dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants We used data from two open-access datasets, each containing structural MRI and resting-state fMRI assessments in healthy adult human subjects. The larger of these datasets, the PIOP1 dataset of the Amsterdam Open MRI Collection  (Snoek et al. , 2021)  was used as the discovery sample. 201 subjects (122 women, 79 men; mean age = 22. 2, SD = 1. 77) from the PIOP1 dataset were included in the present analyses. The MPI-Leipzig Mind-Brain-Body dataset  (Mendes et al. , 2019)  hereafter referred to as the 'MBB dataset' -was used as a replication sample. 68 subjects (32 women, 36 men; mean age (5-year bins) = 24. 5-29. 5) from the MBB dataset were included in the present analyses. We selected the larger dataset to be our discovery dataset given that it is more adequately powered. The putative 'true' non-inflated effect size of multivariate brainbehavior associations has been found to be in the range of r = ~0.
30-0. 40  (Marek et al. , 2022). Our discovery sample (n = 201) provides ≥80% power to detect non-zero correlations r ≥ 0. 15 with 95% confidence intervals, whereas our replication sample (n = 68) provides ≥80% power to detect non-zero correlations r ≥ 0. 25 with 95% confidence intervals. Both datasets, therefore, provide sufficient statistical power to detect putative multivariate brain-behavior associations involving absorption. Nonetheless, we note that, especially given tendencies toward attenuated effect sizes in out-of-sample replications  (Marek et al. , 2022) , the smaller size of the replication sample does constitute an important limitation in evaluating the true replicability of effects observed in the discovery sample. Another important limitation is that site-related differences pertaining to scanner type and imaging acquisition protocols may represent confounds which may hinder replicability.
Self-report measures Self-report measures included in the present analyses are trait Absorption and, for a follow-up analysis (see below), 'openness to experience' from the 'Big 5' personality inventory. Trait Absorption was assessed using the modified (Likert scale) Tellegen Absorption Scale (MODTAS) in both datasets  (Jamieson, 2005; Tellegen and Atkinson, 1974). MODTAS scores are hereby referred to as 'absorption scores' for simplicity. Importantly, however, a shortened (15-item) version of the 34-item scale was used in the PIOP1 dataset. This was done due to due to time constraints in data collection and an assumed high correlation between this subset of measures and all of the measures. Items were selected to maintain relative proportions of five putative MODTAS subscales. We empirically tested (in the MBB dataset which assessed all 34 items) whether the mean score produced by the shortened scale correlates strongly with the mean score based on all items.
This revealed a correlation of r = 0. 96, indicating strong but not perfect correspondence. MODTAS subfactor scores were available for the PIOP1 dataset and were used in secondary analyses. Subfactor (or item-level scores) were not available for the MBB dataset and therefore were not included in the analyses. Trait Openness was assessed in the PIOP1 dataset using the NEO-FFI  (Costa and McCrae, 1989) , a 60-item version of the NEO inventory, and in the MBB dataset using the NEO-PIR, the full 240-item version of the NEO inventory  (Costa Jr and McCrae, 2008).
Discovery sample: PIOP1 dataset Magnetic resonance imaging (MRI) was acquired on a Philips 3T scanner (Philips, Best, Netherlands) equipped with a 32-channel head coil. For each participant a number of scans were recorded over a 60-min scan session. In temporal order, there were faces (fMRI), gender-Stroop (fMRI), T1-weighted scan, emotion matching (fMRI), resting-state (fMRI), phase-difference fieldmap (B0) scan, diffusion-weighted imaging scan, working memory (fMRI), and emotion anticipation (fMRI). Only the T1-weighted and resting-state scans were used in the present analyses. The high-resolution anatomical (i. e. , T1-weighted) image was acquired using a 3D MPRAGE sequence with the following parameters: voxel size = 1. 0 mm isotropic, FOV = 188 × 240 × 220 mm, TR = m, TE = 3. 9 m, flip angle = 8 • , bandwidth = 191.
