dataesr/scientific-paragraphs-categorization huggingface dataset:
Chronic low doses of MPTP induces slow onset of motor symptoms Throughout the MPTP intoxication protocol, animals exhibited a gradual onset of motor symptoms which allows to identify several keys periods. First of all, animals went through a presymptomatic period, lasting from 4 to 6 months characterized by no visible motor symptoms (starting just after the rst MPTP injection) (Fig. 1. B). Next, a period of stabilization of the parkinsonian state characterized by the appearance of slight and unstable motor symptoms started (Fig. 1. A). Data collected during this uctuating period were excluded from analyzes. Finally, animals exhibited a stable parkinsonian syndrome, after a cumulative dose of 5. 2 ± 1. 7mg/kg (motor score of 16. 4 ± 1. 5), for several months without observation of recovery phenomena.
Apparatus. Monkeys were implanted with a polysomnographic equipment, a radio-telemeter transmitter (D70-EEE, Data Science International, France) for longterm recording in freely moving animals. The transmitter had three channels biopotential for recording electroencephalogram (EEG), electro-oculogram (EOG), and electromyogram (EMG) signals with a sampling rate of 500 Hz and a gain of 75 and actimetry count. Signals were acquired via two receivers mounted on the home cage and behavioral cage and then forwarded to a data exchange matrix connected to a computer for data storage and off-line analysis. Surgery. The surgery was performed under aseptic conditions and general anesthesia. Animals were anesthetized with Ketamine (7mg/kg, i. m. ) and Xylazine (0. 6mg/kg, i. m. ) then intubated and switched to iso urane mixed with oxygen.
For all the exams, the animal was anesthetized and a dose of 90 MBq was injected intravenously 3h before the acquisition. Then, the images which had the highest radioactivity count, and the one above and below (three cuts) were selected and averaged. Regions of interest were drawn manually for each striatum and an area of occipital cortex served as a reference for the background noise (region of non-speci c 123 I uptake). The nal activity count was determined as the ratio of striatal activity to occipital activity. Sleep data analysis. Sleep scoring was performed o ine on a software (NeuroScore, Data Science international). EEG and EOG was bandpass-ltered in the range of 0. 3 to 35Hz and EMG was bandpass-ltered in the range of 10 to 100Hz. Sleep stages were manually determined according to the American Academy of Sleep Medicine criteria and performed in 30s epoch  50.
This analysis was performed in healthy, presymptomatic, and symptomatic states to observe any changes between these different conditions. Thus, 300 samples (100 samples for each animal) from random 30s epochs for these different wake and sleep stages and in the different conditions were analyzed. For each 30s epoch, an estimate of the power spectral density was computed using the Fast Fourier Transform and Hamming windowing technique and averaged (Matlab). Data are expressed in mean ± 95% con dence interval. Nighttime sleep quality evaluation. Nighttime sleep data were analyzed from 7 pm to 7 am. Two 12h recording sessions per week were performed until a total of ten recordings were reached at minimum, in each experimental condition. For each 12h period of nighttime, the sleep latency (SL) (min), total sleep time (TST) (min), duration of each stage (%), wake time after sleep onset (WASO) (min) and sleep e ciency (expressed in %, as the ratio of TST to the 12h nocturnal time) were calculated.
Acknowledgments and funding sources The authors would address their thanks to  Luc Loveiko  from  CHUGA  and also  Jessy Hugues Dit Ciles  and  Clarice Froget  for their technical and animal care assistance. This study was supported by  CEA Clinatec Grenoble , the  Fondation de l'Avenir Paris  (n°  AP-RMA-2015-002 ) and the  Fondation de France Paris, France  (n°  00086316 ). Data were acquired on a platform of  France Life Imaging network partly by the grant  " ANR-11-INBS-0006 ". Sponsors had no role in the study design, data collection, data analysis or data interpretation.
We explored whether disease severity of Friedreich ataxia can be predicted using data from clinical examinations. From the database of the European Friedreich Ataxia Consortium for Translational Studies (EFACTS) data from up to five examinations of 602 patients with genetically confirmed FRDA was included. Clinical instruments and important symptoms of FRDA were identified as targets for prediction, while variables such as genetics, age of disease onset and first symptom of the disease were used as predictors. We used modelling techniques including generalised linear models, supportvector-machines and decision trees. The scale for rating and assessment of ataxia (SARA) and the activities of daily living (ADL) could be predicted with predictive errors quantified by root-meansquared-errors (RMSE) of 6.
