dataesr/scientific-paragraphs-categorization huggingface dataset:
| Experimental Paradigms The first release of the IBC dataset comprises twelve tasks. Their majority pertains to already-existing paradigms developed and validated in previous studies. They were thus combined in four different MRI sessions according to this criterion. For further details about the organization of the MRI sessions, consult Table  2  of  Pinho et al. (2018). Concretely, these twelve tasks refer to the reproduction of the ARCHI and HCP batteries, with minor adaptations, as well as to a new task addressing language comprehension and based on a Rapid-Serial-Visual-Presentation (RSVP) paradigm. A summary of the experimental paradigm of each task is given next. Each design is categorical, i. e. it relies on the principle of "cognitive subtraction" as means to isolate effects-of-interest across experimental conditions within every task.
| Data Storage The online access of all data is assured by the Human Brain Project (HBP) EBRAINS platform as well as the Open-Neuro public repository  (Poldrack et al. , 2013)  under the accession number ds000244 (dataset DOI: 10. 18112/openneuro. ds000244. v1. 0. 0). The individual and unthresholded z-maps, obtained from all contrast maps of the aforementioned experimental conditions (see Section Experimental Paradigms and Appendix section B), can be found in the NeuroVault repository  (Gorgolewski et al. , 2015) , under the collection with the id=4438: https://identifiers. org/neurovault. collection:4438. The scripts used for data analysis are publicly available under the Simplified BSD license: https://github. com/ hbp-brain-charting/public_analysis_code. The documentation of the dataset, containing a full description of the experimental designs, acquisition parameters and analysis pipeline, is accessible on the IBC website: https://project.
| Preprocessing Raw data were preprocessed using PyPreprocess (https://github. com/neurospin/pypreprocess). This framework stands for a collection of Python scripts oriented toward a common workflow of fMRI-data preprocessing analysis. It is built upon the Nipype interface  (Gorgolewski et al. , 2011)  v0. 12. 1, thus allowing for the stand-alone use of precompiled modules in SPM12 software package (Wellcome Department of Imaging Neuroscience, London, UK) v6685, and FSL library (Analysis Group, FMRIB, Oxford, UK) v5. 0, used for the preprocessing of neuroimaging data. All fMRI images, i. e. GE-EPI volumes, were collected twice with reversed phase-encoding directions, resulting in pairs of images with distortions going in opposite directions. Susceptibility-induced off-resonance field was estimated from the two Spin-Echo EPI volumes in reversed phase-encoding directions.
| FMRI-Model Specification FMRI data were analyzed using the General Linear Model (GLM). Regressors of the model were designed to capture variations in BOLD response strictly following stimulus timing specifications. They were estimated through the convolution of boxcar functions, that represent per-condition stimulus occurrences, with the canonical Hemodynamic Response Function (HRF), defined according to  Friston, Fletcher, et al. (1998)  and  Friston, Josephs, et al. (1998). To build such models, paradigm descriptors grouped in triplets (i. e. onset time, duration and trial type) according to BIDS Specification were determined from the log files' registries generated by the stimulus-delivery software. To account for small fluctuations in the latency of the HRF peak response, additional regressors were computed based on the convolution of the same task-conditions profile with the time derivative of the HRF.
Nuisance regressors were also added to the design matrix in order to minimize the final residual error. To remove signal variance associated with spurious effects arising from movements, six temporal regressors were defined for the motion parameters. Further, the first five principal components of the signal, extracted from voxels showing the 5% highest variance, were also regressed to capture physiological noise  (Behzadi et al. , 2007). In addition, a discrete-cosine basis was included for high-pass filtering (cutoff= 1 128 Hz). Model specification was implemented using Nistats library v0. 0. 1b, a Python module devoted to statistical analysis of fMRI data (http:// nistats. github. io), that leverages Nilearn  (Abraham et al. , 2014) , a Python library for statistical learning on neuroimaging data (https://nilearn.
