FreeSurfer volumetric approaches and SPM statistical parametric mapping use different algorithms for brain analysis
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The listed sources discuss brain morphometry and software packages like SPM and FSL, but do not directly compare the specific algorithms of FreeSurfer and SPM.
Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.
Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T1-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmen
Abstract Background Processing of large‐scale multimodal MRI and PET data is crucial for advancing Alzheimer's disease (AD) neuroimaging research. However, image processing requires strong expertise on different software packages (SPM/FSL/AFNI/FreeSurfer) and programming languages (R/MATLAB/Python/Bash) and lab‐specific image processing approaches are a major roadblock for harmonization across sites. Therefore, establishing uniform and user‐friendly image processing workflows is crucial for inter‐site standardization and harmonization of neuroimaging data and to reduce bias introduced by different preprocessing strategies. Therefore, we developed the containerized, state‐of‐the‐art, fully automated, neuroimaging toolbox ADprep that integrates robust preprocessing of structural/functional MRI, and multi‐tracer PET (amyloid/tau/FDG/TSPO), generating standardized nifti and atlas‐based spreadsheet outputs across a broad range of brain atlases. ADprep requires no programming expertise and can facilitate harmonized neuroimaging analyses and data sharing across the AD neuroimaging community and will be fully integrated into the cloud‐based GRIP platform. Methods ADprep works on bids‐formatted data and was fully developed in nipype (Figure 1). Preprocessing for structural MRI includes volumetric and cortical thickness assessments for widely used brain atlases (Desikan‐Killiany/Schaefer100‐600/LPBA/Hammers/Neuromorphometrics/Cobra/Destrieux), plus spatially normalized and smoothed tis
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