Integrating Functional and Diffusion Magnetic Resonance Imaging for Analysis of Structure-Function Relationship in the Human Language Network

The capabilities of magnetic resonance imaging (MRI) to measure structural and functional connectivity in the human brain have motivated growing interest in characterizing the relationship between these measures in the distributed neural networks of the brain. In this study, we attempted an integration of structural and functional analyses of the human language circuits, including Wernicke's (WA), Broca's (BA) and supplementary motor area (SMA), using a combination of blood oxygen level dependent (BOLD) and diffusion tensor MRI.Functional connectivity was measured by low frequency inter-regional correlations of BOLD MRI signals acquired in a resting steady-state, and structural connectivity was measured by using adaptive fiber tracking with diffusion tensor MRI data. The results showed that different language pathways exhibited different structural and functional connectivity, indicating varying levels of inter-dependence in processing across regions. Along the path between BA and SMA, the fibers tracked generally formed a single bundle and the mean radius of the bundle was positively correlated with functional connectivity. However, fractional anisotropy was found not to be correlated with functional connectivity along paths connecting either BA and SMA or BA and WA. for use in diagnosing and determining disease progression and recovery

Fuzzy anatomical connectedness of the brain using single and multiple fibre orientations estimated from diffusion MRI.

A new fuzzy algorithm for assessing white matter connectivity in the brain using diffusion-weighted magnetic resonance images is presented. The proposed method considers anatomical paths as chains of linked neighbouring voxels. Links between neighbours are assigned weights using the respective fibre orientation estimates. By checking all possible paths between any two voxels, a connectedness value is assigned, representative of the weakest link of the strongest path connecting the voxel pair. Multiple orientations within a voxel can be incorporated, thus allowing the utilization of fibre crossing information, while fibre branching is inherently considered. Under the assumption that paths connected strongly to a seed will exhibit adequate orientational coherence, fuzzy connectedness values offer a relative measure of path feasibility. The algorithm is validated using simulations and results are shown on diffusion tensor and Q-ball images

In-vivo brain anatomical connectivity using diffusion magnetic resonance imaging and fuzzy connectedness.

Diffusion-weighted (DW) magnetic resonance imaging is the only non-invasive and in-vivo method available for studying brain white matter anatomical connectivity. Tractography algorithms have been developed to reconstruct neuronal tracts utilizing DW images. In this study, a new tractography method is presented. This is based on a fuzzy framework, as suggested by the intrinsic fuzzy nature of medical images. The proposed technique checks all possible paths -defined on the discrete image grid- between any pair of voxels and assigns a connectivity value, representative of the strength of the strongest path. Path branching, which is not well captured by binary streamline techniques, is inherently considered. Compared to other distributed tractography approaches, our method combines a) converged connectivity values for all image voxels, b) connectivities that do not drop systematically with the distance from the seed, c) path propagation with relatively high angular resolution and d) fast execution times. Results are shown on both simulated and real images, where predicted tracts agree well with a-priori anatomical knowledge

Robust Graph-Based Tracking Through Crossing Fibre Configurations.

Graph-based distributed tractography of the brain provides an alternative to streamline approaches. However, graph-based tracking through complex fibre configurations has not been extensively studied and existing methods have inherent limitations. In this study, we discuss these limitations and present a new approach for robustly propagating through fibre crossings, as these are depicted by the Q-ball orientation distribution functions (ODFs). Complex ODFs are decomposed to components representative of single-fibre populations and an appropriate image graph is created. Path strengths are calculated using a modified version of Dijkstra's shortest path algorithm. A comparison with existing methods is performed on simulated and on human Q-ball imaging data. © 2009 IEEE

Brain tractography using Q-ball imaging and graph theory: Improved connectivities through fibre crossings via a model-based approach.

Brain tractography techniques utilize a set of diffusion-weighted magnetic resonance images to reconstruct white matter tracts, non-invasively and in-vivo. Streamline tracking techniques propagate curves from a seed to imply connectivity to distal voxels. Alternative approaches have been developed that attempt to quantify connection strength between all voxels and the seed. Tractography based on graph theory is amongst them. Despite its potential, graph-based tracking through complex fibre configurations has not been extensively studied and existing methods have inherent limitations. Anatomically unlikely connections may be identified in fibre crossing regions, by assigning relatively high connection strengths to all crossing populations. In this study, we discuss these limitations and present a new approach for robustly propagating through fibre crossings, as described by the orientation distribution functions (ODFs) derived from Q-ball imaging. Each image voxel is treated as a graph node and graph arcs connect neighbouring voxels. Weights representative of both structural and diffusivity features are assigned to each arc. To account for the existence of crossing fibre populations within a voxel, complex ODFs are decomposed into components representative of single-fibre populations and an image multigraph is created. The multigraph is searched exhaustively, but efficiently, to find the strongest paths and assign connectivity strengths between a seed and all the other image voxels. A comparison with the existing graph-based tractography as well as Q-ball driven front evolution tractography is performed on simulated data, and on human Q-ball imaging data acquired from five healthy volunteers. The new approach improves the connection strengths through fibre crossing regions, reducing the strengths of paths that are less anatomically plausible

A regularized two-tensor model fit to low angular resolution diffusion images using basis directions.