| Summary Statistics Because data from each task were collected, at least in two acquisitions, with opposite phase-encoding directions (see Section Imaging-data acquisition for details), statistics of their joint effects were calculated for each subject with a fixed-effects (FFX) model. Specifically, t-statistics were computed at every voxel for every contrast map. After completing the within-subject level analysis, a group-conjunction analysis  (Heller et al. , 2007)  was performed on the main contrasts; significance was assessed against the null hypothesis asserting that less than a fourth of the subjects display activity in a given voxel. The motivation to employ conjunction analysis instead of the random-effects (RFX) linear model relates to the small size of the cohort, i. e. thirteen subjects.
We observe that the median of the indices is higher for the conjunction analysis while its interquartile range is smaller, indicating that group-level conjunction contrasts are more consistent across different samples of the population. We also denote that there is a likely difference between RFX results obtained for smoothed and unsmoothed data, whereas there is no difference between the conjunction counterparts; this evidence also hints at a greater consistency of the second-level results obtained from conjunction, regardless different post-processing choices. A z-transform of the t-maps was always performed both on individual and group-level statistical maps. Such procedure allows for standardized results, which are not anymore dependent on the number of degrees of freedom. This is particularly useful when comparing results from different datasets (see Sections 2.
| Assessing Reproducibility and Functional Variability In order to assess the reproducibility of the main results, group-level contrast z-maps of the IBC dataset were compared to the corresponding ones obtained from the original ARCHI dataset, n=78  (Pinel et al. , 2019)  as well as to those obtained from a subset of the HCPS900 dataset, n=786. To evaluate how consistent the task-specific functional signatures are among participants, the correlation of the contrast maps was computed both between and within subjects, i. e. both between all subjects' pairs of FFX individual maps and between pairs of "PA" and "AP" maps of the same task per subject. The mean and the 95% Confidence Interval (CI) were then estimated from the distribution of this measure, as a way to quantify the functional variability of the contrasts within and across individuals.
| Dictionary Learning To obtain individual topographies that summarize the spatial representation of the effects-of-interest, dictionary learning was applied to statistical maps featuring the main contrasts of each experiment, in each subject. This sparse decomposition method extracts spatially distinct components from the joint distribution of contrast values. It thus provides a low-dimension representation of the overlapping neural support of the contrasts probed in the dataset. We used a multiple-subject dictionary learning model that captures functional correspondence across datasets, as described in  Varoquaux et al. (2013). Such hypothesis allows the factorization of the individual contrast maps into a dictionary of cognitive profiles (common to all subjects) plus subject-specific spatial maps (aka loadings).
The estimated subject-specific spatial components (U s ), s ∈ [n] can be interpreted as individual topographies; these components may overlap, although their values are zero in most regions. The λ parameter was calibrated in order to yield a sparsity of around 75%. As the estimation problem is non-convex, initialization matters; here, we created an initial V matrix by clustering the voxels across subjects into k = 20 clusters and took the normalized average of the cluster signal. The implementation relies on the mini-batch k-means and the dictionary-learning methods of scikit-learn v0. 21. 3  (Pedregosa et al. , 2011) , a Python machine-learning library (https://scikit-learn. org/stable/). For display purposes, a hard assignment of vertices to components was undertaken by ascribing each vertex to the component that gets the largest loading (note that all U s are nonnegative): label s i =        0 if U s i ,j = 0 , j ∈ [c] argmax j ∈[c] U s i ,j , otherwise Finally, group maps Ū were obtained by first computing the median of the components, as follows: Ūi,j = median s ∈[n] U s i ,j Then, the same labeling strategy, as the one described above, was employed at every vertex.
| Prediction of Contrast Maps across Tasks Because functional activations are overlapping between the IBC tasks due to common underlying cognitive components, we hypothesized that such similarities allow for predicting these contrast maps from the contrast maps of other tasks. Keeping the notation of the previous section, let us consider the problem of predicting one contrast of one subject, which refers to the column X s j of X s , j ∈ [c]. We shall also consider the remaining columns X s -j that represent brain activity from the remaining contrasts, as well as all data from the other subjects X i , i s. To this end, we used Ridge regression: w s,λ,j = argmin w∈ c-1 i s X i j -X i -j w 2 + λ w 2 , in which λ was chosen by generalized cross-validation. The prediction output was defined as follows: X s j = X s -j w s,λ,j.
