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Mapping cyto- and receptor architectonics to understand brain function and connectivity

  • Daniel Zachlod
    Correspondence
    Corresponding author: Daniel Zachlod,
    Affiliations
    Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
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  • Nicola Palomero-Gallagher
    Affiliations
    Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany

    C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

    Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen, and JARA - Translational Brain Medicine, Aachen, Germany
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  • Timo Dickscheid
    Affiliations
    Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany

    Helmholtz AI, Research Centre Jülich, Jülich, Germany

    Department of Computer Science, Heinrich-Heine-University Düsseldorf, Germany
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  • Katrin Amunts
    Affiliations
    Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany

    C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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Open AccessPublished:September 20, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.09.014

      Abstract

      This review focuses on cyto- and receptor architectonics as biological correlates of function and connectivity. It introduces the three-dimensional cytoarchitectonic probabilistic maps of cortical areas and nuclei of Julich-Brain Atlas, available at EBRAINS, to study structure-functions relationships. The maps are linked to the BigBrain as microanatomical reference model and template space. The siibra software tool suite enables a programmatic access to the maps and to receptorarchitectonic data that are anchored to the brain areas. Such cellular and molecular data are tools for studying MR-connectivity including modeling and simulation. At the end we highlight perspectives of the Julich-Brain, as well as methodological considerations. Thus, microstructural maps as part of a multimodal atlas enable one to elucidate the biological correlates of large-scale networks and brain function with high anatomical detail, which provides a basis to study brains of patients with psychiatric disorders.

      Key words

      1. Introduction

      Understanding the anatomical basis for functional specialization and segregation requires one to approach the brain as a system that is organized on multiple spatial scales, from the micro- to the macro level, and that is also acting on multiple temporal scales. MR imaging is capable to investigate brain function and underlying networks in the living human brain, but is less capable to reveal the underlying microstructure including the cellular and the fiber architecture (cyto- and myeloarchitecture, respectively). Cyto- and myeloarchitecture have been considered important biological correlates of brain function and dysfunction since the beginning of the 20th century (

      Campbell AW (1905): Histological studies on the localisation of cerebral function. University Press.

      ,
      • Vogt C.
      • Vogt O.
      Allgemeinere Ergebnisse unserer Hirnforschung (English Translation: Results of our brain research in a broader context).
      ), and their systematic analysis and mapping lead to a subdivision of the cerebral cortex into microstructurally different cortical areas. Brodmann based his work on the assumption that every cytoarchitectonically defined area is contributing to a function in a specific way although this could not be tested rigorously at that time for most of the areas (

      Brodmann K (1909): Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Barth.

      ). He published one of the most influential maps, which is still widely used to relate brain function and networks to the underlying brain areas. This and other’s work formed the conceptual basis for the development of modern multi-modal atlases, which allow one to link data from postmortem observations with in vivo imaging findings including long-range connectivity, functional networks and activations (
      • Amunts K.
      • Hawrylycz M.
      • Van Essen D.
      • Van Horn J.D.
      • Harel N.
      • Poline J.B.
      • et al.
      Interoperable atlases of the human brain.
      ).
      The focus of this paper is on two main organizational principles, cyto- and receptorarchitecture. The latter describes the distribution of receptors for different neurotransmitters as key elements of signal transduction. We describe our approach to map cytoarchitectonic areas in a sample of ten postmortem brains and to compute probabilistic maps in 3D space. These Julich-Brain probabilistic maps are part of the EBRAINS multilevel atlas to study structure-function relationships and can be accessed by the siibra software tool suite (
      • Amunts K.
      • DeFelipe J.
      • Pennartz C.
      • Destexhe A.
      • Migliore M.
      • Ryvlin P.
      • et al.
      Linking Brain Structure, Activity, and Cognitive Function through Computation.
      ). The concept of the BigBrain as micro-anatomical model and template space that is applied, e.g., to studies of connectivity, is elucidated (
      • Amunts K.
      • Lepage C.
      • Borgeat L.
      • Mohlberg H.
      • Dickscheid T.
      • Rousseau M.E.
      • et al.
      BigBrain: An ultrahigh-resolution 3D human brain model.
      ). We present tools that enable the application of cellular and molecular data from the atlas for MR-studies of connectivity and modeling and discuss perspectives and pitfalls in this context.

      2. Cellular architecture and probabilistic maps

      The brain contains about 86 billion neurons, the basic building blocks (
      • West M.J.
      New stereological methods for counting neurons.
      ,
      • Azevedo F.A.
      • Carvalho L.R.
      • Grinberg L.T.
      • Farfel J.M.
      • Ferretti R.E.
      • Leite R.E.
      • et al.
      Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain.
      ). While neurons in subcortical nuclei are distributed mainly in clusters, those in the neo- or isocortex form a six-layered architecture. The allocortex is phylogenetically older, and consists of the paleocortex (e.g., olfactory bulb/tract, piriform cortex, superficial amygdala) and the archicortex (e.g., hippocampus). The number of layers varies between areas (e.g., eleven in the entorhinal cortex, three in the hippocampus). The six neocortical layers are arranged in parallel to the cortex, show a specific input and output pattern and differ in the distribution of cells and cell types. E.g., while layers III and V contain pyramidal cells, layers II and IV appear rather granular; for an overview on cytoarchitecture see (

      Zilles K, Amunts K (2015): Anatomical basis for functional specialization. fMRI: From Nuclear Spins to Brain Function: Springer, pp 27-66.

