A B S T R A C TNeuroimaging 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.

3-T T2*-weighted brain MRI distinguishes perivenous MS lesions from microangiopathic lesions. Clinical application of this technique could supplement existing diagnostic algorithms.

Background: Pathology in the cervical spinal cord is considered an important cause of disability in multiple sclerosis. However, the majority of serial studies have failed to find a correlation between spinal cord atrophy and disability. Objectives: To use a highly reproducible and accurate method to quantify spinal cord area change on three dimensional magnetic resonance imaging and relate this to disability change in patients with multiple sclerosis. Methods: 38 patients with multiple sclerosis (20 with the relapsing-remitting (RRMS) form and 18 with the secondary progressive (SPMS) form) were imaged at baseline and at months 6, 12, 18, and 48 during two treatment trials of the high dose subcutaneous thrice weekly interferon β-1a (IFNβ, Rebif). Thirty one healthy subjects were also imaged at baseline. Upper cervical cord area (UCCA) was measured using Sobel edge detection. Results: The intraobserver coefficient of variation of the method was 0.42%. A significant reduction in UCCA was detected at month 6 in the placebo group (p = 0.04) and at month 12 for INFβ (p = 0.03). The mean reduction of UCCA at month 48 was 5.7% for patients initially on placebo who received treatment at 24 months (RRMS) or at 36 months (SPMS), and 4.5% for those on IFNβ throughout the study (p = 0.35). The change in UCCA was significantly correlated with change in the expanded disability status scale at month 12 (r = 0.4, p = 0.016), month 18 (r = 0.32, p = 0.05), and month 48 (r = 0.4, p = 0.016) in the total cohort. Conclusions: Despite the small number of patients studied and the possible confounding effects of interferon treatment, this study showed that edge detection is reproducible and sensitive to changes in spinal cord area, and that this change is related to changes in clinical disability. This suggests a role for measurement of spinal cord atrophy in monitoring disease progression and possible treatment effects in clinical trails.

Tobacco smoking has been linked to an increased risk of multiple sclerosis. However, to date, results from the few studies on the impact of smoking on the progression of disability are conflicting. The aim of this study was to investigate the effects of smoking on disability progression and disease severity in a cohort of patients with clinically definite multiple sclerosis. We analysed data from 895 patients (270 male, 625 female), mean age 49 years with mean disease duration 17 years. Forty-nine per cent of the patients were regular smokers at the time of disease onset or at diagnosis (ever-smokers). Average disease severity as measured by multiple sclerosis severity score was greater in ever-smokers, by 0.68 (95% confidence interval: 0.36–1.01). The risk of reaching Expanded Disability Status Scale score milestones of 4 and 6 in ever-smokers compared to never-smokers was 1.34 (95% confidence interval: 1.12–1.60) and 1.25 (95% confidence interval: 1.02–1.51) respectively. Current smokers showed 1.64 (95% confidence interval: 1.33–2.02) and 1.49 (95% confidence interval: 1.18–1.86) times higher risk of reaching Expanded Disability Status Scale scores 4 and 6 compared with non-smokers. Ex-smokers who stopped smoking either before or after the onset of the disease had a significantly lower risk of reaching Expanded Disability Status Scale scores 4 (hazard ratio: 0.65, confidence interval: 0.50–0.83) and 6 (hazard ratio: 0.69, confidence interval: 0.53–0.90) than current smokers, and there was no significant difference between ex-smokers and non-smokers in terms of time to Expanded Disability Status Scale scores 4 or 6. Our data suggest that regular smoking is associated with more severe disease and faster disability progression. In addition, smoking cessation, whether before or after onset of the disease, is associated with a slower progression of disability.

We investigate chaotic electron transport in the lowest miniband of a semiconductor superlattice with a tilted magnetic field. This experimentally accessible non-Kolmogorov-Arnol'd-Moser system involves only stationary electric and magnetic fields, but is dynamically equivalent to a time-dependent kicked harmonic oscillator. The onset of chaos strongly delocalizes the electron orbits, thus raising the electrical conductivity. When the cyclotron and Bloch frequencies are commensurate, the phase space is threaded by a stochastic web, which produces a further resonant increase in the conductivity.

In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the “cleaned” residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series.

Background: Current magnetic resonance imaging (MRI) outcome measures such as T2 lesion load correlate poorly with disability in multiple sclerosis. Diffusion tensor imaging (DTI) of the brain can provide unique information regarding the orientation and integrity of white matter tracts in vivo. Objective: To use this information to map the pyramidal tracts of patients with multiple sclerosis, investigate the relation between burden of disease in the tracts and disability, and compare this with more global magnetic resonance estimates of disease burden. Methods: 25 patients with relapsing-remitting multiple sclerosis and 17 healthy volunteers were studied with DTI. An algorithm was used that automatically produced anatomically plausible maps of white matter tracts. The integrity of the pyramidal tracts was assessed using relative anisotropy and a novel measure (L t ) derived from the compounded relative anisotropy along the tracts. The methods were compared with both traditional and more recent techniques for measuring disease burden in multiple sclerosis (T2 lesion load and "whole brain" diffusion histograms). Results: Relative anisotropy and L t were significantly lower in patients than controls (p < 0.05). Pyramidal tract L t in the patients correlated significantly with both expanded disability status scale (r = −0.48, p < 0.05), and to a greater degree, the pyramidal Kurtzke functional system score (KFS-p) (r = −0.75, p < 0.0001). T2 lesion load and diffusion histogram parameters did not correlate with disability. Conclusions: Tract mapping using DTI is feasible and may increase the specificity of MRI in multiple sclerosis by matching appropriate tracts with specific clinical scoring systems. These techniques may be applicable to a wide range of neurological conditions.

The excess mortality in MS relative to the general population has not changed over the past 50 years. Female patients with MS have higher survival disadvantage compared to that of males. Death due to cardiovascular diseases, suicide and infection is higher in patients with MS compared to the general population.