Statistical parametric mapping of brain morphology: Sensitivity is dramatically increased by using brain-extracted images as inputs

Abstract

A major attraction of voxel-based morphometry (VBM) is that it allows researchers to explore large datasets with minimal human intervention. However, the validity and sensitivity of the Statistical Parametric Mapping (SPM2) approach to VBM is the subject of considerable debate. We visually inspected the SPM2 gray matter segmentations for 101 research participants and found a gross inclusion of non-brain tissue surrounding the entire brain as gray matter in five subjects, and focal areas bordering the brain in which non-brain tissue was classified as gray matter in many other subjects. We also found many areas in which the cortical grey matter was incorrectly excluded from the segmentation of the brain. The major source of these errors was the misregistration of individual brain images with the reference T1-weighted brain template. These errors could be eliminated if SPM2 operated on images from which non-brain tissues (scalp, skull, and meninges) are removed (brain-extracted images). We developed a modified SPM2 processing pipeline that used brainextracted images as inputs to test this hypothesis. We describe the modifications to the SPM2 pipeline that allow analysis of brain-extracted inputs. Using brain-extracted inputs eliminated the non-brain matter inclusions and the cortical gray matter exclusions noted above, reducing the residual mean square errors (RMSEs, the error term of the SPM2 statistical analyses) by over thirty percent. We show how this reduction in the RMSEs profoundly affects power analyses. SPM2 analyses of brainextracted images may require sample sizes only half as great as analyses of non-brain extracted images.

Pitfalls of Voxel-Based Amyloid PET Analyses for Diagnosis of Alzheimer’s Disease: Artifacts due to Non-Specific Uptake in the White Matter and the Skull

“…These AS errors were seen in 25% (14/56) of all subjects. Similar errors have been reported in the analysis of T 1 -weighted MRI using SPM2 and were eliminated by the removal of signals from non-brain tissues (Fein et al 2006). On the other hand, 3D-SSP is relatively insulated from the influence of such tracer distributions because it uses a number of landmarks for AS.…”

Brain atrophy in long-term abstinent alcoholics who demonstrate impairment on a simulated gambling task

“…SPM2, in its standard form implements voxel-based morphometry using T1-weighted brain images that include the scalp, skull and meninges as inputs, and incorporates a morphological clean-up step to remove 'non-brain' tissue. In a recent manuscript (Fein et al, 2006), we found that: 1) SPM2 does a poor job removing the non-brain tissue, 2) poor alignment of individual brains with the brain in the MNI template, resulting in an incorrect delineation of cortical gray matter, and 3) that this incorrect delineation of cortical gray matter dramatically increases the error term in the SPM2 analyses, reducing the sensitivity of standard SPM2 to detect experimental effects. We modified the SPM2 processing pipeline to accept skull-stripped inputs and demonstrated that the modification reduced the error term by about one third, with concomitant increases in the sensitivity to detect experimental effects.…”

“…We examined these brain regions, using T1-weighted MRIs, in the 101 participants from our previous study using voxel-based morphometry (VBM). VBM was performed using a modification we developed (Fein et al, 2006) of Baron's procedure, (Baron et al, 2001), in which we use skullstripped images as input. We also restricted the analysis to a ROI consisting of the amygdala and VMPFC as defined by the Talairach Daemon resource.…”

Accurate and robust extraction of brain regions using a deformable model based on radial basis functions

“…In the past decade, voxel-based morphometry (VBM) (Ashburner and Friston, 2000) has been extensively applied to statistically reveal regions with significant structural discrepancy between image groups (Good et al, 2001a, b;Beyer and Krishnan, 2002;Brenneis et al, 2003;Karas et al, 2003). Recent studies indicated that accurate brain extraction can improve the validity of VBM results because of better tissue segmentation and brain registration (Fein et al, 2006;Acosta-Cabronero et al, 2008).…”