We propose a linear-elastic registration method to register diffusion-weighted MRI (DW-MRI) data sets by mapping their diffusion orientation distribution functions (ODFs). The ODFs were reconstructed using a q-ball imaging (QBI) technique to resolve intravoxel fiber crossing. The registration method is based on mapping the ODF maps represented by spherical harmonics which yield analytic solutions and reduce the computational complexity. ODF reorientation is required to maintain the consistency with transformed local fiber directions. The reorientation matrices are extracted from the local Jacobian and directly applied to the coefficients of spherical harmonics. The similarity cost of the registration is defined by the ODF shape distance calculated from the spherical harmonic coefficients. The transformation fields are regularized by linear elastic constraints. The proposed method was validated using both synthetic and real data sets. Experimental results show that the elastic registration improved the affine alignment by further reducing the ODF shape difference; reorientation during the registration produced registered ODF maps with more consistent principle directions compared to registrations without reorientation or simultaneous reorientation.
Non-rigid registration of diffusion MRI is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.
Diffusion MRI; orientation distribution function (ODF); spherical harmonics; ODF reorientation; registration; diffeomorphisms
Twist-related protein 1 (Twist1), also known as class A basic helix-loop-helix protein 38 (bHLHa38), has been implicated in cell lineage determination and differentiation. Previous studies demonstrate that Twist1 expression is up-regulated in gastric cancer with poor clinical outcomes. Besides, Twist1 is suggested to be involved in progression of human gastric cancer. However, its biological functions remain largely unexplored. In the present study, we show that Twist 1 overexpression leads to a significant up-regulation of FoxM1, which plays a key role in cell cycle progression in gastric cancer cells. In contrast, knockdown of Twist 1 reduces FoxM1 expression, suggesting that FoxM1 might be a direct transcriptional target of Twist 1. At the molecular level, we further reveal that Twist 1 could bind to the promoter region of FoxM1, and subsequently recruit p300 to induce FoxM1 mRNA transcription. Therefore, our results uncover a previous unknown Twist 1/FoxM1 regulatory pathway, which may help to understand the mechanisms of gastric cancer proliferation.
In the title compound, [CoCl(C10H7N3S)2]Cl·2H2O, the CoII atom is five-coordinated by four N atoms from two chelating 2-(1,3-thiazol-4-yl)-1H-benzimidazole ligands and one Cl atom in a distorted trigonal–bipyramidal geometry. In the crystal, N—H⋯O and O—H⋯Cl hydrogen bonds and π–π interactions between the thiazole, imidazole and benzene rings [centroid-to-centroid distances 3.546 (2), 3.683 (2) and 3.714 (2) Å] link the complex cations, chloride anions and uncoordinating water molecules into a three-dimensional network.
Preclinical models have consistently demonstrated the importance of the mesocorticolimbic (MCL) brain reward system in drug dependence, with critical molecular and cellular neuroadaptations identified within these structures following chronic cocaine administration. Cocaine dependent individuals manifest alterations in reward functioning that may relate to changes induced by cocaine or to pre-existing differences related to vulnerability to addiction. The circuit level manifestations of these drug-induced plastic changes and predispositions to drug dependence are poorly understood in preclinical models and virtually unknown in human drug dependence. Using whole-brain resting-state fMRI connectivity analysis with seed voxels placed within individual nodes of the MCL system, we report network-specific functional connectivity strength decreases in cocaine users within distinct circuits of the system, including between ventral tegmental area (VTA) and a region encompassing thalamus/lentiform nucleus/nucleus accumbens, between amygdala and medial prefrontal cortex (mPFC), and between hippocampus and dorsal mPFC. Further, regression analysis on regions showing significant functional connectivity decrease in chronic cocaine users revealed that the circuit strength between VTA and thalamus/lentiform nucleus/nucleus accumbens was negatively correlated with years of cocaine use. This is the first evidence of circuit-related changes in human cocaine dependence and is consistent with the range of cognitive and behavioral disruptions seen in cocaine dependence. As potential circuit level biomarkers of cocaine dependence, these circuit alterations may be usefully applied in treatment development and monitoring treatment outcome.
Estimating the effective signal dimension of resting-state functional MRI (fMRI) data sets (i.e., selecting an appropriate number of signal components) is essential for data-driven analysis. However, current methods are prone to overestimate the dimensions, especially for concatenated group data sets. This work aims to develop improved dimension estimation methods for group fMRI data generated by data reduction and grouping procedure at multiple levels. We proposed a “noise-blurring” approach to suppress intragroup signal variations and to correct spectral alterations caused by the data reduction, which should be responsible for the group dimension overestimation. This technique was evaluated on both simulated group data sets and in vivo resting-state fMRI data sets acquired from 14 normal human subjects during five different scan sessions. Reduction and grouping procedures were repeated at three levels in either “scan–session–subject” or “scan–subject–session” order. Compared with traditional estimation methods, our approach exhibits a stronger immunity against intragroup signal variation, less sensitivity to group size and a better agreement on the dimensions at the third level between the two grouping orders.
