dataesr/scientific-paragraphs-categorization huggingface dataset:
3  (Kärnä and Baptista, 2016). The root mean percent error is normalized to the standard deviation of the FTIR data in (σF) used in the respective analysis. 𝑁𝑅𝑀𝑆𝐸 = 1 𝜎 𝐹 √[∑ (𝐶 𝑚,𝑖 -𝐶 𝐹,𝑖 ) 2 𝑁 𝑖=1 ]. (3) In addition to evaluating the models using every available FTIR data point in the analysis years, the monthly mean annual cycles are also presented. The mean partial columns are calculated by taking the mean of every point in a month, for  2008, 2009, 2014, and 2015. The model means are made in the same manner, using only the smoothed partial columns that have a corresponding matching FTIR measurement, as defined above. The mean percent difference is the mean value from Eq. 2 for each month across the years, where error bars represent the standard deviation of this mean. The mean of these monthly mean differences is used to calculate the overall mean percent difference for each model, where the uncertainty is the standard deviation.
Results and discussion This section presents the analyses described above, for CH4, CO and O3, and discusses the findings in the context of the 2021 AMAP SLCF Assessment Report, and other related literature. Given the volume of data (three species, five locations, and 11 models), only selected plots are shown in the main text, with the remaining figures provided in Appendices A-C. The results from Eureka, Canada are shown in the main text as this location is at the highest latitude and has the most measurement points for all species. All the comparisons shown are for a 0-7 km partial column, where the model output is smoothed as described by Eq. 1.
CH4 CH4 is the second most important greenhouse gas (GHG) after CO2, and its emissions are expected to increase in the Arctic due to melting permafrost (IPCC, 2021). CH4 is also involved in the formation of tropospheric O3, which is the third strongest anthropogenic GHG and an air pollutant at the surface. Therefore, it is important for both air quality and climate models to represent CH4 accurately. It should be noted that the CH4 concentrations in these models have been prescribed and only three of the 11 participating models provided 3-hourly CH4 concentrations  (Whaley et al. , 2022). The prescribed concentrations are input at the bottom model layer and all come from the same dataset  (Prather et al. , 2012; Olivié et al. , 2021) , but the resulting CH4 concentrations differ based on the processes within each model.
Acknowledgements Funding for this work was provided by the  Canadian Space Agency  under the Earth System Science Data Analyses Program (grant  21SUASABBC ). The authors acknowledge the use of model datasets from the 2021 AMAP SLCF Assessment Report. The authors would like to thank all the people involved with the data collection and maintenance of the high-latitude NDACC FTIR instruments used in this study. The Eureka FTIR measurements were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  by the  Canadian Network for the Detection of Atmospheric Composition Change (CANDAC) , which has been supported by the  Atlantic Innovation Fund/Nova Scotia Research Innovation Trust ,  Canada Foundation for Innovation ,  Canadian https://doi. org/10. 5194/egusphere-2023-1161Preprint.
Data availability The FTIR data are publicly available on the NDACC data repository (https://wwwair. larc. nasa. gov/missions/ndacc/data. html (last access: 14 Feb 2023), and the model data are available at https://doi. org/10. 18164/e0a0ac5c-d851-45b9-b6d9-4abc29d7d419 (last access: 14 Feb 2023).
Due to taphonomic processes, the estimation of this history with reference to extinct taxa has come to heavily rely on measures derived from cranial endocasts. Such measures of endocranial volume  3  , shape  20  , or osteological landmarks  21  all represent the outer meningeal surface and thus lack fidelity regarding the true cortical surface. This loss in anatomic precision limits the translation across both extant and extinct taxa and has led to conflicting theories and continued controversy regarding, for example, primate brain evolution  [22] [23] [24]. The recent availability of large open image datasets of rodents  25  , non-human primates  26, 27  , human subjects  28, 29  and fossil taxa  30  has facilitated comparative analyses of brain organisation and function  31, 32. However, studies are either limited to phylogenetically closely related species  33, 34  , a-priori defined spatial homologies  6, 35  , or avoid establishing correspondences between morphologically and functionally divergent domains altogether by focusing on global descriptions of brain organisation  5, [36] [37] [38].
