dataesr/scientific-paragraphs-categorization huggingface dataset:
Children with a left perinatal stroke showed larger lateralization indices of both structural and functional connectivity of the dorsal language pathway towards the right hemisphere that, in turn, were associated with better language outcomes. Importantly, the pattern of structural asymmetry was significantly more rightlateralized in children with a left perinatal brain insult than in a group of matched healthy controls. These results strongly suggest that early lesions of the left dorsal pathway and the associated perisylvian regions can induce.
Significance Statement The prevalent theories explaining the functional recovery of language functions after perinatal ischemic stroke strikingly differ on the role of perilesional functionally spared regions as opposed to the homologous non-affected contralesional brain areas. Here, we assessed how patterns of brain functional and structural reorganization were associated with language outcomes in a group of four-year-old children with left perinatal arterial ischemic stroke (PAIS). Larger lateralization indices of both functional and structural connectivity toward the right hemisphere were associated with higher levels of language development. Thus, interhemispheric plasticity through structural and functional hyperconnectivity mechanisms might be crucial in early damage, probably through the degeneration of neurons projecting from temporal to frontal areas together with contralateral axonal sprouting over the right hemisphere.
, 2017). Interestingly, neuroimaging studies have revealed that newborns may recruit a bilateral neural network with an already left predominance for linguistic stimuli  (Shultz et al. , 2014; Dehaene-Lambertz and Spelke, 2015). Although it is still under debate, some of the white-matter tracts such as the AFs are already developed and functional at birth, while others such as the UF are still developing due to a delayed maturation  (Zhang et al. , 2007; Perani et al. , 2011; Brauer et al. , 2013; Bajada et al. , 2015). Despite the fact that low-level language functions may already be present at birth, as reflected by left cortical activations for specific spectro-temporal features of the speech input  (Dehaene-Lambertz et al. , 2002 , 2006; Telkemeyer et al. , 2009; Perani et al. , 2010) , the later development of higher-order language functions may involve a graded lateralization toward the left hemisphere emerging with age  (Brown et al.
Here, we assessed for the first time how patterns of brain structural and functional reorganization were associated with language outcomes in a group of four-year-old children with left PAIS. To this aim, we gathered fine-grained measures of receptive and productive aspects of language as well as standardized measures of cognitive development. We also collected both structural and functional neuroimaging data under light anesthesia. Importantly, we used a group of healthy children from the Pediatric MRI Data Repository [National Institutes of Health (NIH) MRI Study of Normal Brain Development) matched in age and gender to serve as a control for the structural connectivity analyses. Considering the role of perilesional functionally spared regions as opposed to the homologous non-affected contralesional brain areas in the current theories of functional recovery after early stroke, we expected children with perinatal stroke affecting the left AF to be the most impaired in language functions.
This pattern of results would confirm that the functional integrity of the left hemisphere is mandatory to achieve normal language development  (Raja-Beharelle et al. , 2010). However, if the right hemisphere is able to "take over" language functions usually supported by the left hemisphere  (Staudt et al. , 2002; Tillema, et al. , 2008; Tivarus et al. , 2012) , then we should observe a significant relationship between structural parameters (i. e. , microstructural properties of languagerelated WM pathways) and/or functional activations in the right hemisphere and measures of receptive and expressive language, in line with previous evidence from adults on the role of the right dorsal pathway in recovery from left stroke-induced aphasia  (Forkel et al. , 2014; Pani et al. , 2016).
Infants with (1) clear sign of tissue loss or atrophy in MRI suggestive of an old infarction that occurred before birth; (2) vascular malformation; (3) congenital or chromosomic anomalies; (4) metabolic or infectious diseases; (5) complex heart congenital diseases or infants with extra corporeal membrane oxygenation, were excluded from the study. Specifically, MRI-based diagnostics were performed on anatomic data acquired on a 1. 5 T whole-body MRI scanner with a specific neonatal head coil within 7 d after the occurrence of the neurologic symptoms. These MRI data allowed classifying the lesions according to the arterial territory involved (i. e. , four segments: M1-M4). The classification of each infarct was obtained by two independent neurologists blind to the clinical data. Discrepancies in the classification of the PAIS were discussed and solved by consensus at joint revaluation among three observers: AGA and two neurologists blind to the clinical data.
