Search results

Relationships of brain white matter microstructure with clinical and MR measures in relapsing-remitting multiple sclerosis.

30 March 2018

PURPOSE: To assess the relationships of microstructural damage in the cerebral white matter (WM), as measured by diffusion tensor imaging (DTI), with clinical parameters and magnetic resonance imaging (MRI) measures of focal tissue damage in patients with multiple sclerosis (MS). MATERIALS AND METHODS: Forty-five relapsing-remitting (RR) MS patients (12 male, 33 female; median age = 29 years, Expanded Disability Status Scale (EDSS) = 1.5, disease duration = 3 years) were studied. T2-lesion masks were created and voxelwise DTI analyses performed with Tract-Based Spatial Statistics (TBSS). RESULTS: T2-lesion volume (T2-LV) was significantly (P < 0.05, corrected) correlated with fractional anisotropy (FA) in both lesions and normal-appearing WM (NAWM). Relationships (P = 0.08, corrected) between increasing EDSS score and decreasing FA were found in the splenium of the corpus callosum (sCC) and along the pyramidal tract (PY). All FA associations were driven by changes in the perpendicular (to primary tract direction) diffusivity. No significant global and voxelwise FA changes were found over a 2-year follow-up. CONCLUSION: FA changes related to clinical disability in RR-MS patients with minor clinical disability are localized to specific WM tracts such as the sCC and PY and are driven by changes in perpendicular diffusivity both within lesions and NAWM. Longitudinal DTI measurements do not seem able to chart the early disease course in the WM of MS patients.

Investigation of white matter pathology in ALS and PLS using tract-based spatial statistics.

30 March 2018

OBJECTIVE: We aimed to investigate differences in fractional anisotropy (FA) between primary lateral sclerosis (PLS) and amyotrophic lateral sclerosis (ALS) and the relationship between FA and disease progression using tract-based spatial statistics (TBSS). METHODS: Two scanners at two different sites were used. Differences in FA between ALS patients and controls scanned in London were investigated. From the results of this analysis, brain regions were selected to test for (i) differences in FA between controls, patients with ALS and patients with PLS scanned in Oxford and (ii) the relationship between FA and disease progression rate in the Oxford patient groups. RESULTS: London ALS patients showed a lower FA than controls in several brain regions. Oxford patients with PLS showed a lower FA than ALS patients and than controls in the body of the corpus callosum and in the white matter adjacent to the right primary motor cortex (PMC), while ALS patients showed reduced FA compared with PLS patients in the white matter adjacent to the superior frontal gyrus. Significant correlations were found between disease progression rate and (i) FA in the white matter adjacent to the PMC in PLS, and (ii) FA along the cortico-spinal tract and in the body of the corpus callosum in ALS. CONCLUSIONS: We described significant FA changes between PLS and ALS, suggesting that these two presentations of motor neuron disease show different features. The significant correlation between FA and disease progression rate in PLS suggests the tissue damage reflected in FA changes contributes to the disease progression rate.

Relating functional changes during hand movement to clinical parameters in patients with multiple sclerosis in a multi-centre fMRI study.

30 March 2018

We performed a prospective multi-centre study using functional magnetic resonance imaging (fMRI) to better characterize the relationships between clinical expression and brain function in patients with multiple sclerosis (MS) at eight European sites (56 MS patients and 60 age-matched, healthy controls). Patients showed greater task-related activation bilaterally in brain regions including the pre- and post-central, inferior and superior frontal, cingulate and superior temporal gyri and insula (P < 0.05, all statistics corrected for multiple comparisons). Both patients and healthy controls showed greater brain activation with increasing age in the ipsilateral pre-central and inferior frontal gyri (P < 0.05). Patients, but not controls, showed greater brain activation in the anterior cingulate gyrus and the bilateral ventral striatum (P < 0.05) with less hand dexterity. An interaction between functional activation changes in MS and age was found. This large fMRI study over a broadly selected MS patient population confirms that movement for patients demands significantly greater cognitive 'resource allocation' and suggests age-related differences in brain responses to the disease. These observations add to evidence that brain functional responses (including potentially adaptive brain plasticity) contribute to modulation of clinical expression of MS pathology and demonstrate the feasibility of a multi-site functional MRI study of MS.

Fully Bayesian spatio-temporal modeling of FMRI data.

30 March 2018

We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).

Probabilistic independent component analysis for functional magnetic resonance imaging.

4 April 2018

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

Imaging how attention modulates pain in humans using functional MRI.