      ). Modern fMRI, in particular at high field, allows one to resolve activity with increasing accuracy, and to distinguish activations in supra- from those in infragranular layers. This is the basis to study, for example, mechanisms of feedforward and feedback pathways in the living human brain (
      • Lawrence S.J.D.
      • Formisano E.
      • Muckli L.
      • de Lange F.P.
      Laminar fMRI: Applications for cognitive neuroscience.
      ,
      • Larkum M.E.
      • Petro L.S.
      • Sachdev R.N.S.
      • Muckli L.
      A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements.
      ).
      The cerebral cortex can be subdivided into areas based on cytoarchitectonic differences including layer thickness, cell density, and special cell types, e.g., Betz cells in the primary motor cortex or von Economo cells in the agranular insular and cingulate cortex (

      Zilles K, Palomero-Gallagher N, Bludau S, Mohlberg H, Amunts K (2015): Cytoarchitecture and maps of the human cerebral cortex. In: Toga AW, editor. Brain Mapping: An Encyclopedic Reference. San Diego: Elsevier Academic Press, pp 115-135.

      ). The existence and precise localization of cytoarchitectonic borders between areas has been proven using image analysis and statistical tests (
      • Schleicher A.
      • Palomero-Gallagher N.
      • Morosan P.
      • Eickhoff S.
      • Kowalski T.
      • de Vos K.
      • et al.
      Quantitative architectonic analysis: A new approach to cortical mapping.
      ) and provides a solid basis for building a modern cytoarchitectonic atlas (
      • Amunts K.
      • Mohlberg H.
      • Bludau S.
      • Zilles K.
      Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture.
      ). Borders between areas coincide between different modalities, e.g., cyto-, receptor- and fiber architecture, if the respective modality is sensitive to structural changes between two areas (Fig. 1).
      Figure thumbnail gr1
      Figure 1Correspondence of fiber- and cytoarchitecture. A: Polarized light image (PLI) of the hippocampus showing the fiber orientation at μm resolution. B: Same section cell-body stained. Hippocampal subdivisions correspond in both modalities (most prominent borders for areas PreS and EC). Areas are labeled and borders are indicated by white and black lines, respectively. alv – Alveus; CA – Cornu ammonis; EC – entorhinal cortex; FD – fascia dentata; HATA – hippocampal-amygdaloid transition area; PaS - parasubiculum; PreS - presubiculum; ProS - prosubiculum; Sub – subiculum.
      In addition to the laminar pattern of the cortex, cell bodies and nerve fibers are arranged vertically to the cortical surface such as mini-columns (
      • Schlaug G.
      • Schleicher A.
      • Zilles K.
      Quantitative analysis of the columnar arrangement of neurons in the human cingulate cortex.
      ). The visibility of columns represents another distinguishing feature of cortical areas (e.g., the rain shower formation in extrastriate areas (

      von Economo CFK, G.N. (1925): Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Berlin: Springer.

      )).
      Differences between cytoarchitectonic areas are the most prominent indicators of cortical specialization and form the basis of cortical maps. Combined cytoarchitectonic and electrophysiological studies, pioneered, e.g. by the Parma group have demonstrated that response properties of neurons change at the border of cortical areas (
      • Luppino G.
      • Matelli M.
      • Camarda R.
      • Gallese V.
      • Rizzolatti G.
      Multiple representations of body movements in mesial area 6 and the adjacent cingulate cortex: an intracortical microstimulation study in the macaque monkey.
      ,
      • Nelissen K.
      • Luppino G.
      • Vanduffel W.
      • Rizzolatti G.
      • Orban G.A.
      Observing others: multiple action representation in the frontal lobe.
      ), i.e., demonstrate the relationship of the brain’s micro-anatomy and activity. However, to replicate such phenomena in the living human brain is not possible and more indirect measures have to be applied. E.g., cytoarchitectonic similarity is greater between connected areas, suggesting that microscale cortical cytoarchitecture is closely related to macroscale brain connectivity organization (
      • Wei Y.
      • Scholtens L.H.
      • Turk E.
      • Van Den Heuvel M.P.
      Multiscale examination of cytoarchitectonic similarity and human brain connectivity.
      ) and that disruption in connectivity in brain disorders like schizophrenia is associated with alterations in microstructure (
      • van den Heuvel M.P.
      • Scholtens L.H.
      • de Reus M.A.
      • Kahn R.S.
      Associated microscale spine density and macroscale connectivity disruptions in schizophrenia.
      ). Maps of cortical areas are also predictive for local gradients of functional maps (
      • Dohmatob E.
      • Richard H.
      • Pinho A.L.
      • Thirion B.
      Brain topography beyond parcellations: Local gradients of functional maps.
      ).
      However, additional principles of brain segregation have been identified, both within an area and beyond the areas (
      • Amunts K.
      • Zilles K.
      Architectonic mapping of the human brain beyond Brodmann.
      ). Within an area, the border tuft and the fringe area within areas of the visual cortex have been described in the sixties (
      • Sanides F.
      • HGf Vitzthum
      Die Grenzerscheinungen am Rande der menschlichen Sehrinde.
      ) and later verified using Polarized Light and diffusion imaging (
      • Caspers J.
      • Palomero-Gallagher N.
      • Caspers S.
      • Schleicher A.
      • Amunts K.
      • Zilles K.
      Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus.
      ). Callosal connections in the occipital cortex have been shown in the border regions of early visual areas as demonstrated by the Nauta method (
      • Clarke S.
      Callosal connections and functional subdivision of the human occipital cortex. Functional organisation of the human visual cortex.
      ), and ocular dominance columns represent another change in connectivity within those areas.
      The neocortex can also be classified according to the appearance of an internal granular layer IV into agranular (i.e., layer IV is not present in the adult brain such as the motor cortex, Brodmann area 4), dysgranular (i.e., it does exist, but not well visible as an independent cortical ribbon such as in Brodmann area 44) and granular cortex (containing a well-defined layer IV such as in the frontal pole). I.e., this view combines different areas into larger groups based on shared features of layer IV. Furthermore, receptorarchitecture can be used to identify “families” of cortical areas (see paragraph 3).
      More “low-frequency” changes across several areas have been described like myelination trends (
      • Sanides F.
      Comparative architectonics of the neocortex of mammals and their evolutionary interpretation.
      ), and gradations, i.e. directed, incremental changes at the transition zone between iso- and allocortex, the proisocortex (
      • Vogt C.
      • Vogt O.
      Allgemeinere Ergebnisse unserer Hirnforschung (English Translation: Results of our brain research in a broader context).
      ,

      Sanides F (1962): Die Architektonik des menschlichen Gehirns. Berlin: Springer-Verlag.