Motor impairment was considered when infants had clear clinical signs of monoplegia/hemiplegia (GMFCS at least I and/or BFMF at I level). Infants with minor abnormalities such as precocious handedness, mild abnormalities of tone or reflexes that did not interfere with functions were not considered to have a motor impairment. Importantly, while all children presented with seizures during the three first days following birth, no children suffered from epilepsy at the time of the MRI scan Brain MRI was also acquired at 3. 8 years (SD ϭ 0. 26) to control for the residual lesion and to obtain the functional and structural data reported here. These MRI data demonstrated that all children had infarctions of the MCA affecting several brain regions (Table  1 ; Fig. 1 ). At the time of the study, all the children showed a right-hand preference.
Neurologic convention is used. B, Individual DMNs for each patient, obtained using group spatial ICA on the resting state data (see Materials and Methods). Individual data are in z-scores (p Ͻ 0. 05, uncorrected) and presented over each patient's T1 registered to a common space (four-year-old MRI brain template obtained from the Neurodevelopmental MRI Database; see Materials and Methods). This was done as a demonstration of data quality, given that propofol can reduce connectivity in cortical areas of significance for the current study  (Boveroux et al. , 2010). On the right, the average DMN for all patients is depicted, using t values (p Ͻ 0. 01, uncorrected) over a canonical four-year-old MRI brain template. C, Probability templates for the left arcuate (anterior, posterior, and long segment) were extracted from the Tractotron atlas (Thiebaut de  Schotten et al.
, 2011; Rojkova et al. , 2016) , thresholded at a 80% (only voxels having a 80% probability of being part of the AF according to the atlas are shown) and registered to a common space (the four-year-old MRI brain template obtained from the Neurodevelopmental MRI Database). For each patient, here we depict the arcuate probabilistic atlas (in red), the lesion (in blue), and the overlap between them (in pink) over a sagittal slice of the left hemisphere. Neurologic convention is used. L, left hemisphere. of utterance (pMLU; i. e. , phonological complexity based on the accuracy in producing consonants, diphthongs, and complex syllabic structure) and phonological wholeword proximity to the corresponding adult word-form (i. e. , proximity) scores were obtained for complexity and closeness to the target productions, respectively  (Ingram, 2002).
The pMLU of the word list from this tool is 8. 31. This would be the value obtained if all target words were correctly produced, the proximity score in this case (child's pMLU/target pMLU) being 1. Analysis of the original data from a sample (N ϭ 50) of four-year-old children from  Bosch (2004) , used as a reference group, yielded the following values: mean pMLU ϭ 7. 55 (SD ϭ 0. 55) and mean proximity score ϭ 0. 91 (SD ϭ 0. 064). Expressive language complexity was assessed from recordings of spontaneous utterances produced in a semistructured conversation/play situation using toys and picture books. The children's spontaneous utterances were transcribed so that the values of the MLU in words and the MLU of the five longest utterances (max-L) could be obtained. These measures could then be compared to the reference values from an age-matched control group of N ϭ 50 Spanish-speaking children at four years old  (Vázquez and Alonso, 2007).
MRI scanning MRI data were collected on a 1. 5 T whole-body MRI scanner at a location which will be identified if the article is published (GE Signa HD). The acquisition of a highresolution T1-w structural image (magnetization-prepared rapid acquisition gradient echo sequence; TR ϭ 12. 396 ms, TE ϭ 5. 22 ms, slice thickness ϭ 0. 4297 mm, 1-mm in-plane resolution, 180 slices, matrix size ϭ 512 ϫ 512) was followed by a DW-MRI sequence. DW-MRI scans were acquired with a spin-echo echoplanar imaging (EPI) sequence (TR ϭ 16500 ms, TE ϭ 100 ms, 48 axial slices, slice thickness 2. 5 mm, FOV: 270, acquisition matrix: 100 ϫ 100, reconstruction matrix: 256 ϫ 256, voxel size: 1. 05 ϫ 1. 05 ϫ 2. 5 mm 3 ). One run with one non-diffusion weighted volume (using a spin-echo EPI sequence cov-erage of the whole head) and 29 diffusion-weighted volumes (b value of 1500 s/mm 2 ) was acquired.
