ArticleHuman brain mapping2014
A hierarchical method for whole-brain connectivity-based parcellation.
Article in Human brain mapping, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Dynamic functional connectivity following cochlear implantation: Predicting speech outcomes and exploring brain network dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Global organisation of structural covariance networks derived from parcellated cortical surface area in atypical populations.bioRxiv : the preprint server for biology · 2025Article
- Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants.Human brain mapping · 2025Article
- CoCoNest: A continuous structural connectivity-based nested family of parcellations of the human cerebral cortex.Network neuroscience (Cambridge, Mass.) · 2024Article
- Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Changes in white matter functional networks during wakefulness and sleep.Human brain mapping · 2022Article
- On the definition, construction, and presentation of the human cerebral sulci: A morphology-based approach.Journal of anatomy · 2022Article
- Latency structure of BOLD signals within white matter in resting-state fMRI.Magnetic resonance imaging · 2022Article
- The medial occipital longitudinal tract supports early stage encoding of visuospatial information.Communications biology · 2022Article
- Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution.BME frontiers · 2022Article
- Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation.Frontiers in neuroscience · 2021Article
- Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism.Frontiers in neuroinformatics · 2021Article
- From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas.Frontiers in neuroinformatics · 2020Article
- Article
- Parcellation of the Human Cerebral Cortex Based on Molecular Targets in the Serotonin System Quantified by Positron Emission Tomography In vivo.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Using diffusion MRI to discriminate areas of cortical grey matter.NeuroImage · 2018Article
- Parcellating Cerebral Cortex: How Invasive Animal Studies Inform Noninvasive Mapmaking in Humans.Neuron · 2018Review
- Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration.NeuroImage · 2018Article
- An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks.NeuroImage · 2018Review
- Concurrent white matter bundles and grey matter networks using independent component analysis.NeuroImage · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps. Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function-anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole-brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them. Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole-brain parcellation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.