dataesr/scientific-paragraphs-categorization huggingface dataset:
Future studies will precisely characterize the anatomical pathways of the distinct alpha-band oscillations, possibly involving cortical dynamics in the ventral and dorsal streams  (Capilla et al. , 2014)  or cortico-thalamic connections (Lopes da  Silva et al. , 1980; Halgren et al. , 2019). Concerning the anatomical pathway of waves' propagation, our analysis based on EEG recordings prevents us from clearly determining whether the observed waves propagate through the cortex or whether more localized dipoles generate such widespread observations at the sensor level. A previous source-analysis study on different visual-task datasets  (Lozano-Soldevilla and VanRullen, 2019)  leaves both possibilities open. However, recent simulations on perceptual echo data (related to bottom-up, sensory waves,  Zhigalov and Jensen, 2022)  suggest that two dipoles in occipital and parietal regions could be responsible for the generation of the waves propagating in the occipital-to-frontal direction.
Methods Participants. EEG data were recorded from 16 volunteers (aged 20-32 years old, four women, three left-handed). All subjects reported normal or corrected-to-normal vision, and they had no history of epileptic seizures or photosensitivity. All participants gave written informed consent before starting the experiment, following the Declaration of Helsinki. This study adheres to the guidelines for research at the "Centre de Recherche Cerveau et Cognition," and the protocol was approved by the local ethical committee "Comité de protection des Personnes Sud Méditerranée 1" (ethics approval number N˚ 2019-A02641-56). Furthermore, we included EEG recordings from two additional publicly available datasets investigating distinct scientific questions and using different analyses than our study.
In the first one, 31 participants performed a visual working memory task involving spatial attention. The data were published in a previous study  (Feldmann-Wüstefeld and Vogel, 2019, data  available online at https://osf. io/a65xz/). In the second dataset, 16 participants performed a task involving covert spatial attention. These data were published in another study  (Foster et al. , 2017 , data available online at https://osf. io/m64ue). The number of participants in our dataset was estimated based on a power analysis of previous studies investigating travelling waves in vision  (Luo et al. , 2021)  and to match the number of participant in the third dataset  (Foster et al. , 2017). Our dataset is also available online at https://osf. io/pn784/. Experimental procedure. The following describes the experimental procedure to collect the data never published before.
Participants reported whether they saw a target only at each trial's end to prevent motor activity from confounding the EEG signals. All stimuli were generated in MATLAB using the Psychtoolbox  (Brainard, 1997). We included two additional datasets in this study. In both studies, participants performed a visual attention task while keeping their fixation in the center of the screen. Regarding the Feldmann-Wüstefeld and Vogel, 2019 study, participants were asked to memorize the colors of two stimuli while ignoring a set of distractors stimuli. We analyzed uniquely those trials in which the visual stimuli were presented to the left or right side of the screen, while the distractors were placed above or below the fixation cross. After 500ms of the fixation cross, two colored 'target' stimuli were presented for 200ms.
Participants were asked to memorize these stimuli, and a new 'probe' stimulus was shown after an additional second. Participants reported whether the probe matched the target stimuli or not. We analyzed the traveling waves in the 500ms following the target stimulus onset. Participants performed a spatial attention task in the second dataset from Foster et al. 2017. First, the fixation cross cued participants to covertly attend one of eight possible spatial positions uniformly distributed around the center of the screen. After one second, a digit was displayed either in the cued location or in any other one. The remaining locations were filled with letters. Participants were instructed to report the displayed digit. We analyzed the waves the second before the stimuli onset when participants were attending to the locations cued to the left or right side of the screen (we discarded trials in which participants attended locations above or below the fixation cross).
For additional details about both experimental procedures, we refer the reader to  Foster et al. , 2017 and Feldmann-Wüstefeld and Vogel, 2019. EEG recording and Preprocessing. Throughout the experiment, we recorded EEG signals using a 64-channel active BioSemi EEG system (1024Hz sampling rate), with three additional ocular electrodes. The preprocessing consisted of down-sampling the data to 256Hz, followed by a high-pass (>1Hz) and a notch (47-53Hz) filter. Data were then averagereferenced and segmented from 500ms before trial onset to its end. We performed the preprocessing in EEGlab  (Delorme and Makeig, 2004). Importantly, we carefully discarded from all analyses all trials in which the EOG signals revealed eye movements. Traveling wave analysis. As in our previous studies  (Alamia et al.
