dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Sample Calculation The main objective is to compare the central neuroinflammation measured by the TSPO distribution volume assessed by PET among both currently depressed and control groups. To our knowledge, this is the first study using the [18F] DPA-714 as biomarkers of neuroinflammation in depression with this type of study design. So, we cannot calculate the power of the study. However, few studies of this type are available in the scientific literature. There are studies using other markers of inflammation, such as [18-F]FEPPA  (45). In the absence of sufficient literature data on which to base the calculation, a population of 20 patients per group seems appropriate considering the size of neuroimaging studies in the literature. In addition, 20 patients per group will allow us demonstrating, with a power of 80%, a statistically significant difference in the distribution volume between patients with a major depressive episode and controls, in the event the measured volume is similar according to the markers (based on data from the Setiawan et al.
Analysis of PET Imaging Data To evaluate the brain kinetics of [18F] DPA-714, PET images will be co-recorded on the MNI atlas with PMOD R software. To construct time-activity curves of [18F] DPA-714, the PET images will be segmented into 13 regions of interest (ROI): precuneus, anterior cingulum, posterior, frontal, temporal, parietal, occipital cingulum, hippocampus, semi-oval centrum, anterior putamen, posterior putamen, caudate nucleus, and intersections, as defined in the MNI-AAL atlas. The binding potential (BP) is the ratio of B max (receptor density) to KD (dissociation constant): BP = B max / KD shall be calculated. Additionally, the distribution volume ratio (DVR) will be estimated from the region of interest and a reference region using, for example, Logan graphical analysis  (66)  with the cerebellum as reference region for the images obtained between 0 and 90 min  (46).
In this study, the DVR will be calculated as the slope of the linear part of the Logan analysis using PMOD software. We will calculate the BP for the 13 ROIs mentioned above, as well as for the entire brain. For the primary hypothesis, PET data will be analyzed by multivariate ANOVA with TSPO Volume in the 13 ROIs as the dependent variables and diagnosis and genotype as fixed factors. Main effects will be considered significant at the conventional P ≤ 0. 05. Effects in each region, will be analyzed by univariate ANOVA. As a secondary analysis, a MANOVA including every brain region sampled (including all cortical and subcortical regions) will be performed to assess the effect of diagnosis on TSPO Volume. A partial correlation will be used in a secondary analysis to quantitate the relationship between TSPO Volume in the primary regions of interest and severity of symptoms of MDD measured by total MADRS score.
Analysis of MRI Data For structural MRI analysis, image processing will be performed using FSL 4. 1 (FMRIB Software Library; www. fmrib. ox. ac. uk/ fsl/) and an in-house software developed in Matlab (version 6. 5, The MathWorks), with procedures similar to those described earlier  (67) (68) (69) (70). Using T1 anatomical image, we will identify and segment hippocampi and amygdala. Concerning diffusion weighted imaging, a diffusion tensor imaging model will be fit at each voxel, generating mean diffusivity (MD) maps. The MD maps were then registered to brain-extracted wholebrain volumes from T1-weighted images using a full affine (correlation ratio cost function) alignment with nearest-neighbor resampling. As a result of this processing, the MD maps were corrected for head movements, and shared an identical reference space with the anatomical T1-weighted volumes.
For standard volumetric analysis, we will extract volume values from each segmented structure and divided this value by the total intracranial volume (TIV) to normalize for participant ′ s brain size. For each subject, the region of interest segmentation results, coregistered MD were all superimposed onto the original T1weighted volume. The segmented structures defined the binary masks, where the mean values of MD will be calculated for each individual. For functional MRI analysis, image processing will be performed using SPM12 (https://www. nitrc. org/projects/ spm), CONN toolbox (https://www. nitrc. org/projects/conn) and an in-house software developed in Matlab (version 6. 5, The MathWorks). Individual correlation maps between the defined regions (hippocampus, amygdala, anterior cingulate gyrus, frontal cortex-these areas of interest are anatomical zones defined in known and classical stereotactic spaces in neuroimaging) and the whole brain will be determined.
