dataesr/scientific-paragraphs-categorization huggingface dataset:
). Overall, 943 patients on CPAP or APAP n=30 235 patients in ESADA database n=877 sleep studies excluded Type unspecified (n=736) Limited channel (n=138) PAP log data (n=3) n=29 358 sleep studies included Attended PSG (n=14 616) Unattended PSG (n=353) PG (n=14 388) n=5145 AHI <5 h per h or unspecified AHI <5 per h (n=4687) AHI unspecified (n=458) n=24 213 with AHI ≥5 per h n=9714 missing data needed for phenotyping ESS (n=617) Avg. sleep length (n=1482) Insomnia diagnosis (n=422) Atrial fibrillation (n=6128) Hierarchical filtering SysBP and/or DiaBP (n=1065 n=14 499 cohort for final analysis n=1081 follow-up data available (time frame 24-36 months) n=138 treatment other than CPAP/APAP n=943 follow-up on CPAP/APAP # ) FIGURE 2 Flow-chart of the inclusion process. ESADA: European Sleep Apnoea Database; PAP: positive airway pressure; PSG: polysomnography; PG: polygraphy; AHI: apnoea-hypopnoea index; SysBP: systolic blood pressure; DiaBP: diastolic blood pressure; CPAP: continuous positive airway pressure; APAP: automatic positive airway pressure.
An important perspective is the development of methods for the fully automated recognition of cortical folding patterns  (Snyder et al. , 2019; Borne et al. , 2021). An alternative approach is based on graph-based analysis of the spatial organization of the ''sulcal pits''  (Im et al. , 2011; Takerkart et al. , 2017) , the local maxima of depth of the cortical surface  (Lohmann et al. , 2008). A majority of these methods to visualize and analyze the sulcal morphology are freely available in ''Morphologist'' toolbox, within BrainVISA software 1.
We offer and recommend posttest support and followup to all individuals, regardless of the results of the test. Data retrospectively collected from subjects' medical records, information about their motivation to take the test, and familial disease load of either disease were reviewed. We calculated a familial disease load score for each individual as follows: each first-degree relative with ALS or FTD scored 1 point, each second-degree relative with ALS or FTD scored 0. 5 points, and other more distant relatives with one of these diseases scored 0. 1 points. The sum for each individual was used to evaluate the final score of disease load for each phenotype in their families. This score has not been validated and is thus only indicative. Statistical results involving this disease load score should be interpreted with caution.
This approximation suggests that there is a higher percentage of demands for PT for ALS/FTD than HD. With a few exceptions,  15  published studies report low uptake rates of PT among at-risk populations, ranging from 7% to one-third of the subjects at risk. 23 Our study suggests that there is a nonnegligible number of demands for PT arising from the relatively rare genetic diagnoses. Familial ALS cases will likely be increasingly sought and recognized in the era of new promising gene-specific treatments for the 2 most common genes in familial ALS: SOD1 24 and C9ORF72 (NCT03626012). The association of an increase in the number of genetic diagnoses and the hope of specific treatment may lead to an increase in the demand for genetic counseling and PT in affected families. The FTD field appears, in particular, to be ahead in the generalization of genetic discussions and the sampling of patients with FTD.
We believe that the practices between the ALS and FTD communities should be harmonized. Our study illustrates how virtually the same families may encounter both diseases in most cases. Despite our efforts to ascertain any potential bias, certain limitations could not be circumvented. First, we collected the data retrospectively. Certain relevant information that was not systematically collected was unfit for analysis. Furthermore, the results all came from a single center under the French legal frame. Finally, the follow-up of testees is ongoing, and thus, the outcome is pending. However, our principal aim was to understand the uptake and interest in ALS/FTD PT on the basis of our experience and contribute to discussion in this field. In conclusion, requests for PT for ALS and FTD have been progressively growing over the last several years and may continue to do so.
Cell Quantification Quantifications were performed in the DDG granule cell layer of 12 sections per animal (n = 6 to 7/group) separated by 50 µm. BrdU + , BrdU + /NeuN + and BrdU + /GFAP + cells were counted manually with an Olympus microscope (BX60) coupled with mapping software (Mercator Pro, Explora Nova, La Rochelle, France) or with a confocal microscope (Carl Zeiss MicroImaging, LSM 780 NLO, Jena, Germany) at objective x20. The total surface of granule cell layers and the DDG were established by software (BX60 microscope, Mercator Pro; Explora Nova, La Rochelle, France) to obtain the densities of BrdU + cells, i. e. , the number of BrdU + cells/µm 2 of surface. To obtain the percentage of BrdU + /NeuN + and BrdU + /GFAP + cells (38-to 43-day-old), the phenotype of at least 300 BrdU + cells per group was analyzed.