333) suggests a lack of effect  (Smith, 2001; Masson, 2011). We performed all analyses in JASP  (Love et al. , 2015) , considering default uniform prior distributions. The code to analyze the travelling waves is freely available at the following link: https://github. com/artipago/Travelling-waves-EEG-2. 0  (Alamia, 2023). the European Union's Horizon 2020 research and innovation program (grant agreement No 101075930).
These waves were computed on trials without target or distractors. Positive (negative) values reflect more (less) waves than the chance level (as quantified by the surrogate distribution), whereas values around 0 indicate no difference between the real and the null distribution. E) Simulated data providing a schematic representation of forward and backward waves in the time domain in a given line of electrodes (from more frontal E1 to more occipital E7). A positive and a negative phase shift characterized forward and backward waves, respectively. Attending to visual stimuli modulates traveling waves. In this analysis, we investigated.
The middle row shows schematic representations of the screen during the tasks: the central panel illustrates the task from Feldmann-Wustefeld et al. 2019, where D and T stand for Distractor and Target, respectively. In the task from Foster et al. 2017, the screen was empty, as the eight circles were not displayed during the task but here illustrate the stimulus positions (Foster et al. , 2017 )(Foster et al. , 2017). The lower panels represent the lines of electrodes in all datasets.
Error bars represent standard errors of the mean. The middle row shows schematic representations of the screen during the tasks: the central panel illustrates the task from Feldmann-Wustefeld et al. 2019, where D and T stand for Distractor and Target, respectively. In the task from Foster et al. 2017, the screen was empty, as the eight circles were not displayed during the task but here illustrate the stimulus positions (Foster et al. , 2017 )(Foster et al. , 2017). The lower panels represent the lines of electrodes in all datasets.
Indeed, expansion of the ansiform from macaques to chimps to humans  69  occurs alongside expansion of the PFC  72  , as well as the termination zone that connects them in the cerebral peduncles  46. Currently, it remains unclear how the ansiform scales relative to the CCS, when taking allometry (scaling relationships) and phylogeny (evolutionary relationships) among species and traits into account. Here we assessed primate CCS evolution through phylogenetic comparative analysis of cerebellar volumes. We used a large (34 species, 63 specimens) open MRI dataset alongside modern phylogenetic comparative methods to consolidate previous evidence of isometric (volume of region y and region x expand at the same rate) scaling between cerebellum and cerebrum  31, 32, 75. We evaluated potential differences in slopes or intercepts of cerebellar scaling in apes and non-apes  35, 37  , as well as strepsirrhines and haplorhines.
54 ± 1. 93 cm 3 ; 12. 4%) (Table  1 , Supplementary Figs. 1, 2 ). Human volumes were systematically higher in males. The chimpanzee data were generally similar between sexes (Supplementary Fig. 3 ). Fig. 1  Consensus phylogenetic tree for the 34 primate species in this study. We obtained the consensus tree for the 34 species in our dataset from 10kTrees Arnold et al. 142. It represents the best-supported evolutionary relationships between the primates in our sample. Archeological epochs are superimposed on the tree to provide a temporal perspective of predicted species bifurcations. Internal node numbers are plotted and can be used to identify ancestral characters (Supplementary Table  1 ). Extant species included in the current study are provided on the right and are colored by clade membership.
Evolutionary BM was best supported, significantly more so than Early Burst. Chi-squared testing revealed further support for the λ = 1. 0 versus the λ = 0. 0 model (χ 2 = 53. 68). Thus, we adopted BM (λ = 1. 0) for subsequent analyses. Ancestral character estimations. Next, we aimed to estimate ancestral traits based on BM evolution. We provide ancestral volumes for cerebellar, cerebral, and ansiform volumes in Supplementary Table  1  and full ancestral character estimations (ACEs) can be found on GitHub. Ratios are confounded by allometry. To visualize the evolutionary dynamics of the CCS we mapped ancestral character estimations (ACEs) to the phylogenetic tree. We plotted 95% confidence intervals for ACEs (Fig. 2 ), with uncertainty intuitively increasing with time to present (Supplementary Fig.
