dataesr/scientific-paragraphs-categorization huggingface dataset:
TLS cloud and the photogrammetric clouds, both segmented following the same class definition used for the images, are available in addition to the images for each building. These multiple-source data can useful to perform comparisons and assessments such as: (i) compare the accuracy of point-based and multi-view based methods on the same dataset, (ii) compare the accuracy of multi-view based approach on heritage benchmark with that obtained on standard buildings, (ii) assess the of point-based networks on two types (TLS and photogrammetric) of point cloud data. Hence, the presented dataset can be (i) integrated with ARCHdataset, (ii) used to tailor existing network architectures on the CH building case, (iii) exploited to develop new hybrid networks that can leverage on both images and point clouds.
To this aim, we analyzed data from our previous study  (Tommasin et al. , 2017; Tommasin et al. , 2018)  in which we collected BOLD fluctuations in a block-design fashion employing prolonged epochs of alternated resting-state and sustained working-memory task conditions. Leveraging on the peculiar acquisition protocols, both long and with multiple conditions within the same run, we evaluated the condition-specific performance of the pipelines according to benchmarks based either on minimizing motion-related artifacts or at maximizing network identifiability. In addition, in order to corroborate our findings, we evaluated the pipelines in a further, larger dataset, composed of two separate acquisitions of resting-state and stop-signal task scans  (Poldrack et al. , 2016). Finally, we examined the robustness of previously reported task-associated modulations in within-network FC  (Tommasin et al.
| Subjects and datasets Denoising strategies were evaluated in two fMRI datasets. The first, which we refer to as "Centro Fermi" (CF) dataset (40 runs from 20 healthy subjects), is composed of data collected at our laboratory for the evaluation of FC modulations following sustained task execution  (Tommasin et al. , 2017; Tommasin et al. , 2018). Each CF run comprises multiple long-lasting epochs of either rest or working-memory task. Despite the limited sample size, the CF dataset was used as our primary dataset given its peculiar paradigm (detailed in the following section). For the second dataset, which serves for result corroboration and to counteract the sample-size limitation of the CF dataset, we used a subset of data from the Consortium for Neuropsychiatric Phenomics (CNP  Poldrack et al.
, 2016). The CNP dataset comprises resting-state scans as well as six scans of BOLD acquisitions under different task conditions. We chose to compare the resting state to the stop-signal task since such task showed the greatest difference in motion compared to rest, as reported in a previous study  (Huijbers et al. , 2017). From the entire pool of healthy subjects (130 subjects), we selected 120 subjects that included (a) a complete resting-state acquisition, (b) a complete stop-signal task acquisition, and (c) a T1-weighted structural scan. The CF data were collected on a 3T MRI Scanner (Magnetom Allegra, Siemens Healthineers, Erlangen, Germany) equipped with a standard birdcage coil. Functional images were acquired using a Gradient-Echo Planar Imaging (GE-EPI) sequence (TR = 2,100 ms, TE = 30 ms, FA = 70 , voxel size 3 × 3 × 2.
5 mm 3 , 1. 25 mm skip). Each run lasted 24 min and 38 s yielding 704 volumes (four dummy scans included). The slices were positioned starting from the vertex of the brain and covered the whole cerebrum. The cerebellum was not consistently included in the field of view of each subject. High-resolution T1-weighted images were acquired for anatomic reference and tissue segmentation purpose using a Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE, TE = 4. 38 ms, TI = 910 ms, TR = 2000 ms, FA = 8 , voxel size 1 × 1 × 1 mm 3 ). Subjects gave written informed consent in accordance with the Declaration of Helsinki and European Union regulations. The CNP data were collected on one of two Siemens Trio 3T scanners. Rest and task runs were acquired on the same day with a GE-EPI sequence (TR = 2000 ms, TE = 30 ms, FA = 90 , voxel size 3 × 3 × 4 mm 3 ).