Statistical Analysis Data normality was checked by Q-Q plot methods. One (dose or model) or two-way (dose x model) repeated-measures ANOVA was used for intra-and inter-group analysis to study spatial learning performances. To compare the interaction dose x model, we randomly chose eight sham mice to become the respective controls for both models. One simple t-test relative to 25% was used to compare long-term memory performances against chance, during the probe test. An intergroup comparison was made using t-test if the data were distributed according to the normal law, otherwise, a Mann-Whitney was applied (Sigma ® v11. 0). For immunochemistry data, a general equation analysis was conducted for intergroup comparisons (R version 3. 4. 4, R Core Team, Boston, MA, USA; version 1. 1. 423, RStudio).
Acknowledgments: The authors thank the GSEA of  IRSN  for technical assistance with the animals,  Gwennhael Autret  of  INSERM UMR970, PARRC-HEGP, Paris, France, from the "Plateforme Imageries du Vivant, Université Paris Descartes, Sorbonne Paris Cité, Faculté de médecine, Paris, France " for MR imaging and also  DR S. Laroche  for advice and discussion. The authors are also grateful to  A. Villard ,  A. Legendre  and  M. Benadjaoud  from  IRSN  for their advice on statistical studies.
To ensure methodological rigor, acquisition and filtering pipelines, as well as raw data, were reported  ( Leff et al. , 2011 ). To discriminate between physiological noise and cerebral meaningful signals, the physiological data (i. e. , heart and respiratory rates) were recorded throughout the experimental session so that frequency bands of such signals could be regressed from the f NIRS data. An automatic tracking of the headset was also used to monitor the exact position of the optodes in reference to the cranio-cerebral correlates of the NIR channels. Motor timing at the spontaneous motor tempo would lead to less prefrontal (i. e. , anterior and dorsolateral prefrontal cortices) and motor (i. e. , premotor and primary motor cortex) activation when compared to performing the motor task at either fast or slow tempi (  1 ).
Furthermore, action production at slow tempi would lead to a significantly greater increase in cerebral oxygenation over the prefrontal lobe when compared to task execution at a fast tempo, for which the increase in cerebral oxygenation would involve motor areas (  2 ). Similar patterns Fig. 1. Raw f NIRS data from a participant with hair vs. a participant without hair. Note. Raw data for HbO 2 , HHb and HbT. Panel A: A typical participant with hair. Pannel B: A typical participant without hair. These data were obtained from a pretest using two participants who performed the finger-tapping task synchronized to an auditory metronome at an interstimuli interval of 500 ms. HbO 2 = oxygenated hemoglobin; HHb = deoxygenated hemoglobin; HbT = total hemoglobin. of activation would be observed regardless of the complexity of the motor task being undertaken (  3 ).
Participants Healthy adults were recruited for the present study from among the corpus of University of Lille staff and students. Participants were right-handed  ( Oldfield, 1971  , assessed by use of the Edinburgh Handedness Inventory;), had normal to corrected-to-normal vision, and did not present motor dysfunction or neurological/psychiatric disorders. A pilot test conducted on two participants confirmed that both hair density and length could induce significant noise in f NIRS data (. 1 ). Ongoing work in our laboratory is targeting potential solutions to address this limitation that pertains to f NIRS technology. Nevertheless, for the present study, only male participants with very short haircuts ( < 1 cm) or shaven heads were included to avoid hair-related issues  ( McIntosh et al. , 2010; Pringle et al.
, 1999 ). Participants were informed of the tasks to be performed and the measurements to be taken at least 48 hr prior to their participation. After reading an information sheet, each participant was invited to provide written informed consent. At this point, demographic data were collected (sex, age, and musical expertise). The sample size required for the critical statistical test of each research hypothesis was calculated using G * Power (3. 1. 9. 2; see Table  1  ). For  1 ,  2 , and  3 , the f NIRS results of  Abiru et al. (2016)  were used as group parameters. For  1 and  2 , Tukey post hoc tests were the critical statistical tests. Because a Tukey test is essentially a modified t statistic that corrects for multiple comparisons, required sample size was also computed for paired-samples t tests.