      ) and periallocortex (
      • Filimonoff I.
      A rational subdivision of the cerebral cortex.
      ), respectively. More recently, gradients have been described for receptors (
      • Goulas A.
      • Zilles K.
      • Hilgetag C.C.
      Cortical Gradients and Laminar Projections in Mammals.
      ), and functional connectivity (
      • Genon S.
      • Bernhardt B.C.
      • La Joie R.
      • Amunts K.
      • Eickhoff S.B.
      The many dimensions of human hippocampal organization and (dys)function.
      ,
      • Margulies D.S.
      • Ghosh S.S.
      • Goulas A.
      • Falkiewicz M.
      • Huntenburg J.M.
      • Langs G.
      • et al.
      Situating the default-mode network along a principal gradient of macroscale cortical organization.
      ).
      The term “gradients” has been used in different context and meaning. For example, the combined analysis of microstructure, receptor- and transcriptomic data revealed hierarchical gradients when moving from one area to the next in four functional systems processing motor, somatosensory, auditory and visual information (
      • Zachlod D.
      • Bludau S.
      • Cichon S.
      • Palomero-Gallagher N.
      • Amunts K.
      Combined analysis of cytoarchitectonic, molecular and transcriptomic patterns reveal differences in brain organization across human functional brain systems.
      ). Such covarying gradients across areas show hierarchies of gene expression and function across the neocortex (
      • Hansen J.Y.
      • Markello R.D.
      • Vogel J.W.
      • Seidlitz J.
      • Bzdok D.
      • Misic B.
      Mapping gene transcription and neurocognition across human neocortex.
      ). Some authors observed a divergence for transmodal areas processing working memory, social cognition and cognitive control, and hypothesized that the decoupling of microstructural and functional gradients might enable functional diversity and flexibility in transmodal areas (
      • Paquola C.
      • Vos De Wael R.
      • Wagstyl K.
      • Bethlehem R.A.
      • Hong S.-J.
      • Seidlitz J.
      • et al.
      Microstructural and functional gradients are increasingly dissociated in transmodal cortices.
      ).
      In summary, evidence has been provided that cytoarchitecture is in a systematic way linked to molecular architecture and connectivity. Cytoarchictectonic areas provide a suitable reference to interpret findings on connectivity and function, while multiple effects are contributing to brain segregation.
      To identify areas based on the folding pattern is not reliable, since borders of most areas do not coincide with anatomical landmarks, except for a few, e.g. primary sensory and motor cortex. Borders vary between brains regarding their relationship to sulci and gyri. E.g., areas 44 and 45 of Broca’s region show a considerable intersubject variability, while other areas, e.g., the primary visual cortex, vary to a lesser degree (
      • Amunts K.
      • Mohlberg H.
      • Bludau S.
      • Zilles K.
      Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture.
      ). The Julich-Brain probabilistic maps of cytoarchitectonic areas reflect these differences between brains by integrating and superimposing individual maps from ten postmortem brains into a common reference space (
      • Amunts K.
      • Mohlberg H.
      • Bludau S.
      • Zilles K.
      Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture.
      ). They quantify the probability of an area at each position in stereotaxic space and display it in a color spectrum from low to high. Maximum probability maps (MPM) have been calculated, which show brain areas in a non-overlapping mode by assigning each position in the reference space to the area with the highest probability (
      • Eickhoff S.
      • Stephan K.E.
      • Mohlberg H.
      • Grefkes C.
      • Fink G.R.
      • Amunts K.
      • et al.
      A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data.
      ).

      3. Molecular architecture, receptor fingerprints and maps

      Neurotransmitter receptors are key molecules of information transfer between neurons, and can be studied using quantitative receptor autoradiography (

      Zilles K, Schleicher A, Palomero-Gallagher N, Amunts K (2002): Quantitative analysis of cyto-and receptor architecture of the human brain. Brain mapping: the methods: Elsevier, pp 573-602.

      ). They are expressed at varying intensities throughout the human brain (e.g., Zilles and Amunts (
      • Zilles K.
      • Amunts K.
      Receptor mapping: architecture of the human cerebral cortex.
      ), Palomero-Gallagher, Amunts (

      Palomero-Gallagher N, Amunts K, Zilles K (2015): Transmitter receptor distribution in the human brain.

