Imaging-based parcellations of the human brain

By Simon B. Eickhoff, B. T. T. Yeo, S. Genon. Nature Reviews Neuroscience, 2018

486 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/344b70fa5b4127642cc6a3f75b8239687b7e38b0

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Citations in the corpus

Comparing spatial null models for brain maps (2021, unknown): “How brain regions are defined and ultimately represented in analyses is an important choice in neuroimaging (Eickhoff et al. 2018), as recent work has highlighted the role of brain region definition in analyses including test-retest reliability (Arslan et al.”
BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets (2019, background): “…magnetic resonance imaging, MRI) to cognition has focused on identifying discrete regions and modules and their speci ﬁ c functional roles 10 , recent conceptual and methodological developments have provided the data and methods that allow macroscale brain features mapped to low…”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, background): “For example Farrant and Uddin (2015) note in their work that while some investigators see the high degree of functional and anatomical overlap between the ventral attention network and salience network as evidence that they are part of the same system (Kucyi et al.”
The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives (2024, methodology): “In order to identify such network constituents, a large number of approaches is employed to parcellate the brain (Eickhoff et al., 2018; Amunts et al., 2020; Bijsterbosch et al., 2020; Royer et al., 2022b) and to identify and characterize white matter ﬁ bers (Mori and Van Zijl, 2002; Jeurissen et…”