Intensity Segmentation of the Human Brain with Tissue dependent Homogenization

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of the acquisition hardware. We propose an intensity segmentation which is free from any shape prior. It uses for the first time alternatively grey (GM) or white matter (WM) based homogenization. This new tissue dependency was introduced as the analysis of 60 high resolution MRI datasets revealed appreciable differences in the axial bias field corrections, depending if they are based on GM or WM. Homogenization starts with axial bias correction, a spatially irregular distortion correction follows and finally a noise reduction is applied. The construction of the axial bias correction is based on partitions of a depth histogram. The irregular bias is modelled by Moody Darken radial basis functions. Noise is eliminated by nonlinear edge preserving and homogenizing filters. A critical point is the estimation of the training set for the irregular bias correction in the GM approach. Because of intensity edges between CSF (cerebro spinal fluid surrounding the brain and within the ventricles), GM and WM this estimate shows an acceptable stability. By this supervised approach a high flexibility and precision for the segmentation of normal and pathologic brains is gained. The precision of this approach is shown using the Montreal brain phantom. Real data applications exemplify the advantage of the GM based approach, compared to the usual WM homogenization, allowing improved cortex segmentation

Dynamic models in fMRI

Most statistical methods for assessing activated voxels in fMRI experiments are based on correlation or regression analysis. In this context the main assumptions are that the baseline can be described by a few known basis-functions or variables and that the effect of the stimulus, i.e. the activation, stays constant over time. As these assumptions are in many cases neither necessary nor correct, a new dynamic approach that does not depend on those suppositions will be presented. This allows for simultaneous nonparametric estimation of the baseline as well as the time-varying effect of stimulation. This method of estimating the stimulus related areas of the brain furthermore provides the possibility of an analysis of the temporal and spatial development of the activation within an fMRI-experiment

Diffusion Tensor Imaging: on the assessment of data quality - a preliminary bootstrap analysis

In the field of nuclear magnetic resonance imaging, diffusion tensor imaging (DTI) has proven an important method for the characterisation of ultrastructural tissue properties. Yet various technical and biological sources of signal uncertainty may prolong into variables derived from diffusion weighted images and thus compromise data validity and reliability. To gain an objective quality rating of real raw data we aimed at implementing the previously described bootstrap methodology (Efron, 1979) and investigating its sensitivity to a selection of extraneous influencing factors. We applied the bootstrap method on real DTI data volumes of six volunteers which were varied by different acquisition conditions, smoothing and artificial noising. In addition a clinical sample group of 46 Multiple Sclerosis patients and 24 healthy controls were investigated. The response variables (RV) extracted from the histogram of the confidence intervals of fractional anisotropy were mean width, peak position and height. The addition of noising showed a significant effect when exceeding about 130% of the original background noise. The application of an edge-preserving smoothing algorithm resulted in an inverse alteration of the RV. Subject motion was also clearly depicted whereas its prevention by use of a vacuum device only resulted in a marginal improvement. We also observed a marked gender-specific effect in a sample of 24 healthy control subjects the causes of which remained unclear. In contrary to this the mere effect of a different signal intensity distribution due to illness (MS) did not alter the response variables

Is the Brain Cortex a Fractal?

The question is analysed if the human cerebral cortex is self similar in a statistical sense, a property which is usually referred to as being a fractal. The presented analysis includes all spatial scales from the brain size to the ultimate image resolution. Results obtained in two healthy volunteers show that the self similarity does take place down to the spatial scale of 2.5 mm. The obtained fractal dimensions read D=2.73±.05 and D=2.67±.05 correspondingly, which is in good agreement with previously reported results. The new calculational method is volumetric and is based on the fast Fourier Transform of segmented three dimensional high resolved magnetic resonance images. Engagement of FFT enables a simple interpretation of the results and achieves a high performance, which is necessary to analyse the entire cortex

Segmentierung des Gehirns auf der Basis von MR-Daten

Es wird ein Segmentierungsverfahren vorgestellt, das bei T1-gewichteten MR Aufnahmen Liquor, Cortex und weisse Materie trennt. Das Verfahren korrigiert in mehreren Schritten aufnahmetechnisch bedingte Artefakte und bestimmt die Substanzen durch 2 globale Schwellen. Das Verfahren erfordert an mehreren Stellen eine interaktive Justierung von Parametern und ist entsprechend flexibel

Graph Theoretic Analysis of Brain Connectomics in Multiple Sclerosis: Reliability and Relationship to Cognition

Research suggests that disruption of brain networks might explain cognitive deficits in multiple sclerosis (MS). The reliability and effectiveness of graph-theoretic network metrics as measures of cognitive performance were tested in 37 people with MS and 23 controls. Specifically, relationships to cognitive performance (linear regression against the Paced Auditory Serial Addition Test [PASAT-3], Symbol Digit Modalities Test [SDMT] and Attention Network Test [ANT]) and one-month reliability (using the intra-class correlation coefficient [ICC]) of network metrics were measured using both resting-state functional and diffusion MRI data. Cognitive impairment was directly related to measures of brain network segregation and inversely related to network integration (prediction of PASAT-3 by small-worldness, modularity, characteristic path length, R2=0.55; prediction of SDMT by small-worldness, global efficiency and characteristic path length, R2=0.60). Reliability of the measures over one month in a subset of 9 participants was mostly rated as good (ICC>0.6) for both controls and MS patients in both functional and diffusion data but was highly dependent on the chosen parcellation and graph density, with the 0.2-0.5 density range being the most reliable. This suggests that disrupted network organisation predicts cognitive impairment in MS and its measurement is reliable over a 1-month period. These new findings support the hypothesis of network disruption as a major determinant of cognitive deficits in MS and the future possibility of the application of derived metrics as surrogate outcomes in trials of therapies for cognitive impairment