Methods Subjects. All patients included in this analysis participated in the annual examinations for the EFACTS longitudinal database and had genetically confirmed FRDA. Retrieved data included only patients for which monitoring had been completed up to the fifth annual visit (fourth follow-up), leading to 602 patients with a total of 2306 data points. All subjects or their authorised surrogates gave informed consent before participation and all procedures were reviewed and approved by the institutional ethics committee of the medical faculty of RWTH Aachen University (reference number: EK 057/10) and carried out in accordance with the Declaration of Helsinki  16. Modelling implementations. Data analysis and modelling were carried out using the R programming language version 4. 0  17  and the Python programming language version 3.
In case that value was 2 or higher, we considered hypertrophy to be likely present in a patient, and for modelling the variable was then dichotomised into unlikely hypertrophy ( < 2 ) and likely hypertrophy ( ≥ 2). For modelling the selected targets, relevant predictors (clinical and genetic routine features) were selected from the data. A critical point here was to ensure no feature leakage (including features that implicitly contain information about the target) would be present in the modelling, as many of the variables available in the EFACTS database measure disease progression in some way. By correlating features with targets and selecting variables based on topic knowledge we identified a core set of features, namely age at disease onset, age at examination, GAA-repeats on both FXN alleles separately, gender, first symptom of the disease and presence of problems during the neonatal phase.
Stability of results from rating scales is both a challenge and a limitation when applying techniques of statistical learning to clinical instruments and at the moment there is not much data to draw conclusions on how FRDA and the instruments commonly used here are affected by this issue. Numerous more predictors were included in the present work compared to a priori information included in previous works. With a wider range of available predictors there might be potential for improved predictive accuracy if the used predictors convey additional information. However, the data presented here suggests that in some cases rather small predictor sets led to a superior predictive performance and even if the full predictor set led to the best performance, the difference to the smaller ones was usually rather minor.
Despite being to some degree sensitive to the applied (pre-) processing, measures characterising the central nervous system are suffering less from issues regarding objectivity than clinical scales. Characteristic alterations of the spinal cord, brain stem and cerebellum in FRDA have recently been shown  50, 51  and thus seem like a viable variable to include in predictive modelling of the disease course. Limitations. There are limitations in the work presented here. First, while in the context of EFACTS large amounts of data are regularly gathered in a standardised manner, missing data was the main drawback in this work. Especially when researching a rare disease, it is much better to have a smaller, but complete dataset, than having a wide range of variables, but large amounts of missing data.
Several studies have reported that the network topology is altered in AD and MCI patients compared to HC  [47, 48,. Studies have shown that the AD group deviates from the optimal small-world topology towards a more random one compared to HC  [50] [51] [52]. For the other topological parameters, contradictory results have been observed in the literature  [50] [51] [52] [53] [54] [55] 57, 65, 67]. This is mainly due to the use of sparse data sets with different characteristics as well as methodological differences. More precisely, EEG databases with different characteristics are used in the literature for comparing AD patients to HC and MCI. In addition, they are prone to experimental constraints that do not match the reality on the ground, such as including strict patient inclusion and exclusion criteria and considering normal healthy subjects as controls.
In addition, most studies consider undirected binary networks, which require the application of an arbitrary threshold on adjacency connectivity matrices. This factor affects directly the resulting network. The choice of using weighted or binary matrices to estimate network graph has mostly been arbitrary to date. Recently, some studies addressing this issue  [65, 66]  found that preserving real-valued weights produces consistent results by exploiting the additional topological information that is stored in the weights. Moreover, several measures can be exploited to quantify the connectivity in brain networks. These measures may reflect different processes, which lead to different network topologies. The majority of studies used a specific measure without comparing it to others on the same database and without studying the contribution of graph analysis with respect to classical connectivity analysis.