Allometric scaling relationships Accelerated ansiform scaling in primates. Next, we aimed to formally quantify scaling relationships within the CCS. More specifically, we assessed scaling between cerebellum and cerebrum, and ansiform and cerebellum or cerebrum. We employed phylogenetic generalized least squares (PGLS) regression in the context of BM evolution (Fig. 3 ). Median cerebellar volumes were regressed on median cerebral volume for the full 34-species data (Fig. 3a ) and for the 13 species with complete data (Fig. 3b ). The Species median measurements are provided for cerebellar, cerebral, and ansiform volumes (in mm 3 ). Species ratios between median cerebellar and cerebral volumes, and ansiform and cerebellar volumes are also given (in percentages). Importantly, these ratios are reported merely to illustrate how allometric scaling of cerebello-cerebral system components may lead to wide-ranging ratios reported in the literature.
245), with a lower intercept for the cerebrum (intercept cerebrum = -2. 784) than for the ROC (intercept ROC = -1. 833). Together, the ansiform scaled hyperallometrically to both the ROC and cerebral volumes. No distinct allometries across major primate bifurcations. To explore divergent patterns across primate groups, we split the data into strepsirrhine (9 species) and haplorhine (  25 ) subsets (Fig. 4a-e ). We also split the data into ape and non-ape subsets for cerebellum regressions (7 apes (N = 24); 27 non-apes (N = 39)) and ansiform (5 apes (N = 22); 8 non-apes (N = 8)) (Fig. 4f-i ). Main cerebellar ~cerebral regressions are replotted in (Fig. 4a, f ). ACEs for cerebellar (a), cerebral (b), and relative cerebellar-to-cerebral (c) volumes were calculated from, and mapped to, the full 34-species tree, whereas ansiform (d) and relative ansiform-to-cerebellar volumes (e) were calculated from, and mapped to, the 13-species tree.
Replication in the Stephan dataset. To further validate the stability of our results, we replicated PGLS analysis in an alternative set of primates, the Stephan dataset  82  (Fig. 5 ). This dataset contains postmortem primate brains, with volumes corrected for shrinkage. BM was again the best-supported model (AIC = -19. 13), with BM ≈ Early Burst (AIC = -17. 51) ≈ OU (diagonal α; AIC = -16. 03). It significantly outperformed the star-model (χ 2 = 84. 60). We adopted BM to map the ancestral character estimations (ACEs) to the tree. Cerebellar and cerebral volume.
1 to ~1. 6) (Supplementary Fig. 7g-j ). Such hyperscaling likely drives much of the lateral cerebellar expansion observed in large-brained hominoidea  68  , and primates more generally  67  , and perhaps occurs alongside cerebral transmodal   82  collection reported cerebellar and cerebral volumes for 34 species available in 10kTrees Arnold et al. 142. Ancestral character estimations (ACEs) (a) and phylogenetic generalized squares regression (PGLS) (b) were repeated in this complementary dataset. Despite only partial overlap in species, ACEs for cerebellar-tocerebral volume ratios (a) largely mirrored the main analysis (Fig. 2c ). PGLS regression between cerebellar and cerebral volumes in the Stephan collection (b) illustrated generally similar scaling as the primary analysis (Fig. 3a ), albeit reduced at approximately 0.
92 (black line), and with its 95% confidence intervals (black dotted line), but not 99% confidence intervals (red dotted line), excluding isometry (blue line). All apes and lemurs fell on or above the regression line, approaching isometry and mirroring the main analysis (Fig. 3a ). Altogether, the analyses again show how allometry causes diverse ratios between brain areas to arise across species. Legend: apes are colored as in Fig. 3a , with legend again shown here in (b). Lemuriformes are colored here as well, with solid grays representing species overlapping with the main analysis, and solid blacks indicating lemur species unique to the Stephan dataset (a, b). Lastly, other species overlapping between both datasets, that were not specifically considered, are colored a shaded gray (a, b).
3). Our data generally resemble prior observations for mean ansiform volumes in humans (current study: 43. 94 cm 3 ; Balsters et al. : 53. 65 cm 3 ; Makris et al. : 43. 01 cm 3 ), intraspecific variability (5. 00 cm 3 ; 8. 01 cm 3 ; 6. 38cm 3 ), and relative ansiform-to-cerebellum fraction (30. 76%; 36. 51%; 29. 58%)  69, 83. Reanalysis of ansiform scaling in larger datasets will be necessary to confirm our observations as they may be partially driven by an underpowered sample and influenced by specimen-specific idiosyncrasies  81, 98, 117. Our study compared brain areas within specimens, helping to minimize the influence of variability across brains. Nonetheless, shrinkage could alter exact scaling formulae and would ideally be accounted for. We assumed that intraspecific variability, compounded by measurement error due to unequal shrinkage, may lead to a twofold difference in volumetric estimates  77, 79, 80.