| CF dataset CF functional images were acquired during a block-design stimulation paradigm consisting of alternated long-lasting epochs of eyes-open resting state and sustained auditory working memory task (4 min and 54 s each, starting with a resting-state epoch). The auditory working memory task involved continuous n-back trials administered in epochs at either "high" load (2-back) or "low" load (1-back). Each trial was composed of a 500-ms window, in which subjects were aurally presented with a vowel (pseudo-randomly chosen among A, E, or O), and a subsequent 1,600-ms response window, during which subjects had to report, via an MRI compatible 2-button keyboard, whether the current vowel was the same as the one presented one stimulus prior (1-back) or two stimuli prior (2-back). During the entire functional run, subjects were asked to maintain their gaze on the center of the screen, which was marked by a one-degree diameter circle over a uniform black background.
| CNP dataset The CNP rest and task data were acquired in separate runs, yet within the same acquisition session. During resting-state acquisitions, no stimulation was presented and subjects were asked to remain relaxed and keep their eyes open. The stop-signal task runs consisted of 128 trials in which a "go" stimulus (left or right pointing arrow) was visually presented with or without an aurally presented "stop" signal (a 500 Hz tone). Subjects were required to respond to the go stimulus (via a left or right button press) as quickly and accurately as possible, but to withhold the response in case of the stop tone. Trials were separated by rest periods (with a black screen) whose duration was pseudorandomly chosen between 0. 5 and 4 s, with a mean of 1 s. While the experimental design is aimed at identifying the specific response to the stop signal, we treated the runs as block-designed, thus ignoring potential instantaneous changes of motion associated with the different structure of the trials (i.
| Functional image preprocessing Image preprocessing was performed with FC toolbox  (CONN 18. a, Whitfield-Gabrieli & Nieto-Castanon, 2012) , which is based on SPM12 routines (http://www. fil. ion. ucl. ac. uk/spm), and was run on Matlab 2016b (The Mathworks Inc. , Natick, MA). Preprocessing of functional data included the following steps: (a) rigid body registration for inter-frame head motion, (b) application of the unwarp algorithm to reduce the susceptibility-by-movements effects  (Andersson, Hutton, Ashburner, Turner, & Friston, 2001) , (c) compensation of systematic slice-dependent time shifts by phase shift in the Fourier domain, (d) direct normalization to Montreal Neurological Institute (MNI) space (voxel size 3 × 3 × 3 mm 3 ) using as source image the EPI mean volume obtained from step a, and (e) intensity normalization to global mode 1,000 units.
The direct normalization to MNI space (i. e. , without using high-resolution structural information) was chosen in order to mitigate the impact of geometric distortion artifacts, as shown in  (Calhoun et al. , 2017). Subject-specific whole-brain (WB) masks were defined to retain voxels fully covered by the field of view of the EPI sequence. Such masks were obtained by intersecting an a priori brain mask with subject-specific masks composed of voxels with a mean intensity above the 20% of the global mode. The WB mask was also used to compute the global signal. To allow a fair comparison between rest and task conditions, scans were trimmed so that the two functional conditions had the same number of volumes. In the CF dataset, we discarded the first resting-state epoch, since there were three rest and two task epochs.
Following the cut, the final number of volumes in the run was 560 (280 volumes per condition, approximately 10 min). In the CNP dataset, we discarded the first 32 volumes of the task run, so that each functional condition was composed of 152 volumes (approximately 5 min). Finally, after removing constant and linear trends, the rest and task runs of the CNP dataset were concatenated, yielding a total run length of 304 volumes.
Subsequently, the ensuing map was thresholded at 99% (95% for the CNP dataset, since in such dataset the 99% threshold resulted in a few subjects with empty masks) and underwent a 2-voxel level erosion. The resulting mask was normalized to MNI space and was deprived of any nonventricle structure using an a priori mask. In case the final mask contained less than 10 voxels, the procedure was replicated using a 1-voxel level erosion. Additional information regarding the constructed masks are reported in Figure  S2  and Table  S1. Once confounding signals were extracted with the above-defined masks, we further masked functional data by retaining voxels with a gray matter probability >75%. Such last masking aimed at increasing the specificity of FC estimates and at lightening the computational burden.
