Inter-Method Inconsistencies of Brain Volume Estimation and Inter-Site Variability in Autism Gajendra Jung Katuwal1,2, Nathan Cahill2,3, Chase Dougherty1, Eli Evans1, Stefi Baum2,4, Greg Moore1, Andrew Michael1,2,5 1Autism and Developmental Brain Medicine Institute, Geisinger Health System (GHS); 2Center for Imaging Science, Rochester Institute of Technology (RIT); 3School of Mathematical Sciences, (RIT); 4Faculty of Science, University of Manitoba, Canada; 5Institute of Advanced Application, GHS
Inter-method Inconsistencies
Introduction Automated software tools (ASTs) are increasingly applied in neuroimaging studies to preprocess structural MRI (sMRI) data to investigate associations between brain morphometry and brain disorders ASTs are more objective than manual tracing but have inherent method specific bias and variance Previous studies have shown inter-method inconsistencies [2,3] This bias and variance may arise due to the use of different image processing algorithms, normalization templates and other prior information In this work we investigate on how different ASTs change the estimation of the following brain volumes: total intracranial volume (TIV), gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) We also investigate how autism spectrum disorder (ASD) vs. typically developing control (TDC) group differences in brain volumes vary with site and AST Algorithmic reasons for the inter-AST inconsistencies in brain volume estimation are explored
TIV estimation Comparison (without regression)
TIV estimation Comparison (TIVFSL estimated using method same as that of FS)
Method Data: 417 patients with ASD & 459 TDC from ABIDE dataset [1] Preprocessing: SPM, FSL & FS were applied to calculate the aggregate brain features: TIV, GM, WM and CSF volumes TIV Calculation SPM & FSL: TIV = GM +WM + CSF volumes in native space FreeSurfer: estimated TIV (eTIV) = K/det(T); where K is a scaling constant derived from the atlas mask volume and T is affine transformation matrix that takes native space image to MNI space Statistical Analyses Age, sex. IQ measures and site (only for all sites) were regressed out ASD vs. TDC two sample t-test p-values were calculated for each site as well as across all sites on corrected TIV
Distribution of the brain volumes estimates Figure A: Inter-method inconsistencies in brain volumes estimation
Figure B: Standard deviation of TIV estimates for FreeSurfer and ASD group were generally higher
Inter-method inconsistencies A(i)
A(ii)
SPM bias towards TIVASD B(i)
B(ii)
C(i)
C(ii)
Segmentation Discrepancies
D(i)
GM and WM segmentation discrepancies were mainly observed in cerebellum, subcortical structures and intertissue borders CSF segmentation discrepancies were higher in inter-sulcal region and the surrounding region of the brain.
D(ii)
Brain volumes produced by the three ASTs did not agree and differing inter-method trends were observed in the estimated brain volumes Inter- method correlation coefficients of estimated volumes across all subjects were low (Table 1) The direction of significant group difference changed with site even using the same AST (e.g. WM in CALTECH & UCLA) The direction of group difference changed with AST even within a single site (e.g. TIV in YALE). ALL sites: SPM: TIVASD >*TIVTDC, FSL: TIVASD TIVTDC Individual site preprocessing method agreement / disagreement In 5 sites all three methods agreed TIVASD > TIVTDC In 2 sites all three methods agreed TIVASD < TIVTDC In 8 sites at least one method disagreed on directionality The largest group difference in TIV was 0.03L while the inter-method difference in TIV was as large as 0.15L. Across all subjects, only the group difference in TIV, GM and CSF estimate by SPM were statistically significant whereas all inter method differences were significant except TIVSPM vs. TIVFS In summary, across all subjects, TIV_SPM > TIV_FS > TIV_FSL, GM_SPM > GM_FS > GM_FSL, WM_FSL > WM_SPM > WM_FS and CSF_SPM > CSF_FS
..
Conclusion One of the more consistent findings in ASD is that ASD patients have larger TIV relative to TDC. Results in this study indicate that even this aggregate brain feature is site dependent and preprocessing method dependent. Sub-disorder analyses and better methods are needed to find robust brain biomarkers.
References [1] http://fcon_1000.projects.nitrc.org/indi/abide/ [2] K. a Clark, R. P. Woods, D. a Rottenberg, A. W. Toga, and J. C. Mazziotta, “Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images.,” Neuroimage, vol. 29, no. 1, pp. 185–202, Jan. 2006. [3] F. Klauschen, A. Goldman, V. Barra, A. Meyer-Lindenberg, and A. Lundervold, “Evaluation of automated brain MR image segmentation and volumetry methods.,” Hum. Brain Mapp., vol. 30, no. 4, pp. 1310–27, Apr. 2009.
[email protected]