Data reduction; Data-driven analysis; Dimension estimation; Group fMRI; Resting state; Signal dimension
Most current automated segmentation methods are performed on T1- or T2-weighted MR images, relying on relative image intensity that is dependent on other MR parameters and sensitive to B1 magnetic field inhomogeneity. Here, we propose an image segmentation method based on quantitative longitudinal magnetization relaxation time (T1) of brain tissues. Considering the partial volume effect, fractional volume maps of brain tissues (white matter, gray matter, and cerebrospinal fluid) were obtained by fitting the observed signal in an inversion recovery procedure to a linear combination of three exponential functions, which represents the relaxations of each of the tissue types. A Look-Locker acquisition was employed to accelerate the acquisition process. The feasibility and efficacy of this proposed method were evaluated using simulations and experiments. The potential applications of this method in the study of neurological disease as well as normal brain development and aging are discussed.
magnetic resonance imaging; automated segmentation; brain tissue; partial volume effect; fractional volume; fast T1 mapping
Most MRI studies of Alzheimer's disease (AD) and frontotemporal dementia (FTD) have assessed structural, perfusion and diffusion abnormalities separately while ignoring the relationships across imaging modalities. This paper aimed to assess brain gray (GM) and white matter (WM) abnormalities jointly to elucidate differences in abnormal MRI patterns between the diseases. Twenty AD, 20 FTD patients, and 21 healthy control subjects were imaged using a 4 Tesla MRI. GM loss and GM hypoperfusion were measured using high-resolution T1 and arterial spin labeling MRI (ASL-MRI). WM degradation was measured with diffusion tensor imaging (DTI). Using a new analytical approach, the study found greater WM degenerations in FTD than AD at mild abnormality levels. Furthermore, the GM loss and WM degeneration exceeded the reduced perfusion in FTD whereas, in AD, structural and functional damages were similar. Joint assessments of multimodal MRI have potential value to provide new imaging markers for improved differential diagnoses between FTD and AD.
In the title compound, C15H10Cl2N4O2, the dichloropyrimidine and methoxyphenoxy parts are approximately perpendicular [dihedral angle = 89.9 (9)°]. The dihedral angle between the two pyrimidine rings is 36.3 (4)° In the crystal, there are no hydrogen bonds but the molecules are held together by short intermolecular C⋯N [3.206 (3) Å] contacts and C—H⋯π interactions.
The Zn atom in the title compound, [Zn(C7H5O2)2(C12H8N2)(H2O)], is five-coordinate in a distorted trigonal–bipyramidal coordination environment involving two O atoms of two monodentate benzoates, two N atoms of a 1,10-phenanthroline molecule and one O atom of a water molecule. The axial positions are occupied by a carboxylate O atom from the benzoate ligand and an N atom from the 1,10-phenanthroline ligand [N—Zn—O = 146.90 (7)°]. The water molecule forms an intramolecular O—H⋯O hydrogen bond; an intermolecular O—H⋯O hydrogen bond gives rise to a dimer.
In diffusion tensor imaging (DTI), interpreting changes in terms of fractional anisotropy (FA) and mean diffusivity or axial (D||) and radial (D⊥) diffusivity can be ambiguous. The main objective of this study was to gain insight into the heterogeneity of age-related diffusion changes in human brain white matter by analyzing relationships between the diffusion measures in terms of concordance and discordance instead of evaluating them separately, which is difficult to interpret. Fifty-one cognitively normal subjects (22–79 years old) were studied with DTI at 4 Tesla. Age was associated with widespread concordant changes of decreased FA and increased MD but in some regions significant FA reductions occurred discordant to MD changes. Prominent age-related FA reductions were primarily related to greater radial (D⊥) than axial (D||) diffusivity changes, potentially reflecting processes of demyelination. In conclusion, concordant/discordant changes of DTI indices provide additional characterization of white matter alterations that accompany normal aging.
Diffusion tensor imaging; Aging; Non-parametric analysis; Concordance; Discordance; Fractional anisotropy; Mean diffusivity; Axial diffusivity; Radial diffusivity; Demyelination
Group independent component analysis (gICA) was performed on resting-state data from 14 healthy subjects scanned on 5 fMRI scan sessions across 16 days. The data were reduced and aggregated in 3 steps using Principal Components Analysis (PCA, within scan, within session and across session) and subjected to gICA procedures. The amount of reduction was estimated by an improved method that utilizes a first-order autoregressive fitting technique to the PCA spectrum. Analyses were performed using all sessions in order to maximize sensitivity and alleviate the problem of component identification across session. Across-session consistency was examined by three methods, all using back-reconstruction of the single session or single subject/session maps from the grand (5 session) maps. The gICA analysis produced 55 spatially independent maps. Obvious artifactual maps were eliminated and the remainder were grouped based upon physiological recognizability. Biologically relevant component maps were found, including sensory, motor and a ‘default-mode’ map. All analysis methods showed that components were remarkably consistent across session. Critically, the components with the most obvious physiological relevance were the most consistent. The consistency of these maps suggests that, at least over a period of several weeks, these networks would be useful to follow longitudinal treatment-related manipulations.
default-mode; dimensionality; fMRI; longitudinal studies
Diffusion is an important mechanism for molecular transport in living biological tissues. Diffusion magnetic resonance imaging (dMRI) provides a unique probe to examine microscopic structures of the tissues in vivo, but current dMRI techniques usually ignore the spatio-temporal evolution process of the diffusive medium. In the present study, we demonstrate the feasibility to reveal the spatio-temporal diffusion process inside the human brain based on a numerical solution of the diffusion equation. Normal human subjects were scanned with a diffusion tensor imaging (DTI) technique on a 3-Tesla MRI scanner, and the diffusion tensor in each voxel was calculated from the DTI data. The diffusion equation, a partial-derivative description of Fick’s Law for the diffusion process, was discretized into equivalent algebraic equations. A finite-difference method was employed to obtain the numerical solution of the diffusion equation with a Crank-Nicholson iteration scheme to enhance the numerical stability. By specifying boundary and initial conditions, the spatio-temporal evolution of the diffusion process inside the brain can be virtually reconstructed. Our results exhibit similar medium profiles and diffusion coefficients as those of light fluorescence dextrans measured in integrative optical imaging experiments. The proposed method highlights the feasibility to non-invasively estimate the macroscopic diffusive transport time for a molecule in a given region of the brain.
diffusion equation; diffusion tensor imaging (DTI); numerical solution; spatio-temporal process