Simulating the effect of introducing this volumetric variability in cerebello-cerebral PGLS (Supplementary Fig. 7e, f ), we found generally reduced allometries relative to our main PGLS. This result was consistent with analysis of the Stephan dataset, which used shrinkage-corrected volumes  82. Although sometimes assumed to be of minor importance 101 , within-brain shrinkage was expected to play a larger role. Cerebellar, cerebral, and ansiform shrinkage may differ 118 , perhaps based on white-matter content 119. Simulating this shrinkage (Supplementary Fig. 7c, d, ) revealed that conclusions on cerebello-cerebral scaling may differ depending on how areas have shrunk with respect to one another. This may also explain contradictory conclusions on near-isometric cerebellar scaling across datasets  35.
Ansiform hyperscaling was robust to this shrinkage. In the future, standard recording of fresh brain weights, and calculation of time-related shrinkage across brain areas may help combat uncertainty in volumetric estimates and, potentially, contradictory results  81, 98. Systematic analysis of larger datasets containing both many species and sizable intraspecific samples might resolve previous controversies  78, 81, 98, 120, 121. Creating community-wide guidelines for data collection and statistical handling can facilitate the integration of data from primate studies  50, [122] [123] [124]. Statistical methods to incorporate intraspecific samples already exist  [125] [126] [127]. To further data integration, databases can be used to earmark primate brains with unique identifiers, linked to relevant metadata including sex, age, body weight, captivity status, data availability, and inclusion in specific studies.
However, larger datasets are needed to elucidate exact primate scaling and determine if species or groups of species deviate from allometry more than expected. In conclusion, within the tightly coevolving cerebello-cerebral system, the ansiform-transmodal cerebrum functional module may be a primary area of interest in the study of human and comparative cognitive correlates in the future. Large datasets, in combination with relevant metadata and sizable intraspecific samples, will increase understanding of the behavioral implications of primate hyperscaling of this module within the cerebellocerebral system.
Methods Data provenance and acquisition. Primate comparative data are rare and sizable datasets are difficult to obtain. We used the collection of primate species collated from different sources in the Brain Catalogue Primates project  89  , available on BrainBox  83. It included 35 species (N = 66), which covered the primate phylogenetic tree relatively well, with 9 infraorders from strepsirrhine and haplorhine suborders. Cebidae (7 species), Hominoidea (7), Lemuriformes (6), and Papionini (5) were the most extensively sampled (Fig. 1 ). The data combines MRI images obtained from diverse sources, including donations, open data, and acquisitions from Heuer et al. (  2019 )  89  (Supplementary Data 1). The current study followed the institutional review board guidelines of the corresponding institutions.
org, a bonobo, gibbon, and a western lowland gorilla (T1w; 1. 5 T) from the NCBR  96  , and finally 10 human brains (T1w; 3 T) from the ABIDE-I dataset  132. ABIDE-I gained approval from the local Institutional Review Boards and had the 18 Health Insurance Portability and Accountability-protected health information identifiers removed  132. No new primate brains were obtained in his study. Together, T1-or T2-weighted (T1w or T2w) MRI anatomical scans were obtained at either 1. 5 T, 3 T, 7 T or 11. 4 T for up to 12 h. For all brains, 3D gradient-echo sequences (FLASH) images were obtained  89. MRI quality descriptors including scanning resolutions, and signal-to-noise (SNR) can be found in Supplementary Data 1. For full provenance and acquisition parameters, see Heuer et al. 89. Data preprocessing and quality control.
Human 132,133 and nonhuman primate (NHP)  89  data were already preprocessed. SNRs of the primate scans be found in Supplementary Data 1. Comparative datasets often vary in data quality, due to differences in brain size, tissue conservation (in vivo, in situ, or extracted brains), MRI resolution, and SNR, as well as geometrical brain differences, and more idiotypic differences related to individual specimens. During manual segmentation (see the following) extensive visual inspection of the data was performed by NM (as well as previously by KH and RT)  89. The red howler monkey was excluded due to irreparable damage. Extensive damage was also noted in the black-and-white ruffed lemur, eastern gorilla, and gibbon. Additionally, two cerebellar were not noticeably missing any tissue, but were deformed.
