dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors are indebted to  Prof. Klaus-Armin Nave  and  Prof. Michael Sereda  for CMT1A rats kindly supplied by the  Max Planck Institute for Experimental Medicine (University of Göttingen, Germany). We thank  Emilie Pinault  for the proteomic analysis and  Stephanie Durand  for the following statistical tests,  Dr. Mathilde Duchesne  for his expertise in muscle histology and  Sandrine Robert  and the entire anatomo-pathology service for histological sections, and  Claire Carrion  for her help in microscopic analysis, as well as the animal facility in the  Faculty of Medicine and Pharmacy-Limoges. We thank the ' Conseil Régional du Limousin ' for financial support. We are indebted to  Dr. Ian Darby  ( Melbourne, Australia ) for professional editing of this manuscript.
FUNDING This work was supported by grants from the  Fond National de Recherche  within the  PEARL programme  ( FNR/P13/6682797 ) and the  National Centre for Excellence in Research on Parkinson's disease  ( NCER-PD ) programme and by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No 692320 (WIDESPREAD;  CENTRE-PD ).
Data were digitized with Digidata 1550, collected with Clampex. The amplitudes of EPSCs were analyzed with Clampfit and graphs were drawn with Excel. The amplitudes were normalized to the mean of the 10-min baseline recording before cholinergic pairing or disinhibition pairing. Values were presented as mean ± SEM. EPSCs were evoked every 60 seconds by stimulating the SC pathway with an electrode placed in the stratum radiatum through a stimulator (Grass S88X). The stimulation intensity was 1-10 µA for 0. 1 ms. To study the effects of cholinergic coactivation on SC to CA1 synaptic plasticity in Figure  2 , cholinergic terminals in the hippocampus were optogenetically activated (10 pulses at 10 Hz, 1 sec before SC stimulation) through ChR2 that was selectively expressed in ChAT-cre positive (cholinergic) neurons.
Acknowledgments This research was supported by the  Intramural Research Program  of the  NIH, National Institute of Environmental Health Sciences. IG and BSG acknowledge support from the  Fondation pour la Recherche sur Alzheimer ,  CNRS ,  INSERM ,  ANR-17-EURE-0017  and  ANR-10-IDEX-0001-02. BSG acknowledges funding from  HSE   Basic Research Program  and the  Russian Academic Excellence Project  " 5-100. ".
Manual placement Manual ROI placement consists in meticulous delineation of anatomical regions (in a subject or group of subjects), which are often small enough to isolate a specific group of fibers, yielding precisely defined bundles. This kind of approach is highly time consuming, limiting its application to only a few subjects and a reduced amount of regions. Also, this approach depends on the expertise of the operator, which limits its reproducibility. One of the first works of this kind was the one developed by  Catani et al. (2003). The fiber segmentation was performed from a DTI dataset averaged across 11 subjects, and only for the occipital and temporal regions. This work was not specifically focused on the short fibers and therefore no detailed description about them was given, just the mention of their existence within the data and a rough description of their position.
Another example of manual segmentation of short fibers is the work by  Wu et al. (2016). The authors utilized regions of avoidance (ROA) (i. e. region used to exclude fibers passing through it) drawn in DSI studio software 1  in order to extract SWM connections. They focused in the temporal, parietal and occipital lobes. The segmentation was performed over the DSI data of 10 subjects and a publicly available template composed by 90 subjects (NTU-90 Atlas, composed by the averaged DSI data from 45 males and 45 female subjects)  (Yeh and Tseng, 2011). These results were compared with previous DTI reports, evidencing that higher resolution images allow a better description of the connectivity patterns. Postmortem dissections were also performed showing high agreement with the tractography results.
Another example is exposed in  (Burks et al. , 2017) , where the authors used 10 subjects from the HCP database to study the connections in the inferior parietal lobe. They applied user-defined seed ROIs, based on expert knowledge, in DSI studio software for tracking the fibers and isolate the tracts. These fiber tracking were also validated by postmortem dissection. The fact that this study was only focused in a specific region of the brain, allowed a good anatomical and functional description of the fiber bundles connecting the analyzed areas. The authors found short fibers connecting the supramarginal and angular gyri, and connecting both of these gyri to the superior parietal lobule. Finally, in a region-specific study of the parietal lobe  (Catani et al. , 2017) , the authors studied short connections in human and monkey brain.