Addressing these shortcomings and adequately studying the distributed patterns and sources of cortical variability and evolution requires a common analytical framework  31  that bridges the gaps between the analysis of humans, extant mammals and the fossil record. Here, we link phylogenetic and geometric methods to establish a common reference frame for the cerebral cortex of Euarchontoglires, the supra-order containing both humans as well as commonly used neuroscientific model animals such as mice, rats, macaques and marmosets. By establishing group-wise correspondences between all cortical surfaces in a dataset of ninety different species, we show how ecological adaptations are reflected in the shape of the surfaces of cerebral cortices of living members of Euarchontoglires. We use spatial statistics to quantify the process of modal segregation of brain function.
Finally, we combine ancestral state reconstruction with statistical decoding of brain activation to derive a historical time-line of modules of evolutionary adaptations of the cortical shape in the deep ancestral human lineage since the last common ancestor of rodents and primates. The resulting aligned surface meshes for all extant species, their coordinates for commonly used template spaces and tools for mapping between them as well as their estimated ancestral shapes are available at https://github. com/cirmuw/EvolutionOfCorticalShape.
Neuroecology of cortical morphology The relationship between the morphology of a-priori defined cortical areas and sensory specialisation has been studied extensively in a variety of species, both in their natural state as well as by manipulation such as post-natal enucleation  44. Using the common cortical reference space, we performed a data-driven analysis to study if the relationship between the morphology of cortical areas and sensory specialisation can be observed at the whole-brain level. First, we performed phylogenetic principal component analysis (pPCA)  45  to reduce the dimensionality of our dataset and tested for the association between the extracted modes of variation describing the shape of the cerebral cortex and ecological and behavioural niches. We found 5 pPCA components out of 21 (Supplementary Fig.
5 ). Using the cosine distance between sensory specialisation measures we then performed semivariogram modelling with respect to geodesic distance on all cortical surfaces. In spatial statistics, the largest distance at which two measurements are spatially autocorrelated is called the range of the model. A decreasing range value corresponds to increasing segregation into modality specific areas (Supplementary Fig. 9 ). Fig. 1  | Phylogenetic tree of the dataset used in this study. The dataset covers 90 species (58 primates, 28 rodents, 2 lagomorphs, 1 scandentia, 1 dermoptera), for which 5 binary ecological and behavioural categorical indicator variables were collected (Large group size, diurnality, arboreality, terrestriality and fossoriality, see Supplementary Datas 1a, 2a and 2b). Estimates of the morphology of potential ancestral shapes were computed after iterative pairwise alignment of sister species.
This revealed both a significant increase in relative cortical surface area (β(105) = 0. 15, p < 1e-6, 95% CI [0. 1341, 0. 1659]) as well as a positive interaction with ancestral time (β(105) = 0. 0017, p < 1e-6, 95% CI [0. 0014, 0. 0020]) for the limbic areas of the cerebral cortex in mice Fig. 5  | The ancestral state of regional functional modal specialisation of cortical areas. Cortical surface shape alignment across species and to common ancestors allows for the estimation of the spatial distribution of primary sensory areas (vision: blue, auditory: green, somatosensory: red) in the ancestral state of primates and rodents. The ancestral state can be mapped to all surfaces in the dataset to estimate the distribution and evolution of the relationship between modal specificity and surface shape.
Renderings of the mapping of the ancestral state estimate onto all surfaces of the dataset are provided in Supplementary Data 11. relative to humans, whereas the human lineage showed an increase of motor (β(105) = 0. 0011, p < 1e-6, 95% CI [0. 0007, 0. 0014]) but also to a lesser degree default mode (β(105) = 4. 3230e-04, p = 0. 011, 95% CI (0. 0001, 0. 0008)) and frontoparietal (β(105) = 4. 7530e-04, p = 0. 005, 95% CI (0. 0001, 0. 0008)) areas with time, indicating a possible evolutionary sensory specialisation for olfaction in mice and for complex motor and association capabilities in humans.
