dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Statistical analysis was performed in MATLAB 2021b. We analyzed all the comparisons among the I-self, the I-others and the I-diff values using permutation testing, where the labels of the two groups were randomly allocated 10,000 times. Thus, we obtained a null distribution of the randomly determined differences by computing the absolute value of the difference of the group averages at each iteration  (Nichols and Holmes, 2002). The relationship between variables was studied with Pearson's correlation and the results were corrected for multiple comparisons using the False Discovery Rate (FDR)  (Benjamini and Hochberg, 1995) , setting the significance level at p-value < 0. 05.
Such endeavour could be achieved, in principle, by using the Functionnectome  (30, 31) , a recently developed method that combines fMRI with tractography by projecting the grey matter BOLD signal onto white matter pathways. In the present study, we extended our previous approach -the Functionnectome methodology (30) -to RSNs, integrating the grey matter resting-state signal with white matter connections, and analysed the resulting data through ICA. In so doing, we produced the most comprehensive atlas of 30 RSNs specifying their grey matter maps together with their white matter circuitry -the WhiteRest atlas. This atlas unlocks the systematic exploration of white matter components supporting resting-state networks. The atlas comes with companion software, the WhiteRest tool, a module of the Functionnectome that will facilitate this exploration and assist the investigation of brain lesions' effects on RSNs and cognition.
Extraction of white matter and grey matter components Projection of the functional signal to the white matter In order to explore the white matter structures of resting-state networks, we projected the functional signal from the rs-fMRI scans onto the white matter using the Functionnectome (30, 31) (https://github. com/NotaCS/Functionnectome). The Functionnectome is a recently introduced method that unlocks the functional study of the white matter. Briefly, the Functionnectome takes an fMRI scan and a grey matter mask as inputs, combines grey matter BOLD signal with white matter anatomical priors, and outputs a new functional volume (called a functionnectome), with the same dimensions as the original fMRI scan (same 3D space, same number of time-points), but with the functional signal associated to the white matter.
In this functionnectome volume, the signal of a white matter voxel results from the combination of the BOLD signals from the voxels within the grey matter mask that are structurally connected to it (weighted by the probability of connection). The structural connectivity probability is given by the anatomical priors provided with software (customisable priors option available). Using the Functionnectome thus allows the analysis of the functional signal in a connectivity-oriented framework by integrating the signal from distant but structurally connected grey matter voxels or clusters of voxels. For our analysis, each of the 150 rs-fMRI scans from the dataset were processed with the Functionnectome, along with a grey matter mask (the same mask for all the subjects). This mask was computed using the brain parcellation data from all the participants: the mask corresponds to the voxels that were identified as part of the grey matter in at least 10% of the participants.
Visualisation The 3D z-maps presented in figure  1 , 2, 3, 4 & 5 were generated using Surf Ice (https://www. nitrc. org/projects/surfice/), with the default mni152_2009 brain volume as background template. The 2D brain slices of figure  5  & 6 were displayed on a standard template in MRIcron (https://www. nitrc. org/projects/mricron). Each white matter map was masked in order to remove the grey matter part of the volume to improve readability. The mask used corresponded to voxels defined as white matter in at least 10% of the 150 participants, according to the parcellation provided with the HCP datasets. In figure  5 , the RSN count was saturated at 14 on the displayed map to improve readability, as only a handful of voxels presented higher values. No. 818521; MTS) and the Marie Skłodowska-Curie programme (grant agreement No.
Data availability The WhiteRest atlas (non thresholded maps) is freely available on Neurovault. org, with both the grey matter maps (https://neurovault. org/collections/SBZVMFMK/) and the white matter maps (https://neurovault. org/collections/AFVTMGXJ/). The WhiteRest module is open-source and freely available as part of the Functionnectome software, which can be found at http://www. bcblab. com/BCB/Opendata. html or directly downloaded from https://github. com/NotaCS/Functionnectome.