Replication dataset: MPI-leipzig MBB dataset MRI data was acquired on a Siemens 3T scanner (Magnetom Verio, Siemens Healthcare, Erlangen, Germany) equipped with a 32-channel Siemens head coil at the Day Clinic for Cognitive Neurology, University of Leipzig. For each participant the following scans were obtained: 1) a high-resolution structural scan, 2) four resting-state fMRI scans, 3) two gradient echo fieldmaps and, 4) two pairs of spin echo images with reversed phase encoding direction. The high-resolution structural image was acquired using a 3D MP2RAGE sequence with the following parameters: voxel size = 1. 0 mm isotropic, FOV = 256 × 240 × 176 mm, TR = 5000 m, TE = 2. 92 m, TI1 = 700 m, TI2 = 2500 m, flip angle 1 = 4 • , flip angle 2 = 5 • , bandwidth = 240 Hz/Px, GRAPPA acceleration with iPAT factor 3 (32 reference lines), duration = 8.
Anatomical data preprocessing Anatomical data preprocessing was conducted with the fMRIprep software  (Esteban et al. , 2019). The following description has been copied from this software to best facilitate reproducibility. The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with 'N4BiasFieldCorrection', distributed with ANTs 2. 2. 0  (Avants et al. , 2009) , and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype implementation of the 'antsBrainExtraction. sh' workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and grey-matter (GM) was performed on the brain-extracted T1w using fsl fast  (Jenkinson et al. , 2012). Brain surfaces were reconstructed using FreeSurfer recon-all  (Fischl, 2012)  and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical grey-matter.
Functional data preprocessing Functional data preprocessing was conducted with the fMRIprep software  (Esteban et al. , 2019). For each of the BOLD runs found per subject, the following preprocessing steps were performed: First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer  (Fischl, 2012) ;) which implements boundary-based registration. Co-registration was configured with six degrees of freedom. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using McFlirt (FSL  (Jenkinson et al. , 2012) ;). BOLD runs were slice-time corrected using '3dTshift' from AFNI  (Cox, 1996).
Functional data denoising Functional data denoising was conducted with xcpEngine. The '36P + SCRUB' pipeline of xcpEngine (https://xcpengine. readthedocs. io/o verview. html) was applied to the fMRIprep preprocessed data in both datasets. This stringent pipeline is consistent with the motion artefact removal pipeline originally proposed by  Power et al. (2012 Power et al. ( , 2014)). The following denoising steps were applied: demeaning and detrending, nuisance regression, bandpass filtering (0. 01-0. 08 Hz) and scrubbing (FD > 0. 5 mm). Scans with ≥15% of volumes removed (i. e. , interpolated) due to scrubbing were excluded from analyses. Nuisance regressors included 6 motion alignment parameters (R) and their derivatives and expansions (R 2 R t-1 R t-1 2 R t-2 R t-2 2 ) for a total of 36 motion regressors, as well as mean CSF signal as computed within an eroded mask and its derivative, mean WM signal as computed within an eroded mask and its derivative, and the whole-brain global signal and its derivative.
Voxel-based morphometry analyses 2. 5. 1. GMD calculation GMD was calculated from the individual-subject T1-weighted images of each dataset using voxel-based morphometry (VBM) as implemented in CAT12, a SPM12 add-on  (Ashburner and Friston, 2000; Gaser and Dahnke, 2016). Raw T1-weighted images were first spatially normalized to a high-resolution stereotactic space using the DARTEL template and then underwent automated segmentation into grey matter, white matter, and cerebrospinal fluid (CSF). CAT12 uses a tissue probability map (TPM) prior to skull strip the data and initialize the segmentation, and segmentation is conducted using a hypothesis-free adaptive maximum a posteriori (AMAP) segmentation approach  (Gaser and Dahnke, 2016). This approach estimates the amount of brain tissue type within each voxel and allows for the control of partial volume effects.