Coordinate based random effect size meta-analysis of neuroimaging studies

Low 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 necessary 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

Altered connectivity of the right anterior insula drives the pain connectome changes in chronic knee osteoarthritis

Resting-state functional connectivity (FC) has proven a powerful approach to understand the neural underpinnings of chronic pain, reporting altered connectivity in three main networks: the default mode (DMN), central executive (CEN), and the salience network (SN). The interrelation and possible mechanisms of these changes are less well understood in chronic pain. Based on emerging evidence of its role to drive switches between network states, the right anterior insula (rAI, an SN hub) may play a dominant role in network connectivity changes underpinning chronic pain. To test this hypothesis, we used seed-based resting-state FC analysis including dynamic and effective connectivity metrics in 25 people with chronic osteoarthritis (OA) pain and 19 matched healthy volunteers. Compared to controls, participants with painful knee OA presented with increased anticorrelation between the right anterior insula (SN) and DMN regions. Also, the left dorsal prefrontal cortex (CEN hub) showed more negative FC with the right temporal gyrus. Granger causality analysis revealed increased negative influence of the right anterior insula on the posterior cingulate (DMN) in OA patients in line with the observed enhanced anticorrelation. Moreover, dynamic FC was lower in the DMN of patients and thus more similar to temporal dynamics of the SN. Together, these findings evidence a widespread network disruption in patients with persistent osteoarthritis pain, and point toward a driving role of the rAI

Carotid plaque hemorrhage on magnetic resonance imaging strongly predicts recurrent ischemia and stroke

Objective There is a recognized need to improve selection of patients with carotid artery stenosis for carotid endarterectomy (CEA). We assessed the value of magnetic resonance imaging (MRI)-defined carotid plaque hemorrhage (MRIPH) to predict recurrent ipsilateral cerebral ischemic events, and stroke in symptomatic carotid stenosis. Methods One hundred seventy-nine symptomatic patients with ≥50% stenosis were prospectively recruited, underwent carotid MRI, and were clinically followed up until CEA, death, or ischemic event. MRIPH was diagnosed if the plaque signal intensity was >150% that of the adjacent muscle. Event-free survival analysis was done using Kaplan–Meier plots and Cox regression models controlling for known vascular risk factors. We also undertook a meta-analysis of reported data on MRIPH and recurrent events. Results One hundred fourteen patients (63.7%) showed MRIPH, suffering 92% (57 of 62) of all recurrent ipsilateral events and all but 1 (25 of 26) future strokes. Patients without MRIPH had an estimated annual absolute stroke risk of only 0.6%. Cox multivariate regression analysis proved MRIPH as a strong predictor of recurrent ischemic events (hazard ratio [HR] = 12.0, 95% confidence interval [CI] = 4.8–30.1, p < 0.001) and stroke alone (HR = 35.0, 95% CI = 4.7–261.6, p = 0.001). Meta-analysis of published data confirmed this association between MRIPH and recurrent cerebral ischemic events in symptomatic carotid artery stenosis (odds ratio = 12.2, 95% CI = 5.5–27.1, p < 0.00001). Interpretation MRIPH independently and strongly predicts recurrent ipsilateral ischemic events, and stroke alone, in symptomatic ≥50% carotid artery stenosis. The very low stroke risk in patients without MRIPH puts into question current risk–benefit assessment for CEA in this subgroup

In vivo assessment of brainstem depigmentation in Parkinson’s: potential as severity marker for multi-centre studies

Purpose: To investigate the pattern of neuromelanin signal intensity loss within the substantia nigra pars compacta (SNpc), locus coeruleus, and ventral tegmental area in Parkinson disease (PD); the specific aims were (a) to study regional magnetic resonance (MR) quantifiable depigmentation in association with PD severity and (b) to investigate whether imaging- and platform-dependent signal intensity variations can be normalized. Materials and Methods: This prospective case-control study was approved by the local ethics committee and the research department of Nottingham University Hospitals. Written informed consent was obtained from all participants before enrollment in the study. Sixty-nine participants (39 patients with PD and 30 control subjects) were investigated with neuromelanin-sensitive MR imaging by using two different 3-T platforms and three differing protocols. Neuromelanin-related volumes of the anterior and posterior SNpc, locus coeruleus, and ventral tegmental area were determined, and normalized neuromelanin volumes were assessed for protocol-dependent effects. Diagnostic test performance of normalized neuromelanin volume was investigated by using receiver operating characteristic analyses, and correlations with the Unified Parkinson’s Disease Rating Scale scores were tested. Results: Reduction of normalized neuromelanin volume in PD was most pronounced in the posterior SNpc (median, −83%; P < .001), followed by the anterior SNpc (−49%; P < .001) and the locus coeruleus (−37%; P < .05). Normalized neuromelanin volume loss of the posterior and whole SNpc allowed the best differentiation of patients with PD and control subjects (area under the receiver operating characteristic curve, 0.92 and 0.88, respectively). Normalized neuromelanin volume of the anterior, posterior, and whole SNpc correlated with Unified Parkinson’s Disease Rating Scale scores (r2 = 0.25, 0.22, and 0.28, respectively; all P < .05). Conclusion: PD-induced neuromelanin loss can be quantified across imaging protocols and platforms by using appropriate adjustment. Depigmentation in PD follows a distinct spatial pattern, affords high diagnostic accuracy, and is associated with disease severity