Analysis of all Data PET and MRI imaging, clinical symptomatology and biological markers. PET and MRI images will be co-recorded using a method previously used by our team  (71)  [e. g. ,  (71) ] so that the maps of all multimodal markers have the spatial consistency needed for their statistical comparison. Firstly, for each marker map, we will perform t-tests to compare the groups with each other (SPSS 25. 0). This will allow us to quantify the structural, functional and inflammatory alterations that characterize each group (i. e. , treatment-resistant MDD vs. control, responsive MDD vs. control, treatment-resistant MDD vs. responsive MDD). Secondly, we will verify that there is no spatial collocation between the atrophy marker (MRI) and the inflammation marker (DPA714 -PET) that can induce a different interpretation of PET results.
Finally, we will correlate the inflammation marker maps with the structural and functional marker maps generated with MRI. This will allow us to understand whether neuroinflammation is linked to brain changes (e. g. , iron deposits, functional alteration). To study the correlation between the severity of depressive symptoms, suicide risk and the intensity of neuroinflammation, graphic representations will be made to observe the distribution of these data in the two groups concerned. The Pearson correlation coefficient will be calculated in each instance. The same analysis strategy will be used to measure the correlation between plasma concentrations of biological markers of peripheral inflammation and the intensity of neuroinflammation in the three study groups.
ACKNOWLEDGMENTS CHU Toulouse as sponsor of this study. This study was supported by the  French National Agency for Research  (" Investissements d ′ Avenir " no. ANR-11-LABX-0018-01 ), IRON. We thank the technical staff of  CERRP (Centre d ′ Etude et de Recherche sur les RadioPharmaceutiques), Tours , for their assistance. We are grateful to the  Treatment Resistant Depression Fundamental Network :  Djamila Bennabi ,  Marion Garnier ,  Frank Bellivier ,  Thierry Bourgerol ,  Thierry D ′ Amato ,  Olivier Doumy ,  Frédéric Haesebaert ,  Jérôme Holtzmann ,  Christophe Lançon ,  Philippe Vignaud ,  Guillaume Vaiva ,  Ali Amad ,  Michel Walter ,  Isabel Nieto ,  Raphaëlle Marie Richieri ,  Philippe Domenech ,  Corentin Rabu ,  Luc Mallet ,  Liova Yon ,  Florian Stephan. We also thank the  Clinical Investigation Centre in Toulouse (Calvas F.
Temperature Recording Core and skin temperatures were monitored using the VitalSense R system (precision ± 0. 01 • C, Mini Mitter, Respironics, Herrsching, Germany). A wireless Jonah TM ingestible thermometer pill, swallowed 5-7 h before the testing session, was used to measure T core. The validity of ingestible thermometer pills for monitoring T core has been confirmed during both rest and exercise, making them a viable substitute for more invasive methods  (Casa et al. , 2005). Wireless XTP dermal adhesive temperature patches were used to measure chest (T chest ), hand (T hand ), and calf (T calf ) skin temperatures. Both internal and external sensors sent data by telemetry to a single data logger every 60 s. Mean skin temperature (T skin ) was calculated using  Burton's (1934)  weighted formula: 0.
Cognitive Testing The OTS test was performed upon reaching 39 • C in HOT or after 35 min of seated rest in CON. This test has been used in previous studies investigating the effect of hot environmental conditions on cognitive performance and was shown to be a valid tool to differentiate the effects of heat on simple and complex tasks  (Gaoua et al. , 2011a (Gaoua et al. , , 2012)). This test was also used because instead of categorizing different tasks as simple and complex, it manipulates the complexity within the same task  (Gaoua, 2010). Hence, the mechanism required to perform the task and the brain area being assessed remain constant, but the cognitive load required to successfully complete the task is manipulated. The OTS was performed during each trial using Cantab software (CANTABeclipse, Cambridge Cognition, Cambridge, United Kingdom) and hardware (a tactile screen and a touch pad).
EEG Recordings Continuous EEG data was recorded using the NicoletOne LTM system (Viasys Healthcare, Madison, WI, United States). Genuine gold cup electrodes (10 mm diameter, Grass Technologies, West Warwick, RI, United States) were affixed to the scalp with conductive paste (EC2, Grass Technologies, West Warwick, RI, United States) and secured with a small gauze pad. A lightweight hairnet was used to prevent the electrodes from moving. The primary recording electrode was placed at the Fz position and recorded with a paired mastoid reference and grounding electrode at the Fpz position according to the International 10-20 System  (Homan et al. , 1987). The frontal midline area has been shown to be a primary activity region within the brain during working memory tasks  (Gevins et al. , 1997; Ishii et al.
