RESUMEN
Voxel based morphometry (VBM) is a widely utilized neuroimaging technique for spatially normalizing brain structural MRI (sMRI) onto a common template. The DARTEL technique of VBM takes into account the spatial intensity distribution of sMRIs to construct a study specific group template. The group template is then used to create final individual normalized tissue maps (FINTM) for each subject in the group. In this study, we investigate the effect of group on FINTM, i.e., we evaluate the variability of a constant subject's FINTM when other subjects in the group are iteratively changed. We examine this variability under the following scenarios: (1) when the demographics of the iterative groups are similar, (2) when the average age of the iterative groups is increased, and (3) when the number of subjects with a brain disorder (here we use subjects with autism) is increased. Our results show that when subject demographics of the group remains similar the mean standard deviation (SD) of FINTM gray matter (GM) of the constant subject was around 0.01. As the average age of the group is increased, mean SD of GM increased to around 0.03 and at certain brain locations variability was as high as 0.23. A similar increase in variability was observed when the number of autism subjects in the group was increased where mean SD was around 0.02. Further, we find that autism vs. control GM differences are in the range of -0.05 to +0.05 for more than 97% of the voxels and note that the magnitude of the differences are comparable to GM variability. Finally, we report that opting not to modulate during normalization or increasing the size of the smoothing kernel can decrease FINTM variability but at the loss of subject-specific features. Based on the findings of this study, we outline precautions that should be considered by investigators to reduce the impact of group on FINTM and thereby derive more meaningful group differences when comparing two cohorts of subjects.
RESUMEN
Previous studies applying automatic preprocessing methods on Structural Magnetic Resonance Imaging (sMRI) report inconsistent neuroanatomical abnormalities in Autism Spectrum Disorder (ASD). In this study we investigate inter-method differences as a possible cause behind these inconsistent findings. In particular, we focus on the estimation of the following brain volumes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and total intra cranial volume (TIV). T1-weighted sMRIs of 417 ASD subjects and 459 typically developing controls (TDC) from the ABIDE dataset were estimated using three popular preprocessing methods: SPM, FSL, and FreeSurfer (FS). Brain volumes estimated by the three methods were correlated but had significant inter-method differences; except TIVSPM vs. TIVFS, all inter-method differences were significant. ASD vs. TDC group differences in all brain volume estimates were dependent on the method used. SPM showed that TIV, GM, and CSF volumes of ASD were larger than TDC with statistical significance, whereas FS and FSL did not show significant differences in any of the volumes; in some cases, the direction of the differences were opposite to SPM. When methods were compared with each other, they showed differential biases for autism, and several biases were larger than ASD vs. TDC differences of the respective methods. After manual inspection, we found inter-method segmentation mismatches in the cerebellum, sub-cortical structures, and inter-sulcal CSF. In addition, to validate automated TIV estimates we performed manual segmentation on a subset of subjects. Results indicate that SPM estimates are closest to manual segmentation, followed by FS while FSL estimates were significantly lower. In summary, we show that ASD vs. TDC brain volume differences are method dependent and that these inter-method discrepancies can contribute to inconsistent neuroimaging findings in general. We suggest cross-validation across methods and emphasize the need to develop better methods to increase the robustness of neuroimaging findings.