      )). In general terms, receptors for the excitatory neurotransmitter glutamate and the inhibitory neurotransmitter GABA are present at considerably higher densities in the cerebral cortex than receptors for modulatory neurotransmitters, and most receptors are found at higher densities in the superficial than the deep cortical layers (
      • Palomero-Gallagher N.
      • Zilles K.
      Cortical layers: Cyto-, myelo-, receptor-and synaptic architecture in human cortical areas.
      ). The laminar distribution patterns of neurotransmitter receptors correlate with synaptic densities, but differences in receptor densities should not be interpreted as directly reflecting cytoarchitectonic layers or cell packing densities (
      • Palomero-Gallagher N.
      • Zilles K.
      Cortical layers: Cyto-, myelo-, receptor-and synaptic architecture in human cortical areas.
      ). Simultaneous analysis of the distribution of cell bodies and of multiple receptor types along the cortical ribbon has shown that changes in receptor densities are indicative of borders between areas, and that receptorarchitectonic borders occur at positions comparable to those of cytoarchitectonic borders both in the isocortex and allocortex (e.g., Caspers, Palomero-Gallagher (
      • Caspers J.
      • Palomero-Gallagher N.
      • Caspers S.
      • Schleicher A.
      • Amunts K.
      • Zilles K.
      Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus.
      ), Palomero-Gallagher, Kedo (
      • Palomero-Gallagher N.
      • Kedo O.
      • Mohlberg H.
      • Zilles K.
      • Amunts K.
      Multimodal mapping and analysis of the cyto-and receptorarchitecture of the human hippocampus.
      )), and are sometimes more sensitive regarding borders than cytoarchitectonics (
      • Geyer S.
      • Ledberg A.
      • Schleicher A.
      • Kinomura S.
      • Schormann T.
      • Bürgel U.
      • et al.
      Two different areas within the primary motor cortex of man.
      ). However, what makes this multimodal approach so powerful is actually the fact that not all receptors reveal all possible cytoarchitectonic borders, so that each neurotransmitter receptor can identify “families” of cytoarchitectonically distinct but neurochemically related areas (
      • Palomero-Gallagher N.
      • Zilles K.
      Cyto-and receptor architectonic mapping of the human brain. Handbook of clinical neurology.
      ). Indeed, similarities between areas in their receptor fingerprints, i.e., in their specific co-distribution patterns of multiple receptors, constitute the molecular underpinning of communication between these areas, and thus confers them with the ability to build a network which subserves a specific functional system (
      • Zilles K.
      • Bacha-Trams M.
      • Palomero-Gallagher N.
      • Amunts K.
      • Friederici A.D.
      Common molecular basis of the sentence comprehension network revealed by neurotransmitter receptor fingerprints.
      ). Furthermore, receptor fingerprints differ between functional systems, segregate cortical types and reveal hierarchical processing levels (e.g., isocortex vs. allocortex or unimodal vs. multimodal areas (
      • Palomero-Gallagher N.
      • Zilles K.
      Cortical layers: Cyto-, myelo-, receptor-and synaptic architecture in human cortical areas.
      ,
      • Goulas A.
      • Changeux J.-P.
      • Wagstyl K.
      • Amunts K.
      • Palomero-Gallagher N.
      • Hilgetag C.C.
      The natural axis of transmitter receptor distribution in the human cerebral cortex.
      )). I.e., receptor fingerprints enable the analysis of the brain’s structural segregation and of its functional connectivity principles.
      Importantly for translational studies, the regional differences in receptor distribution patterns as revealed by means of receptor PET are comparable to those obtained with in vitro receptor autoradiography, provided that the same ligand (or different ligands, but of comparable specificity and type) is used for both modalities (e.g., Hurlemann, Matusch (
      • Hurlemann R.
      • Matusch A.
      • Eickhoff S.B.
      • Palomero-Gallagher N.
      • Meyer P.T.
      • Boy C.
      • et al.
      Analysis of neuroreceptor PET-data based on cytoarchitectonic maximum probability maps: a feasibility study.
      ), Kumar and Mann (
      • Kumar J.D.
      • Mann J.J.
      PET tracers for 5-HT1A receptors and uses thereof.
      ), Paterson, Kornum (
      • Paterson L.M.
      • Kornum B.R.
      • Nutt D.J.
      • Pike V.W.
      • Knudsen G.M.
      5‐HT radioligands for human brain imaging with PET and SPECT.
      )). Recently, a 3D normative receptor atlas has been provided by Hansen, Markello (
      • Hansen J.Y.
      • Markello R.D.
      • Vogel J.W.
      • Seidlitz J.
      • Bzdok D.
      • Misic B.
      Mapping gene transcription and neurocognition across human neocortex.
      ). The PET derived receptor data showed brain structure and function coupling, correspondence to connectivity, neural dynamics (MEG) and neuroatrophy in brain disorders like ADHD, autism and temporal lobe epilepsy. Another study by Kaulen, Rajkumar (
      • Kaulen N.
      • Rajkumar R.
      • Régio Brambilla C.
      • Mauler J.
      • Ramkiran S.
      • Orth L.
      • et al.
      mGluR5 and GABAA receptor‐specific parametric PET atlas construction—PET/MR data processing pipeline, validation, and application.
      ) provided an atlas showing the in-vivo distribution of glutamate and GABAA with potential benefit for psychiatric diseases. While receptor PET has the advantage of studying molecular dynamics at the receptor level and can reveal their relationship to behavior and disease in healthy subjects and patients (e.g. da Cunha-Bang and Knudsen (
      • da Cunha-Bang S.
      • Knudsen G.M.
      The modulatory role of serotonin on human impulsive aggression.
      )), in vitro receptor autoradiography has the advantage of providing a higher spatial resolution, and of addressing many different receptor types in the same sample. Therefore, in vitro receptor autoradiography represents a powerful tool for the analysis of the pathogenesis of neuropsychiatric disorders, where receptor alterations are often associated with more than one transmitter system. To combine data from receptor studies (both in vivo and in vitro) with findings from cytoarchitecture, connectivity studies and functional imaging, it is necessary to integrate them into a common reference space, and to use an atlas, where different data modalities across the different scales are represented.