The success of the prediction is quantified by the cross-validated coefficient of determination, that is computed at each location i: R 2 i (j ) = 1 -mean s ∈[n] X s i ,j -X s i ,j 2 X s i ,j 2 In practice, a leave-three-subjects-out cross-validation experiment was employed, i. e. training sets were composed of the contrasts from ten subjects and three were left for the test set. Contrasts from the same task were predicted jointly, as they are correlated; hence, predicting one from the other would lead to optimistic bias. Ward's clustering method was used for parceling the brain volume into 100 sub-regions. The aforementioned analysis was performed in these sub-regions, similarly to what was done in  Tavor et al. (2016). Prediction accuracy at every voxel was measured in terms of R 2 -scores obtained from each data partition.
One should note that pinv X (s) X (s) is a linear projection operator that transforms R into R (s) , according to the subject-specific functional information embedded in X (s). This approach is an adaptation of the two stages featuring the dual-regression approach (consult Figure  1  and equations (1) and (3) of  Nickerson et al. (2017) ) and its notation is adapted to the type of data used in the present context. The number of contrasts maps c amounts to 43 because these are the 51 main IBC contrast maps from the first release minus those 8 selected to draw their functional profiles (see Section 3. 5). In order to generate the corresponding subject-specific region masks, the maps obtained in R (s) were then transformed into binary maps according to a prespecified threshold; this threshold was set in order to yield the same number of voxels in individual regions as in the group region.
Training sets were composed of data from twelve participants and prediction was performed in the data from the remaining participant; each group was thus referring to the data from one participant. Predictions were made between all possible combinations of two regions from the original collection of six regions-of-interest. Accuracy prediction at every voxel was measured in terms of classification accuracy per region pair. This analysis was performed using scikit-learn. To compute the chance level, the same procedure was employed using the scikit-learn's dummy classifier.
On the other hand, spatial consistency is higher at the individual level in 27/33 contrasts displayed in Figure  3. Note that, since per-session data are used to compute the individual-maps consistency, the fSNR in every map is lower for the intrasubject consistency analysis than for the intersubject consistency. These correlations also reflect imperfectly corrected PA/AP spatial distortions. Overall, the fact that maps are generally more consistent within subjects than across subjects hints at subject-specific topographies. We have also compared intersubject consistency of the main-contrast maps (except those from the RSVP Language task) estimated from the IBC dataset and the original datasets, i. e. ARCHI and HCP datasets (see Figures C. 1 and C. 2 in Appendix section C). Results show that, in the large majority of the cases, considerably higher correlations were achieved using the IBC data than the ARCHI data as well as the HCP data.
| A Dictionary of Cognitive Components We then analyzed the topographies of twenty cognitive components obtained by decomposing, per subject, the original data of all tasks-i. e. 51 main-contrast maps-using sparse dictionary learning. These individual topographies are displayed as spatial maps (see top section of Figure  4 ), wherein each map represents the functional profile of a component in each subject and hemisphere (i. e. maps of the top-left and top-right panels refer to the left and right hemispheres, respectively). According to S. M. Smith et al. (2009)  and given the total number of IBC contrasts, k = 20 represents a consensual number of components to feature this study. A larger number of components was deliberately not chosen for the sake of readability of the present results. These components synthesize the cognitive information across all contrasts, making between-subject comparison easier.
