dataesr/scientific-paragraphs-categorization huggingface dataset:
e produce many distinct spatial maps which in turn would lead more local optimum. Interestingly, this would imply lower truncations not only help reduce the dimensionality of parameter space but also reduce the structural degeneracy in Epileptor model by constraining the spatial maps of parameters to those that have smooth spatial transitions. However, such smooth spatial maps would have the trade off that size of the inferred EZ could be underestimated to avoid false positives. To ensure that effect of hyperparameters and observation noise on accuracy are not specific to the EZ hypothesis, the same parameter sweeps are performed using bad EZ hypothesis. Their effects are found to be similar to the case with good EZ hypothesis (Fig. 3  d, e, f ). Similar analysis performed on a different dataset is shown in supplementary Fig.
This can lead to division by zero errors, hence, if such an error occurred for any of the patients at a particular threshold then the precision is set to zero. Identification of seizure onset zone using purely data driven signal analysis of SEEG time series such as EI, EM and ER  [2, 3, 5]  are by construction spatially constrained to identify EZ within the implanted regions. Similar to previous VEP studies  [16, 18, 17, 31] , the generative model used in this study also includes a forward model, the so called the gain matrix, transforming latent source activity to the SEEG time series. The forward model could account for seizure activity originating in regions beyond the implanted regions on the observed SEEG. The efficacy of the forward model in discovering EZ beyond implanted regions is studied by analyzing the number of regions in the predicted EZ that are beyond the implanted regions.
7c. Lastly, the accuracy of the neural field extension of the VEP (VEP-NFM) framework proposed in this study is compared against the previous VEP approaches with neural mass model (VEP-NMM)  [18, 31]  against the same retrospective dataset. In the seizure free group, at an onset tolerance of 10 seconds, the number of false positives have drastically reduced as can be seen from the precision in Fig. 8a, b. A paired samples t-test was performed to compare precision and recall between VEP-NMM and VEP-NFM. There is a statistically significant difference in precision between VEP-NFM (M=0. 85, SD=0. 21) and VEP-NMM (M=0. 56, SD=0. 34); t(28)=5. 41, p¡0. 01. For recall, there is not a significant difference between VEP-NFM (M=0. 25, SD=0. 21) and VEP-NMM (M=0. 29, SD=0. 27); t(28)=-1. 48, p=0. 15.
The retrospective dataset used for validation is small and thereby validation against larger datasets is needed. Future research should consider more comprehensive datasets to enhance confidence of the proposed approach. Model inversion is performed using MAP which provides point estimates using an optimization algorithm. Hence the drawbacks of an optimization algorithms such as getting stuck in a local minima also apply to our results. One common technique to partially mitigate this challenge is to run multiple optimizations with different initial conditions, which we did employ in this study. To address issues with MAP, we are currently exploring variational inference using more sophisticated density approximation methods such as Masked auto-regressive flows  [41]  and Free-form Jacobian of reversible dynamics (FFJORD)  [42].
Acknowledgments This research was supported by the  Fondation pour la Recherche Médicale  (Grant  DIC20161236442 ),  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 945539  ( SGA3 )  Human Brain Project , and the  SATT Sud-Est (TVB-Epilepsy). This work has been carried out within the  Fédération Hospitalo-Universitaire EPINEXT  with the support of the  Recherche Hospitalo-Universitaire EPINOV  (Grant  ANR-17-RHUS-0004 ) funded by the " Investissements d'Avenir" French Government program  managed by the  French National Research Agency (ANR).