, 1999; Mizuhara et al. , 2004). All electrode impedances were maintained below 10 kOhm. EEG data was sampled at 256 Hz, low pass filtered at 0. 3 Hz, high pass filtered at 35 Hz, and stored on a computer hard disk for subsequent analysis. A Fast Fourier Transformation (FFT) was calculated with 2-s bins using a Hanning window with 75% overlap to yield the absolute power values for the theta (3-8 Hz) and alpha (8-12 Hz) frequency bands. These signal frequencies have been previously shown to have very high test-retest reliability when measured in the context of working memory tasks  (McEvoy et al. , 2000). Each measure was obtained by averaging the values from consecutively recorded 2-s data segments preceding correct responses during all OTS-4 and OTS-6 tasks. Digital markers were applied during data acquisition to represent the start and end (correct answer) of each task.
Statistical Analysis We used Shapiro-Wilk test and confirm that all data were normally distributed. Data were coded and analyzed in SPSS Version 17 software (SPSS, Chicago, IL, United States). A oneway within-subjects ANOVA was performed to study the effect of condition (CON, HOT). In addition, a two-way withinsubjects ANOVA was performed to analyze the effect of condition as well as the effect of task (i. e. , PRE, OTS-4, and OTS-6) and potential interaction on EEG data. All variables were tested using Mauchly's procedure for sphericity. If a significant condition × task interaction was found, pairwise comparisons using a Bonferroni correction were used to compare the effect of condition at each time interval. The level of statistical significance was set at p < 0. 05. Moreover, effect-sizes are described in terms of partial eta-squared (η 2 ; with η 2 ≥ 0.
Surgery and data acquisition A subdural intracranial electrocorticogram (ECoG) recording electrode was positioned in the cortex overlying the hippocampus (coordinates x: Ϫ2. 25; y: Ϫ2. 46). A reference electrode was implanted either in the skull of the contralateral hemisphere, or above the cerebellum. Electrodes were either bare wire, or skull screws. An EEG transmitter (A3028B, Open Source Instruments) was implanted on the back of the animal subcutaneously. Animals were left to recover for at least 24 h after surgery before the commencement of telemetry data acquisition. Telemetric ECoG data were acquired for ϳ3 d from each animal. Recording was either conducted continuously between days 1 and day 3 after surgery, or day 1, followed by day 5 to day 6. ECoG data were acquired using an Opensource Instruments data acquisition system at 512 sps as previously described  (Chang et al.
ECoG data processing The raw ECoG data were analyzed using custom written Tcl and C processors. ECoG data were analyzed in 8-s intervals. For each interval we extracted measures of data loss, spike count, ␦ power (0. 1-3. 9 Hz) and power (4 -12 Hz). We defined intervals in which data loss exceeded 20% of samples as "lossy" intervals. Intervals in which ␦ power exceeded 0. 16 mV 2 were classified as artifacts. Lossy and artifact intervals were excluded. IIS in rodent ECoG were detected as follows. Each 8-s interval of EEG was treated as a two-dimensional path. One dimension is voltage, which was normalized by dividing by the mean absolute step size of the voltage in the 8-s interval. The mean absolute step size is the sum of the absolute changes in voltage from one sample to the next, divided by the number of samples.
Slides were stored at RT. Imaging was performed on a Zeiss AX10 microscope using StereoInvestigator Software with a 5x/0. 16 (420630-9900) apochromat air objective. Quantification was performed using StereoInvestigator Software "Optical Fractioner Workflow" probe with the following settings. Thickness of 50 m was manually defined, and regions were selected using a 1. 25x/0. 03 (420310-9900) apochromat air objective for low magnification and then counted with a 10x/ 0. 45 (420640-9900) apochromat air objective for high magnification. The border between medial septum (MS) and diagonal band of Broca (DB) was defined as a line between the two major island of Caleja. The regions were separated using different lines. The counting frame used was a square of 75-m size and the grid was a square of 150-m size.