      4. Cyto- and receptorarchitectonic maps as part of a multilevel brain atlas

      Cytoarchitectonic probabilistic maps have been used to build the EBRAINS multilevel brain atlas (https://ebrains.eu/service/human-brain-atlas/, https://julich.brainatlas.eu), a 3D reference atlas that links different aspects of brain organization at microscopic and macroscopic scales. The core idea of this atlas is to integrate different whole-brain maps as well as regional data in a common framework, and to superimpose them in the same template. Therefore, the atlas supports multiple templates at different spatial scales and contains explicit links between the spaces.
      The microscopic scale of the atlas is represented by the BigBrain model and template space (http://bigbrain.brainatlas.eu), i.e., a 3D-reconstruction of a full series of cell body-stained sections of a human postmortem brain at 20 μm spatial resolution, a Terabyte-sized dataset (
      • Amunts K.
      • Lepage C.
      • Borgeat L.
      • Mohlberg H.
      • Dickscheid T.
      • Rousseau M.E.
      • et al.
      BigBrain: An ultrahigh-resolution 3D human brain model.
      ). The gray values of the BigBrain represent cellular densities and allow to distinguish details cortical layers and even large cells. Automated mapping workflows have been proposed for this microstructural template, resulting, e.g., in a complete map of the six layers of the isocortex (http://layers.brainatlas.eu, (
      • Wagstyl K.
      • Larocque S.
      • Cucurull G.
      • Lepage C.
      • Cohen J.P.
      • Bludau S.
      • et al.
      BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices.
      )). It uses a classification of 3D cortical intensity profiles and 1D Convolutional Neural Networks (CNNs). In addition, highly detailed maps of cortical and subcortical areas based on image segmentations across thousands of histological sections have been computed, whereby 2D CNNs were trained on a scarce set of cytoarchitectonically identified areas (
      • Schiffer C.
      • Spitzer H.
      • Kiwitz K.
      • Unger N.
      • Wagstyl K.
      • Evans A.C.
      • et al.
      Convolutional neural networks for cytoarchitectonic brain mapping at large scale.
      ).
      The macroscopic scale of the atlas is represented by the MNI reference space (
      • Evans A.C.
      • Janke A.L.
      • Collins D.L.
      • Baillet S.
      Brain templates and atlases.
      ), which is employed in many neuroimaging and clinical applications. It incorporates different templates including the MNI Colin 27 single-subject average, as well as the ICBM 152 2009c asymmetric multi-subject average (spatial resolution of 1 mm). Both templates are fully mapped by probabilistic cytoarchitectonic maps. Since these follow the same delineation principles as the maps available in BigBrain space, they constitute a direct anatomical link across the scales. The BigBrain served as a basis to provide the first whole brain model of cortical layers (
      • Wagstyl K.
      • Larocque S.
      • Cucurull G.
      • Lepage C.
      • Cohen J.P.
      • Bludau S.
      • et al.
      BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices.
      ) and to study the 3D topology of the hippocampus (
      • DeKraker J.
      • Lau J.C.
      • Ferko K.M.
      • Khan A.R.
      • Köhler S.
      Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain.
      ). Ultra-high resolution 3D reconstructions of cortical areas and subcortical nuclei have been developed (
      • Schiffer C.
      • Spitzer H.
      • Kiwitz K.
      • Unger N.
      • Wagstyl K.
      • Evans A.C.
      • et al.
      Convolutional neural networks for cytoarchitectonic brain mapping at large scale.
      ,

      Brandstetter A, Bolakhrif N, Schiffer C, Dickscheid T, Mohlberg H, Amunts K (2019): Deep learning-supported cytoarchitectonic mapping of the human lateral geniculate body in the BigBrain. International Workshop on Brain-Inspired Computing: Springer, Cham, pp 22-32.

      ) and openly provided to the community. In addition, several new tools, e.g. the BigBrain warp toolbox (
      • Paquola C.
      • Royer J.
      • Lewis L.B.
      • Lepage C.
      • Glatard T.
      • Wagstyl K.
      • et al.
      The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging.
      ) and PET tracer simulation (
      • Belzunce M.A.
      • Reader A.J.
      ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas.
      ) have been developed during the past years, and links to other community tools have been established; for an overview see https://bigbrainproject.org/.
      For more than 30 of these cytoarchitectonic areas, neurotransmitter receptor densities have been obtained using quantitative in vitro autoradiography (
      • Palomero-Gallagher N.
      • Zilles K.
      Cyto-and receptor architectonic mapping of the human brain. Handbook of clinical neurology.
      ). The datasets are directly linked to the multilevel atlas in the form of regional data features, identified by the cytoarchitectonic localization of the underlying tissue samples that are openly accessible.
      Most recently, the volumetric cytoarchitectonic maps have been projected to the fsaverage surface space (https://surfer.nmr.mgh.harvard.edu/ (
      • Dale A.M.
      • Fischl B.
      • Sereno M.I.
      Cortical surface-based analysis. I. Segmentation and surface reconstruction.
      ,
      • Fischl B.
      • Sereno M.I.
      • Dale A.M.
      Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.
      )).