Finally, our specific within-brain study design, comparing volume within specimens, should appreciably lessen the effect of shrinkage across brains. Manual segmentation procedure. To provide the most accurate volumetric measurements in the highly variable primate data, we manually delineated the areas of interest directly on the MRI data  134. Before this manual segmentation, we semi-automatically obtained initial cerebellar masks (serial 2D-outlines in MRI sections used to reconstruct the cerebellum in 3D). We did this by subtracting existing cerebral masks from whole brain masks with Stereo-taxicRAMON (github. com/neuroanatomy/StereotaxicRAMON) or using Thresholdmann (github. com/neuroanatomy/thresholdmann) to interpolate between thresholds at manually selected locations. Initial cerebellar masks were uploaded to BrainBox 135 for collaborative segmentation.
Since data quality was variable, segmentation was performed in a subset of representative specimens where fissures were identifiable: 5 apes (N = 22; including 10 humans and 9 chimpanzees) and 8 non-apes (N = 8). Segmentations were again iteratively performed until satisfaction. An interobserver strategy validated segmentations made by NM. For six species, a secondary observer (MA) provided blinded segmentations alongside NM. Additionally, a tertiary observer (VS) segmented human and chimpanzee ansiforms in consultation with NM. All segmentations were checked by KH and SLV. Reliability of segmentations. Reliability of ansiform segmentations was assessed by ANOVA: Ansiform volume $ species þ observer ð1Þ Human cerebellar and ansiform manual segmentations were compared with automated segmentations from CERES 114 at volbrain.
upv. es: Cerebellar volume $ method ð2Þ and Ansiform volume $ method ð3Þ Intraclass correlation (ICC) was additionally calculated 137. ICC of 0. 75-0. 90 indicates good reliability and 0. 90-1. 00 excellent reliability  138. Cerebellar volume was significantly related to method (Pr(>F) < 0. 01). ICC was. 26 (95% confidence interval (CI): -0. 38 < ICC < 0. 74). Inspection revealed that manual segmentations were on average 20. 4 cm 3 (16. 2%) larger than CERES segmentations. Ansiform volume was not related to method (Pr(>F) = 0. 286), with ICC of 0. 84 (95% CI:. 51 < ICC < 0. 96). Manual volumes were only 2. 7 cm 3 (6. 6%) larger. One specimen differed by 6. 9 cm 3 (18. 2%). It represented an average-sized cerebellum, unlikely to alter the results substantialy. Full results can be found on GitHub.
1 ) by Shapiro-Wilk tests 139 and outliers were visualized on boxplots (± 1. 5 IQR from Q1 and Q3). This led to MRI: T1w or T2w the exclusion from subsequent analyses of one rhesus and one crab-eating macaque (Supplementary Fig. 2 ). Uncertainties in comparative data. Primate datasets are rare and generally work on an all-you-can-get-basis. Consequently, covariates known to have substantial influence on brain volumes, such as sex and age, are generally unavailable. In our study, we could not account for sex or age across the sample. Provenances of the data were diverse, potentially leading to differences in the extent of shrinkage of the included brains  82, 140. These sources of measurement variability may compound intraspecific variability in primate brain traits  80  and together lead to large uncertainties in volumetric estimates, a problem well-known to comparative primatologists  81.
1 ) and ansiform analyses were obtained from the 10kTrees 142 primate database (10ktrees. nunn-lab. org; version 3). They were constructed from 17 genes and 7 different loci 142 (10ktrees. nunn-lab. org/ downloads/10kTrees_Documentation. pdf). We used R version R 4. 1. 0 for statistical analyses  143. Extant primate traits are not statistically independent, sharing variable amounts of evolutionary history 144. Brain traits of species that diverged more recently are more likely to be similar. We detected substantial phylogenetic signal, and correlations between cerebello-cerebral traits illustrated severe collinearity (Fig. 8 ), demonstrating the importance of accounting for phylogeny and allometry. Therefore, before performing evolutionary analyses, we assessed what evolutionary model best described trait evolution among our species.