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

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

Coordinate Based Random Effect Size meta-analysis of neuroimaging studies

1AbstractLow power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta‐ analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density.Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is vital to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely.</jats:p

Easy to interpret Coordinate Based Meta-Analysis of neuroimaging studies: Analysis of Brain Coordinates (ABC)

AbstractFunctional MRI and voxel-based morphometry (VBM) are important approaches to testing hypotheses in neuroscience, helping us to understand neurological disease, and brain function and development. However, they are technically challenging with no one optimal generalisable method, and the multiple popular techniques have been shown to produce different results. Furthermore, results may be sensitive to settings, such as smoothing or statistical thresholding, that can be difficult to optimise per hypothesis. It is useful, therefore, to be able to meta-analyse published results from such studies that tested a similar hypothesis potentially using different analysis methods, scanners, and protocols as well as different subjects. Coordinate based meta-analysis (CBMA) offers this using only commonly reported summary results. It is the aim of CBMA to find those results that indicate replicable effects across studies. However, just like the multiple analysis methods offered for neuroimaging, there are now multiple CBMA algorithms each with specific features and empirical parameters/assumptions. Results derived from CBMA are inevitably conditional on the algorithm used, so conclusions are clearer when the analysis approach is easy to understand. With this in mind a new CBMA method (Analysis of Brain Coordinates; ABC) is presented, with the aim of being easy to interpret by eliminating empirical assumptions where possible and by relating statistical thresholding directly to replication of effect.</jats:p

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

Enhancing emotion regulation with an in situ socially assistive robot among LGBTQ plus youth with self-harm ideation: protocol for a randomised controlled trial

INTRODUCTION: Purrble, a socially assistive robot, was codesigned with children to support in situ emotion regulation. Preliminary evidence has found that LGBTQ+ youth are receptive to Purrble and find it to be an acceptable intervention to assist with emotion dysregulation and their experiences of self-harm. The present study is designed to evaluate the impact of access to Purrble among LGBTQ+ youth who have self-harmful thoughts, when compared with waitlist controls. METHODS AND ANALYSIS: The study is a single-blind, randomised control trial comparing access to the Purrble robot with waitlist control. A total of 168 LGBTQ+ youth aged 16-25 years with current self-harmful ideation will be recruited, all based within the UK. The primary outcome is emotion dysregulation (Difficulties with Emotion Regulation Scale-8) measured weekly across a 13-week period, including three pre-deployment timepoints. Secondary outcomes include self-harm (Self-Harm Questionnaire), anxiety (Generalised Anxiety Disorder-7) and depression (Patient Health Questionnaire-9). We will conduct analyses using linear mixed models to assess primary and secondary hypotheses. Intervention participants will have unlimited access to Purrble over the deployment period, which can be used as much or as little as they like. After all assessments, control participants will receive their Purrble, with all participants keeping the robot after the end of the study. After the study has ended, a subset of participants will be invited to participate in semistructured interviews to explore engagement and appropriation of Purrble, considering the young people's own views of Purrble as an intervention device. ETHICS AND DISSEMINATION: Ethical approval was received from King's College London (RESCM-22/23-34570). Findings will be disseminated in peer review open access journals and at academic conferences. TRIAL REGISTRATION NUMBER: NCT06025942