      5. Accessing and analyzing atlas data through the siibra tool

      The different maps can be directly accessed using the siibra software tool suite (software interface for interacting with brain atlases), which provides both interactive and programmatic interfaces. This recently developed tool suite comprises a fully interactive web interface (siibra-explorer), as well as a comprehensive python library (siibra-python). In addition, an HTTP API is provided for connecting external applications (siibra-api; hosted at https://siibra-api-stable.apps.hbp.eu/). The underlying reference templates, parcellations and data are stored as curated datasets in the EBRAINS Knowledge Graph (https://search.kg.ebrains.eu). The siibra tool integrates these datasets into the multilevel atlas, and links them with other resources, e.g., from studies of connectivity.
      The interactive siibra-explorer https://atlases.ebrains.eu/viewer provides a three-planar view of a reference volume, combined with a rotatable overview of the 3D surface. Different templates and maps can be selected using the layer navigation panel (Fig. 2), allowing one to change between MRI-scale views and the full resolution of the BigBrain as well as a 3D view for convoluted and inflated surfaces. Precomputed nonlinear transformations will be used to preserve the view across spaces in terms of its 3D position, orientation, and zoom level (Fig. 3), thus presenting the corresponding anatomical region in the new space. By selecting a brain region, a side panel is presented which provides a description and link to detailed metadata in the Knowledge Graph, a list of regional data features linked from the Knowledge Graph and additional online resources, as well as an interactive browser for regional connectivity profiles from different imaging cohorts. Regions can be selected by clicking, or by navigating an interactive, searchable region hierarchy tree. The viewer provides an extensible plugin architecture, which includes an annotation mode for creating and sharing named locations, lines and polylines, as well as some tools for interactive data analysis such as JuGEx (
      • Bludau S.
      • Muehleisen T.W.
      • Eickhoff S.B.
      • Hawrylycz M.J.
      • Cichon S.
      • Amunts K.
      Integration of transcriptomic and cytoarchitectonic data implicates a role for MAOA and TAC1 in the limbic-cortical network.
      ).
      Figure thumbnail gr2
      Figure 2User interface of siibra-explorer, the interactive 3D viewer for accessing the multilevel brain atlas hosted at https://atlases.ebrains.eu/viewer. Julich-Brain cytoarchitectonic maps depicted in different colors in MNI Colin 27 space at 1mm resolution, layer navigator panel opened, which showed the selectable reference spaces and parcellation schemes.
      Figure thumbnail gr3
      Figure 3User interface of the siibra explorer showing the approximately corresponding view as in which the viewer presents after switching to BigBrain space with 20 μm resolution. Cytoarchitectonically mapped areas in the BigBrain are labeled with different colors.
      The interactive functionality of the siibra-explorer as well as advanced features are available for integration in computational workflows through the software library siibra-python, which is directly installable from https://pypi.org/project/siibra/. The library offers data types and predefined objects for parcellations, maps, reference spaces, and multimodal data features, and provides compatibility with common libraries such as numpy, pandas, matplotlib, nibabel and nilearn. A detailed documentation is available at https://siibra-python.readthedocs.io, which also provides downloadable code examples with explanations.

      6. Application scenarios

      The multilevel atlas is designed for integrating multimodal data from in vivo and postmortem studies and is linked with a growing number of data (for accessible tools and data see Table 1). In the following, the scope of several applications will be illustrated.
      Table 1Overview of available tools and data for multivariate analyses of connectivity and brain function.
      ToolDescriptionLink or reference
      Atlas viewerProvides parcellation maps of cyto- and fiber architecture and connectivity in different reference spaceshttps://atlases.ebrains.eu/viewer
      JuGExAnalysis of differential gene expression in cytoarchitectonic areashttps://ebrains.eu/service/jugex/(59)
      siibra software tool suiteProgrammatic access to the maps of the atlas in pythonhttps://siibra-api-stable.apps.hbp.eu/
      EBRAINS KnowledgegraphFlexible and scalable metadata management system with a search user interface, links the atlas to further data stored in the knowledge graphhttps://search.kg.ebrains.eu
      VoluBAAnchoring any volume-of-interest (e.g., high-resolution volume from optical imaging such as PLI) in the BigBrainhttps://voluba.apps.ebrains.eu
      JuSpaceCross-modal correlation between fMRI and PET datasetshttps://github.com/juryxy/JuSpace  (
      • Axer M.
      • Amunts K.
      • Grassel D.
      • Palm C.
      • Dammers J.
      • Axer H.
      • et al.
      A novel approach to the human connectome: ultra-high resolution mapping of fiber tracts in the brain.
      )
      DatasetDescriptionLink or reference
      Julich-Brain cyotoarchitectonic mapsCytoarchitectonic probabilistic maps and MPMs of more than 145 areas, Integrated in the atlas viewerhttps://atlases.ebrains.eu/viewer (
      • Amunts K.
      • Mohlberg H.
      • Bludau S.
      • Zilles K.
      Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture.
      )
      Representation of cytoarchitectonic maps in Freesurfer formatIntegrated in the atlas viewerhttps://atlases.ebrains.eu/viewer https://surfer.nmr.mgh.harvard.edu/
      Structural connectivityMaps of short- and long-range fiber bundles, integrated in the atlas viewerhttps://doi.org/10.25493/NVS8-XS5
      Functional connectivityVisualization and connectivity strength of functionally connected brain areas, Integrated in the atlas viewerhttps://doi.org/10.25493/61QA-KP8
      BigBrain modelMicroscopical brain modelhttps://bigbrainproject.org/hiball.html (
      • Amunts K.
      • Lepage C.
      • Borgeat L.
      • Mohlberg H.
      • Dickscheid T.
      • Rousseau M.E.
      • et al.
      BigBrain: An ultrahigh-resolution 3D human brain model.
      )
      BigBrain segmentationLayer segmentation of the BigBrain for the isocortexhttp://layers.brainatlas.eu
      High resolution maps of the BigBraindeep learning approach to enhance the spatial resolution of brain areas in the BigBrain(
      • Schiffer C.
      • Spitzer H.
      • Kiwitz K.
      • Unger N.
      • Wagstyl K.
      • Evans A.C.
      • et al.
      Convolutional neural networks for cytoarchitectonic brain mapping at large scale.
      )
      IBCFunctional territories, Individual Brain Charting dataset(

      Dukart J, Holiga S, Rullmann M, Lanzenberger R, Hawkins PC, Mehta MA, et al. (2021): JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Wiley Online Library.