Affinity measurements of anti-FLT3 antibody for human and murine FLT3 The surface plasmon resonance (SPR) experiments were performed on a T200 apparatus at 25 • C on CM5S dextran sensor chip in HBS-EP+ buffer (20 mM Hepes Buffer pH7. 4 containing 150 mM of NaCl and 0. 05 % P20 surfactant). Recombinant human and murine FLT3 hFc Chimeric (R&D systems) were captured on anti-human Fc using Ab human Capture Kit (Cytiva) according to the manufacturer's instructions (Cytiva) and increasing concentrations (from 0. 75 to 100 mM) of anti-FLT3 were injected at 50 µl/min. A pulse of 3 M MgCl2 was used as regenerant between cycles. The kinetic constants were evaluated from the sensorgrams after double-blank subtraction with T200 Evaluation software (Cytiva) using a bivalent fitting model.
Statistical analysis Data were analyzed with graphpad prism 9. Area Under Curve (AUC) was calculated based on the trapezoidal rule to have the volume of area above or under the baseline across different time points until full recovery. Data are expressed as the mean ± S. E. M. All sample sizes were chosen based on our previous studies except for animal studies for which sample size has been estimated via a power analysis using the G-power software. The power of all target values was 80 % with an alpha level of 0. 05 to detect a difference of 50 %. Statistical significance was determined by analysis of variance (ANOVA one-way or two-way for repeated measures, over time). In all experiments in which a significant result was obtained, the test was followed by Holms-Sidak post-hoc test for multiple comparisons, as appropriate.
, 2018). Here, we show that Iba1 staining was found quantitatively increased after repeated FL injections (Fig. 4F-G ), supporting a role for FLT3 in central changes. To rule out a possible implication of FLT3 expression in microglia cells after surgery, we performed Flt3 in situ hybridization of spinal cord from the CX3CR1 EGFP mouse line, in which EGFP is expressed in CX3CR1 + cells, known as macrophages (including microglia). Our data revealed an absence of colocalization between Flt3 mRNA and CX3CR1 (Supplementary Fig. 7 ). Then, we evaluated the level of the FLT3 ligand FL after SI or DI in the skin and the dorsal horn of the spinal cord on the ipsilateral side compared to sham animals. Whereas no significant difference was observed between Sham and SI, DI seems to produce an increase in FL concentration in the skin of the ipsilateral hindpaw in comparison with Sham at D3 (Supplementary Fig.
(caption on next page) A. Tassou  et al. Progress in Neurobiology 222 (2023) 102405 Acknowledgments This work was supported by the  University of Montpellier , France; grants from the  READYNOV Région Occitanie and European community program , France;  Société Française d'Etude et Traitement de la douleur  ( Gisèle Guilbaud  2019 price), France; CBS2 doctoral school, France and BIODOL therapeutics, France. MAbA3 was produced by GenAc platform funded by the  French National Research Agency, France , under the program " Investissements d ′ avenir "  Grant Agreement LabEx MAbImprove :  ANR-10-LABX-53  and by the  SIRIC Montpellier-Cancer  under Grant Agreement " INCa-DGOS-Inserm. We are grateful to  F. Perrin  for providing CX3CR1 EGFP mouse line, to  B. Pau  to help for designing FLT3 antibody strategy, to  P.
Open field test The open field chamber measured 40 × 40 cm and was made of white high-density non-porous plastic. The chamber was wiped with 30% ethanol before testing each mouse to remove any odor cues and light was controlled. Mice were video-tracked using EthoVision software XT 16 (Noldus Inc. ). Calibration was performed according to manufacturer's instructions. Mice were familiarized with the room at least 45 min before the test. They were placed in the center of the chamber and were allowed to freely move and explore the arena for 8 min. To analyze mice behavior during this test, the chamber was divided in zones: center, border and corners. Parameters measured were: total distance traveled, locomotion speed, time and frequency of crossing in the three different zones. Y-maze spontaneous alternation test Y-maze was made of white high-density non-porous plastic (Noldus).