We visually inspected the resulting grey matter maps to ensure consistency in orientation across subjects. Once this was confirmed, the homogeneity of the total sample (comprising both datasets) was evaluated in order to identify outliers. Homogeneity was determined to be high across both datasets with average inter-subject spatial correlations ranging from r = 0. 88-0. 90. Prior to analysis, total intracranial volume was regressed from the grey matter maps to more reliably extract individual differences in relative grey matter proportions, and the regressed grey matter maps were then smoothed with an 8 mm Gaussian kernel prior to being used as input to the partial least squares correlation (PLSC) analyses (described below in Section 2. 8). It was noted above that the Leipzig used a MP2RAGE sequence which removes the influence of proton density and T2* from the T1-weighted image, as compared to the more common MPRAGE sequence which does not do this.
We found that this resulted in stronger image quality (IQR) ratings as output by SPM12 in the MBB dataset relative to the PIOP1 dataset (Leipzig range = 1. 8-2; PIOP range = ~2-3), where lower values indicate stronger quality. However, as mentioned, tests of homogeneity indicate that the subjects of the MBB dataset were not outliers with respect to the PIOP1 dataset.
, 2016). Cortical gradients were computed using a diffusion map embedding algorithm, as implemented using the BrainSpace toolbox (https://gith ub. com/MICA-MNI/BrainSpace (de Wael et al. , 2020);) in MATLAB. Diffusion map embedding was applied to individual-subject 400 × RSFC matrices (i. e. , including cortical parcels only). As has been done previously (e. g. ,  Hong et al. , 2019; Margulies et al. , 2016) , RSFC matrices were z-transformed and thresholded row-wise at 90% sparsity in order to retain only the strongest connections. Cosine similarity was then computed on the thresholded z-matrix in order to generate a similarity matrix which captures the similarity in whole-brain connectivity patterns between vertices. This similarity matrix is required as input to the diffusion map embedding algorithm.
dRSFC calculation Dynamic RSFC was computed using a sliding-window RSFC approach  (Lurie et al. , 2018). In particular, RSFC matrices were computed for 45-s Gaussian-tapered sliding windows in each datasetconsistent with past work using this approach  (Daws et al. , 2022; Lurie et al. , 2018). Results for 60-s and 75-s windows are reported in the Supplementary Information. Tapered windows were used as past work has indicated they increase sensitivity to differences between adjacent windows by emphasizing center timepoints  (Allen et al. , 2014; Leonardi & Van De Ville, 2015). Each window was slid for the number of timepoints equivalent to 1/3 of the window length overlapping, consistent with past work  (Cui et al. , 2021; Daws et al. , 2022)  and to reduce computational time-intensiveness.
g. , voxel-wise grey matter density or interregional functional connectivity) and absorption. A separate PLSC analysis was computed for each measure to assess its relationship with absorption. TheX and Y matrix for each analysis was centered and normalized across participants. Singular value decomposition of the cross-correlation matrix X'Y yields several mutuallyorthogonal LVs, each composed of three elements: (i) a left singular vector, containing weights the neural measure; (ii) a right singular vector, containing a weight for absorption; and (iii) a scalar singular value. Squared singular values reflect effect size: they are proportional to the covariance between brain and behavior that is accounted for by each latent variable. The number of latent variables is equal to the rank of X'Y; in the present case, this is 1 for analyses with absorption total and 5 for analyses in the PIOP1 dataset which included the 5 absorption subscales.
Out-of-sample replication To assess the replicability of PLSC neural weights derived from the discovery (PIOP1) dataset in the replication (MBB) dataset, we conducted a BSR-based direct replication. In particular, for results which reached statistical significance in the PIOP1 dataset, we computed the dot product between the neural BSR pattern from the PIOP significant result and the corresponding Leipzig neural input matrix, and then examined the correlation between the resulting values (i. e. , one value per Leipzig subject representing the degree to which its input matrix exhibits the LV pattern) and absorption in the MBB dataset.
Self-report results Descriptive statistics were conducted on the self-report data to assess their reliability and suitability for a linear decomposition method such as PLSC. First, histograms were created for absorption scores in each dataset to determine whether they are approximately normally distributed. This was indeed confirmed (Supplementary Fig. 1 ). Second, we computed the Cronbach's alpha of absorption assessments for each dataset. This revealed high alpha values (PIOP1 alpha = 0. 87, Leipzig alpha = 0. 94), indicating strong internal consistency. Past research has indicated that absorption exhibits a 0. 3-0. 5 correlation with trait openness to experience  (McCrae, 1993) , a reliable personality trait from the canonical NEO 'Big Five' personality assessment  (Costa Jr and McCrae, 2008; McCrae, 1993).