Behavioral data acquisition and preprocessing In the tapping and pointing tasks, inter-response intervals (IRIs) were measured as the time interval between the onset of successive taps. In the drawing task, radiuses from the center of the circle to each target were computed first. Taps were defined as the locus that intersected the participant's finger and each radius. Before conducting the main analyses, the time series were checked in order to detect and remove the IRIs greater than twice the ISI of the given block of trials. These trials were referred to as "temporal omissions " and were not included in the statistical analyses. An IRI error ( IRI error ) was computed as the percentage of absolute difference between an IRI and its reference ISI for a given time interval  ( Eq. (  1 ) ).
f NIRS data acquisition Data were collected using a continuous-wave f NIRS system operating at three near-infrared wavelenghts (780 nm, 805 nm, and 830 nm) and monitored by the associated LabNIRS software. This f NIRS system offers 32 optodes divided into 16 light sources (multicomponent glass bundle fibers) and 16 detectors (multi-alkali photomultipliers detectors). The sampling frequency was set at 2. 27 Hz (i. e. , temporal resolution of 440 ms). The brain regions of interest were the prefrontal cortex (anterior and dorsolateral prefrontal cortices; Brodmann's areas 9 and 10) and motor cortex  ( Homan et al. , 1987; Okamoto et al. , 2004  , premotor and primary motor cortices; Brodmann's areas 4 and 6;). Thus, a 45-channel (28 optodes, 45 source-detector couples) configuration was applied in order to cover these cortices over both brain hemispheres ( Fig.
Controlling for noisy signals Data contamination caused by movement and physiological artifacts in f NIRS is an important consideration with regard to reaping the full potential of the technique for real-life applications  ( Jahani et al. , 2018 ). In the present study, we applied tracking methods to identify sources of noisy data. The synchronization of the different systems was controlled by means of Matlab algorithms. The central command computer controlled for motor tasks; it sent triggers to the other apparatus -including the f NIRS system -to segment the times of onsets and offsets in an automated manner. A similar triggering system was used in our previous studies  ( Blampain et al. , 2018 , e. g. ,) , which revealed a synchronization error of ~35 ms. Visual tags were used to identify the beginning and end of each trial, with specific tags for each trial type.
Cardiorespiratory monitoring. By use of the Fourier transform method, both heart and respiratory rates can be identified in the f NIRS frequency spectrum. The ability to identify these two frequency components serves to ensure the validity of f NIRS measures. In terms of frequency, the neurophysiological detail in the f NIRS signals is lower than that of both heart and respiratory rates  ( Pinti et al. , 2019 , ~2 Hz and ~0. 3 Hz, respectively). Thus, these physiological components were filtered out, which restricted any potential contamination from the raw f NIRS signal. Cardiorespiratory monitoring was done using the MP150 Biopac system (Biopac Systems, Goleta, CA), complemented with two dedicated add-on wearable devices. To avoid recording movement artifacts, heart rate (HR) data (Hz) were captured by use of an ECG Bionomadix module (wireless transmitter and RPEC-R amplifier) and low-pass filtered to 1 Hz.
Two disposable patch electrodes were placed on the participant's right and left clavicles. Respiration rate (Hz) was recorded using the TSD201 respiratory effort transducer, which was wired to the RSP100C amplifier. This respiratory belt was placed around the chest wall, at the level of the sternum. Sampling frequency was set to 250 Hz. Data acquisition was facilitated by the AcqKnowledge software that is included in the MP system. 2. 3. 4. 2. Headset position tracker. Data were collected using three Oqus 5+ cameras (Qualisys MoCap, Göteborg, Sweden) to control for any shift in the headset. The spatial positions were measured in real time using three spherical passive markers taped to the participant's right temple and headset (with the use of one and two markers, respectively). The distance between the temple marker and each of the two headset markers was computed (in cm) based on Cartesian coordinates (i.
e. , x, y , and z ). The Real-Time Motion Capture (RTMocap) Matlab toolbox  ( Lewkowicz and Delevoye-Turrell, 2016 )  was used to interpolate any missing data. The spatial accuracy of the system is 0. 5 mm for each dimension of 3D space. To verify the occurrence of a f NIRS helmet shift, a deformation calculation of the area of the planar triangle connecting the 3D markers was used. This was referred to as the mesh area. Notably, if the 3D markers remain in the same place with respect to each other, the mesh area remains constant. On the other hand, if the two markers of the headset move relative to the reference marker (i. e. , temple marker), the mesh area is modified. The mesh area between the three markers   , with  ∈ {0; 1; 2} , at a given moment in time  ∈ [1; ] , with d defined as the acquisition duration, was computed using a scalar product ( Eq.
