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Improved gray matter surface based spatial statistics in neuroimaging studies

Posted by on Tuesday, May 21, 2019 in Diffusion Weighted MRI, Image Processing, Magnetic resonance imaging, Neuroimaging.

Prasanna Parvathaneni; Ilwoo Lyu; Yuankai Huo; Baxter P. Rogers; Kurt G. Schilling; Vishwesh Nath; Justin A Blaber; Allison E Hainline; Adam W Anderson; Neil D. Woodward; Bennett A Landman. “Improved gray matter surface based spatial statistics in neuroimaging studies.” Magnetic Resonance Imaging, 61, 285-295, 2019.

Neuroimaging often involves acquiring high-resolution anatomical images along with other low-resolution image modalities, like diffusion and functional magnetic resonance imaging. Performing gray matter statistics with low-resolution image modalities is a challenge due to registration artifacts and partial volume effects. Gray matter surface based spatial statistics (GS-BSS) has been shown to provide higher sensitivity using gray matter surfaces compared to that of skeletonization approach of gray matter based spatial statistics which is adapted from tract based spatial statistics in diffusion studies. In this study, we improve upon GS-BSS incorporating neurite orientation dispersion and density imaging (NODDI) based search (denoted N-GSBSS) by 1) enhancing metrics mapping from native space, 2) incorporating maximum orientation dispersion index (ODI) search along surface normal, and 3) proposing applicability to other modalities, such as functional MRI (fMRI). We evaluated the performance of N-GSBSS against three baseline pipelines: volume-based registration, FreeSurfer’s surface registration and ciftify pipeline for fMRI and simulation studies. First, qualitative mean ODI results are shown for N-GSBSS with and without NODDI based search in comparision with ciftify pipeline. Second, we conducted one-sample t-tests on working memory activations in fMRI to show that the proposed method can aid in the analysis of low resolution fMRI data. Finally we performed a sensitivity test in a simulation study by varying percentage change of intensity values within a region of interest in gray matter probability maps. N-GSBSS showed higher sensitivity in the simulation test compared to the other methods capturing difference between the groups starting at 10 percent change in the intensity values. The computational time of N-GSBSS is 68 times faster than that of traditional surface-based or 86 times faster than that of ciftify pipeline analysis.

Flowchart of the N-GSBSS data processing for each subject. (1) The central surface is reconstructed via MaCRUISE (red) (2) and transformed to the MNI space (yellow) using ANTs volume registration. (3) These volumes are diffeomorphically registered to a single target surface. (4) Metrics of interest in other modalities are co-registered to corresponding anatomical T1-weighted image. (5) Cortical ODI search is performed using ODI and Viso from NODDI metrics to search for higher ODI excluding Viso within a given range (6) Data are processed for each modality (NODDI for diffusion microstructure and first level analysis for working memory tasks) to derive metrics of interest for cross-sectional analysis. (7) Metrics of interest are mapped onto the individual surface. (8) The mappings from shape correspondence are used to project intensity values of metrics of interest to the target surface (blue). (9) Vertex-wise spatial statistics on all projected data are performed on the target surface.


Working memory fMRI data were processed for 28 healthy controls and results are reported for (a) correct cue, (b) correct delay (c) correct probe tasks with 2 mm smoothing for VBR, SBR, ciftify, N-GSBSS -S0 with no search and N-GSBSS-S2 with 2mm search methods. Significant p-values after FWE correction based on non parametric randomize one sample t-test with 10000 iterations are reported. Pfwe <0.05 are highlighted in red.