We included 983 participants from the Genetic Frontotemporal dementia Initiative (GENFI [Jonathan D. Rohrer et al. , 2015] ). Voxelwise analysis in a region of interest encompassing the thalamus, striatum, globus pallidus, amygdala, and the cerebellum was performed using deformation-based morphometry (DBM). These regions were selected because of their implication in cerebello-subcortical circuitry  (Palesi et al. , 2017)  and for their potential implication in psychiatric symptoms  (Blithikioti et al. , 2022; Phillips et al. , 2015). We first exam- The issue of heterogeneity in clinical presentations of FTD mutation is complex and we believe that our approach can help us understand this heterogeneity by approaching brain-behavior relationships in a data driven way. Instead of selecting only subjects with FTD symptoms or neuropsychiatric symptoms for example, we attempt to find those relationships across all disease stages and clinical manifestation to identify why some patients develop these symptoms while others do not.
| Deformation based morphometry We used the two-level deformation-based morphometry (DBM) python pipeline developed in the CoBrA Lab (https://github. com/CoBrALab/ twolevel_ants_dbm and described in more details in the Supplementary methods) to investigate voxel-wise morphometry (Figure  1 ). Each individual image was warped using affine and nonlinear registration to create an unbiased average using ANTs tools and a group-wise registration strategy  (Avants et al. , 2011). Relative voxel volume increases and decreases were determined from the deformation fields by estimating the Jacobian determinant at each voxel  (Chung et al. , 2001). This mathematical transformation allows easier statistical analyses and interpretation: positive values indicate that the voxel in template space must be expanded to get to the subject space, and negative values indicate that the voxel in template space must be reduced.
| Linear mixed effect models Vertex-wise linear mixed-effects models (vertexLmer from RMINC_1. 5. 2. 2 package in R 3. 6. 3) were used to test the significance of our relative Jacobians within our mask (Figure  1 ). Similar linear models were tested including voxels of the whole brain to test whether our results in the subcortical and cerebellar regions were still significant when including the entire brain (see supplementary Figure  1 ).
| Flat map visualization Matlab (version R 2014b) and SUIT toolbox were used to display lmer and PLS results into a surface-based flatmap representation of the cerebellum (Jörn  Diedrichsen, 2006; Jörn Diedrichsen et al. , 2009; J. Diedrichsen et al. , 2011; Jörn Diedrichsen & Zotow, 2015). Details on the commands used are described in the supplementary methods.
Biases in our results may arise from the limited number of MAPT mutation carriers in the GENFI dataset, explaining the apparent reduced brain involvement in this group. Only about 18% of our mutation carriers were MAPT carriers. However worldwide, MAPT mutation is the least frequent (23. 2%), followed by GRN mutation (34. 6%) and C9orf72 expansion (42. 1%)  (Moore et al. , 2020). Therefore, our dataset was representative of the general mutation distribution in FTD. Further, while GENFI is the largest dataset of carriers for FTD-linked mutations to date and offers a sufficient number of participants to conduct complex statistical analyses, only 18% of those participants were symptomatic mutation carriers. Secondly, this study aims to understand the brain and CBI-R variables impairment during the presymptomatic phase of the disease, and uses EYO as an estimator of age of symptom onset.
However, as discussed above, EYO accuracy is limited and varies between genetic mutation groups  (Moore et al. , 2020). Therefore, to better characterize the association between brain and behavior during this presymptomatic phase, longitudinal data with documentation of the actual age at onset is necessary. This will eventually be available from GENFI from ongoing follow-up brain imaging and CBI-R assessments. Finally, it is important to note that while our results allowed straightforward qualitative comparisons between each PLS findings, no test has been performed to quantitatively compare the different brain maps. To verify that our differences in brain maps were not driven by a threshold effect, we also plotted the different brain maps using a less stringent significant threshold (Supplementary Figure  1 ), demonstrating that even with a more lenient threshold, different parts of the cerebellum were associated with the behavior scores for each genetic mutation group.
were included from the GENFI2 dataset data release five which includes data from participants across multiple research sites. The participants were either known carriers of a pathogenic mutation in MAPT (n = 104), GRN (n = 243), C9orf72 (n = 256), TANK-binding kinase 1 (TBK1; n = 5) or first-degree relatives of known symptomatic carriers ("noncarriers" group; n = 375). TBK1 carriers were excluded because of the low number of carriers of this mutation. Inclusion and exclusion criteria are detailed in the Supplementary material.