Statistical analyses 2. 4. 1. Data eligible for analysis HbT reflects the overall changes in corpuscular blood volume of the sampling volume. Because HbT is a summation of HbO 2 and HHb changes (see Eq. (  4 ) ), it was not statistically analyzed. The behavioral data were analyzed and reported in a supplementary online file. HHb and HbO 2 data were analyzed only from those blocks of trials characterized by (a) at least 70% level of behavioral accuracy, and (b) an absence of headset shift. A participant's entire data set was removed if > 25% of his data were ineligible and any excluded participants were replaced. Given that an absence of cerebral activation can be informative, both activated and non-activated channels were taken into consideration. In addition, the ratio of activated to non-activated channels (i.
Preprocessing Given that our f NIRS system does not provide access to raw intensities, preprocessing was performed directly on Δ HbO 2 and Δ HHb concentrations. This was achieved using Matlab, with both personal code and algorithm adapted from the Matlab-based toolbox Homer2 (Massachusetts General Hospital, Boston, MA, USA). The preprocessing steps are detailed in Fig. 5. The f NIRS literature details several filtering methods  ( Herold et al. , 2018; Hocke et al. , 2018; Pinti et al. , 2019 )  and a hybrid filtering of the f NIRS time-series was applied in the present study  ( Jahani et al. , 2018 , cf. ). First, the wavelet-based method was used to perform motionartefact correction, as it appeared to apply particularly well to our dataset, and has been shown to be relatively efficient  ( Hocke et al.
e. , an absence of HR in the power spectrum of f NIRS signals). Only Δ HbO 2 raw data were analyzed, as HbO 2 is more sensitive to cardiac oscillation than HHb  ( Pinti et al. , 2019 ). A two-step process was used to check the 45 channels for each participant: First, applying power-spectrum density (PSD; i. e. , frequency domain) to the raw data, the frequency corresponding to maximal peak in the 50-160 beatper-minute (bpm) range was detected. Second, to guarantee that the identified frequency was indeed the HR frequency, it was compared to the HR measurements provided by the Biopac system, with a tolerance threshold of 7 bpm. Following the aforementioned steps, three participants were excluded due to an absence of heart beat oscillations in the f NIRS signal across all channels pertaining to at least one region of interest.
Motion correction: wavelet filtering. f NIRS signals recorded dur- ing body movements are prone to motion artefacts. Accordingly, motion correction was performed to remove motion-induced sharp and fast skips from the raw f NIRS time series (see Fig. 7  ). To this end, the wavelet-based smoothing method described by  Molavi and Dumont (2012)  and implemented in Homer2 (hmrMotionCorrect_Wavelet function, interquartile-range [iqr] = 1. 5) was adapted to process concentrations rather than of optical densities. The motion-corrected data were visually inspected to ensure that the selected iqr value was well suited to the present dataset.
(Siemens Artis Q, Siemens Healthcare, Erlangen, Germany). The protocol used for 4D DSA acquisitions was: 70 kV, pulse width: 12. 5 ms, field of 32 cm, rotation of 260, acquisition time of twelve seconds, and a projection acquired every 0. 85 • (305 projections). The injection rate was 3 ml per second (Mark 7 Arterion, Medrad, Beek, Netherlands), without delay period or dilution of injected contrast medium (Iomeron 250). Post-treatment reconstructions were performed with supplied software (Syngo4D) from the Artis series (VD11). Two time resolved 3D volumes were selected, the first one performed at maximal shunt time to visualize the nidus (Fig. 2 A  and C ), and the second at venous time to visualize normal venous anatomy (Fig. 2 B  and D ). To be compatible with the neuronavigation system, data extraction of time resolved 3D volume selected was done using DICOM.
