From the Guidelines
A cortical parcellation mask is a neuroimaging tool that divides the cerebral cortex into distinct regions based on anatomical or functional characteristics, allowing for systematic analysis of specific brain regions. This tool is crucial in neuroimaging studies, particularly those involving MRI data, as it enables the measurement of regional brain volumes, cortical thickness, or functional activity patterns 1. The process of creating these masks involves assigning tissue samples to parcellated brain regions, taking into account their broad anatomical locations, such as cortex or subcortex, and applying a distance threshold to avoid inaccurate mapping 1. Some key aspects of cortical parcellation masks include:
- Dividing the cerebral cortex into distinct regions based on anatomical or functional characteristics
- Serving as a template for segmenting the brain's outer layer into labeled areas
- Enabling quantitative analysis of brain structure and function across different regions
- Being essential in research involving MRI data to measure regional brain volumes, cortical thickness, or functional activity patterns
- Helping to standardize brain analysis across subjects and studies, making it possible to compare results and identify region-specific changes in various conditions. The use of cortical parcellation masks has significant implications for understanding brain function and structure, particularly in the context of neurodegenerative diseases, psychiatric disorders, or brain injury, as highlighted in recent studies 1.
From the Research
Definition of Cortical Parcellation Mask
A cortical parcellation mask refers to a technique used to divide the cerebral cortex into distinct regions or parcels based on their anatomical and functional characteristics. This process is crucial for understanding the structural and functional organization of the brain [ 2 ].
Purpose of Cortical Parcellation
The primary purpose of cortical parcellation is to identify and delineate the boundaries of different brain regions, which can help researchers and clinicians to better understand the brain's functional and structural organization [ 3 ]. This information can be used to diagnose and treat neurological disorders, as well as to study the brain's development, aging, and function [ 4 ].
Methods of Cortical Parcellation
There are several methods used for cortical parcellation, including:
- Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis [ 2 ]
- Geometric deep learning with adaptive full-band spatial diffusion [ 3 ]
- Connectivity-based parcellation [ 5 ]
- Multi-modal parcellation using magnetic resonance images [ 4 ]
- Convolutional neural networks (ConvNets) [ 6 ]
Key Features of Cortical Parcellation Masks
Cortical parcellation masks have several key features, including:
- Ability to accurately delineate brain regions based on their anatomical and functional characteristics
- Robustness to variations in brain structure and function
- Ability to generalize to unseen data and populations
- High accuracy and efficiency in parcellating brain regions
- Ability to be used in conjunction with other neuroimaging techniques, such as fMRI and diffusion tensor imaging (DTI) [ 2 , 3 , 5 , 4 , 6 ]