, 2020). The addition of brain morphology in the investigation of the structure-function relationship is particularly relevant in epilepsy since brain structural organization is affected by the pathology, for instance as cortical thinning in the context of focal epilepsy  (Galovic et al. , 2019). In this study, we hypothesized that cortical thickness distribution in the brain may be linked to the altered spreading of the large-scale perturbations in relation to epilepsy. To test this hypothesis, we used a multimodal dataset made of source-reconstructed high-density electroencephalography (hdEEG) and structural MRI from 59 patients with TLE. The model of TLE is of particular interest because it represents a rather well-defined group of electroclinical conditions with lower clinical heterogeneity compared to other epilepsy forms.
More specifically: ߪ ൌ ∏ ቀ ೡೞ ሺାଵሻ ೡೞ ሺሻ ቁ భ ಿ ್ షభ ே ್ ିଵ ୀଵ (1) ߪ ൌ ∏ ሺߪ ሻ భ ಿ ೌೡೌ ே ೌೡೌ ୀଵ (2) where i is the branching parameter of the ith avalanche in the dataset, N bin is the total amount of bins in the i-th avalanche, n events (j) is the total number of events active in the j-th bin, and N aval is the total number of avalanche in the dataset. In our analyses the branching ratio was 1 for bin = 2 (corresponding to bins of 8 ms).
All samples were retrospectively tested independently of the proposed criteria. Between June 2007 and December 2020, peripheral blood was collected in ethylenediaminetetraacetic acid (EDTA) tubes from 567 individuals with PD or atypical parkinsonism referred by neurologists or geneticists for a molecular diagnosis. The medical records of all patients for whom an analysis by MLPA was carried out between June 2007 and December 2020 were retrospectively examined. The data collected consisted of the sex, age at onset, number of first-degree and further relatives affected by parkinsonism, and geographical origin. Moreover, the clinical diagnosis of all patients followed in the Lille University Hospital or regional hospitals related to the Lille expert center for PD during the same period were reviewed.
K E Y W O R D S dementia with Lewy bodies, genetic testing, Mendelian transmission, multiple system atrophy, Parkinson's disease Mendelian PD (MRC Holland, Amsterdam, The Netherlands) according to the manufacturer's instructions. Both kits contain 50 MLPA probes for the detection of structural variants in the ATP13A2, LRRK2, PARK7, PINK1, PRKN (PARK2), SNCA, UCHL1 and GCH1 genes. They also include two probes specific for (i) the rare pathogenic variant NM_000345. 3(SNCA):c. 88G> C, p. (Ala30Pro) and (ii) the recurrent pathogenic variant NM_198578. 3(LRRK2):c. 6055G>A, p. (Gly2019Ser). Electrophoresis of the MLPA products was performed on an ABI 3730 DNA analyzer (Applied Biosystems, Foster City, CA, USA). The MLPA data were analyzed using GeneMapper® 2. 5 (Applied Biosystems) and COFFALYSER (MRC Holland) software.
Other studies used different NGS methods in PD  [21, 26]  or early-onset PD  [  Ashkenazi Jewish origin should also be considered. The early onset of PD in our population (≤39 years old) is similar to the findings of the Movement Disorder Society Genetic mutation database that led to recommending testing for PRKN mutations for patients with PD onset before the age of 40 years  [11]. The association of the sex ratio for positive MLPA tests is more puzzling. It was observed that women had a higher probability of being positive. This may reflect that women have a higher risk of developing Mendelian PD. Such a gender effect has previously been observed for PD patients with LRRK2 mutations, especially those carrying the G2019S mutation  [32]. However, it cannot be excluded that men may be less willing to consult a neurologist and/or a medical geneticist, or that men develop a disease with a more marked environmental origin, such as pesticide exposure  [33].
In vivo anatomical and diffusion-weighted magnetic resonance imaging (MRI) data were collected on a 3-Tesla MRI system from 39 chimpanzees. These datasets were subsequently processed using a novel fiber clustering pipeline adapted to the chimpanzee brain, enabling us to create two novel deep and superficial white matter connectivity atlases representative of the chimpanzee brain. These atlases provide the scientific community with an important and novel set of reference data for understanding the commonalities and differences in structural connectivity between the human and chimpanzee brains. We believe this study to be innovative both in its novel approach and in mapping the superficial white matter bundles in the chimpanzee brain, which will contribute to a better understanding of hominin brain evolution.