Discovery analyses Having established the reliability and suitability of absorption scores in both datasets for linear analyses, we applied PLSC to assess whether absorption is associated with inter-individual differences in voxel-wise grey matter density (GMD). First, we conducted a hypothesis-driven PLSC analysis including only voxels within a FPCN mask (as defined based on  Schaefer et al. (2017)  ROIs and the  Yeo et al. , (2011) parcellation). No statistically significant latent variable (LV) was found (p = 0. 89). Given the availability of absorption subscale measurements in the PIOP1 dataset, we conducted an additional PLSC analysis with the five absorption subscales, rather than absorption total. Again, no statistically significant LV was foundalthough, LV 2 trended towards significance (p = 0.
Replication analyses We failed to find a significant association between GMD and absorption total in the discovery (PIOP1) dataset. This null finding was replicated in the MBB dataset, wherein PLSC analyses similarly did not find a significant latent variable linking GMD and absorption (p = 0. 72). Given that the LV from the PIOP1 absorption subscale analysis represents an axis of covariance that pertains to a dissociation between absorption subscales and, given that it only minimally correlated with absorption total (r = 0. 07), a true replication in the MBB dataset (for which absorption subscales are not available) was not possible. Nonetheless, for comprehensiveness, we conducted a direct replication in which we computed brainscores from the PIOP-derived LV 2 BSR weights and the Leipzig individual-subject GMD matrices and assessed their correlation (controlling for sex effects) with absorption total in the MBB dataset.
076) and the latter revealed a non-significant effect (p = 0. 19). We next conducted an exploratory analysis evaluating whether whole-brain interregional RSFC (400 × 400;  Schaefer et al. , 2017)  is associated with absorption. PLSC results with the PIOP1 dataset revealed one statistically significant LV for absorption (p = 0. 01, 58. 19% covariance explained; Fig. 2 ). No additional latent variables were found when computing the model with the five absorption subscales. This LV represents a set of interregional RSFC connections that jointly covary with absorption scores. Post-hoc correlations with absorption subscales revealed that all subscales were strongly positively associated with this effect, with the highest correlations being with the 'altered state of consciousness' and 'aesthetic' subscales.
Replication analyses. To evaluate the replicability of the result with the PIOP1 dataset, we computed brain connectivity scores from the PIOP1-derived LV 1 BSR weights and the Leipzig individual-subject RSFC matrices and assessed their correlation (controlling for sex effects) with absorption in the MBB dataset. This revealed a correlation of r = 0. 04 (as compared to r = 0. 77 in the PIOP1 dataset), indicating a lack of replication at the whole-brain level. To evaluate replicability at a more fine-grained level, we also computed brain connectivity scores for each of the statistically significant network x network pairs as shown in Fig. 3B. This allowed us to derive correlations (for both datasets) between absorption and the extent to which a given network x network RSFC pattern expresses the pattern found in the corresponding network x network BSR weights from PIOP LV 1.
Results are shown in Fig. 3. Results indicate that the correlations in the Leipzig (replication) dataset are unreliable for the network pairs which exhibited significant correlations in the PIOP1 datasets. This indicates a lack of replication of this result across datasets. Finally, for comprehensiveness we also computed univariate correlations between network-averaged RSFC (i. e. , average RSFC across all connections within and between each network) for each of the 17 networks of  Yeo et al. (2011)  and absorption for each of PIOP1 and MBB datasets (Supplementary Fig. 2 ). No overlap between results from each dataset was observed (p < 0. 05 uncorrected), further confirming the absence of a reliable association between RSFC and absorption in the present datasets. The proportions of each of the 17 large-scale networks identified by  (Yeo et al.
