Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

Effects of eight neuropsychiatric copy number variants on human brain structure

peer reviewedMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions. © 2021, The Author(s)

Effects of eight neuropsychiatric copy number variants on human brain structure

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions

Brain hemispheric structural efficiency and interconnectivity rightward asymmetry

To estimate white matter interregional axonal pathways and to infer left and right common anatomical network properties, obtaining global and local measures that allow us to evaluate structural network (dis)similarities between hemispheres, high-angular resolution DW-MRI datasets were acquired in 11 right-handed healthy subjects. T2-weighted images were parcellated into 90 gray matter structures. 3 axonal connectivity values were estimated using 3 fiber tractography algorithms: FSL, PICo, and a graph-based tractography algorithm. Whole-brain network was segmented into left and right hemispheric networks and analyzed in a graph framework: anatomic regions representing nodes and connections obtained from tractography representing arcs. Topological parameters of global efficiency, local efficiency, interconnectivity and betweenness centrality were extracted. Lateralization index was computed for these measures. We found significant differences between right and left hemispheric networks at a hemispheric level for the efficiency and interconnectivity metrics. Also, 21 pairs of human homolog regions were found lateralized according to centrality (15 leftward 6 rightward). These indicate either that the right hemisphere is, at the whole-hemisphere level, more efficient and interconnected and also that the left hemisphere presents more central or indispensable regions for the whole-brain structural network. A greater left hemisphere functional specialization could lead to its apparently ‘worse’ general structural organization. Results are in line with the fact that the left hemisphere has a leading role for highly demanding specific process (e.g.language and motor actions), whereas the right hemisphere has a leading role for more general process (e.g.integration tasks)

Converging patterns of aging-associated brain volume loss and tissue microstructure differences.

Given the worldwide increasing socioeconomic burden of aging-associated brain diseases, there is pressing need to gain in-depth knowledge about the neurobiology of brain anatomy changes across the life span. Advances in quantitative magnetic resonance imaging sensitive to brain's myelin, iron, and free water content allow for a detailed in vivo investigation of aging-related changes while reducing spurious morphometry differences. Main aim of our study is to link previous morphometry findings in aging to microstructural tissue properties in a large-scale cohort (n = 966, age range 46-86 y). Addressing previous controversies in the field, we present results obtained with different approaches to adjust local findings for global effects. Beyond the confirmation of age-related atrophy, myelin, and free water decreases, we report proportionally steeper volume, iron, and myelin decline in sensorimotor and subcortical areas paralleled by free water increase. We demonstrate aging-related white matter volume, myelin, and iron loss in frontostriatal projections. Our findings provide robust evidence for spatial overlap between volume and tissue property differences in aging that affect predominantly motor and executive networks

Main peaks in the 33-degrees phantom data.

<p>Main peaks extracted from the fiber ODFs estimated in the phantom data with inter-fiber angle equal to 33 degrees and Rician noise with a SNR = 15 are shown. Results are based on reconstructions using 200 iterations. Peaks are visualized as thin cylinders.</p

Reconstruction accuracy of RUMBA-SD and dRL-SD measured in phantoms with different volume fractions.

<p>Reconstruction accuracy of RUMBA-SD (blue color) and dRL-SD (red color) is shown in terms of the volume fraction of the smaller fiber bundle (upper panel) and the success rate (middle panel) in the 41 synthetic phantoms with inter-fiber angle equal to 70 degrees, using different volume fractions. The lower panel shows results similar to those depicted in the upper panel but considering only voxels where the two fiber bundles were detected. The discontinuous diagonal black line in the upper and lower panels represents the ideal result as a reference. The continuous coloured lines in each plot denote the mean values for each method. The semi-transparent coloured bands represent the values within one standard deviation to both sides of the mean. Results refer to the datasets with SNR = 15 and dictionary created with the true diffusivities.</p

Main peaks in the 45-degrees phantom data.

<p>Main peaks extracted from the fiber ODFs estimated in the phantom data with inter-fiber angle equal to 45 degrees and Rician noise with a SNR = 15 are shown. Results are based on reconstructions using 200 iterations. Peaks are visualized as thin cylinders.</p

Main peaks from the fiber ODFs estimated in the “HARDI Reconstruction Challenge 2013” phantom.

<p>Visualization of the main peaks extracted from the fiber ODFs reconstructed from the SMF-based data generated with SNR = 20 in a complex region of the “HARDI Reconstruction Challenge 2013” phantom. Results are based on reconstructions using 400 iterations. Peaks are visualized as thin cylinders.</p