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1.  Multi-session statistics on beamformed MEG data☆ 
Neuroimage  2014;95(100):330-335.
Beamforming has been widely adopted as a source reconstruction technique in the analysis of magnetoencephalography data. Most beamforming implementations incorporate a spatially-varying rescaling (which we term weights normalisation) to correct for the inherent depth bias in raw beamformer estimates. Here, we demonstrate that such rescaling can cause critical problems whenever analyses are performed over multiple sessions of separately beamformed data, for example when comparing effect sizes between different populations. Importantly, we show that the weights-normalised beamformer estimates of neural activity can even lead to a reversal in the inferred sign of the effect being measured. We instead recommend that no weights normalisation be carried out; any depth bias is instead accounted for in the calculation of multi-session (e.g. group) statistics. We demonstrate the severity of the weights normalisation confound with a 2-D simulation, and in real MEG data by performing a group statistical analysis to detect differences in alpha power in eyes-closed rest compared with continuous visual stimulation.
Highlights
•Raw beamformer estimates of MEG data exhibit increased variance with depth.•Corrections are typically applied to each session to remove this depth bias.•These corrections confound subsequent multi-session statistical analyses.•This confound can reverse the apparent direction of an effect.•We demonstrate this with a simulation and in real data.
doi:10.1016/j.neuroimage.2013.12.026
PMCID: PMC4073650  PMID: 24412400
MEG; Group statistics; Beamforming; Source reconstruction
2.  Fast transient networks in spontaneous human brain activity 
eLife  2014;3:e01867.
To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.
DOI: http://dx.doi.org/10.7554/eLife.01867.001
eLife digest
When subjects lie motionless inside scanners without any particular task to perform, their brains show stereotyped patterns of activity across regions known as resting state networks. Each network consists of areas with a common function, such as the ‘motor’ network or the ‘visual’ network. The role of resting state networks is unclear, but these spontaneous activity patterns are altered in disorders including autism, schizophrenia, and Alzheimer’s disease.
One puzzling feature of resting state networks is that they seem to last for relatively long times. However, the majority of studies into resting state networks have used fMRI brain scans, in which changes in the level of oxygen in the blood are used as a proxy for the activity of a given brain region. Since changes in blood oxygen occur relatively slowly, the ability of fMRI to detect rapid changes in activity is limited: it is thus possible that the long-lived nature of resting state networks is an artefact of the use of fMRI.
Now, Baker et al. have used a different type of brain scan known as an MEG scan to show that the activity of resting state networks is shorter lived than previously thought. MEG scanners measure changes in the magnetic fields generated by electrical currents in the brain, which means that they can detect alterations in brain activity much more rapidly than fMRI.
MEG recordings from the brains of nine healthy subjects revealed that individual resting state networks were typically stable for only 100 ms to 200 ms. Moreover, transitions between different networks did not occur randomly; instead, certain networks were much more likely to become active after others. The work of Baker et al. suggests that the resting brain is constantly changing between different patterns of activity, which enables it to respond quickly to any given situation.
DOI: http://dx.doi.org/10.7554/eLife.01867.002
doi:10.7554/eLife.01867
PMCID: PMC3965210  PMID: 24668169
magnetoencephalography; resting state; connectivity; non-stationary; hidden Markov model; microstates; human
3.  Is iron overload in alcohol-related cirrhosis mediated by hepcidin? 
In this case report we describe the relationship between ferritin levels and hepcidin in a patient with alcohol-related spur cell anemia who underwent liver transplantation. We demonstrate a reciprocal relationship between serum or urinary hepcidin and serum ferritin, which indicates that inadequate hepcidin production by the diseased liver is associated with elevated serum ferritin. The ferritin level falls with increasing hepcidin production after transplantation. Neither inflammatory indices (IL6) nor erythropoietin appear to be related to hepcidin expression in this case. We suggest that inappropriately low hepcidin production by the cirrhotic liver may contribute substantially to elevated tissue iron stores in cirrhosis and speculate that hepcidin replacement in these patients may be of therapeutic benefit in the future.
doi:10.3748/wjg.15.5864
PMCID: PMC2791283  PMID: 19998511
Alcohol; Iron; Anaemia; Hepcidin; Cirrhosis
4.  Increased hepcidin expression in colorectal carcinogenesis 
AIM: To investigate whether the iron stores regulator hepcidin is implicated in colon cancer-associated anaemia and whether it might have a role in colorectal carcinogenesis.