The neuronavigation system used was Medtronic StealthStation S7 ®, using provided visualization software (StealthMerge and Stealth3D). Fusion was done with 3D Flair MRI and the two selected 3D volumes. Segmentation concerned three elements used for surgical planning: the "cortical surface" of the brain (sulco-gyral anatomy with 3D Flair MRI sequences), the microAVM nidus (using the selected 3D volume at maximal shunt time using Stealth3d software) and the normal venous anatomy (same process at normal venous time) (Fig. 3 ). For neurosurgical planning, pre and peroperative visualization of "the cortical surface", "microAVM", and "normal venous anatomy" was used to localize the nidus (Fig. 3 ). Indocyanine Green Fluorescent Angiogram (IGFA), as routinely performed intraoperatively (Fig. 4 ).
Discussion The present study aimed to compare inhibitory control in children with ADHD and TD children when they were performing a Simon task. We evaluated both reactive and proactive inhibition. The classical analysis of RTs and error rates revealed two main results. First, we observed a larger intra-individual RT variability in children with ADHD compared to TD children. This result is consistent with data found in prior studies, which report increased intra-individual RT variability in various RT tasks  (Klein et al. , 2006; Lipszyc & Schachtar, 2010; Tamm et al. , 2012). This is also consistent with data obtained in the attentional D2 test, which revealed poorer performance in children with ADHD than in TD children. Previous work has proposed that the high intra-individual RT variability of participants with ADHD reflects attentional lapses, corresponding to fluctuations in sustained attention  (Sonuga-Barke & Castellanos, 2007; Tamm et al.
, 2012). Processing of stimuli that arrive during attentional lapses could be impaired and/or delayed, thereby increasing the global variability of RTs in the Simon task, or indeed all other tasks requiring fast responses. But the exact nature of mechanisms and processes responsible for increased RT variability seems not yet clearly identified (for review, see  Kofler et al. , 2013; Kofler et al. , 2014). Second, children with ADHD committed more errors without being faster than TD children, suggesting that they have difficulties with conflict monitoring. This was confirmed by the larger interference effect observed in children with ADHD relative to TD children. These results confirm data from previous studies  (Lansbergen et al. , 2007; Mullane   et  al. , 2009)  and support the idea of a deficit in conflict monitoring in children with ADHD.
, 2012; Jiang et al. , 2021) , and maturation would be reached only in adulthood. In conclusion, our results support the claim that ADHD affects reactive inhibitory control. This is consistent with data coming from neuroimaging studies which show that children with ADHD have abnormal activation in pre-supplementary motor areas (pre-SMA), the right inferior frontal gyrus (rIFG), the subthalamic nucleus, and the globus pallidus  (Booth et al. , 2005; Dickstein et al. , 2006; Rubia et al. , 2010)  which are areas known to be involved in reactive inhibitory control  (Aron, 2007; Forstmann et al. , 2008).
ADHD does not affect proactive inhibitory control We investigated proactive cognitive control through conflict sequential effects (CSE). Our data showed similar CSE for both groups, children with ADHD were able to enhance the level of conflict monitoring after an incongruent trial suggesting normal proactive control. Our data is consistent with some recent findings. An intact CSE has been found in children with ADHD with a modified version of the sequence flanker task  (Bluschke et al. , 2020). In addition, proactive but not reactive inhibition has been found to be normal in ADHD children in a study which used a modified version of the stop signal task to dissociate the two  (van Hulst et al. , 2018). Different models have been proposed to explain the CSE in conflict tasks (for a review, see  Egner, 2007).
Importantly, these mechanisms could not be exclusive. Indeed, CSE could come from additive contributions of conflict adaptation and feature integration effects (for review, see  Egner, 2007; Notebaert et al. , 2006). Finally, adjustements of attentional focus could also be based on subjects' expectancies regarding the nature of an upcoming trial  (Egner, 2007). To conclude, our data show that proactive inhibition is preserved in children with ADHD whose conflict-monitoring improved after incongruent trials, similar to that observed in TD children. We propose that conflict monitoring results from attentional adjustments and, more specifically, from a refocusing of attention on a specific event or on taskrelevant information  (Egner, 2007). Therefore, preserved proactive inhibition in children with ADHD could indicate a preserved ability to efficiently select relevant information or, in other words, preserved selective attention.