One final FLAIR image (TR ϭ 11002 ms, TE ϭ 95 ms, slice thickness ϭ 0. 3711 mm, 4. 5-mm in-plane resolution, 35 coronal slices, matrix size ϭ 512 ϫ 512) was also collected. After structural data were collected, a fMRI language task was conducted. One functional run consisting of 120 (6 min) functional images sensitive to BOLD contrast [echoplanar T2‫-ء‬weighted gradient echo sequence; TR ϭ 3000 ms (as in  Perani et al. , 2010 Perani et al. , , 2011 ) TE ϭ 60 ms, flip angle 80°, acquisition matrix ϭ 64 ϫ 64, 3. 75-mm inplane resolution, 3. 5-mm thickness, no gap, 34 axial slices aligned to the plane intersecting the anterior and posterior commissures] was acquired. Additionally, 100 volumes (5 min) of resting state (no stimuli were presented during acquisition) were collected using the same acquisition parameters as for the passive listening task.
fMRI preprocessing and statistical analysis Data were pre-processed using Statistical Parameter Mapping software (SPM8, Wellcome Trust Center for Neuroimaging, University College, London, United Kingdom; www. fil. ion. ucl. ac. uk/spm/). The functional images (language task and the rs-fMRI data) was first realigned, and then co-registered to the corresponding T1 using FSL's FLIRT (FMRIB's Linear Image Registration Tool;  Jenkinson et al. , 2012). As stated before, we used an age-appropriate template to register the children MRI images to the same space  (Richards et al. , 2016). In particular, we employed FSL's FNIRT (FMRIB's Nonlinear Image Registration Tool;  Jenkinson et al. , 2012) , to normalize our patient's T1-w and its corresponding lesion mask to a four-year-old MRI brain template obtained from the Neurodevelopmental MRI Database (https://jerlab.
sc. edu/projects/neurodevelopmental-mri-database/;  Almli et al. , 2007; Sanchez et al. , 2012; Fillmore et al. , 2015; Richards et al. , 2016). We first linearly registered the individuals' T1-w and the corresponding lesion map to the four-year-old template using FLIRT. Then, we used FNIRT to non-linearly register the children T1 images to the aforementioned age-specific brain template. For all these steps, cost function masking was employed  (Brett et al. , 2001; Andersen et al. , 2010; Ripollés et al. , 2012). The registration parameters obtained during these calculations were applied to the lesion masks, the fMRI and the rs-fMRI data (previously registered to the corresponding T1) to normalize it to the common space (the four-year-old template). Finally, fMRI and rs-fMRI data were spatially smoothed with an 8-mm FWHM kernel.
Gray matter (GM) and WM loss and precise lesion location To precisely locate the areas affected by the lesion in each patient, we used the four-year-old adapted LONI LPBA40 brain atlas included in the Neurodevelopmental MRI Database  (Shattuck et al. , 2008; Richards et al. , 2016)  and the lesion masks that were registered to this space. In addition, to provide a measure of GM and WM loss, we computed an estimation of the lesion size  (Tillema et al. , 2008)  as follows. We segmented the T1-w images into GM and WM using the four-year-old GM and WM segmentation priors included in the Neurodevelopmental MRI Database  (Richards et al. , 2016)  using Unified Segmentation  (Ashburner and Friston, 2005). The cerebellum and brainstem were then masked out using the manual fouryear-old atlas of the Neurodevelopmental MRI Database  (Fillmore et al.
DW-MRI pre-processing Diffusion data processing started by correcting for eddy current distortions and head motion using FMRIB's Diffusion Toolbox (FDT), which is part of the FMRIB Software Library (FSL 5. 0. 1, www. fmrib. ox. ac. uk/fsl/; Jenkinson et al. , 2012). Subsequently, the gradient matrix was rotated  (Leemans and Jones 2009). Following this, brain extraction was performed using the Brain Extraction Tool  (Smith et al. , 2006) , which is also part of the FSL distribution. The analysis continued with the reconstruction of the diffusion tensors using the linear least-squares algorithm included in Diffusion Toolkit 0. 6. 2. 2 (Ruopeng Wang, Van J. Wedeen, TrackVis. org/dtk, Martinos Center for Biomedical Imaging, Massachusetts General Hospital). This pipeline of analysis was chosen as our acquisition parameters where fine-tuned using this procedure  (François et al.