      )
      Table 1: Overview of available tools and data for multivariate analyses of connectivity and brain function.

      a. Sharing and analyzing high resolution microscopic data in the BigBrain space

      The BigBrain is suited for spatial anchoring of regional data from volumes of interest (VOI) from histological experiments with high spatial resolution. VoluBA is a service for upload and interactive anchoring of such volumes into the BigBrain (https://voluba.apps.ebrains.eu) including imaging data from two-photon, light-sheet imaging or x-ray. As an example, a volume from 3D Polarized Light Imaging, 3D-PLI (
      • Axer M.
      • Amunts K.
      • Grassel D.
      • Palm C.
      • Dammers J.
      • Axer H.
      • et al.
      A novel approach to the human connectome: ultra-high resolution mapping of fiber tracts in the brain.
      )) has been anchored to the BigBrain space, showing the distribution of nerve fiber orientations in the hippocampus. The embedding in the atlas allows one to compare modalities and appreciate the orientation, size, and proximity of the VOI to other brain regions superimposed to the cytoarchitectonic BigBrain model (data at https://search.kg.ebrains.eu/instances/Dataset/b08a7dbc-7c75-4ce7-905b-690b2b1e8957).

      b. Comparing activation data from fMRI data with cytoarchitectonic and maps

      When fMRI studies have been transformed to the MNI reference space, areas of activation can easily be compared with brain regions of the atlas. For this scenario, the siibra-explorer allows to overlay a local brain volume with the volumetric atlas by dragging the file onto the browser window. fMRI datasets can also be correlated with nuclear imaging (PET and SPECT maps) of various neurotransmitter systems by using the user interface of the JuSpace toolbox (

      Dukart J, Holiga S, Rullmann M, Lanzenberger R, Hawkins PC, Mehta MA, et al. (2021): JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Wiley Online Library.

      ). Moreover, the atlas provides access to a high resolution task-fMRI dataset (
      • Pinho A.L.
      • Amadon A.
      • Fabre M.
      • Dohmatob E.
      • Denghien I.
      • Torre J.J.
      • et al.
      Subject‐specific segregation of functional territories based on deep phenotyping.
      ).

      c. Cytoarchitectonic maps as seed regions for analyses of connectivity

      The maps have been used for extracting region-averaged connectivity matrices from cohort studies. Several datasets of functional as well as structural connectivity in the form of streamline counts and lengths from diffusion imaging have been linked with the atlas (
      • Caspers S.
      • Moebus S.
      • Lux S.
      • Pundt N.
      • Schütz H.
      • Mühleisen T.W.
      • et al.
      Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS.
      ,

      Domhof J, Eickhoff S, Jung K, Popovych O (2021): Parcellation-based structural and resting-state functional brain connectomes of a healthy cohort. Gehirn & Verhalten.

      ,

      Popovych O, Eickhoff S, Jung K, Domhof J (2020): Averaged structural and functional connectivities of healthy cohorts based on whole-brain parcellations. Gehirn & Verhalten.

      ) providing connectivity profiles as a regional feature type. In the siibra-explorer, they can be used to explore connections of a brain region interactively: After selecting a source region and connectivity dataset, a list of connected target regions is presented, and connection strengths are used to colorize the parcellation map (Fig. 4).
      Figure thumbnail gr4
      Figure 4A: Freesurfer view of cytoarchitectonic maps. B: Pull-down menu with connectivity map of area V1, left. Connected areas are colored showing the connectivity strength from strong (red) to week (blue). C: Gene expression map showing differential expression of gene GABRA3 between primary visual and auditory area in relative units (z-scores; see also next paragraph).

      d. Studying genetics: the JuGEx tool

      The siibra tool suite implements a direct interface to the Allen Brain Atlas API (© 2015 Allen Institute for Brain Science; https://brain-map.org/api/index.html). Microarray measurements are linked to the atlas via the 3D coordinates of the tissue blocks, which are supplied in MNI reference space (Fig. 4C). This interface enabled implementation of the JuGEx workflow, originally implemented in Matlab (
      • Bludau S.
      • Muehleisen T.W.
      • Eickhoff S.B.
      • Hawrylycz M.J.
      • Cichon S.
      • Amunts K.
      Integration of transcriptomic and cytoarchitectonic data implicates a role for MAOA and TAC1 in the limbic-cortical network.
      ) as an extension of the siibra-python package (https://github.com/FZJ-INM1-BDA/siibra-jugex/), and makes it accessible as an interactive plugin in siibra-explorer. By selecting two regions of interest in the atlas, the workflow performs a differential analysis of the expression levels of candidate genes. This tool allowed to disclose subregion specificity in the left medial frontal pole area Fp2 in patients with major depression (
      • Bludau S.
      • Bzdok D.
      • Gruber O.
      • Kohn N.
      • Riedl V.
      • Sorg C.
      • et al.
      Medial prefrontal aberrations in major depressive disorder revealed by cytoarchitectonically informed voxel-based morphometry.
      ).

      e. From large cohort data to individual profiles of brain aging

      Brain aging is an individual phenomenon. Cytoarchitectonic maps can be used for deep phenotyping in the older adult population to link multilevel brain, cognitive and lifestyle data to better understand the normal aging brain, as well as the different brain-behavior relationships in brain disorders with a benefit for clinical psychiatry. In a pilot study, a subcohort of five males scoring low on a dementia screening test was analyzed, which is part to a large population-based cohort study, 1000BRAINS (
      • Caspers S.
      • Moebus S.
      • Lux S.
      • Pundt N.
      • Schütz H.
      • Mühleisen T.W.
      • et al.
      Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS.
      ,
      • Jockwitz C.
      • Bittner N.
      • Caspers S.
      • Amunts K.
      Deep characterization of individual brain-phenotype relations using a multilevel atlas.
      ). As expected, individual cognitive profiles are highly heterogeneous regarding cognitive performance, lifestyle factors and grey matter atrophy. However, cytoarchitectonically defined areas PFt, PG, 3b and 45 of the subcohort deviated more than two standard deviations from the mean of the large cohort and were characterized regarding structural connectivity, receptor density and APOE expression through the JuGEx tool. Such an integrative approach using micro- and macrostructural information was instrumental to explain the individual phenomena and might lead to individual clinical diagnosis and tailored treatment strategies (
      • Jockwitz C.
      • Bittner N.
      • Caspers S.
      • Amunts K.
      Deep characterization of individual brain-phenotype relations using a multilevel atlas.
      )

