years, gender, patient/control status, Figure  1 ).