(Bottom) The boxplots represent the distribution of the intrasubject and intersubject stability of the components and whiskers show the 95% CI. Both intrasubject and intersubject data were split into two halves of the dataset, according to the phase-encoding direction parameter of acquisition. Dictionaries were estimated separately for each dataset. Split-half reproducibility of the spatial loadings was obtained, as their correlation, within and between subjects. While intrasubject correlations are relatively high, intersubject ones are low, thus hinting again at a strong subject effect. Black horizontal lines, going through the boxes, represent the median in both distributions. Overall, this figure illustrates the consistency of some components, while it outlines local and large-scale differences across the Individual Brain Charting (IBC) participants.
For instance, components tagged as math, saccades and consonant strings are observed in similar brain regions across subjects, but one typically dominates the others. In other words, their spatial representation compete across subjects. While their outline is broadly similar, they reveal in detail notable differences at high-resolution. To assess the stability of the dictionary, the dataset was split into two halves, according to the phase-encoding direction parameter. The split-half reproducibility of the twenty components was measured as correlations of the individual topographies both within and between subjects. The topographies were, in this case, obtained through the contrast z-maps estimated from the "PA" and "AP" runs of each task. Intrasubject stability was evaluated through the distribution of the correlations between PA-and AP-related topographies in every subject.
On the other hand, intersubject stability was estimated through the distribution of correlations between PA-and AP-related topographies within pairs of subjects. The results, displayed at the bottom section of Figure  4 , show higher correlations estimated from intrasubject data than intersubject data, highlighting that the variability of such spatial representations is linked to individual differences. Finally, it is also worthy of note that these aggregated components are more stable than the original contrast maps (see Figure  C. 1 of Appendix section C). Overall, means of the distributions for both intrasubject and intersubject correlations of dictionary components (see bottom section of Figure  4 ) are higher than means of the distributions of z-maps correlations (see Figure  3 ).
| Functional Mapping of the Language Network As several contrasts cover the same psychological domains, the IBC dataset provides the appropriate framework to derive a fine and unambiguous characterization of brain regions with respect to cognition. To showcase the benefits resulting from contrast-maps accumulation, we thus investigated the cognitive profile of a set of brain regions known to be linked to language mechanisms. These regions-of-interest refer to: (f posterior Superior Temporal Sulcus (STS). They were subsequently made subject-specific using the dual-regression approach described in Section 2. 7. 8. The maps of their projections onto individual templates are provided in Figure  D. 1 of the Appendix section D. 1. To illustrate the specific function of these regions, Figure  6  shows the activations elicited by-linearly independent-contrasts selected from the ARCHI Standard and RSVP Language tasks, whose effects-of-interest are related to language.
e. the corresponding chance levels of such predictions. All scores outperformed the chance level, although the classifier between IPS pars orbitalis/pars triangularis was close to chance. In order to generalize our conclusions to other cognitive domains and demonstrate the benefits of accumulated task data, the same experiment was also performed with data extracted from all main-contrast maps obtained from the entire dataset. The decoding performance was slightly higher (see The scores were estimated based on the functional activation of voxels from different pairs of regions-of-interest pertaining to the language network. Combinations of two brain regions from a total amount of six regions from the left hemisphere-Inferior Frontal Gyrus (IFG) pars orbitalis, IFG pars triangularis, Temporal Pole, Temporoparietal Junction, anterior Superior Temporal Sulcus (STS) and posterior STS-were employed in a leave-one-subject-out cross-validation experiment to calculate classification accuracy.
The functional profiles of the voxels were extracted from contrasts of the Individual Brain Charting (IBC) dataset that relate to language mechanisms (see Figure  6 ). To further evaluate the generalizability of these results, we conducted the same analysis in another set of regionsof-interest, also pertaining to the language network but extracted from functional signatures obtained from a larger cohort. Concretely, the group-level contrast story versus math of the HCP Language task (for more information about this task, consult Section 2. 4. 2), referring to a subset of the HCP900 dataset (n=786), was used to define functional regions participating in language-related mechanisms. A complete description of the procedures concerning regions' extraction can be found in the Appendix section D.