Human scalp EEG and foramen ovale (FO) electrode recordings Human scalp EEG and FO electrode recordings were performed at the Massachusetts General Hospital, as described in detail previously  (Lam et al. , 2017). Scalp EEG electrodes were placed using the International 10-20 system, with additional T1 and T2 electrodes. Sleep staging in patient data were performed by a boardcertified clinical neurophysiologist (ADL) based on visual analysis of the full scalp EEG data. While dedicated electrooculogram and electromyogram channels were not recorded for these studies, the frontopolar scalp EEG electrodes allowed assessment of eye movements, while the frontopolar, frontal, and temporal electrodes allowed assessment of myogenic activity. Scalp EEG data were reviewed in 30-s epochs in the longitudinal anterior-posterior bipolar montage, using the Python module wonambi (https://github.
com/wonambi-python/wonambi). Each 30-s epoch was classified as awake, non-REM (NREM)1, NREM2, NREM3, or REM, based on the American Academy for Sleep Medicine's manual for sleep scoring. Spike quantification in patient data were performed by a board-certified clinical neurophysiologist (ADL), using a custom-made GUI in MATLAB (Mathworks). The GUI displayed 15-s epochs of left and right sided FO data, in both bipolar and common reference montages (common reference ϭ C2), along with the EKG trace to allow exclusion of EKG artifact. The reviewer could adjust amplitudes for each trace as needed. For the MCI patient analyzed, contact #3 from the left FO electrode did not record properly and was excluded from analysis. The reviewer marked all spikes in each epoch. Epochs were presented in consecutive order, but the reviewer was otherwise blinded to the sleep stage for each epoch during the review.
Instantaneous spike rates were calculated by determining the total number of left FO and right FO spikes detected within all 30-s epochs of the recording (which corresponded to the sleep staging epochs above) and converting these rates to spikes per hour. Average spike rates within each sleep stage were calculated by summing the total number of spikes that occurred during each sleep stage and dividing by the total number of hours the patient spent in each respective sleep stage in the recording. Spectral analysis of the FO electrodes was performed in MATLAB, using the freely available Chronux toolbox  (Mitra and Bokil, 2007). Analysis was performed on the LFO1, LFO2, RFO1, and RFO2 channels, as these were the deepest contacts and thus least prone to noise or artifact. Channels were each normalized to zero-mean, unitvariance.
Multi-taper spectrograms were calculated for each normalized channel, using the Chronux script mtspecgramc with the following parameters: frequency range: 1-20 Hz, window: 30 s; step size: 30 s; time-bandwidth product: 3, tapers: 5. This provided a spectral resolution of 0. 2 Hz. An average spectrogram across all FO channels was then generated, and the average spectral powers within the ␦-band (0 -4 Hz) and -band (4 -12 Hz) were then calculated.
The time of each event was evaluated as a phase of a circadian cycle. Circular data were analyzed using circular statistics by means of the "circular" package  (Agostinelli and Lund, 2013). Circular outliers were identified using "CircOutlier" package  (Rambli et al. , 2016). For tests entailing random variables, linear mixed models were fit using "lme4"  (Bates et al. , 2015). Significance was tested using a log-likelihood test comparing the full model to a null model without the factor of interest. For evaluation of the relationship between spike count and /␦, we described each /␦ value as a member of one of three levels: (1) /␦ Յ 1; (2) 1 Ͻ /␦ Յ 2, and (3) /␦ Ͼ 2. We then modeled spike count (Poisson-distributed) as a function of levels of /␦, using the R package "MCM-Cglmm"  (Hadfield, 2010).
It should be noted that due to poor properties of a single model fitted across all animals (fitting animal as a random effect and /␦ factor as a fixed effect), separate models were fitted to individual animals without including a random effect. Thus, the data do not allow for inference about the population. Event-triggered averages of IISs were evaluated by considering each interval in which an IIS was detected as an event. If no intervals within Ϯ80 s around the event were excluded, then the 160-s window was included in the calculation of the event-triggered averages, else the event was excluded from the averaging. An event-triggered average was also evaluated around 2000 randomly sampled points. For comparing /␦ in intervals with IIS to /␦ in intervals preceding IIS, we considered only interval pairs where the preceding interval did not contain IIS and fit the model ( / ␦ ) 1 / 4 ϳ Index ϩ Subject ϩ where Index was a factor labeling whether the interval contained IIS or the preceding interval and modeled as a fixed effect, and Subject was a random effect with a random intercept.