4 April 2018

Current clinical and experimental literature strongly supports the phenomenon of reduced pain perception whilst attention is distracted away from noxious stimuli. This study used functional MRI to elucidate the underlying neural systems and mechanisms involved. An analogue of the Stroop task, the counting Stroop, was used as a cognitive distraction task whilst subjects received intermittent painful thermal stimuli. Pain intensity scores were significantly reduced when subjects took part in the more cognitively demanding interference task of the counting Stroop than in the less demanding neutral task. When subjects were distracted during painful stimulation, brain areas associated with the affective division of the anterior cingulate cortex (ACC) and orbitofrontal regions showed increased activation. In contrast, many areas of the pain matrix (i.e. thalamus, insula, cognitive division of the ACC) displayed reduced activation, supporting the behavioural results of reduced pain perception.

Monitoring disease activity and progression in primary progressive multiple sclerosis using MRI: sub-voxel registration to identify lesion changes and to detect cerebral atrophy.

30 March 2018

OBJECTIVE: To explore the potential usefulness of two new magnetic resonance imaging (MRI) analysis techniques for assessment of progressive cerebral atrophy and T2 lesion activity in primary progressive multiple sclerosis (PPMS), and thereby assess the relationship between MRI activity and atrophy in this patient group. BACKGROUND: Measurements of cerebral atrophy and net change in T2 lesion volumes are currently used as surrogate markers of disease progression in multiple sclerosis (MS). However, manual implementation of these techniques is time-consuming and the pathological specificity of T2 lesion change is low. Advances in serial scan registration have facilitated the development of a new, fully-automated technique to measure cerebral volume (SIENA; Structural Image Evaluation, using Normalisation, of Atrophy), and a technique to measure the total new T2 lesion volume selectively (MRI difference imaging). METHOD: SIENA measures changes in cerebral size based on sub-voxel detection of shifts in edge contours. The lesion difference imaging method measures differences in lesion volumes over time as defined by a semi-automated outlining technique. The two new methods were validated against the T2 lesion volume contour technique and a previously described measure of partial brain volume (which uses six slices centred on the presumed area of greatest change around the lateral ventricles). All were applied to serially acquired MR images from a cohort of 39 patients with PPMS, who also underwent scoring on the expanded disability status scale (EDSS) twice, two years apart. RESULTS: The two measures reflecting cerebral atrophy correlated strongly (r = 0.58, p < 0.001). T2 lesion load measurements using the two techniques correlated very highly (r = 0.999, p < 0.001). 91% of the total new T2 lesion volume was from enlargement of pre-existent lesions and only 9 % from new, discrete, lesions. No relationship was seen between the traditional measure of net gain in T2 lesion load and either measure of atrophy. However, the fully-automated measure of total new T2 load correlated with both measures of atrophy (SIENA technique, r= -0.37, p= 0.02; six slice measure, r = -0.41, p = 0.01). There was no relationship between the MRI measures and changes in the EDSS. CONCLUSION: Both of the new image analysis techniques appear to be promising as sensitive markers for disease progression in PPMS. The correlation of total new T2 lesion volume with the progression of cerebral atrophy (which is known to be a consequence of axonal loss in progressive disease), compared with a lack of correlation with the traditional net gain in T2 lesion load is interesting and suggests that the total new T2 lesion volume may ultimately be the most useful measure.

Linking cognition to brain connectivity.

3 April 2018

What is the most interesting part of the brain?

28 March 2018

Creative ideas and rigorous analysis are the hallmarks of much impactful science. However, there is an oft-aired suspicion in the neuroscience community that some scientists start with an advantage, simply because of the brain region or behaviour they study. We tested this unstated hypothesis by regressing the journal impact factor against both the pattern of brain activity and the experimental keywords across thousands of brain imaging studies. We found the results to be illuminating.

Model-free group analysis shows altered BOLD FMRI networks in dementia.

30 March 2018

FMRI research in Alzheimer's disease (AD) and mild cognitive impairment (MCI) typically is aimed at determining regional changes in brain function, most commonly by creating a model of the expected BOLD-response and estimating its magnitude using a general linear model (GLM) analysis. This crucially depends on the suitability of the temporal assumptions of the model and on assumptions about normality of group distributions. Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on these assumptions and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. Tensorial probabilistic ICA (T-PICA) is a model free technique that can be used for analyzing multiple subjects and groups, extracting signals of interest (components) in the spatial, temporal, and also subject domain of FMRI data. We applied T-PICA and model-based GLM to study FMRI signal during face encoding in 18 AD, 28 MCI patients, and 41 healthy elderly controls. T-PICA showed activation in regions associated with motor, visual, and cognitive processing, and deactivation in the default mode network. Six networks showed a significantly decreased response in patients. For two networks the T-PICA technique was significantly more sensitive to detect group differences than the standard model-based technique. We conclude that T-PICA is a promising tool to identify and detect differences in (de)activated brain networks in elderly controls and dementia patients. The technique is more sensitive than the commonly applied model-based method. Consistent with other research, we show that networks of activation and deactivation show decreased reactivity in dementia.