      7. Critical discussion and constraints

      Probabilistic cytoarchitectonic maps are reference data of a multilevel atlas to enable the integration of different data of human brain organization that cannot be studied within one and the same brain, or one and the same experiment as a prerequisite to studTPy structure-function relationship across the different spatial scales. There are several challenges: Human brains are variable concerning the anatomy including the folding pattern, the extent and localization of cortical areas, and the relationship of the borders of these areas to the folding pattern. Similar is the case for brain activity and function. Cytoarchitectonic probabilistic maps capture anatomical variability, but also include variability that is caused by methodological aspects. It has been shown that variability is smaller in surface-based maps as compared to volume-based maps (
      • Fischl B.
      • Rajendran N.
      • Busa E.
      • Augustinack J.
      • Hinds O.
      • Yeo B.T.T.
      • et al.
      Cortical folding patterns and predicting cytoarchitecture.
      ), which may increase precision in studying the cortex (
      • Gulban O.
      • Goebel R.
      • Moerel M.
      • Zachlod D.
      • Mohlberg H.
      • Amunts K.
      • et al.
      Improving a probabilistic cytoarchitectonic atlas of auditory cortex using a novel method for inter-individual alignment.
      ). Surface-based MPM have been computed in Freesurfer reference space (https://ebrains.eu/news/new-maps-features-ebrains-multilevel-human-brain-atlas/) to address the need of studying cytoarchitectonic correlates of functional activations in more detail. At the same time, the computation of surface-based maps cannot depict information on cortical depth and structures located deep in the brain, and topological errors cannot be excluded when extracting surfaces. Another limitation includes the limited sample size of ten brains of the probabilistic maps, as well as a natural bias to older subjects although the impact of these factors is deemed to be limited in studies of mapping. Image registration of postmortem brain data to a common reference space introduces inevitable inaccuracies. E.g., when assessing structure-function relationships, more fine-grained structures, like individual cortical layers, but also some small areas (a few millimeters large), are not captured properly at in vivo imaging resolutions, which might lead to weak associations between in vivo and postmortem data, undesired smoothing, and partial volume effects. Finally, several regions reveal a very fine-grained parcellation and a complex and variable folding pattern, e.g. intraparietal sulcus (
      • Choi H.J.
      • Amunts K.
      • Mohlberg H.
      • Fink G.R.
      • Schleicher A.
      • Zilles K.
      Cytoarchitectonic mapping of the anterior ventral bank of the intraparietal sulcus in humans.
      ,
      • Scheperjans F.
      • Hermann K.
      • Eickhoff S.B.
      • Amunts K.
      • Schleicher A.
      • Zilles K.
      Observer-independent cytoarchitectonic mapping of the human superior parietal cortex.
      ,
      • Richter M.
      • Amunts K.
      • Mohlberg H.
      • Bludau S.
      • Eickhoff S.
      • Zilles K.
      • et al.
      Cytoarchitectonic segregation of human posterior intraparietal and adjacent parieto-occipital sulcus and its relation to visuomotor and cognitive functions.
      ) as well as the insula (

      Quabs J, Caspers S, Iannilli F, Mohlberg H, Bludau S, Amunts K (2019): Mapping the cytoarchitectonic basis of socio-emotional and cognitive processing in the insula. Annual Meeting of the Organization of Human Brain Mapping (OHBM). 9 Jun 2019 - 13 Jun 2019, Rome (Italy) Y2 9 Jun 2019 - 13 Jun 2019 M2 Rome, Italy.

      ). Hypothesis-driven imaging experiments are necessary to identify the functional correlates and specific networks in which these areas are involved.

      8. Summary and outlook

      A multimodal reference framework has been provided for investigating brain organization across multiple scales based on Julich-Brain. The atlas is interactive and provides detailed maps of cytoarchitectonic brain areas in widely used templates which are linked to receptor density data, structural DTI-based connectivity data and gene expression data from the Allen Brain institute. It is publicly available to facilitate studies on structure-function relationships. Information about brain areas can be accessed via the Knowledge Graph of EBRAINS. At the same time, the atlas is an ongoing project, new maps will be integrated in the atlas to stepwise substitute the gap maps, and the integration of bigdata volumes will play an increasing role in the future. Deep learning algorithms become more and more powerful to detect and 3D reconstruct brain areas at high resolution, with a benefit for small and geometrically complex brain areas. It is planned that future tools will support the exploration and access to data in the form of 3D graph-like structures, in particular streamlines, vasculature skeletons, neuron morphologies, and polyline annotations. Developing a modern brain atlas requires collaborations with experts in supercomputing is necessary to scale up and improve steps of image processing, e.g., cell recognition. These and other future trends in neuroanatomy can be implemented in the Julich-Brain to keep it an up-to-date tool for exploring brain organization, and providing the knowledge basis to decode the pathomechanisms of brain diseases.

      Acknowledgments:

      The Julich-Brain Atlas project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3) and was supported by the Joint Lab Supercomputing and Modeling for the Human Brain (SMHB). The authors want to thank Markus Axer and Olga Kedo for their contribution to Figure 1.
      Competing interests: The authors report no biomedical financial interests or potential conflicts of interest.

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