This is one of the few works that compare short fiber bundles across different species. They performed the analysis over the data of 21 alive humans and 11 ex vivo datasets, 5 vervet and 6 macaque monkeys. As in most of manual segmentations, only few regions were considered in the study: postcentral gyrus, superior parietal lobule and inferior parietal lobule. The results show a close correspondence between the connections in human brains and in monkey brains (these connections in monkey brains have also been described by axonal tracing methods), showing the evolutionary link between these species and their implication in human functions. The authors also performed a postmortem dissection in order to keep only the bundles found both in tractography and in the actual brain. The postmortem dissection was performed based on the Klingler method  (Ludwig and Karger, 1957) , using the brain of a male donor.
e. different fibers shapes and positions) within them. Although automatic placement of ROIs can ease the extraction of fibers in the whole brain, some works have also utilized this approach to study specific brain regions. This kind of studies can benefit from atlases already defined and available, and obtain a higher ROI reproducibility across subjects. For instance, in  (Bozkurt et al. , 2016)  the authors focused in the anatomy of the supplementary motor area complex. Although the main idea of the work was to segment fibers in postmortem brains, the authors were also able to segment SWM in the pre-SMA and SMA from the tractograms of 2 subjects from the HCP database. The findings regarding the SWM unveiled a short fiber network within this functional areas, however only information about the regions they connect was given, lacking a proper bundle-based description of these fibers.
Semi-automatic placement Finally, combining manual and automatic ROI placement leads to semi-automatic definition of ROIs. There are some works that use this approach to get good results in a reasonable time. For instance, in  (Vergani et al. , 2014a)  the authors utilized the Desikan-Killiany Free-Surfer brain parcellation  (Desikan et al. , 2006)  for the study of the SWM fibers connecting the supplementary motor area with the precentral gyrus and Broca's area in 10 subjects from the HCP dataset. The authors manually added some exclusion regions in order to remove spurious fibers. Since this cortical parcellation is based on major gyri, the ROIs were too large, so these kind of filters allowed the authors to better delineate the bundles. They described five connections within the supplementary motor area.
On the other hand SWM presents more complex configurations and overlapping, which makes them more difficult to reconstruct and variable across subjects. Their entanglement hinders the identification of homogeneous groups and outlier removal even with clustering methods. Despite this difficulty, in the past few years there have been some clustering-based studies focused in SWM. For instance, in  (Guevara et al. , 2012)  the authors used an agglomerative average-link hierarchical clustering and a fiber Euclidean distance measure to group short fibers in the whole brain. The analysis was performed using a HARDI database of 12 subjects, and resulted in an atlas of 47 SWM bundles in the left hemisphere, that were then manually labeled in function of the connected regions. The underlying study was focused in DWM, therefore the clustering was not tuned for SWM fibers.
This could be due to the shape of the fibers, only able to be reconstructed using high quality data because of high curvature. Also, they found that humans present a higher amount of short fibers, which they presume might be due to their relation with the gyrification of the cortex. In  (Yeh et al. , 2018)  the authors clustered by means of single-link clustering both long and short fibers reconstructed from the HCP-842 SDF template using DSI studio. This template was construced from the average of the spin distribution function of 842 subjects (in ICBM-152 standard space) from the HCP dataset, and represents an average diffusion pattern within a normal population. The clusters obtained were then labeled by neuroanatomist experts. However, the association fibers were only included into a generic category and no further information of bundle shape or the regions they connect was given.
Another work, presented in  (Zhang et al. , 2018a) , also performed a clustering for the whole brain over the data of 100 subjects from the HCP database. The aim of this study was to create a curated bundle atlas. First, the tractograms were directly aligned across subjects. Then the authors applied a group-wise spectral clustering and labeled the clusters (bundles) according to a FreeSurfer ROI atlas  (Desikan et al. , 2006). A second dataset composed of 584 subjects coming from different databases was conceived for segmentations purposes. Different type of subjects were considered, including data from newborn babies, autism spectrum disorder, neuropsychiatric disorders (schizophrenia, bipolar disorder and attention deficit), Parkinson disease, brain tumors and healthy subjects. The atlas results were therefore projected to the 584 subjects of the second dataset, showing a high reproducibility of the bundles.