All maze arms are 35 long × 6 wide × 15 high cm. Three equal 120° angles separate each arm. Mice were video-tracked using EthoVision software XT 16 (Noldus Inc. ) and the alternation was manually scored from the videos. As for the open field test, mice were put in the room at least 45 min before, the apparatus was cleaned with 30% ethanol between each mouse and light was controlled. Mice were placed in the starting arm facing the center and were allowed to freely explore the maze for 8 min. Scoring alternations is based on triplets. An alternation is scored when all three arms are explored consecutively. The first two arm visits can be considered samples. For all the following visits, an entrance is rated alternation if the chosen arm has not been visited in the two previous choices. We used the percentage of alternation as an index of spatial working memory using the following formula: % of alternation = [number of alternation/(total number of entries -1)]*100.
Electrophysiological data were acquired by an AxoPatch 200B amplifier coupled to a Digidata 1550B interface using pClamp 10. 7 (Axon Instruments/Molecular Devices, USA), and analyzed offline using Clampfit 10. 7 (Molecular Devices, USA) and MiniAnalysis 6. 0 (Synaptosoft, USA) software. Statistical analysis was performed using Prism 7. 05 (GraphPad Software, USA). CTL data were obtained from CRE-DTR-and CRE + DTR-mice; Mann-Whiney test revealed no significant difference between CRE-DTR-vs. CRE + DTR-values, thus they were pooled as CTL.
Microscopy and quantification Images were captured with a Zeiss apotome system (20 × objective). CC and CX were analyzed in two rostro-caudal locations: above the rostral SVZ (Bregma + 0. 5 to + 1 mm) and at the fornix level (Bregma -0. 3 to -0. 8 mm). A minimum of 3 sections for each location were analyzed per mice. The area of interest was delineated and the cells were manually counted using Zen software (Zeiss) (for PDGFRa + cells) or using automated counting with ImageJ software (for Olig2 + cells).
Acknowledgements We are grateful to  Lydia Kerkerian-le Goff  and  Aziz Moqrich  for discussions and critical reading of the manuscript and  Serge Alonso  for technical advice and help on Southern-Blot analysis. This work was funded by  CNRS ,  Aix-Marseille University  and the  ARSEP  foundation. We also acknowledge  France-bioimaging/PICSL infrastructure  ( ANR-10-INSB-04-01 ),  NeuroMarseille  institute and  NeuroSchool/nEURo * AMU  ( ANR-17-EURE-0029 ).
With cross-validated R 2 scores between 0. 3 -0. 5, prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153.
Datasets We used real-world mobile EEG recordings collected by anonymized users of the Muse S headband (RRID:SCR 014418; InteraXon Inc. , Toronto, Canada). This data was collected in accordance with the privacy policy (July 2020) users agree to when using the Muse headband 1  and which ensured their informed consent concerning the use of EEG data for scientific research purposes. The Muse S is a wireless, dry EEG device (TP9, Fp1, Fp2, TP10, referenced to Fpz) with a sampling rate of 256 Hz. Two types of recordings were used in this study: 1) meditation recordings with neurofeedback and 2) overnight sleep recordings. In both cases, recordings were carried out by users through the Muse application on iOS or Android mobile devices. 2   In meditation recordings, users performed an eyes-closed focused attention task with real-time auditory feedback.
This feedback was driven by the users' EEG using a proprietary machine learning-based algorithm aimed at helping them focus on their breath. In overnight sleep recordings, users had the option to listen to various audio content and/or auditory feedback while falling asleep. We collected four datasets by selecting recordings from InteraXon Inc. 's anonymized database of Muse users (see Table  1 ). First, the Muse Meditation Dataset (MMD), a subset of 971 meditation recordings from unique individuals, was obtained by sampling recordings of at least five minutes, with excellent signal quality, such that the age distribution was approximately uniform between 18 and 81 years of age (see Supplementary materials A for the detailed procedure). Next, the At-home Muse Unlabelled Sleep Dataset (AMUSeD), a subset of 1020 overnight sleep recordings from unique individuals, was obtained in a similar fashion to MMD, i.
