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Imaging-based parcellations of the human brain

Abstract

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.

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Fig. 1: A 2D taxonomy of brain parcellation approaches.
Fig. 2: Mapping of visual areas with local markers.
Fig. 3: Inter-individual variability in functional parcellation.

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Acknowledgements

The work of S.B.E. and S.G. is supported by the Deutsche Forschungsgemeinschaft (DFG, GE 2835/1-1, EI 816/4-1), the Helmholtz Portfolio Theme ‘Supercomputing and Modelling for the Human Brain’ and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1) and Grant Agreement No. 785907 (HBP SGA2). B.T.T.Y. is supported by the Singapore Ministry Of Education Tier 2 (MOE2014-T2-2-016), the National University of Singapore (NUS) Strategic Research (DPRT/944/09/14), the National University of Singapore (NUS) School of Medicine Aspiration Fund (R185000271720), Singapore National Medical Research Council (CBRG/0088/2015), NUS Young Investigator Award and the Singapore National Research Foundation Fellowship (Class of 2017). The authors also thank N. Palomero-Gallagher for helpful discussion and Q. Yang and R. Kong for their help with the figures.

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Nature Reviews Neuroscience thanks M. Joliot, H. Liu and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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S.B.E., B.T.T.Y. and S.G. researched data for the article, made substantial contributions to discussion of content, wrote the manuscript and reviewed or edited the manuscript before submission.

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Correspondence to Simon B. Eickhoff.

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Glossary

Large-scale networks

Constellations of brain areas that are strongly connected to each other, presumably subserving specific functions.

Connectivity fingerprints

Patterns of the interactions of brain regions with other brain regions.

Brain cartography

The study of brain organization with the particular objective of representing the organization of the brain as a map of distinct areas.

Brain areas

Brain regions showing specific structure, function and connectivity.

Universal map

A unique division of the brain into individual areas, each having specific structure, connectivity and function, which can be found in all humans.

Graph theory

The use of graphs to study and model relationships between objects with elements such as nodes and edges.

Cytoarchitecture

Tissue composition with regard to cell characteristics.

Myeloarchitecture

The pattern of myelinated fibres.

Visuotopic mapping

The identification of visual areas based on differential cortical responses to different visual stimuli. An example of a mapping stimulus would be a rotating sector of a flashing checkerboard.

Non-negative matrix factorization

A multivariate statistical approach to factorize data into components promoting a part-based representation of the data.

Spectral clustering

A clustering approach based on the eigenvectors of the matrix of similarity (such as connectivity) between brain locations (voxels or vertices). The term ‘spectral’ refers to the spectrum (eigenvalues) of the similarity matrix.

Hierarchical clustering

A clustering approach that disentangles clusters in a hierarchical fashion, in such a way that relationships between clusters can be visualized as a tree structure.

Principal component analysis

A multivariate statistical approach to factorize data into orthogonal components that best represent variance in the data.

Fuzzy or soft clustering

A clustering approach in which points are not assigned to one single group but have a fractional value that represents their relative membership in each group.

Echo planar imaging

An MRI sequence used for functional and diffusion imaging.

Meta-analytic connectivity modelling

A method that aims to model functional connectivity in the brain based on a co-activation pattern across various activation studies.

Probabilistic tractography

An approach to estimate white-matter tract pathways in the brain from diffusion MRI images.

Structural covariance

The pattern of covariations in measures of morphometry (such as grey-matter volume) across brain regions.

Crossing fibres

Individual white-matter fibres whose spatial direction result in points where they meet or cross each other, complicating the estimation of their respective paths.

k-means

A clustering algorithm that divides a set of data points into k clusters by iteratively optimizing the definition of each cluster centroid and data points assigned to the clusters.

Domains

Spatial units in the brain that are smaller than usual brain regions and show specific functions.

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Eickhoff, S.B., Yeo, B.T.T. & Genon, S. Imaging-based parcellations of the human brain. Nat Rev Neurosci 19, 672–686 (2018). https://doi.org/10.1038/s41583-018-0071-7

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