Table 1. Comparison of commonly used... | Emerald Publishing

Table 1.

Comparison of commonly used brain atlases

Brain atlas	Type	ROIs	Resolution	Main features
Talairach Atlas (Lancaster et al., 2000)	Stereotactic anatomical	N/A	Low	Standardized stereotactic coordinate system for neuroimaging localization
AAL (Nathalie et al., 2002)	Anatomical	90–116	Low	Simple anatomical labeling; widely used in EEG/fMRI connectivity analysis
Desikan-Killiany Atlas (Desikan et al., 2006)	Anatomical surface	68	Low	Gyral/sulcal cortical parcellation widely used in structural MRI and FreeSurfer
LPBA40 (Shattuck et al., 2008)	Probabilistic anatomical	56	Low-medium	Atlas constructed from T1-weighted MRI; captures inter-subject anatomical variability
Brainnetome Atlas (Fan et al., 2016)	Connectivity architecture	246–274	Medium-high	Connectivity-driven multimodal MRI (DWI + resting-state fMRI)
HCP-MMP1.0 (Matthew et al., 2016)	Multimodal integration	360	High	Precision cortical mapping using multimodal MRI and connectivity information
Schaefer Atlas (Schaefer et al., 2018)	Functional connectivity	100–1000	Variable	Multi-scale functional parcellation for resting-state fMRI and connectomics
Jülich Brain Atlas (Amunts et al., 2020)	Probabilistic cytoarchitectonic	100+	High	Microstructural and cytoarchitectonic mapping with probabilistic population maps.
C-Atlas (this work)	Data-driven disease-specific	100–1000	Variable	Machine learning-based system, AD-driven, disease adaptive

Brain atlas	Type	ROIs	Resolution	Main features
Talairach Atlas (Lancaster et al., 2000)	Stereotactic anatomical	N/A	Low	Standardized stereotactic coordinate system for neuroimaging localization
AAL (Nathalie et al., 2002)	Anatomical	90–116	Low	Simple anatomical labeling; widely used in EEG/fMRI connectivity analysis
Desikan-Killiany Atlas (Desikan et al., 2006)	Anatomical surface	68	Low	Gyral/sulcal cortical parcellation widely used in structural MRI and FreeSurfer
LPBA40 (Shattuck et al., 2008)	Probabilistic anatomical	56	Low-medium	Atlas constructed from T1-weighted MRI; captures inter-subject anatomical variability
Brainnetome Atlas (Fan et al., 2016)	Connectivity architecture	246–274	Medium-high	Connectivity-driven multimodal MRI (DWI + resting-state fMRI)
HCP-MMP1.0 (Matthew et al., 2016)	Multimodal integration	360	High	Precision cortical mapping using multimodal MRI and connectivity information
Schaefer Atlas (Schaefer et al., 2018)	Functional connectivity	100–1000	Variable	Multi-scale functional parcellation for resting-state fMRI and connectomics
Jülich Brain Atlas (Amunts et al., 2020)	Probabilistic cytoarchitectonic	100+	High	Microstructural and cytoarchitectonic mapping with probabilistic population maps.
C-Atlas (this work)	Data-driven disease-specific	100–1000	Variable	Machine learning-based system, AD-driven, disease adaptive

Source(s): Authors’ own work