e. , we sampled sleep recordings of excellent signal quality, between five and 11 hours long, and with as uniform of an age distribution as possible were (see Supplementary materials B for more details). To study the long-term dynamics of EEG-based brain age, we additionally curated datasets containing sessions from a few users with a high number of recordings over multiple consecutive weeks and months. Following the same signal quality criteria as for MMD and AMUSeD, we sampled the Longitudinal Muse Meditation Dataset (MMD-long) and Longitudinal At-home Muse Unlabelled Sleep Dataset (AMUSeD-long), each containing recordings from four subjects (distinct subjects in both datasets) of different age groups. This yielded a total of 497 recordings for MMD-long and 539 recordings for AMUSeD-long 3.
Preprocessing Minimal preprocessing steps were applied to EEG data before feeding it to the prediction models. In the case of meditation recordings, the first minute of the meditation exercise (in which signal quality might still be settling) was cropped, and as much as eight of the following minutes of data were kept, depending on the length of the recording. Sleep recordings were kept in their entirety. Next, missing values (which can occur if the wireless connection is weak and Bluetooth packets are lost) were replaced through linear interpolation using surrounding valid samples. A zero-phase FIR band-pass filter between 0. 1 and 49 Hz was applied to both data types  (Engemann et al. , 2022). We additionally applied FIR notch filters to attenuate some hardwarespecific spectral peaks that appeared in some of the recordings (at 16, 21.
1 , right), all models achieved above-chance performance, with models trained on all sleep stages obtaining the highest performance (median R 2 of 0. 47 vs. -0. 01 chance-level). The performance of models trained on either W or R stages alone was the lowest (0. 20 and 0. 22), while N2-and N3-based models both achieved higher performance (both 0. 37). Performance was highest when combining information across all sleep stages, suggesting that additive information was conveyed by different sleep stages. Does this finding imply that sleep is even better suited for age prediction than meditation? While the results do show higher R 2 for sleep-based models, the values cannot be directly compared as the age distribution, recording duration and sample sizes of the meditation and sleep datasets are not the same (as shown by different chance-level mean absolute error distributions, see Fig.
Inspection of trained brain age models What drives the predictions of these models, i. e. , what kind of information is critical for predicting age? Knowing this would not only validate the functioning of our models, but also provide interesting information to identify markers of aging in the human brain. To answer this question, we trained additional brain age models on the meditation and sleep datasets, but this time with inputs that capture different types of information. Results are presented in Fig. 2. For both meditation-and sleep-based models, providing spectral information only (i. e. , frequency band log-powers for each EEG channel) already yielded performance visibly above chance. Adding further spatial information, i. e. , by allowing the model to learn from the correlation between pairs of channels, yielded a clear improvement for both data types, with 100/100 and 85/100 of the trained "spectro-spatial" models reaching better performance across cross-validation iterations than their equivalent "spectral"-only models for meditation and sleep data, respectively.
Discussion In this paper, we showed that brain age measurement, thus far restricted to clinical and laboratory settings, can be extended to at-home settings using low-cost mobile EEG. We presented experiments on over 5200 recordings from unique individuals, combining real-world data collected during meditation exercises and overnight sleep. Results highlighted the usefulness of cross-spatio-spectral information to predict age, and revealed how certain EEG characteristics, such as α low power for meditation, N2 α high power in the spindle range and N3 δ power are important for accurately predicting age. Inspired by how mobile EEG technology could facilitate longitudinal monitoring of brain biomarkers, we investigated the stability of brain age over repeated sessions spanning a few months to over a year for eight individuals.
