A spatially constrained independent component analysis jointly informed by structural and functional network connectivity.

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images.

BACKGROUND: Brain tumor extraction from magnetic resonance (MR) images is challenging due to variations in the location, shape, size and intensity of tumors. Manual delineation of brain tumors from MR images is time-consuming and prone to human errors. METHOD: In this paper, we present a method for automatic tumor extraction from multimodal MR images. Brain tumors are first detected using k-means clustering. A morphological region-based active contour model is then used for tumor extraction using an initial contour defined based on the boundary of the detected brain tumor regions. The contour evolution for tumor extraction was performed using successive application of morphological operators. In our model, a Gaussian distribution was used to model local image intensities. The spatial correlation between neighboring voxels was also modeled using Markov random field. RESULTS: The proposed method was evaluated on BraTS 2013 dataset including patients with high-grade and low-grade tumors. In comparison with other active contour based methods, the proposed method yielded better performance on tumor segmentation with mean Dice similarity coefficients of 0.9179 ( ±â€¯0.025) and 0.8910 ( ±â€¯0.042) obtained on high-grade and low-grade tumors, respectively. CONCLUSION: The proposed method achieved higher accuracies for brain tumor extraction in comparison to other contour-based methods.

Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks.

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study.

Objective.Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data.Approach.A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100-900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales.Main results.Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus.Significance.Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.

Sexual Dimorphisms and Asymmetries of the Thalamo-Cortical Pathways and Subcortical Grey Matter of Term Born Healthy Neonates: An Investigation with Diffusion Tensor MRI.

Diffusion-tensor-MRI was performed on 28 term born neonates. For each hemisphere, we quantified separately the axial and the radial diffusion (AD, RD), the apparent diffusion coefficient (ADC) and the fractional anisotropy (FA) of the thalamo-cortical pathway (THC) and four structures: thalamus (TH), putamen (PT), caudate nucleus (CN) and globus-pallidus (GP). There was no significant difference between boys and girls in either the left or in the right hemispheric THC, TH, GP, CN and PT. In the combined group (boys + girls) significant left greater than right symmetry was observed in the THC (AD, RD and ADC), and TH (AD, ADC). Within the same group, we reported left greater than right asymmetry in the PT (FA), CN (RD and ADC). Different findings were recorded when we split the group of neonates by gender. Girls exhibited right > left AD, RD and ADC in the THC and left > right FA in the PT. In the group of boys, we observed right > left RD and ADC. We also reported left > right FA in the PT and left > right RD in the CN. These results provide insights into normal asymmetric development of sensory-motor networks within boys and girls.

Morphological active contour driven by local and global intensity fitting for spinal cord segmentation from MR images.

BACKGROUND: Spinal cord (SC) segmentation from magnetic resonance (MR) images can be used to study neurological disorders and facilitates group analysis. Variation of intensity inhomogeneity and small cross section of SC are difficulties that restrict automizing SC segmentation. NEW METHODS: In this paper we present a method for accurate SC segmentation from MR images. The proposed morphological local global intensity fitting model (MLGIF) is based on region based morphological active contour model that utilizes local and global information. The local information is obtained using local morphology fitting and has been embedded into region based active contour to deal with images intensity inhomogeneity and variable contrast levels between SC and the cerebrospinal fluid. The contour evolution has been performed using successive application of a set of morphological operators. RESULTS: The proposed method has been validated on 28 T1-weighted and 29 T2-weighted MR images and simulated MR images with different noise levels. Assessment of the results shows the accuracy of the proposed method for SC segmentation. COMPARISON TO EXISTING METHOD(S): The proposed MLGIF method was comparable with existing SC segmentation methods. Between segmented images and corresponding ground truth images, the mean DICE similarity coefficient, mean conformity coefficient and mean Hausdorff distance were 0.90 (092), 0.8 (0.83) and 0.85 mm (0.70 mm), respectively, for T1(T2)-weighted images. CONCLUSION: The MLGIF model was proposed to achieve a robust method to deal with intensity inhomogeneity and lack of contrast between SC and surrounding tissues. Moreover, accuracy and less sensitivity to initial curve were seen.