Data collection We considered data coming from 39 healthy in vivo chimpanzees including 23 females and 16 males, imaged between 9 and 35 years old (mean = 19 years old) and housed at the Emory National Primate Research Center/ENPRC. Chimpanzee MRI scans were obtained from a data archive of scans acquired prior to the 2015 implementation of US Fish and Wildlife Service and National Institutes of Health regulations governing research with chimpanzees. All the scans reported in this publication were completed by the end of 2012 and have been used in previous studies (e. g. Vickery et al. , 2020 or Bogart et al. , 2012). Each individual was scanned using a 3 T Trio MRI system (Siemens, Erlangen) using a birdcage coil with a dedicated imaging protocol including: a high resolution 3D T1-weighted inversion recovery fast gradient echo anatomical scan (MPRAGE sequence) of 0.
Data preprocessing, local modeling and fiber tractography Anatomical and diffusion data were processed using a Python pipeline dedicated to the chimpanzee species using the in-house C++ Ginkgo toolbox available at https://framagit. org/cpoupon/gkg. All T1weighted images of the 39 chimpanzees' brains were matched to the Juna. chimp chimpanzee template (template release : https://www. chimpanzeebrain. org/)  (Vickery et al. , 2020). To this aim, direct and inverse non-linear 3D registration transformations from each individual brain T1-weighted scan to the Juna. chimp template were computed using the ANTs software  (Avants et al. , 2008) , relying on a Symmetric Normalization (SyN) with a mutual information similarity criterion. The Juna. Chimp template is released with a high spatial resolution suitable for our project and corresponds to the size of a standard chimpanzee brain (from anterior to posterior = 106 mm; from up to bottom = 72 mm; from left to right = 87 mm).
Intra-subject fiber clustering The intra-subject fiber clustering relies on eight consecutive steps (see Fig. 1 ): • step 1: streamlines reconstructed using fiber tracking from each individual diffusion MRI dataset are transformed into the Davi130 template space using a diffeomorphic transformation calculated with the ANTs toolbox  (Avants et al. , 2008 ); • step 2: transformed streamlines are separated into four subgroups corresponding to the brain area they mostly belong to. A first subgroup connecting the left hemisphere; a second group connecting the right hemisphere, a third group connecting both left and right hemispheres and a last group connecting brainstem and cerebellum regions. This step is achieved using a mask of these regions defined from the Davi130 atlas  (Vickery et al. , 2020 ); • step 3: each streamline subgroup is then subdivided into nine new subgroups corresponding to nine equally spaced fiber length ranges between 7 mm and 133 mm; • step 4: for each of the nine subgroups and each brain area, a fiber density mask is computed and thresholded to provide a binary mask corresponding to the brain region populated by the streamline of the chosen length range; • step 5: a k-means algorithm is used to compute a parcellation of each binary mask with parcels sharing of similar size; • step 6: an affinity matrix is computed between parcels that simply quantify the level of connectivity of each pair of parcels; • step 7: a hierarchical clustering algorithm is applied to this affinity matrix providing a dendrogram allowing to define clusters of strongly connected parcels; • step 8: parcel clusters are finally intersected with the input streamline subgroup of interest, yielding the set of fiber clusters (or fascicles) corresponding to the fiber length range and brain area of interest, and the centroid of each fascicle corresponding to the streamline of the fascicle being the closest to all the other streamlines (using a symmetric mean of mean closest point distance) is computed to provide a synthetic representation of the fascicle that will be used during the next stage operated at the population level.
However, studies have also shown that in the case of higher fiber complexity, deterministic tractography may not be sufficient and be very sensitive to the principal estimated direction and noise  (Descoteaux et al. , 2008; Jones, 2010). We could alternatively use probabilistic or even better global approaches, but we have decided at a first glance to consider the regularized deterministic approach. We intend investigate probabilistic and global approaches in the future to study their impact on the reconstructed white matter bundle atlases. The millimeter resolution of the anatomical and diffusion MRI datasets we used is obviously a limiting factor. Nevertheless, the ENPRC biobank currently represents the largest cohort of imaging data acquired from the chimpanzee brain in vivo. The emergence of new high-end connectome gradients and high-field MRI scanners might change the future of the exploration of the chimpanzee brain.
