FreeSurfer is a suite of tools for analyzing neuroimaging data, providing algorithms to quantify the functional, connectional, and structural properties of the human brain. Initially developed for generating surface representations of the cerebral cortex, it now automatically creates models of most macroscopically visible brain structures from T1-weighted MRI images. FreeSurfer is freely available, runs on various platforms, and is open source. It includes tools for volumetric segmentation, cortical folding pattern parcellation, white matter fascicle segmentation using diffusion MRI, and estimation of architectonic boundaries. The software also maps cortical gray matter thickness and constructs surface models of the cerebral cortex. The development of FreeSurfer was motivated by the need for accurate surface models to solve the EEG/MEG inverse problem, which involves reconstructing brain currents from measured electromagnetic signals. The surface models provided constraints that improved the accuracy of the inverse solution. Early attempts to construct surface models faced challenges due to the difficulty of accurately representing the pial surface, leading to topological defects. Anders Dale and Marty Sereno's work provided a solution by focusing on the gray/white boundary, which is easier to model due to greater spacing between adjacent cortical banks. FreeSurfer's surface deformation techniques allow for accurate modeling of the gray/white and pial surfaces, accounting for tissue variability and acquisition artifacts. The software also includes whole-brain segmentation, enabling the labeling of voxels into semantically meaningful classes such as hippocampus, amygdala, and thalamus. This was achieved using a Bayesian approach that incorporated realistic image likelihood terms and sophisticated prior models. FreeSurfer has evolved significantly since its inception, incorporating features such as automated tractography, cross-modal intra-subject registration, and longitudinal analysis. It has been used to study various neurological disorders, the genetic basis of neuroanatomical variability, and healthy development and aging. The software is widely used in research and clinical settings, with ongoing improvements and extensions to enhance its capabilities. FreeSurfer's open-source nature allows for broad use and adaptation, making it a valuable tool in neuroimaging research.FreeSurfer is a suite of tools for analyzing neuroimaging data, providing algorithms to quantify the functional, connectional, and structural properties of the human brain. Initially developed for generating surface representations of the cerebral cortex, it now automatically creates models of most macroscopically visible brain structures from T1-weighted MRI images. FreeSurfer is freely available, runs on various platforms, and is open source. It includes tools for volumetric segmentation, cortical folding pattern parcellation, white matter fascicle segmentation using diffusion MRI, and estimation of architectonic boundaries. The software also maps cortical gray matter thickness and constructs surface models of the cerebral cortex. The development of FreeSurfer was motivated by the need for accurate surface models to solve the EEG/MEG inverse problem, which involves reconstructing brain currents from measured electromagnetic signals. The surface models provided constraints that improved the accuracy of the inverse solution. Early attempts to construct surface models faced challenges due to the difficulty of accurately representing the pial surface, leading to topological defects. Anders Dale and Marty Sereno's work provided a solution by focusing on the gray/white boundary, which is easier to model due to greater spacing between adjacent cortical banks. FreeSurfer's surface deformation techniques allow for accurate modeling of the gray/white and pial surfaces, accounting for tissue variability and acquisition artifacts. The software also includes whole-brain segmentation, enabling the labeling of voxels into semantically meaningful classes such as hippocampus, amygdala, and thalamus. This was achieved using a Bayesian approach that incorporated realistic image likelihood terms and sophisticated prior models. FreeSurfer has evolved significantly since its inception, incorporating features such as automated tractography, cross-modal intra-subject registration, and longitudinal analysis. It has been used to study various neurological disorders, the genetic basis of neuroanatomical variability, and healthy development and aging. The software is widely used in research and clinical settings, with ongoing improvements and extensions to enhance its capabilities. FreeSurfer's open-source nature allows for broad use and adaptation, making it a valuable tool in neuroimaging research.