Following this idea, in  (Guevara et al. , 2017)  a FreeSurfer parcellation  (Desikan et al. , 2006)  is applied to the tractography data of 79 subjects of a HARDI database. This parcellation was used to extract fibers with length 20-80 mm, connecting two cortical ROIs (adjacent or not). To overcome the entanglement of the fibers connecting two ROIs that are often large, a first intra-subject hierarchical clustering was applied to group fibers. This yielded groups of fibers with similar shape and position within each pair of regions (bundles). A second clustering was then performed across subjects, for keeping only the bundles present in most of the population. These results were validated by applying the method to two different groups and by automatically projecting the bundles into a third group.
Comparison of atlases As already mentioned before, most of the short fibers studies have been restricted to specific areas or bundles. Bundles are often represented in image volumes as probability maps, or tractography datasets. In order to compare the bundle output generated from different methods, we compared the publicly available atlases described in  (Guevara et al. , 2017 ) (atlas1) (Rom an et al. , 2017), (atlas2) and  (Zhang et al. , 2018a) (atlas3). Some bundle examples can be seen in Fig. 1. The bundles from atlas1 can be interactively visualized online as volumes and meshes along   brain anatomical references in the HBP website. 4 Their probability maps can also be downloaded. 5 Atlas2 is available upon request from the authors, 6 and atlas3 is available at: https://dmri. slicer.
org/atlases/. These atlases were constructed using two different datasets and three different approaches. Though our comparison we intend to evaluate the consequences of the method and input data utilized for the inference of the atlases, as well as the consistency of the bundles obtained across them. Both atlas1 and atlas2 were constructed from a HARDI database (ARCHI)  (Schmitt et al. , 2012) , and for atlas3 the HCP database was used. Atlas1 (50 bundles in each hemisphere) and atlas2 (44 bundles in the left hemisphere and 49 in the left hemishpere) are composed only of short bundles, therefore each bundle in one atlas is compared against the ones from the other. For both of these atlases, the bundles composing them are named based on the cortical regions (major gyri) they connect, using the Desikan-Killiany atlas  (Desikan et al.
Then, a bundle is considered to be similar or contained in another one, if its percentage of similar fibers is at least of 80%. Notice that when one bundle fulfills this amount of similarity to the fibers of another bundle, it does not mean that the reverse property is true. In some cases a bundle in one atlas is over-segmented into a group of bundles in another atlas. This could be due the better spatial resolution in the underlying dataset or the clustering algorithm. Then in order to perform a strict comparison, the equivalence between two bundles requires the threshold to be reached in the two directions. The results for this experiment are presented in Table  2 , where atlasX atlasY presents the results of the comparison between two atlases, including the number of similar bundles in each atlas and their average similarity percentage.
3a  (100 bundles). Next, atlas2 used a minimum connectivity criterion of 50% of the fibers connecting a pair of defined regions (also based on Desikan-Killiany atlas). Hence, we selected the bundles present in atlas2 that were not identified in atlas1, displayed in Fig. 3b  (17 bundles). A main difference between atlas1 and atlas2 is that atlas2 includes intra-gyri bundles which are not detected by atlas1 method. Also, as atlas2 method used non-linear registration, some extra bundles were detected connecting adjacent gyri. On the other hand, atlas3 was constructed from a superior quality database (HCP) (HCP Young Adult, 1200), with higher spatial resolution and a multi-shell acquisition, which increases the amount of tractable short fibers and the possibility to disentangle large U-fiber bundles into smaller clusters.
, 2019). Using a subject of the HCP database (HCP Young Adult, 1200) the authors found that probabilistic tractography, based on constrained spherical deconvolution  (Tournier et al. , 2007) , calculated using MRtrix software 8 lead to a better reconstruction of some SWM bundles than deterministic tractography based on Generalized Q-sampling Imaging (GQI)  (Yeh et al. , 2010)  using DSI Studio software. The study also found that SWM bundles are more sensitive to the number of fibers used in tractography than DWM bundles. A value of 1:5M fibers was found to be adequate for DSI Studio and 3M fibers for MRtrix. The sensitivity to other parameters was not quantitatively evaluated, but a set of parameters for both softwares was empirically tuned for SWM reconstruction (listed below). We extended this evaluation to the compilation of SWM bundles from the three atlases, described in section 5 and listed in Tables A.