We then illustrated the practical significance of this individualized mapping approach, by showing how to adapt population-level regions-of-interest to individuals. This framework allows for probing unique functional profiles associated with specialized brain regions, giving the flexibility of adapting to an individual configuration and assessing functional specialization across individuals, while avoiding circular reasoning. We review all these steps in further detail over the next sections. A small-n, yet reliable functional mapping Fifty one main-contrast maps were investigated in this cross-task analysis, yielding a unique high-coverage cognitive mapping. Despite the low number of subjects, group-average results are consistent with those from previous larger-scale datasets, as highlighted by the results presented on Section 3.
Topographies of cognitive components The taskwise structure of the IBC dataset can support the investigation of common functional profiles or fingerprints and link them to experimental tasks. Here, we use dictionary learning to extract these functional profiles in an automatic fashion. Note that, other approaches would also be possible, such as independent components analysis or clustering. Our data-driven analysis, exploiting individual topographies across tasks, highlight the intersubject variability inherent to cognitive mapping (see Figure  4 ). Similarly to the results in Section 3. 2, intrasubject consistency was higher than intersubject consistency. Interestingly, the mean of intrasubject correlation for all components (see bottom section of Figure  4 ) is about twice the intrasubject consistency registered for the contrast maps themselves (see Figure  3 ).
Prediction of contrasts within the dataset Since many contrasts share the same cognitive components, they display overlapping functional patterns. This shared variability can be learnt and used to predict other contrasts. Such a successful prediction reveals the existence of a latent structure underlying them (see Figure  5  -bottom). Additionally, predictions are higher when subject identity is not randomly shuffled, highlighting once again that functional anatomy varies across subjects. Interestingly, this experiment, together with the reproduction of contrast maps from the ARCHI and HCP datasets (see Figure  1 ), implies that some brain maps could be transferred from IBC to these aforementioned datasets. For instance, we hypothesize the possibility of predicting the topographies of the HCP participants from the individual contrast maps of the IBC-ARCHI or RSVP-language tasks.
Cognitive regional profiles of the language network The taskwise organization of the dataset also opens the possibility to investigate the functional profiles of brain regions linked to cognitive domains covered by the contrasts (see Section 3. 5). For instance, the results shown in Section 3. 5 can identify which regions elicit effects modulated by word-level (e. g. left posterior STS) and sentence-level semantics (e. g. left Temporoparietal Junction and left Temporal Pole). On the other hand, effects modulated by syntax are difficult to isolate in the context of brain mapping. Some studies have suggested that syntactic processing is distributed throughout an ensemble of brain regions that support high-level linguistic processing. Therefore, they cannot be separated from other aspects regarding language comprehension, like lexico-semantic processing  (Blank et al.
, 2016) , overall suggesting that the language network might be more strongly concerned with meaning than structure  (Siegelman et al. , 2019). Yet, as an attempt to address this issue, we plan to integrate, in a future release, a naturalistic language-comprehension paradigm, dedicated to syntactic-composition modulation  (Bhattasali et al. , 2019). The more tasks are included in the dataset, the greater the richness of the contrasts that can be used to not only disambiguate the cognitive role of functional regions but also delineate finer demarcations of their anatomical boundaries.
Figure1displays the correlation of group-average z-maps between corresponding contrasts obtained from: (a) IBC versus ARCHI datasets and (b) IBC versus HCP datasets. The diagonal of the matrix refers to homologous contrasts and, therefore, it shows how successfully the maps of ARCHI and HCP were reproduced in IBC. The high correlations present on the diagonal confirm that the functional signatures of the same contrast maps were preserved across different datasets, providing clear evidence that the original results obtained in these large datasets are reproduced using the IBC dataset. One exception stands for the punishement-reward contrast map of the HCP Gambling task; nonetheless, results for this contrast map were also very variable within and between subjects for the original dataset (see Figure C.