By analysing the principal modes of variation of the dataset using activation maps obtained from human functional neuroimaging studies, we showed how neuroanatomical correlates of specific socio-ecological niches are associated with the shape of the cerebral cortex in extant nonhuman species. Furthermore, we showed how the complexity of both their ecological and social environments is associated with the selective expansion of functionally specific cortical regions. Using spatial statistics, we demonstrated a gradual evolutionary decrease in the spatial range at which the modal specificity of these cortical areas becomes independent, suggesting the emergence of areas with multiple co-located associations  51. By decoding the neurological function of the estimated cortical expansion in the deep ancestral human lineage, we were able to propose, for the first time, a sequence of both temporally and spatially circumscribed neuromorphological events that shaped the cerebral cortex of our ancestors over the past 77 million of years.
We established correspondence between the cortical surface meshes of two species, by matching their cortical shape encodings in a highdimensional embedding space. Dense surface correspondences could then be established from the resulting alignment between these two aligned representations (Fig. 2b ). To estimate the potential shape of the cortical surface of an ancestor of two sister species in the dataset, the ancestral state reconstruction was first performed on global shape properties. This allowed the positioning of the ancestral shape along the geodetic connection in the space of smooth shells between the global shape properties of the two species. Since linear interpolation does not reflect the geometric properties of the surfaces, shape interpolation was performed in the space of smooth shells to estimate the ancestral shape 2c).
Dataset description Brain imaging. For the present study, we collected imaging data of 90 different species from various sources (Supplementary Data 1a), consisting of 58 primates, 28 rodents, 2 lagomorphs, 1 scandentian and 1 dermopteran. In order to collect a maximum number of different species, we combined imaging data from different modalities of both in-vivo and ex-vivo specimens. In total, 75 specimens were imaged using MRI, 7 using diffusible iodine-based contrast-enhanced Computed Tomography (DiceCT) and 8 using serial histological sections. For the serial histology section data, StackReg 141 was used to correct for rigid displacement between slices. Data for 10 species was available in vivo, while 80 scans were obtained from ex vivo data. Of the 80 exvivo specimens, 43 were fixated ex-situ, while 37 were imaged inside the skull.
The data for 7 species consist of atlas averages of multiple individuals. For 7 specimens, previously published segmentations of the cerebral structures were available. For 31 specimens, segmentation of the cerebrum was available via the BrainCatalogue 5. For the remaining 52, we manually segmented the two cerebral hemispheres using ITK Snap  142. In cases where it remained intact after preparation, the olfactory bulb was manually segmented as a separate label, as well as the Corpus Callosum. We further included commonly used geometric spaces for model animals (Mus musculus, Rattus norvegicus, Macaca mulatta, Callithrix jacchus, Homo sapiens) in the dataset (Supplementary Data 1a). Unfortunately, it was not possible to obtain multiple individual scans for all species. We therefore limit our dataset to one exemplary shape per species, as the morphological variability is limited for the cerebral cortex in most species.
First, we use a method proposed for the matching of developing fetal brain surfaces in humans  154  , which shows a comparable range of geometric variability as our dataset from lissencephalic first trimester of gestation to strongly gyrated in the third trimester. Instead of defining corresponding points in the two shapes directly using nearest neighbours in the embedding space, this method first performs edge-based smoothing of the embedding coordinates, resulting in smoother maps. In order to further enforce diffeomorphic correspondences between the shapes, the resulting correspondences are used as inputs to a joint Laplacian matching step  149, 155. Contrary to the original procedure of computing alignments between individual embeddings of the two shapes, this method is based on the spectral decomposition of a joint Laplacian matrix.
Instead, we represent labels as pseudoprobabilities of the extent of a structure at a specific position of the cerebral cortex and use optimal transport geometry to compute averages of these distributions on the estimated surface models of ancestral states using Convolutional Wasserstein Distances  158. This procedure results in estimates of the distribution for each label in the ancestral state, but also breaks the commutativity of label correspondence between parent and daughter states. We therefore perform an additional matching step between the newly labelled ancestral shape and its daughter shapes to reestablish consistency between the shape and label correspondences. Consistent topology of cortical morphology. After iterative pairwise matching of the surface models of individual cortical shapes, global correspondence between all shapes in the dataset can be established by propagating the correspondences throughout the phylogenetic tree.