Biomarker measurement in real-world conditions with mobile EEG The results presented in this work were obtained on recordings carried out at-home by users themselves, without expert supervision or a complicated experimental setup, relying instead on off-the-shelf, low-cost mobile EEG hardware. Because it is both an easier and cheaper way to record data, this approach could enable collecting larger and more diverse samples. Concretely, we trained models on datasets containing more than 5200 of at-home EEG recordings (see Table  1 ). This is an order of magnitude above much of the published work on EEG-based brain age and in line with the previous studies with the highest sample sizes  (Brink-Kjaer et al. , 2022; Engemann et al. , 2022; Sun et al. , 2019). Critically, these studies focused on clinical or research datasets which are much more expensive and time-consuming to collect.
Despite working on noisier real-world EEG data, brain age prediction performance was in a similar range as these published results, e. g. , 0. 47 vs. 0. 33-0. 61 R 2 in  Engemann et al. (2022)  on their largest reported dataset, and 7. 6 (Fig. S2 , "All stages") vs. 7. 8 years mean absolute error in  Sun et al. (2019). Likely, this similarity in performance reflects the fact that sleep EEG is a rich source of physiological information and that a few channels are sufficient to capture some of its age-related features well. Moreover, our results support the use of a prediction pipeline based on filterbank covariance matrices and Riemannian geometry, as previously proposed for brain age modelling  (Engemann et al. , 2022; Sabbagh et al. , 2020 Sabbagh et al. , , 2022)). The ease-of-use and low friction provided by mobile EEG devices also make it easier to collect longitudinal data over extended periods of time.
S3. These results confirm overnight sleep EEG is a rich source of information for modeling brain age. Despite lower performance, models based on awake meditation EEG also yielded reasonable brain age predictions. Previous work on very similar cross-sectional data but from a descriptive, rather than predictive, point-of-view has shown that the characteristics of the α band vary significantly throughout adulthood  (Hashemi et al. , 2016). For instance, the α peak frequency decreases with age (a widely reported finding, see e. g. ,  Chiang et al. (2011) ;  Klimesch (1999) ;  Knyazeva et al. (2018) ;  Merkin et al. (2023) ). Additionally, a small but significant increase in α and β power with age has also been reported in conjunction with a decrease in δ and θ power  (Hashemi et al. , 2016). Indeed, our filterbank models heavily relied on the information contained in the low-and mid-α bands, i.
Limitations The datasets used in our experiments were collected in uncontrolled, at-home conditions, i. e. , no expert monitoring was available to ensure compliance with the recording instructions. This contrasts with existing work on brain age prediction, where datasets are usually collected in clinical or research settings. Similarly, only minimal metadata was available in the datasets. As a result, the datasets did not contain the necessary information to separate healthy individuals (used for training) from pathological individuals (used for testing), as is commonly done in brain age studies. Since our models were likely exposed to some pathological data during training, their sensitivity to pathology-related EEG activity might be lower than in previous studies realized in clinical settings.
Likewise, we did not have access to clinical cognitive assessments that would have allowed exploration and validation of brain age semantics, e. g. , relating positive brain age ∆ to cognitive slowing  (Denissen et al. , 2022; Engemann et al. , 2020). Finally, the datasets were not collected under controlled conditions typical of laboratory-or clinical-based studies, leaving open the possibility that individuals did not enter their actual age when creating their user profile. However, thanks to the type of sample size enabled by mobile EEG devices, much larger than traditional studies, our models will be less sensitive to the occasional incorrect label. For instance, a study using similar datasets produced results that agreed with earlier lab-based studies  (Hashemi et al. , 2016) , supporting the validity of our dataset for brain age analysis.
Behavioral Results The metrics extracted from the participants' behavioral data were their performance (PF), reported choices (CH), reaction time (RT), and visual discrimination (VD) sensitivity. The PF is a single-episode metric assuming values from 0 (worst) to 1 (best); it is calculated as the percentage of reward value obtained throughout the episode normalized by the maximum and minimum that could have been obtained. CH was the choice made by the participant in each trial, in terms of small or large reward stimulus. The RT was calculated as the time difference between the simultaneous presentation of both stimuli (the GO signal), and the onset of the movement. The VD is the ability to visually discriminate between stimuli, i. e. , identifying which one is the bigger/smaller (see Methods for further details).