Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex
Open accessAbstract
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
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No single parcellation consistently outperforms the others across all evaluation criteria.
“Importantly, no single parcellation consistently outperforms the others across all evaluation criteria (Arslan et al., 2018).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Arslan et al., 2018
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- Authors
- Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Arslan, Salim
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 170
- Pages
- 5-30
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2606796488
- Cited by
- 374
- References
- 126
- Citations by year
- 2026: 23 · 2025: 40 · 2024: 39 · 2023: 38 · 2022: 47 · 2021: 51 · 2020: 53 · 2019: 33 · 2018: 34 · 2017: 16
- FWCI
- 21.37
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- European Research Council (319456, 1U54MH091657)
- NIH Blueprint for Neuroscience Research
- National Institutes of Health
- Seventh Framework Programme (FP/2007-2013)
- McDonnell Center for Systems Neuroscience
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain parcellation, human cerebral cortex, resting-state fMRI, anatomical parcellation, network nodes, cytoarchitecture, Human Connectome Project
- MeSH terms
- Brain Mapping, Cerebral Cortex, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Theoretical
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