Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

Abstract

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.

Metabolic patterns across core features in dementia with lewy bodies

“…20,21 In detail, analysis was based on the following steps. 20,21 In detail, analysis was based on the following steps.…”

BrainSync: An Orthogonal Transformation for Synchronization of fMRI Data Across Subjects

“…Resting fMRI has been used for identification of contiguous areas of cortex that exhibit similar functional connectivity to define a functional parcellation [16,2]. One major problem in using rfMRI for single subject parcellation is that the amount of data from a single subject is usually not sufficient to reliably parcellate the cerebral cortex brain into a large number of regions [13,2]. Since BrainSync makes data across subjects directly comparable, this synchronized data can be easily pooled and a joint parcellation of a large number of subjects is possible.…”

“…Resting fMRI (rfMRI) is being increasingly used to study brain connectivity and functional organization [2]. It is also used for longitudinal studies of brain development and as a diagnostic biomarker in cross-sectional studies for various neurological and psychological diseases and conditions [12].…”

“…It is also used for longitudinal studies of brain development and as a diagnostic biomarker in cross-sectional studies for various neurological and psychological diseases and conditions [12]. Large scale connectivity information derived from fMRI can be used to delineate functional regions [2]. By extension, identification of multiple contiguous areas, each of which exhibits distinct connectivity to the rest of the brain, can be used to define a functional parcellation of the entire cerebral cortex [16,2].…”

“…Large scale connectivity information derived from fMRI can be used to delineate functional regions [2]. By extension, identification of multiple contiguous areas, each of which exhibits distinct connectivity to the rest of the brain, can be used to define a functional parcellation of the entire cerebral cortex [16,2]. …”