8 and A. 9 (Fig. 3 ). For the analysis, we used multi-shell diffusion MRI of 50 subjects of the Human Connectome Project (HCP) database (HCP Young Adult, 1200), from the 1200 Subjects Data Release (S1200 Data). The dMRI data was collected over three b-values (1000, 2000, 3000 s= mm 2 ), with an isotropic voxel size of 1:25mm. We worked with the HCP pre-processed data  (Glasser et al. , 2013) , where diffusion imaging distortion corrections were applied  (Andersson et al. , 2003; Andersson and Sotiropoulos, 2015). Diffusion data is aligned to the native structural space. We calculated whole brain tractography in the subject space, for different numbers of fibers, using DSI Studio, based on the Generalized Qsampling Imaging (GQI)  (Yeh et al. , 2010)  method. Deterministic tractography algorithm  (Yeh et al.
, 2013)  was applied using the following parameters: angular threshold ¼ 90 ∘ , step size ¼ 0:25mm, minimum length ¼ 30mm, maximum length ¼ 250mm, smoothing ¼ 0. 5, and QA threshold ¼ 0. Datasets were calculated for 500k, 1M, 1:5M and 2M streamlines. Furthermore, using the same parameters, the tractographies were also calculated based on DTI model, using DSI Studio. We also calculated tractographies using MRtrix, based on constrained spherical deconvolution model  (Tournier et al. , 2007) (IFOD2). Probabilistic tractography  (Christiaens et al. , 2015)  with Anatomically-Constrained Tractography (ACT)  (Smith et al. , 2012)  was calculated, using a step size ¼ 0:1*voxelsize, angle threshold ¼ 90 ∘ , minimum length ¼ 30mm, maximum length ¼ 250mm and cutoff value ¼ 0. 06. Finally, a Spherical-deconvolution Informed Filtering of Tractograms (SIFT)  (Smith et al.
, 2013)  was applied, keeping the 10% of the fibers. Final datasets of 1M, 2M, 3M, and 5M streamlines were calculated. Then, an automatic WM bundle segmentation algorithm  (Labra et al. , 2017)  was applied to each dataset to extract the bundles included in the compilation of SWM atlas bundles described above and listed in Tables A. 8 and A. 9. The algorithm calculates a distance between each fiber of the atlas and each fiber of the subject's tractography. It uses the maximum Euclidean distance between corresponding points of the two fibers, normalized by the difference between the length of the atlas fiber and the fiber of the subject (d me). Before this processing, the fibers were transformed to MNI space, by applying the non-linear transformation from structural to MNI152 space, calculated with FNIRT software  (Andersson et al.
, 2007) , provided with the HCP database. The segmentation algorithm uses a distance threshold parameter, that was set between 6 and 8mm for deterministic tractography, according to the bundle length  (Rom an et al. , 2017). In the case of MRtrix, with probabilistic tractography, fibers have more irregular trajectories, hence higher thresholds, between 8 and 10mm, were used. A first analysis evaluated the difference between the segmentation results using different number of fibers, based on a fiber similarity measure, as described in  (Rom an et al. , 2019). The tests were conducted for the three local diffusion model/tractography configurations: DTI and deterministic tractography with DSI Studio (called DTI), GQI and deterministic tractography with DSI Studio (called GQI), and IFOD2 and probabilistic tractography using ACT þ SIFT with MRtrix (called MRtrix).