It should offer the possibility to scan chimpanzees at a much higher spatial resolution (from submillimer scale to mesoscale) and to significantly improve the SNR of the dMRI dataset, which should enable more precise mapping of connections entering the cortical ribbon, leading to more reliable reconstructions of superficial white matter bundles. In its current implementation, our two-step fiber clustering algorithm provides a robust approach to inferring white matter bundles, automatically and efficiently discarding outliers. However, this was not the case with a few remaining artifactual fascicles, mostly located superficially at the boundary of the brain mask in regions where the mask was not perfectly delineated. In this case, the remaining artifacts had to be discarded manually with carefully attention.
DESIGN, SETTING, AND PARTICIPANTS This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded.
Lesion Location Mapping Lesion location mapping methods were used to test whether lesions associated with poststroke epilepsy map to a specific brain region. 10 We assessed lesion overlap (or damage) of each lesion to the cortex, subcortex, cortical lobes and vascular territories. Since larger lesions are more likely to lead to epilepsy,  19  we controlled for lesion volume in all analyses. [22]  Lesion Network Mapping Lesion network mapping was used to test whether lesions associated with poststroke epilepsy map to a specific brain network. As described previously,  11, 23, 24  we computed seed-based functional connectivity between each lesion location and all other brain voxels using the resting-state functional connectivity data (2 × 2 × 2-mm resolution) from 1000 healthy participants (human brain connectome from the Brain Genomics Superstruct Project: https://dataverse.
harvard. edu/dataverse/GSP). 25, 26  This process results in a lesion network for each lesion location (Figure  2A  and 2B; eFigure 1 in Supplement 1). To identif y the func tional connec tions (ie, lesion network nodes) associated with epilepsy, we performed a voxel-based permutation test on a whole-brain level using the software Permutation Analysis of Linear Models (PALM) (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/PALM)  27  and controlled for lesion volume as a covariate, as in our prior work. 14, 18 or detailed description of the control analyses, see the eMethods in Supplement 1. We performed multiple control analyses to assess the consistency of our lesion network mapping findings using different control data sets, connectome preprocessing methods, covariates, and subgroups. We used statistical mediation analysis to determine the association between lesion connectivity, lesion volume, and damage to the cortex or subcortex.
Analysis Statistical analyses were performed in R version 2022. 12 (R Foundation) and MATLAB version 2020b (MathWorks). Power analyses were performed in G*Power version 3. 1 (Heinrich Heine Universität Düsseldorf). 36 Data were analyzed from September 2018 through December 2022. Group differences in lesion volume, damage to brain regions, or functional connectivity on a voxel-wise level were tested using an Aspin-Welch test, and the V statistic was re- ported. To assess the association between lesion connectivity and epilepsy, multivariate models were fitted with logistic regression and corrected for potential confounders as covariates. Statistical mediation analysis was performed to assess the relationship between epilepsy, lesion connectivity, and covariates. Proportions of patients with epilepsy across categories were compared using a χ 2 test.
Fingerprint analysis Based on the FCs (Fig. 1 ), we estimated the brain-fingerprinting of both patients and controls. We started by creating frequency-specific identifiability matrices (IM) for each group, using the Pearson's correlation coefficient between the test and re-test FCs. Specifically, the test FC of each subject was correlated to the retest FCs of all the subjects belonging to the same group (including her/himself)  (Amico and Goñi, 2018). So, the resultant IM embodies, in the main diagonal, the information inherent to homo-similarity (I-self, the similarity between FCs of the same individual), whilst data about hetero-similarity (I-others, i. e. , the similarity of that subject's FC with the whole group) are represented by the off-diagonal elements. Then, we extracted the differential identifiability (I-diff), a score that estimates the subject-specific fingerprint level of a specific brain dataset, by subtracting the I-others value from the I-self value  (Amico and Goñi, 2018).