Materials and Methods The present study was conducted on a cohort containing rsEEG data of three populations (SCI, MCI and AD patients) recorded in real-life clinical conditions. In order to investigate the differences between SCI, MCI and AD, we first studied the functional connectivity in the three groups using the entropy measure (EpEn). To assess the effectiveness of our entropy metric, we compared it to two alternative measures, commonly used in the literature: the Magnitude Square Coherence (MSC) and Phase Lag Index (PLI). Then, by means of graph theory analysis on the obtained EpEn matrices, we studied the organizational properties of brain networks in SCI, MCI and AD. Later, we describe the EEG database used and present the three connectivity measures as well as the topological parameters used for the graph analysis.
Study Population The cohort contains rsEEG data of 102 subjects recorded in real-life clinical conditions at the Charles-Foix Hospital in France. Subjects who complained of memory impairment were referred to the outpatient memory clinic of the hospital to undergo a battery of clinical and neuropsychological tests for brain disorders. For each subject, a diagnosis was established at the memory clinic on the basis of the clinical assessment, brain imaging, psychometric findings, interviews and neuropsychological tests, conducted by a multi-disciplinary medical staff, according to the standard diagnostic criteria: DSM-IV, NINDS, Jessen criteria for SCI and Mc Keith criteria for Lewy body dementia  [4, 5, 77]. Patients with epilepsy were excluded, and EEG was not used to establish the diagnosis.
This retrospective study was approved by the institutional review board of the local Ethics Committee Paris 6, on 16 May 2013. Before conducting this retrospective study, an information letter on the research study was sent to the patients, detailing the possibility of opposing the use of their collected data. All the data were fully anonymized before use in our research. The study population includes rsEEG recordings of 22 SCI subjects, 52 MCI patients and 28 mild to moderate AD patients. Table  1  reports information about the demographic and clinical characteristics of the patients.
EEG Recording and Preprocessing EEG data were recorded during the resting state eyes-closed condition using a Deltamed digital EEG acquisition system with 30 scalp electrodes positioned over the whole head according to the 10-20 international system: Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T3, C3, Cz, C4, T4, TP7, CP3, CPz, CP4, TP8, T5, P3, Pz, P4, T6, O1, Oz and O2. All data were digitalized in a continuous recording mode for a minimum of 20 min with a 256 Hz sampling frequency. The EEG recordings were pre-processed off-line on MATLAB software (MathWorks Inc. , Natick, MA, USA). For each subject in the database, continuous epochs of 20 s, free from artifacts (eye movements, eye blinks, muscular activity, instrumental noise etc. ), were manually selected. To do that, an EEG expert visually inspected the EEG signals and discarded the parts of the signals presenting artifacts.
Statistical Analysis Statistical analyses were performed using MATLAB R2020a software. We compared characteristics between the SCI, MCI and AD groups using the Kruskal-Wallis test. This statistical test is a nonparametric version of the one-way ANOVA and is an extension of the Wilcoxon rank sum test to more than two groups. The results with a p-value lower than 0. 05 are considered to be statistically significant. To evaluate the differences among the three groups in terms of functional connectivity measures, the statistical test was applied in Section 3. 1 on each of the 30 electrodes and at each frequency band. We also assessed the significant difference between groups in terms of the graph parameters computed on the average connectivity matrices. The Kruskal-Wallis test was performed on each frequency band considering, for each group, the 30 clustering coefficient values associated to the 30 electrodes (Section 3.
Discussion Previous rsEEG studies on functional organization of the brain network in the context of AD reported conflicting results  [50] [51] [52] [53] [54] [55] 57, 65, 67]. These discrepancies among studies could be related to methodological differences and the use of databases with different characteristics, which are sometimes prone to experimental constraints that do not match the reality on the ground. In light of this, in the present study, we used a real-life clinical database containing rsEEG data of 102 patients at the SCI, MCI and AD stages. To our knowledge, this is the first study to date employing graph theory to characterize the evolution of brain networks When comparing the spatial distribution of the obtained modules in the alpha band for SCI and AD groups (Figure  13a, c ), we first notice that two modules among the three ones observed in SCI extend from anterior to posterior regions.