Throughout the iterative surface mapping, we use the more densely sampled cortical surfaces of the two sister shapes as topology of the estimated ancestral shape. Once the LCA of rodents and primates at the root of the tree is reached, we resample all shapes in the dataset to fsaverage6 topology to allow the easy application of published results on cortical topography in humans. The resulting surface models are available at https://github. com/ cirmuw/EvolutionOfCorticalShape.
Phylogenetic modelling Estimation of phylogenetic relationships. We obtain the phylogenetic relationships for the taxa used in the present study from a timecalibrated phylogeny of the mammalian class  39. The phylogeny is constructed using a Bayesian "backbone-and-patch" method from a 31 gene Supermatrix, resulting in probabilistic estimates of the branch lengths and divergence times in the tree. We draw 100 samples from the posterior distribution of trees of the 90 species contained in our dataset and compute the consensus tree to obtain a single phylogenetic topology and divergence times. As the human brain showed extensive expansion compared to other species, we augment the resulting consensus tree with that of hominin fossils obtained from Melchionna et al. 159. We merge the two trees using phylogenetic inference under matrix representation  160.
Phylogenetic modelling of global cerebral morphology. Estimation of the ancestral shape of the cortical surface models of two sister species requires a of the degree of morphological changes each sister species has been subjected to. However, divergence time estimates in the used phylogeny are based on genetic information, and there is no unique universal scaling factor that would relate changes to the genetic code to cortical morphology. On the other hand, estimation of an evolutionary model for changes in cortical shape requires a priori correspondences between surfaces. To avoid this circular problem, we assume a linear relationship between the changes to the global shape measures of surface area (SA) and gyrification and the degree of local changes to cortical morphology. As a first step in our phylogenetic analysis, we then use the combined tree of extant and extinct species to determine an appropriate evolutionary model for these global morphological properties of the cerebral cortex.
The resulting estimates of the surface areas of the cerebral cortex and its convex hull are then available throughout the phylogenetic tree. We use the ancestral state reconstructions of these values to determine the interpolation factors between two matched cortical surface models to reconstruct their putative ancestral state without a priori matching of all shapes in the dataset as described above. Phylogenetic geometric morphometrics of cortical morphology. After having obtained an estimate for the rate of evolutionary change in global morphological properties of the primate and rodent cerebrum, we aim at determining the shape space of the cerebral cortices of rodentia, lagomorphs, scandentia, dermoptera and primata. However, the dimensionality (40962 vertices per hemisphere, as defined in FreeSurfer fsaverage6 space) of the dataset is too large in relation to the sample size (90 cortical surface models).
We therefore use lowresolution icosahedral subdivision to first reduce the dimensionality of the data to 162 vertices per hemisphere, and remove any samples in the medial wall, which results in 900-dimensional representations of each pair of hemispheres for each taxon in the dataset. We fit both simple (Brownian Motion (BM), Early Burst (EB) and Ornstein-Uhlenbeck (OU)) as well as OU models with evolutionary shifts (OU_shift) to the shape data  165. Note that the position of these evolutionary shifts have been determined a priori from the global cortical shape parameters, and the multivariate OU models used for modelling the localised evolution of the cortical shapes are not subject to the same simplifying assumption as those used in estimating the rate shifts. This approach results in a strong link between changes in evolutionary rates of the global and local cortical morphology, but nonetheless allows for the independent estimation of correlation structure for the local change in cortical morphology during evolution.
