Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture

Science ◽  
2020 ◽  
Vol 369 (6506) ◽  
pp. 988-992
Author(s):  
Katrin Amunts ◽  
Hartmut Mohlberg ◽  
Sebastian Bludau ◽  
Karl Zilles

Cytoarchitecture is a basic principle of microstructural brain parcellation. We introduce Julich-Brain, a three-dimensional atlas containing cytoarchitectonic maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which enables it to account for variations between individual brains. Building such an atlas was highly data- and labor-intensive and required the development of nested, interdependent workflows for detecting borders between brain areas, data processing, provenance tracking, and flexible execution of processing chains to handle large amounts of data at different spatial scales. Full cortical coverage was achieved by the inclusion of gap maps to complement cortical maps. The atlas is dynamic and will be adapted as mapping progresses; it is openly available to support neuroimaging studies as well as modeling and simulation; and it is interoperable, enabling connection to other atlases and resources.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryuta Mizutani ◽  
Rino Saiga ◽  
Yoshiro Yamamoto ◽  
Masayuki Uesugi ◽  
Akihisa Takeuchi ◽  
...  

AbstractThe cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in different brain areas or between the neurons of different individuals have not been delineated. Here we report a nanometer-scale geometric analysis of brain tissues of the superior temporal gyrus of schizophrenia and control cases. The results of the analysis and a comparison with results for the anterior cingulate cortex indicated that (1) neuron structures are significantly dissimilar between brain areas and that (2) the dissimilarity varies from case to case. The structural diverseness was mainly observed in terms of the neurite curvature that inversely correlates with the diameters of the neurites and spines. The analysis also revealed the geometric differences between the neurons of the schizophrenia and control cases. The schizophrenia cases showed a thin and tortuous neuronal network compared with the controls, suggesting that the neuron structure is associated with the disorder. The area dependency of the neuron structure and its diverseness between individuals should represent the individuality of brain functions.


Author(s):  
G. Jacobs ◽  
F. Theunissen

In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by using a combination of three dimensional reconstruction techniques, quantitative analysis and computer visualization techniques to build a probabilistic atlas of a neural map in an insect sensory system. The principal goal of this study was to derive a quantitative representation of the map, based on a uniform sample of afferents that was of sufficient size to allow statistically meaningful analyses of the relationships between structure and function.


2017 ◽  
Author(s):  
Roel M. Willems ◽  
Franziska Hartung

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.


2016 ◽  
Vol 67 (5) ◽  
pp. 471-494 ◽  
Author(s):  
Matúš Hyžný

AbstractDecapod associations have been significant components of marine habitats throughout the Cenozoic when the major diversification of the group occurred. In this respect, the circum-Mediterranean area is of particular interest due to its complex palaeogeographic history. During the Oligo-Miocene, it was divided in two major areas, Mediterranean and Paratethys. Decapod crustaceans from the Paratethys Sea have been reported in the literature since the 19thcentury, but only recent research advances allow evaluation of the diversity and distribution patterns of the group. Altogether 176 species-level taxa have been identified from the Oligocene and Miocene of the Western and Central Paratethys. Using the three-dimensional NMDS analysis, the composition of decapod crustacean faunas of the Paratethys shows significant differences through time. The Ottnangian and Karpatian decapod associations were similar to each other both taxonomically and in the mode of preservation, and they differed taxonomically from the Badenian ones. The Early Badenian assemblages also differed taxonomically from the Late Badenian ones. The time factor, including speciation, immigration from other provinces and/or (local or global) extinction, can explain temporal differences among assemblages within the same environment. High decapod diversity during the Badenian was correlated with the presence of reefal settings. The Badenian was the time with the highest decapod diversity, which can, however, be a consequence of undersampling of other time slices. Whereas the Ottnangian and Karpatian decapod assemblages are preserved virtually exclusively in the siliciclastic “Schlier”-type facies that originated in non-reefal offshore environments, carbonate sedimentation and the presence of reefal environments during the Badenian in the Central Paratethys promoted thriving of more diverse reef-associated assemblages. In general, Paratethyan decapods exhibited homogeneous distribution during the Oligo-Miocene among the basins in the Paratethys. Based on the co-occurrence of certain decapod species, migration between the Paratethys and the North Sea during the Early Miocene probably occurred via the Rhine Graben. At larger spatial scales, our results suggest that the circum-Mediterranean marine decapod taxa migrated in an easterly direction during the Oligocene and/or Miocene, establishing present-day decapod communities in the Indo-West Pacific.


2020 ◽  
Vol 24 (4) ◽  
pp. 2061-2081 ◽  
Author(s):  
Xudong Zhou ◽  
Jan Polcher ◽  
Tao Yang ◽  
Ching-Sheng Huang

Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.


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