The comparison was performed between the dataset with more fibers and the remaining datasets, separately for each configuration. We calculated the percentage of fibers reconstructed for each smaller dataset, and observed a saturation trend for the maximum number of fibers. The comparison confirmed that 1:5M fibers for GQI and 3M fibers for MRtrix are sufficient to obtain about a 95% of fiber similarity with respect to the maximum tested value (2M and 5M fibers, respectively), without any visual difference between the segmented bundles and also no significant difference when comparing bundle masks with 1:25mm of resolution. For DTI, a number of 1:5M was found to be appropriate, with a 97% of fiber reconstruction with respecto to 2M fibers. Next, we evaluated the reproducibility and variability of the SWM bundles across the 50 subjects for the three local diffusion model/tractography configurations (DTI, GQI and MRtrix).
, 2014). Here, the authors defined ROIs from fMRI activations in prospective memory tasks, and then created a common ROI for the three category of subjects: healthy young (13), healthy older adults (13) and patients with mild Alzheimer disease (17). The ROI was warped into the DTI map of each subject in order to reconstruct the fibers passing through. Only short fibers measuring from 4 mm to 35 mm were selected. Increased values of the MD, RD and AD in the SWM of the temporal region have also been reported to be related to Alzheimer disease  (Reginold et al. , 2016). The study utilized tractography datasets calculated from the DTI data from 24 controls and 16 patients. Fibers of interest were segmented by manually placing ROIs for each lobe. Another study showed that the values of FA are inversely related with age in the SWM for healthy individuals, especially in the fronto-parietal and occipital regions  (Nazeri et al.
, 2015). The study was carried out using the DTI data of 141 healthy individuals, across the adult lifespan (18-86 years old), from which fibers were later reconstructed using probabilistic tractography. Only SWM fibers were selected by seeding exclusively in the cortical gray/white matter boundary and excluding DWM using exclusion masks. In order to perform the analysis, the SWM tracts were then binarized and averaged across subjects, creating a SWM probabilistic mask. The mask was used to obtain the mean FA, which was then skeletonized to perform TBSS analysis. Additionally, a MNI probabilistic atlas of cortical structures was employed to identify the SWM from frontal, parietal, occipital and temporal areas. Another study focused on age changes in SWM is presented in  (Malykhin et al. , 2011).
The authors utilized 69 DTI datasets from subjects aged 22-84 years for studying the prefrontal white matter connections, since functions in this lobe generally decline with age. In order to do that, they manually parcellated the prefrontal white matter by placing ROIs. The results showed that there is an increase in the AD and RD with age, starting from the 60s. Other age-focused work studied the development of the SWM in children and adolescents  (Wu et al. , 2014). The authors measured the FA, MD, RD and AD in 133 healthy subjects aged 10-18 years. These diffusion measures were averaged along the direction normal to the WM surface and projected into a WM template from FreeSurfer. The analysis results showed an increasing FA and decreasing MD and RD as the age increases, beneath bilateral motor sensory cortices and superior temporal auditory cortex, as well as an increase in FA and AD in bilateral orbitofrontal regions and insula.
Regarding the autism spectrum disorder (ASD), it is thought that it is generated by changes in the overall brain connectivity. Therefore, a special interest in this field has arisen not only for the study of DWM but also of the SWM. Recently a study of ASD utilized a SWM atlas of 63 bundles  (Guevara et al. , 2017)  to identify specific bundles implicated in this pathology (d  'Albis et al. , 2018). These bundles were segmented from the tractography datasets of 27 patients and 31 control subjects and the mean generalized FA among them was used as a measure of integrity. It was found that a deficit of the connectivity comprising 13 bundles, mostly from frontal, temporal and parietal regions, is associated with the severity of the disorder. Similar results were found in  (Sundaram et al. , 2008) , where the authors also found an FA significantly lower in ASD.
In this study the authors only focused in the temporal lobe. The analysis was performed by manual delineation of ROIs in order to extract the fibers from whole brain tractography datasets computed from the DTI data of 50 patients and 16 controls. Studies relating schizophrenia with changes in SWM have also been performed. A recent study used tractography datasets of 31 patients with schizophrenia and 54 healthy controls, to segment SWM bundles using the same atlas that in (d'  Albis et al. , Czechet al. ). This analysis exposed that there is a lower generalized FA in bundles connecting the frontal gyri, in patients with schizophrenia  (Ji et al. , 2018). This relationship has also been described previously by means of TBSS for the frontal and parieto-occipital connections (no specific bundles)  (Nazeri et al.