Sleep/wake modulation of IIS Since IIS predominantly occurred in the normal inactive phase of the circadian cycle, we next asked whether this circadian modulation of IIS could be accounted for by the sleep/wake state of the animals. In a subset of J20s, we acquired simultaneous video recordings while recording ECoG data (n ϭ 4). We manually scored the video and classified periods as sleep or wake (see Materials and Methods). Two of these four J20 animals showed strong circadian phase coupling of IIS, and two showed weak phase coupling. For the two animals that showed strong phase coupling of IIS, IIS occurred more frequently in sleep than during waking (Fig. 3A, B ). In contrast, the modulation of IIS probability did not show a consistent pattern in animals showing weak phase coupling (Fig.
Sleep stage modulation of IIS in human AD The first intracranial recordings in humans with AD were recently reported and demonstrated marked activation of mesial temporal lobe (mTL) IIS during sleep compared to the awake state  (Lam et al. , 2017). We further analyzed the combined scalp EEG and intracranial electrode recordings from these two patients to better understand the relationship between sleep stage and mTL IIS rate in AD patients. One patient with advanced AD did not achieve REM sleep but showed mTL IIS preferentially during NREM sleep as opposed to waking states (Table  2 , patient 1). The second patient was a 67-year-old woman with amnestic MCI (aMCI), an early stage of AD that is thought to correspond to the early stage of AD modeled in our young J20 mice. The data from this patient were used to compare the frequency of IIS in wake, NREM, and REM states.
Code and data accessibility The processor script used for quantification of IIS, , and ␦ power in rodent ECoG data are available from http:// www. opensourceinstruments. com/Electronics/A3018/HTML/ SCPP4V1. tcl. Code used for quantifying IIS in human data are available from https://github. com/mauriceaj/GUI-EEG_Spike_ Annotation. The datasets used for Figures  1 2 3 4 5 6 (rodent data) are available from http://dx. doi. org/10. 7488/ds/2319.
Anterior planar 64 × 64 pixel images were acquired at 10 minutes (early phase) and 3 hours (delayed phase) after intravenous administration of 123 I-MIBG (185 MBq). Thyroid was blocked by oral administration of 130 mg of potassium iodine 1 hour before 123 I-MIBG injection  41. The early and delayed (i. e. , the most reliable MIBG biomarker) heart to mediastinum (H/M) uptake ratios were calculated by dividing the mean count in a manually-drawn, left ventricular region of interest (ROI) by the mean count in a 7 × 7-pixel ROI square placed in the upper mediastinum to avoid lung activity. The MIBG washout rate (i. e. , the rate of MIBG washed out between the early and the delayed images) was calculated by comparing the cardiac count in the ventricular ROI of the early and delayed images  41. The physicians who analyzed the scintigraphic data (DMG, MB) and calculated the delayed and early H/M ratios and MIBG washout rate were blinded to the diagnosis.
Another THI questionnaire was completed after the endovascular procedure at Day 1 and 60. A HRCT (GE GSI Revolution; GE Healthcare) of the petrous bone was performed. Slices were acquired helically in the axial plane, at a nominal 0. 625 mm slice thickness with a 50% overlap of 0. 312 mm as recommended when a SSCD syndrome is suspected  (14). All images were obtained in ultrahigh resolution at 140 kV and 200 mAs/section. The primary images were retargeted to the axial and coronal planes of the lateral semicircular canal, to a 60 mm field of view with a 512 matrix for an isometric voxel. The retargeted axial scans were then reformatted in the Pöschl plane, using AW Server software, GE Healthcare.
3 mM NaGTP (pH adjusted to 7. 3 with KOH). CA1 neurons were voltage-clamped at -65 mV. For miniature EPSC (mEPSC) recording, 1 M TTX and 100 M picrotoxin (to block GABA A R) was added in the ACSF. In this study, all cells had a resting membrane potential ranging from Ϫ55 to Ϫ75 mV. Cells with a resting membrane potential more positive than -55 mV were discarded. Analyses were made offline using the Mini analysis software. Neuronal excitability was assessed with a standard input/output curve obtained from an I-clamp step protocol (0 -100 pA), in the presence of kinurenic acid and picrotoxin.
Exogenous LGI1 Exogenous LGI1 was produced in 293E cells (RRID: CVCL_8869) and purified using a two-step tandem purification as previously described  (Thomas et al. , 2010). LGI1 protein was eluted in PBS and purity was assess by silver staining SDS page gels. The concentration of the purified protein was determined to be 16. 75 M. LGI1 was first added to complete neural basal growth media at 750 nM concentration and for control treatment an equal volume of PBS was added. Treatments were added as a one third media change for a final LGI1 concentration of 250 nM.