| Assessment of in-scanner motion The realignment transformation matrices, estimated during the interframe rigid-body registration, were used to compute the Framewise Displacement (FD), defined as the root mean square deviation of the relative transformation matrices (i. e. , the transformation "error" between two consecutive volumes), over an 80-mm radius sphere  (Jenkinson, Bannister, Brady, & Smith, 2002). Although there are several alternative FD metrics, we adopted the one defined by Jenkinson and colleagues as it has been shown to be the most closely related to voxel-specific metrics of displacement  (Yan et al. , 2013). For the CF dataset, we split the FD series in its five epochs, according to the experimental paradigm. As for the EPI series, the FD series of the first epoch was discarded.
When censoring was required, the marked volumes were put to zero value both in the data and in the regressor matrix, consuming a number of tDoF equal to the number of excised volumes. For the CNP dataset, in order to take into account the phase shift between the concatenated rest and task runs, two orthogonal sets of trend and band-pass blocks were used (see Figure  S3  for CF and CNP representative denoising matrices). The linear regression was calculated via ordinary least squares method using the functional run as dependent variable and the above defined regressor matrix as explanatory variables. The denoised series was obtained by computing the residuals of the regression model. The simultaneous denoising approach is available in AFNI via the function 3dTproject  (Cox, 1996) , however, for speeding up calculations (e.
g. , by avoiding loading multiple times the same dataset that undergoes different regression models) we rewrote the algorithm in Matlab. The function is freely available from GitHub (https://github. com/dmascali/fmri_denoising), along with code to construct suitable regressors for denoising. We note that the range of frequency selected for bandpass filtering is slightly wider compared to similar previous denoising studies (e. g. ,  Parkes et al. , 2018; Yan et al. , 2013). The cut-off frequencies were selected to minimize the impact of the filter on the residual tDoF while at the same time allowing for low-pass filtering below 0. 1 Hz, which in previous studies has shown to be effective in mitigating motion artifacts  (Satterthwaite et al. , 2013).
Conversely, P-censoring ensures a fair comparison between conditions, but removes potentially good volumes in the condition with less motion. In the CF dataset, the 0. 2 mm and 25% thresholds were chosen so to leave at least 39 tDoF and at least 5 min of data in each functional condition, which has been shown as an adequate amount of data to achieve stable estimates of FC  (Van Dijk et al. , 2010). Given the short acquisition, the CNP dataset could not meet these two criteria for any sensible threshold, therefore, we did not explore censoring in this dataset.
Such an approach ensures that the extracted PCs are maximally predictive. Note: For each pipeline the table shows the number of explanatory variables used by each set of confounders, along with the total number of variables (including the number of censored volumes) and the residual nominal tDoF. When more than one number is present in a cell, the first refers to the CF dataset, the second to the CNP dataset; when this distinction is absent, the number is common to both datasets. When the number of confounders is variable across the dataset, the mean and the standard deviation are reported. • ICA-AROMA (ICA-based strategy for Automatic Removal of Motion Artifacts). ICA-AROMA is a data-driven method to identify and remove motion-related artifacts  (Pruim, Mennes, Buitelaar, et al. , 2015; Pruim, Mennes, van Rooij, et al.
| FC estimates Before estimating FC, the denoised series were split and merged in order to obtain two functional series, one for the rest and one for the task condition, from which we extracted condition-specific FC estimates. If the pipeline included censoring, the censored volumes, originally set to zero value, were removed. 1 In order to compute benchmark measures, we parcellated the cortex in 333 node regions using the Gordon and colleagues parcellation, which provides higher homogenous FC than other available parcellations  (Gordon et al. , 2016). Five (34) node regions were discarded because they did not overlap consistently with the EPI data in the CF (CNP) dataset, resulting in a total of 328 (299) exploitable nodes. Average time series were extracted from each node and a FC matrix was obtained by calculating the Pearson's correlation coefficient between average time series of each pair of nodes, resulting in 53,628 (44,551) unique FC estimates.