Discussion Previous rsEEG studies on functional organization of the brain network in the context of AD reported conflicting results  [50] [51] [52] [53] [54] [55] 57, 65, 67]. These discrepancies among studies could be related to methodological differences and the use of databases with different characteristics, which are sometimes prone to experimental constraints that do not match the reality on the ground. In light of this, in the present study, we used a real-life clinical database containing rsEEG data of 102 patients at the SCI, MCI and AD stages. To our knowledge, this is the first study to date employing graph theory to characterize the evolution of brain networks throughout different clinical stages of cognitive decline, including healthy elders with subjective cognitive impairments (SCI), MCI patients and patients with AD.
Data Availability Statement: We conducted a retrospective study on EEG data of patients referred to the outpatient memory clinic of the Charles-Foix hospital (France). This retrospective study was approved by the institutional review board of the local Ethics Committee Paris 6, on 16 May 2013 (France). All data were fully anonymized before exploiting them in our research work. Before conducting this retrospective study, an information letter on the research study was sent to the patients, with the possibility of them opposing the use of their collected data. Informed consent was thus waived in this context. Data are available upon request to researchers qualified to handle confidential data. Data requests can be made to Laurent Capelle (email: cppidf6. salpetriere@yahoo. fr, Comité de protection des personnes Ile-de-France VI) or from the corresponding author (kiyoka.
) , reached scores ≥ 4 (children, mean: 5. 94, range: 3-7; adults: mean: 5. 54, range: 3-7), typical of close partners such as best friends  (Gächter et al. ; Myers and Hodges). Nine children and one adult were discarded due to claustrophobia, sleepiness, joystick malfunction, misunderstood instructions, or excessive motion in the scanner. One of the remaining children had missing fMRI data and one of the remaining adult had missing behavioral data due to recording issues. The final samples of subjects therefore comprised 25 children and 22 adults for behavioral analyses, and 24 children and 23 adults for fMRI analyses. All participants were native French speakers, had no visual deficit, no MRI contra-indications and no history of neurological and psychiatric disorder. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the CPP Sud Est II Ethics Committee on November 7, 2018 (ClinicalTrials.
MRI data acquisition MRI scans were obtained from a MAGNETOM Prisma 3. 0 T scanner (Siemens Healthcare, Erlangen, Germany) at the Lyon Primage neuroimaging platform (CERMEP, Imagerie du vivant, Lyon, France). The fMRI blood oxygenation level-dependent (BOLD) signal was measured with a susceptibility weighted single-shot echo planar imaging (EPI) sequence. The following parameters were used: TR = 2,000 ms, TE = 24 ms, flip angle = 80°, matrix size = 128 x 120, field of view = 220 x 206 mm, voxel size = 1. 72 x 1. 72 mm, slice thickness = 3 mm (0. 48 mm gap), number of slices = 32. Between the two functional runs, a high resolution T1-weighted 3D structural image was acquired for each participant (TR = 3,000 ms, TE = 2. 93 ms, flip angle = 8°, matrix size = 320 x 280 mm, field of view = 280 x 320 mm, slice thickness = 0.
Preprocessing Data analysis was performed using SPM12 (www. fil. ion. ucl. ac. uk/spm accessed on 15 September 2021). Functional images were corrected for slice acquisition delays, spatially realigned to the first image of the first run to correct for head movements, and spatially smoothed with a Gaussian filter equal to twice the voxel size (4 x 4 x 7 mm³ full width at half maximum). Functional image runs were inspected using ArtRepair (cibsr. stanford. edu/tools/human-brain-project/artrepairsoftware. html accessed on 15 September 2021); functional volumes with a global mean intensity greater than 3 standard deviations from the average of the run or a volume-to-volume motion greater than 2 mm were identified as outliers and substituted by the interpolation of the 2 nearest non-repaired volumes.
Acknowledgments We thank the  CERMEP Primage MRI engineers Franck Lamberton , and  Danielle Ibarrola  for invaluable help in data collection. We thank the  Centre Hospitalier le Vinatier  for the promotion of this study. We thank  Jérôme Redouté  for his support throughout the study and his comments on the manuscript, and  Fadila Hadj-Bouziane  for helpful discussions. We extend our thanks to all the volunteers who took part in the study, with special thanks to the children.