, 2016). For the control group, we directly used the tensors provided by the NIH Pediatric MRI Data Repository (already preprocessed using the standard TORTOISE pipeline;  Pierpaoli et al. , 2010). We chose to use the already processed tensors to use the data with the best quality provided by the repository. These tensors were transformed into a compatible format with Diffusion Toolkit using in-house scripts (the code is available at https:// github. com/pripolles/Diffusion-Tools/). For both patient and control data, whole-brain tractography was performed using Diffusion Toolkit 0. 6. 2. 210 and the interpolated streamlines algorithm. Tractography was started only in voxels with an fractional anisotropy (FA) value Ͼ0. 2 and was stopped when the angle between two consecutive steps was larger than 35°.
Deterministic tractography Given the nature of our dataset (pediatric individuals with lesions) we decided to use manual deterministic tractography to dissect the tracts of interest, as it allowed us to draw specific ROIs adapted to each individual. In other words, for each individual and tract we drawn in native space-specific ROIs that took into account the neuroanatomy of each participant. For each dataset, the three segments of AF were defined following the procedure reported by  Catani et al. (2007)  and also used in López-Barroso et al. (  2013 ),  Tuomiranta et al. (2014), and François et al. (2016). All ROIs were defined using the FA or RGB FA map as a reference. These FA and RGB-FA maps were generated from diffusion tensors that were reconstructed using the linear least-squares method provided in the Diffusion Toolkit.
This process was conducted for both the left and the right hemisphere. As a control, we also performed virtual in vivo dissections of the IFOF, ILF, and UF. These dissections were performed for both hemispheres. For each dataset, we placed three spherical ROIs (as in  Ripollés et al. , 2017)  at the level of the anterior temporal lobe (temporal ROI), the posterior region located between the occipital and temporal lobe (occipital ROI) and the anterior floor of the external/extreme capsule (frontal ROI). to define each of the tracts of interest we applied, again, a two-ROI approach. The ILF was obtained by connecting the temporal and occipital ROIs. The streamlines passing through the occipital lobe and frontal ROIs were considered as part of the IFOF. The frontal capsule ROI was united to the temporal ROI to delineate the UF.
The lateralization index was calculated [lateralization index ϭ (values on the R Ϫ values on the L)/(values on the R ϩ values on the L)] and was included in the analysis to see whether right WM reorganization had a relation with language development. The lateralization index ranges from -1 to 1: negative values represent left lateralization, values around zero symmetrical distribution, and positive values right lateralization (López-  Barroso et al. , 2013). As a control, we also calculated the LI for the IFOF, ILF, and UF using the same procedure. We compared the LIs for the AF, ILF, IFOF, and UF between controls and patients using nonparametric Wilcoxon Mann-Whitney tests. Due to the limited number of patients, confirmatory Bayesian statistical analyses were computed for all these between group comparisons using JASP with default  (Rouder and Morey, 2012; Morey and Rouder, 2015; JASP Team, 2018).
rs-fMRI functional connectivity analysis The rs-fMRI data were analyzed following the rationale of the tractography analyses. The already preprocessed rs-fMRI data were analyzed using the functional connectivity (CONN) toolbox v17f (Whitfield-Gabrieli and Nieto-Castanon, 2012). After preprocessing, the images were bandpass filtered to 0. 008 -0. 09 Hz and denoised by regressing out the six motion parameters obtained during realignment and the white-matter and cerebrospinal fluid signals. Finally, data were detrended. Given the tractography results (see Results) which pointed to a crucial involvement of the arcuate fasciculus in language functions in the studied patient population, we performed an rs-fMRI analysis that further confirmed these results. With this aim, we selected the two main regions connected by the arcuate fasciculus: the superior temporal gyrus (STG) and the inferior frontal gyrus (IFG; including both part opercularis and triangularis).