METHODS: Mass spectrometry (MALDI-TOF MS and SELDI-TOF MS) was employed to measure hepcidin in urine collected from 56 patients with colorectal cancer. Quantitative Real Time RT-PCR was utilized to determine hepcidin mRNA expression in colorectal cancer tissue. Hepcidin cellular localization was determined using immunohistochemistry.
RESULTS: We demonstrate that whilst urinary hepcidin expression was not correlated with anaemia it was positively associated with increasing T-stage of colorectal cancer (P < 0.05). Furthermore, we report that hepcidin mRNA is expressed in 34% of colorectal cancer tissue specimens and was correlated with ferroportin repression. This was supported by hepcidin immunoreactivity in colorectal cancer tissue.
CONCLUSION: We demonstrate that systemic hepcidin expression is unlikely to be the cause of the systemic anaemia associated with colorectal cancer. However, we demonstrate for the first time that hepcidin is expressed by colorectal cancer tissue and that this may represent a novel oncogenic signalling mechanism.
doi:10.3748/wjg.14.1339
PMCID: PMC2693679  PMID: 18322945
Iron; Hepcidin; Colon; Cancer; Anaemia; Mass spectrometry
5.  Measuring functional connectivity using MEG: Methodology and comparison with fcMRI 
Neuroimage  2011;56(3):1082-1104.
Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the β band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response.
Research highlights
► The utility of beamforming as a source space projection algorithm for use with functional connectivity (FC) measurements is explored. ► Simulations are described and prove to be a robust methodology to eliminate spurious FC from source space MEG measurements. ► The spatial signature of resting state FC between left and right sensorimotor cortices can be measured independently using MEG and fMRI. ► Excellent agreement between motor cortex FC measured using both MEG and fcMRI. ► Multiple MEG FC metrics exploit the direct nature of MEG.
doi:10.1016/j.neuroimage.2011.02.054
PMCID: PMC3224862  PMID: 21352925
MEG; fMRI; 7T; Functional connectivity; Neural oscillations; BOLD; Resting state; Coherence; Imaginary coherence; Envelope correlation
6.  Investigating spatial specificity and data averaging in MEG 
Neuroimage  2010;49(1):525-538.
This study shows that the spatial specificity of MEG beamformer estimates of electrical activity can be affected significantly by the way in which covariance estimates are calculated. We define spatial specificity as the ability to extract independent timecourse estimates of electrical brain activity from two separate brain locations in close proximity. Previous analytical and simulated results have shown that beamformer estimates are affected by narrowing the time frequency window in which covariance estimates are made. Here we build on this by both experimental validation of previous results, and investigating the effect of data averaging prior to covariance estimation. In appropriate circumstances, we show that averaging has a marked effect on spatial specificity. However the averaging process results in ill-conditioned covariance matrices, thus necessitating a suitable matrix regularisation strategy, an example of which is described. We apply our findings to an MEG retinotopic mapping paradigm. A moving visual stimulus is used to elicit brain activation at different retinotopic locations in the visual cortex. This gives the impression of a moving electrical dipolar source in the brain. We show that if appropriate beamformer optimisation is applied, the moving source can be tracked in the cortex. In addition to spatial reconstruction of the moving source, we show that timecourse estimates can be extracted from neighbouring locations of interest in the visual cortex. If appropriate methodology is employed, the sequential activation of separate retinotopic locations can be observed. The retinotopic paradigm represents an ideal platform to test the spatial specificity of source localisation strategies. We suggest that future comparisons of MEG source localisation techniques (e.g. beamformer, minimum norm, Bayesian) could be made using this retinotopic mapping paradigm.
doi:10.1016/j.neuroimage.2009.07.043
PMCID: PMC3224863  PMID: 19635575
MEG; Spatial specificity; Spatial resolution; Source localisation; Linearly constrained minimum variance beamformer; Retinotopic mapping; Regularisation; Information content

Results 1-6 (6)