| Subject's in-scanner movement As expected, the analysis of the FD series showed a marked tendency for subjects to move more during the resting periods than when they were engaged in the working-memory (Figure  1 ) or in the stop-signal task (Figure  S4 ). The motion characteristics of the two datasets are reported in Table  2. Considering the CF dataset (Figure  1 ), the resting epochs were (Figure  1b ). As shown in Figure  1c , the mFD was found significantly greater at rest than at task (mFD task : 0. 074 ± 0. 029 mm; mFD rest : 0. 114 ± 0. 042 mm paired t-test, mFD rest > mFD task : t = 5. 0, p = 7. 8 × 10 -5 , dof = 19, after averaging the two runs). As the result of the pronounced difference in the distribution of FD values for the two functional conditions, a simulation of censoring at several thresholds resulted in more volumes above threshold at rest than at task, for all considered thresholds (Figure  1d ).
| DVARS results Head motion induces spurious signal changes that are apparent in DVARS series. Given the differential impact of head motion between rest and task conditions, we expect DVARS to reflect such differences by showing higher values for the resting state. Here, we used a balanced DVARS value between the two functional conditions as an indicator of pipeline efficacy. Convergent results were obtained with the CNP dataset (Figure  2b ), yet with one exception. The most noticeable is the drop in performance of aCompCor50%, which showed a much higher median absolute value due to a distribution of Δrms(DVARS NOISE ) markedly shifted toward positive values, indicating greater DVARS during task than at rest. Such an inversion may be the result of an overaggressive denoising that may have overfitted the data.
To investigate the type and the time course of variance removed by different models, we compared, subject by subject, the FD series against the DVARS GM series. Figure  3   Another noticeable feature is the suppression of DVARS GM values under the baseline level occurring simultaneously with huge movements, which can be observed with 24RP regression and, even more, with aCompCor-based pipelines but not with ICA-AROMA (see for example volumes 150, 430, and 550 in Figure  3a ). This kind of depression in DVARS series has already been reported  (Hallquist, Hwang, & Luna, 2013)  and may indicate the goodness of these kinds of models in removing BOLD signal fluctuations following head movements that heavily affect the entire brain. The effect, being particularly marked for aCompCor50%, may explain the change in the sign of Δrms(DVARS GM ) for the 24RP + aCompCor50% pipeline seen in the CNP dataset.
| QC-FC correlations The results of the QC-FC analyses, designed to assess the association between FC estimates and in-scanner motion, are reported in Figure  4  for the CF dataset (similar results were obtained with the CNP dataset Figure  4c  shows the residual distance-dependent artifacts, quantified as the Spearman's correlation between FC estimates and the Euclidean distance between pairs of nodes. For all considered models, the correlation was negative, indicating a higher association between FC and motion at short distance rather than at longer distances. Among pipelines without censoring, models that were effective at minimizing global artifacts were generally not as effective with distance-dependent artifacts. No pipeline was associated with correlations both low and even across conditions, although, even in the worst case, the magnitude of the effect was modest (Spearman's r <.
| Network modularity This result highlights the critical role of tDoF in the across-subject reproducibility of network structures. Among pipelines without GSR, aCompCor-based models demonstrated relatively good results, particularly in maximizing modularity, with 24RP + aCompCor50% that showed the highest values in both rest and task conditions. Nevertheless, divergent results were seen when comparing the partitions' similarity of the aCompCor50% model between the CF (Figure  6 ) and CNP (Figure  7 ) dataset. Indeed, while in the CF dataset switching between aCompCor to aCompCor50% improved the partitions' similarity for the task condition, the same switching in the CNP dataset yielded reduced similarity for both functional conditions and for their difference. Such discrepancy between the datasets may once again be related to a low number of residual tDoF.