Results from the RETeval device for this subject showed some ERG waveform measures to be out of the normal range compared with the RETeval normative database  [14, 15]  in both ISCEV standard LA single flash (3 cd s m-2, 2 Hz) and ISCEV standard LA flicker stimulation (3 cd s m-2, 28. 3 Hz) stimulations. For both eyes, the LA single flash b-wave amplitude was below the 3% percentile of the reference distribution (3% and 2%, respectively), and the retinal response to the LA flicker stimulation was smaller than all references value at a 0% percentile of the reference distribution  [14, 15]  (Fig. 1A ). Similar differences in the subject's ERG are evident when current results are compared with normative reference values of a separate age-similar cohort of healthy volunteers  [12]. Indeed, ISCEV standard LA single flash (3 cd s m-2, 2 Hz) analysis revealed that the mean b-wave amplitude of the KCNMA1 +/-subject was significantly decreased (p = 0.
Acknowledgements The authors acknowledge and thank  Dr. Paul Constable ,  Professor Isabelle Ranchon-Cole ,  Michèle Ollier , and  Jemila Houacine  for their contributions to the study, as well as  Professor Dominique Bonneau  (principal investigator),  Professor Sylvie Odent  (investigator),  Dr. Thierry Prazuck  (investigator),  Dr. Yves Donazzolo  (Optimed's investigator), and the  Fragile X France  and Mosaïques associations for assistance in recruiting study participants, as well as LKC for technical assistance with the RETeval device and providing the REACT database, and  Laure-Anne Giannone  from Optimed for statistical analysis. We thank the patient and his family for their participation in and contribution to this study.
Neonatal complications that reduce OT release, such as neonatal maternal separation, have shown to delay the GABA-switch, which induces an imbalance to excitation/inhibition, that is characteristic of many neurodevelopmental disorders  [96, 97]. Major Functional Part of the Oxytocin Network: The Amygdala One area that has been extensively linked to OT functioning is the amygdala. Indeed, the study by  Knobloch and colleagues [30]  found a high number of OT fibers in the central and medial regions of the adult rat amygdala. This has been shown in humans as well  [56] , including on the transcriptome level, where amygdala-associated cognitive terms "anxiety", "fear" and "emotional" showed a 97. 5% stronger correlation with OTR mRNA expression than any other genes in a 20. 737 gene dataset  [90].
(3) A third possible pathway of effect involves additional neurotransmitter systems such as serotonin (5-HT). OT can influence serotonin due to the presence of OTR on serotonergic neurons. Serotonin can bind to microglia via microglial 5-HTR expression. Created with BioRender. com. To our knowledge, little to no OTR expression on microglia has yet been revealed in vivo. Available databases of in vivo microglia single-cell RNA Seq analysis (see http:// www. brainrnaseq. org/, http://www. microgliasinglecell. com/, https://myeloidsc. appspot. com/, all accessed on 1 October 2022) show a trend where OTR transcripts are little-to not detected in the embryo and early postnatal ages, but only in adult mice (Table  2 )  [169] [170] [171] [172] [173]. Moreover, if OTR mRNA is found, it is with very low read counts, below the cutoff threshold of quality assessment measures  [169, 171, 173].
Database: http: //www. brainrnaseq. org/, accessed on 1 October 2022 In vivo P7: no P14: no P21: no P60: yes (Mean = 0. 120 LPS stimulation (5 mg/kg, single intraperitoneal injection at P60) LPS treatment depleted OTR expression at P60 (Mean = 0, SD = 0 FPKM) SD = 0. 120 FPKM) Microglia and Bulk RNA Seq: Illumina [173] J Neurosc macrophages from Tie2-EGFP transgenic mice; sex P7 HiSeq 2000. Database: http: //www. brainrnaseq. org/, In vivo Yes: (Mean = 0. 1 SD = 0 FPKM) None Not applicable unspecified accessed on 1 October 2022 [170] Immunity FACS-sorted microglia from C57BL/6 mice, male and female E14. 5, P4/P5, P30, P100, P450 Single cell RNA Seq: accessed on 1 October 2022 Illumina NextSeq500 Database: http://www. microgliasinglecell. com/, In vivo Not detected at any time-point. None Not applicable [171] Neuron FACS-sorted microglia from male C57BL/6 mice E14.