We perform phylogenetic model selection based on the Generalized Information Criterion (GIC)  166  , which selects an adaptive OU model with 2 rate shifts (BM: -460733. 9, OU: -480972. 5, EB: -460731. 9 OU_shift: -496364. 8) (Supplementary Fig. 1 ). We perform phylogenetic PCA  165  on the low-resolution representation of the dataset in order to account for phylogenetic correlation in the shapes. We then use L2regularisation as proposed in Clavel et al. 167  to estimate the phylogenetic covariance matrix, as its unregularised computation is still illposed due to the large number of landmarks compared to the number of samples. The ratio of one to ten between dimensionality of the samples and their number is comparable to that used in other works on phylogenetic brain shape analysis with similar or smaller sample sizes  168.
Analysis methods for cortical surface maps Meta-analytic decoding. We use meta-analytic decoding to analyse the cortical surface maps obtained from phylogenetic modelling (pPCA, ancestral state reconstruction). We first map the measurements obtained on the cortical surface into the standard MNI space  46. The resulting volumetric maps are then correlated with 3228 individual maps obtained from 14371 individual neuroscientific studies of brain activation in humans  54. We retain the most strongly correlated 1% of these terms to interpret the expansion maps in terms of neuroanatomical localisation and neurological function (Supplementary Datas 3 and 4). We note the anthropocentrism inherent to the utilisation of human reference maps for the decoding maps related to evolutionary processes. However, to the best of our knowledge there unfortunately does not exist to date any comparable dataset for other species which would improve on this.
From the selected model, we estimate the sill (maximum value of spatial independence) and range (distance at which the sill is reached, e. g. measurements of modal specificity at pairs of points separated from each other by a higher geodesic distance can be considered independent), where only the latter is of interest due to the a-priori unit-normalisation of both regressor and regressed variables. Software. All analysis was implemented in Matlab R2014a and R2019a, Python 2. 7. 13 and 3. 7. 3 as well as R 3. 6. 0 and 4. 0. 3. Additional processing was performed using Convert 3D 1. 1. 0, FSL 6. 0. 4, FreeSurfer 6. 0. 0 and 7. 1. 1, ANTs 2. 3. 4 and ImageJ 1. 49 u.
Acknowledgements We gratefully acknowledge everyone who contributed data to the project. In alphabetical order,  Eike Budinger  of the  Leibniz Institute for Neurobiology  for contributing data of Meriones unguiculatus,  Marc Dhenain  of the  Institut de biologie Francois Jacob  for contributing data of Microcebus murinus,  Hao Lei  of  Huashan Hospital  for contributing data of Tupaia belangeri,  Kyoko Miura  of  Kumamoto University  for contributing data of Heterocephalus glaber,  Thomas Morrison  for contributing data of Mesocricetus auratus,  Brian Tainor  and  Vanessa A. Minnie  (with support from  NIH   R01 MH121829 ) for contributing data of Peromyscus californicus,  Cody W. Thompson  of the  University of Michigan Museum of Zoology  for contributing data of Tamias striatus and  Sarael Alcauter Solorzano  of the  Universidad Nacional Autonoma de Mexico  and  Jason R.
ES has received financial support for this project by the  Austrian Research Fund (FWF)  grant I  3925-B27  in collaboration with the  French National Research Agency (ANR). RT and KH were funded by the  French National Research Agency and National Science Foundation  grant agreement project  NeuroWebLab  ( ANR-19-DATA-0025 ),  DMOBE  ( ANR-21-CE45-0016 ), the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant agreement No101033485 (KH Individual Fellowship ), and the  STIC-AmSud  programme project  STIC-AMSUD + CLANN 22-STIC-03. GL has received funding by the  Austrian Research Fund (FWF)  grant  P 35189 , the  Vienna Science and Technology Fund (WWTF)  grant  10. 47379/LS20065 , and the  European Commission  grant  765148   TRABIT.
Data availability All quantitative data supporting the findings of this study are provided as Supplementary Information to the article. Sources of all imaging data used in the study as well as reference publications are available in Supplementary Data 1. Aligned surface models used to define the proposed common reference frame, as well as ancestral state estimates obtained from it are publicly available at https://github. com/ cirmuw/EvolutionOfCorticalShape and https://doi. org/10. 5281/ zenodo. 7713847. Expansion maps used for meta-analytic decoding are additionally made publicly available at https://neurovault. org/ collections/IHFSXSES/.