Indeed, the shortest dataset, CNP, had 51 tDoF left after the application of 24RP + aCompCor50%, a much lower number compared to that of the longest dataset, CF (130 tDoF). Regardless of the dataset, ICA-AROMA pipelines exhibited mixed results. In combination with GSR, ICA-AROMA showed the highest modularity, whereas without GSR it demonstrated intermediate performance in agreement with benchmarks based on motion-related artifacts. Nevertheless, partitions' similarity was relatively low, with values that were comparable to those of RP-based models. When evaluating partition's similarity, it should be considered that noise could inflate such a metric in case motion-induced connectivity patterns are reproducible across subjects. This possibility cannot be excluded a priori, especially considering that previous studies have shown motion to increase test-retest reliability of FC  (Parkes et al.
| Effect of motion on task-associated change in within-network FC Using the CF dataset, we previously reported a marked reduction of within-network FC in several large-scale networks following the execution of the working-memory task  (Tommasin et al. , 2018). Here, the task-associated reduction in within-network FC was found to be modulated in magnitude depending on the denoising model applied, but it was always significantly different from zero (see Figure  S9 ). In addition, the incremental censoring analysis showed the stability of the effect sign under progressive elimination of the most motion-affected volumes. Results of such analysis applied to the best performing pipeline, 24RP + aCompCor50%, are reported in Figure  S10 , while in Figure  8  we show results for the 24RP + aCompCor pipeline, that, demanding fewer tDoF, allowed us to explore a wider range of censoring thresholds.
| DISCUSSION The current study evaluated the efficacy of commonly adopted denoising pipelines in balancing the residual motion-related artifacts between functional conditions differently prone to in-scanner motion. First, we confirmed the marked difference in subjects' motion between resting epochs and epochs of continuous performance of either a working-memory task or a stop-signal task, with task epochs characterized by a minor number of bulky movements and lower average motion. Second, we found that many denoising pipelines Thus, results from our multiple-condition datasets suggest that exploiting signals from tissue compartments that encompass various orthogonal sources of variance is both more effective and more efficient than accounting for phase lags (first derivatives) and nonlinear effects (squared terms) in tissue-mean signals.
Despite each of the above-described pipelines provided incremental benefits in reducing global motion artifacts, they all showed a relatively high differential efficacy in cleaning the two functional conditions, especially in the CF dataset. The effect was markedly reduced by using the 24RP + aCompCor50% pipeline, that is, by increasing the number of PCs used as confounding variables. Indeed, among pipelines without GSR, F I G U R E 8 Incremental censoring analysis for within-network FC considering models (a) RP24 + aCompCor and (b) RP24 + aCompCor + 2GSR. In each panel, the left plot shows a P-censoring analysis while the right plot a T-censoring analysis. In P-censoring an equal number of volumes were excised from rest and task conditions ensuring conditions comparability in terms of tDoF, yet at the expense of removing potentially good volumes in the task condition.
Yet, the latter effect was not seen in the CNP dataset, where the application of aCompCor50% resulted in reduced across-subject partitions' similarity compared to the aCompCor variant. The discrepancy between the two datasets is likely to be tDoF related. Indeed, aCompCor50% had a great cost in terms of tDoF, which resulted in a poor residual nominal tDoF for the CNP but not for the CF dataset. In light of such results, the application of aCompCor50%, despite its good efficacy in removing global motion artifacts, should be evaluated in concert with the consumed and available tDoF, especially in short acquisition protocols. One interesting feature that emerged from the inspection of QC-FC plots in the CF dataset (Figure  4 ) is that the above-described condition-dependent pipeline fostered the functional condition most affected by motion, that is, the rest condition.
Such result indicates that five PCs for tissue type are not sufficient to evenly clean the datasets. While we cannot generalize the statement to different dataset types, we suspect that the need for a large set of PCs in order to effectively minimize motion artifact is due to the specific complexity of multiple-condition experiments or of long-acquisition scans. Indeed, this type of experiments tends to be affected by a richer spectrum of physiological noise compared to single-condition or shortacquisition scans. Nonetheless, a clear advantage of aCompCor50% is the data-driven selection of the number of PCs, which makes the method particularly flexible, being able to tune the number of regressors according to the specific physiological noise within the data. Compared to previous evaluation studies, we optimized aCo-mpCor by extracting PCs from tissue signals orthogonalized with respect to the confounding variables that composed the model, yielding a set of PCs with a greater noise prediction power compared to the standard variant.