These ROIs were extracted from the four-year-old adapted LONI LPBA40 brain atlas included in the Neurodevelopmental MRI Database  (Shattuck et al. , 2008; Richards et al. , 2016). Data for all voxels within an ROI were averaged and one correlation per participant and hemisphere was calculated between the STG and the IFG. These correlation coefficients were converted to z values using Fisher's r-to-z transformation. Right hemispheric z values for each of the four correlations were subtracted from their left-hemispheric counterparts to obtain an rs-fMRI laterality index analog to the one obtained for tractography (for a similar analysis, see  López-Barroso et al. , 2013). Associations between these laterality indexes and (1) the two measures of phonological development (phonological complexity and proximity scores) and (  2 ) the two measures of expressive language complexity (MLU and L-max) were then explored using Spearman's correlations.
Brain-behavior relationships Given that the dorsal pathway was the most affected in patients (the laterality of the entire AF was significantly more right-lateralized in patients than in controls), we computed the laterality indices for both dorsal structural and functional data (see Materials and Methods). Due to the limited number of patients, confirmatory Bayesian statistical analyses were computed for all the brainbehavior correlations with the software JASP using default priors and the Bayesian correlation module (using Kendall's -b; JASP Team, 2018;  Morey and Rouder, 2015; Rouder and Morey, 2012). We used Bayes factors (BF 10 ), which for the comparison at hand, reflect how likely data are correlated (e. g. , a BF 10 ϭ 3 implies that a positive correlation is three times more likely to be observed under the alternative hypothesis).
7B ), although these analyses did not survive the FDR correction. Nonetheless, this finding suggests that the greater the reorganization was to the right hemisphere (i. e. , the greater the intrinsic functional connectivity between these two regions in the right hemisphere), the better the productive aspects of language. This pattern of results strikingly parallels the one found for the lateralization of the AF using deterministic tractography and provides converging evidence that greater right hemispheric engagement was related to better productive aspects of language in this group of children.
This result evidences that the more lateralized to the right the intrinsic connectivity between the regions connected by the AF, the better the productive aspects of language. A 3D render of the four-year-old template is displayed with the ROIs used in this analysis. Correlation index and p values are displayed. for multiple comparisons). Taken together, these results indicate that reorganization of the language network over the right hemisphere can be accomplished through structural and functional hyperconnectivity between right temporal and frontal areas. An increased GM density in the right temporoparietal regions has been associated with a right-lateralized language network in a large group of children with drug-resistant left-sided lesional focal epilepsy  (Pahs et al. , 2013). The authors suggested that the size of the unaffected planum temporale may present a "reserve capacity" for interhemispheric language reorganization in the presence of lesions within left perisylvian regions.
This contralateral reorganization mechanism may, in turn, foster contralateral increased GM plasticity changes reflected by structural and functional hyperconnectivity. The present findings provide important results allowing the refinement of a recent model describing the emergence of increasingly complex linguistic functions through development  (Catani and Bambini, 2014). Building on previous neuroanatomical models of language processing  (Friederici 2011 (Friederici , 2012;; Catani et al. , 2005; Hickok and Poeppel, 2007; Rauschecker and Scott, 2009) , this model links the stepwise evolution of language functions to the neuro-anatomic changes occurring during early development with a specific role of the posterior and long segments of the AF, necessary for normal language development and especially in word-learning, syntactic processing and verbal working memory  (Lebel and Beaulieu, 2009; López-Barroso et al.
Therefore, the enhanced involvement of the right dorsal pathway after early stroke may induce functional and structural changes within a more flexible language network than lesions acquired in a highly specialized functional network during adulthood  (Levine et al. , 2016). Future research is needed to specify the role of genetic predispositions contributing to the left hemisphere specialization as opposed to the neuroplastic mechanisms occurring over the right hemisphere in the face of early left lesions. The present study presents some limitations. The sample size is small, which is an intrinsic and recurrent problem in studying rare neonatal conditions. This limitation is particularly evident for the fMRI results which were not corrected for multiple comparisons. In addition, the fMRI task used here is novel in this type of population and results from a control group of healthy children and/or a group of adults without sedation is needed to further interpret our fMRI data in terms of functional reorganization.