Control analysis with openness to experience Our analyses failed to find a reliable association between absorption and brain structure or resting-state function across the two investigated datasets. To ensure that this was not due to an issue pertaining to the present datasets in general, we also computed associations between GMD and RSFC and the personality trait of openness to experience. As mentioned, openness is a reliable and stable personality trait from the canonical NEO 'Big 5' personality inventory and exhibited a statistically significant correlation with absorption in the present datasets. PLSC analyses with the PIOP1 dataset revealed no significant latent variable for GMD (p = 0. 60), and one statistically significant latent variable for whole-brain interregional RSFC (r = 0. 78, p = 0.
Discussion The present study constitutes the first comprehensive assessment of the association between trait absorption and brain structure and function. In particular, PLSC was applied to assess multivariate associations between absorption and GMD along with several measures of both static (time-averaged) and dynamic (time-varying) RSFC. Analyses were conducted on a discovery dataset (n = 201), and an out-of-sample (n = 68) replication was attempted for statistically significant effects. Results revealed no statistically significant association between GMD and absorption in either dataset. Across the multiple measures of static and dynamic RSFC, only whole-brain interregional RSFC was significantly associated with absorption in the discovery dataset (p < 0. 05) -however, this effect did not replicate in the replication dataset.
Additional analyses revealed that univariate assessments of associations between absorption and GMD or interregional RSFC also failed to yield replicable effects. Finally, as an evaluation of the reliability of the datasets themselves, we analyzed brain-behavior associations involving trait openness to experience and found that, in contrast to absorption, this trait was associated with interregional RSFC in a manner that replicated across datasets. This set of results indicates a lack of reliable associations between trait absorption and brain structure or function as measured by neuroimaging. The lack of reliable brain-absorption associations in the present study can likely be attributed to a combination of two factors: (i) the conceptual and psychometric properties of the construct of absorption itself and (ii) broader concerns related to neuroimaging-based individual-differences research in general.
It is very possible that a more refined assessment of absorption that features greater reliability and clear construct validity may yield reliable associations with brain structure at the sample sizes employed in the present study  (DeYoung et al. , 2022; Tiego and Fornito, 2022). Indeed, this latter point is supported by our successful replication of a brain-behavior association between interregional RSFC and trait openness to experience (r = 0. 78 discovery, r = 0. 28 replication; Supplementary Fig. 4 ). This finding is consistent with a notable large-scale study (n = 889), which found that among the Big 5 personality traitsopenness, conscientiousness, extraversion, agreeableness, and neuroticism -RSFC-based predictions were most reliable for openness  (Dubois et al. , 2018). We highlight, however, that although a significant correlation was found in the replication dataset, this value was far lower (a difference of r = -0.
50) than in the discovery datasetbroadly consistent with the attenuated out-of-sample effect sizes found by  (Marek et al. , 2022). This suggests the likelihood of overfitting in the discovery dataset. Nonetheless, it is notable that the discovery effect size of this association is very similar to the discovery effect size found between interregional RSFC and absorption (r = 0. 77). As such, we might reasonably expect the out-of-sample effect size of this latter finding to also be in the range of r ~ = 0. 25. Given that our replication sample (n = 68, four 15-min resting-state scans) is sufficiently powered to detect effects of r ≥ 0. 25 with a 95% confidence interval (see Methods), it should, therefore, be of sufficient sensitivity to detect the effect found in the discovery dataset. This further suggests that our null findings may be due to the non-replicability of brain associations with trait absorption in particular, rather than solely due to the use of underpowered datasets.
We failed to find a significant association between GMD and absorption or openness to experience in either dataset, using both PLSC and standard GLM-based univariate approaches. At first glance, this appears at odds with previous neuroimaging studies which have found links between personality and brain structure, including with respect to openness (e. g. ,  Vartanian et al. , 2018)  and absorption  (Grant et al. , 2013). However, the present null findings are less surprising when considering that past studies examining GMD-behavior associations have predominantly used small and underpowered (mostly n < 50) samples and have produced effects which rarely replicate  (Avinun et al. , 2020; Boekel et al. , 2015; Genon et al. , 2022; Marek et al. , 2022; Masouleh et al. , 2019). Indeed, in a recent large-scale (n = 1107) GMD-personality study,  Avinun et al.