PURPOSE: To resolve and regularize orientation estimates for two crossing fibers from images acquired with conventional diffusion tensor imaging (DTI) sampling schemes. MATERIALS AND METHODS: Partial volume causes artifacts in DTI. Given that routine use of high angular resolution diffusion imaging (HARDI) is still tentative, a regularized two-tensor model to resolve fiber crossings from conventional DTI datasets is presented. To overcome the problems of fitting multiple tensors, a model that exploits the planar diffusion profile in regions with fiber crossings is utilized. A regularization scheme is applied to reduce noise artifacts, which can be significant due to the relatively low number of acquired images. A set of basis directions is used to convert the two tensor model to many models of lower dimensionality. Relaxation labeling is utilized to select from amongst these models those that preserve continuity of orientations across neighbors. Revised fractional anisotropy (FA) and mean diffusivity (MD) values are computed. RESULTS: Spatial regularization improves the orientation estimates of the two-tensor model in simulations and in human data and estimates agree well with a priori anatomical knowledge. CONCLUSION: Orientational, anisotropy, and diffusivity information can be resolved in regions of two fiber crossings using full brain coverage scans acquired in less than six minutes

Reduced EDSS progression in multiple sclerosis patients treated with modafinil for three years or more compared to matched untreated subjects

Background: Modafinil is a wakefulness-promoting drug used to treat narcolepsy, obstructive sleep apnoea, and shift-work sleep disorder. Modafinil has also been used for the treatment of fatigue and excessive sleepiness in other neurological disorders including multiple sclerosis, psychiatric disorders, and for cognitive enhancement. Recent preclinical studies suggest a potential neuroprotective effect of modafinil in neurodegenerative diseases. Therefore, we investigated its neuroprotective potential in multiple sclerosis. Objective: To retrospectively assess disease progression in a group of MS patients that had received treatment with modafinil, and a matched group that received no treatment with modafinil. Methods: We assessed the expanded disability status scale (EDSS) score change, over at least three years, in 30 patients with MS treated with modafinil, and in 90 patients who did not receive modafinil. The two groups were matched for initial EDSS, age, sex, type of disease, disease duration, duration of follow-up, and concomitant disease modifying therapies. Statistical analysis was performed using a general linear regression model. Results: In relapsing-remitting (RR) patients treated with modafinil there was no significant EDSS change over the follow-up period. In RR patients not treated with modafinil, the mean EDSS increased significantly (0.94; p=0.0001) over the follow-up period. Independent of modafinil treatment status, our model indicated an additional mean EDSS increase of 1.1 point (p=0.0002) for progressive patients i.e. mean EDSS change was 1.1 point for modafinil treated, and 1.10.94=2.04 points for modafinil-untreated patients. Conclusion: Our results support the hypothesis that modafinil has neuroprotective potential, and may play a role in the treatment of multiple sclerosis. A prospective study will need to confirm this finding. © 2012 Elsevier B.V

Ten simple rules for neuroimaging meta-analysis

Neuroimaging has evolved into a widely used method to investigate the functional neuroanatomy, brain-behaviour relationships, and pathophysiology of brain disorders, yielding a literature of more than 30,000 papers. With such an explosion of data, it is increasingly difficult to sift through the literature and distinguish spurious from replicable findings. Furthermore, due to the large number of studies, it is challenging to keep track of the wealth of findings. A variety of meta-analytical methods (coordinate-based and image-based) have been developed to help summarise and integrate the vast amount of data arising from neuroimaging studies. However, the field lacks specific guidelines for the conduct of such meta-analyses. Based on our combined experience, we propose best-practice recommendations that researchers from multiple disciplines may find helpful. In addition, we provide specific guidelines and a checklist that will hopefully improve the transparency, traceability, replicability and reporting of meta-analytical results of neuroimaging data

Novel Morphological and Appearance Features for Predicting Physical Disability from MR Images in Multiple Sclerosis Patients

Abstract Physical disability in patients with multiple sclerosis is determined by functional ability and quantified with numerical scores. In vivo studies using magnetic resonance imaging (MRI) have found that these scores correlate with spinal cord atrophy (loss of tissue), where atrophy is commonly measured by spinal cord volume or cross-sectional area. However, this correlation is generally weak to moderate, and improved measures would strengthen the utility of imaging biomarkers. We propose novel spinal cord morphological and MRI-based appearance features. Select features are used to train regression models to predict patients ’ physical disability scores. We validate our models using 30 MRI scans of different patients with varying levels of disability. Our results suggest that regression models trained with multiple spinal cord features predict clinical disability better than a model based on the volume of the spinal cord alone.