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Functional magnetic resonance spectroscopy (fMRS) has been used to assess the dynamic metabolic responses of the brain to a physiological stimulus non-invasively. However, only limited information on the dynamic functional response of γ-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the brain, is available. We aimed to measure the activation-induced changes in GABA unambiguously using a spectral editing method, instead of the conventional direct detection techniques used in previous fMRS studies. The Mescher-Garwood-semi-localised by adiabatic selective refocusing (MEGA-sLASER) sequence was developed at 7 T to obtain the time course of GABA concentration without macromolecular contamination. A significant decrease (−12±5%) in the GABA to total creatine ratio (GABA/tCr) was observed in the motor cortex during a period of 10 minutes of hand-clenching, compared to an initial baseline level (GABA/tCr = 0.11±0.02) at rest. An increase in the Glx (glutamate and glutamine) to tCr ratio was also found, which is in agreement with previous findings. In contrast, no significant changes in NAA/tCr and tCr were detected. With consistent and highly efficient editing performance for GABA detection and the advantage of visually identifying GABA resonances in the spectra, MEGA-sLASER is demonstrated to be an effective method for studying of dynamic changes in GABA at 7 T.

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.

Several lines of evidence implicate glycogen synthase kinase 3 beta (GSK3beta) in mood disorders. We recently reported associations between GSK3beta polymorphisms and brain structural changes in patients with recurrent major depressive disorder (MDD). Here we provide supporting observations by showing that polymorphisms in additional genes encoding proteins directly related to GSK3beta biological functions are associated with similar regional grey matter (GM) volume changes in MDD patients. We tested specifically for associations with genetic variation in canonical Wnt signaling pathway genes and in genes that encode substrate proteins of GSK3beta. We applied a general linear model with non-stationary cluster-based inference to examine associations between polymorphisms and regional voxel-based morphometry GM volume differences in recurrent MDD patients (n=134) and in age-, gender-, and ethnicity-matched healthy controls (n=144) to test for genotype-by-MDD interactions. We observed associations for polymorphisms in 8/13 canonical Wnt pathway genes and 5/10 GSK3beta substrate genes, predominantly in the temporolateral and medial prefrontal cortices. Similar associations were not found for 100 unrelated polymorphisms tested. This work suggests that identifying SNPs related to genes that encode functionally-interacting proteins that modulate common anatomical regions offers a useful approach to increasing confidence in outcomes from imaging genetics association studies. This is of particular interest when replication datasets are not available. Our observations lend support to the hypothesis that polymorphisms in GSK3beta play a role in MDD susceptibility or expression, in part, by acting via the canonical Wnt signaling pathway and related substrates.

The last two decades have witnessed great progress in mapping neural networks associated with task-induced brain activation. More recently, identification of resting state networks (RSN) paved the way to investigate spontaneous task-unrelated brain activity. The cardinal features characterising RSN are low-frequency fluctuations of blood oxygenation level dependent (BOLD) signals synchronised between spatially distinct, but functionally connected brain areas. Simultaneous EEG/fMRI has been previously deployed to study the neurophysiological signature of RSN by comparing EEG power with BOLD amplitudes. We hypothesised that band-limited EEG power may be directly related to network-specific functional connectivity (FC) of BOLD signal time courses. Hence, we studied the association between individual EEG signature and FC in a core RSN, the so-called default mode network (DMN). Combined EEG/fMRI data of 20 healthy volunteers collected during a 15-minute rest period were analysed. Using an inter-subject analysis design, we demonstrated a network and frequency specific relation between RSN FC and EEG. In a multiple regression model, EEG band-powers explained 70% of DMN FC variance, with significant partial correlations of DMN FC to delta (r=-0.73) and beta (r=0.53) power. The identified EEG pattern has been previously associated with increased alertness. Conversely, an established EEG-derived sedation index (spectral edge frequency SEF95) closely correlated with DMN FC. The study presents an approach that opens a new perspective to EEG/fMRI correlation. Direct evidence was provided for a distinct neurophysiological correlate of DMN FC. This finding further validates the biological relevance of network-specific intrinsic FC and provides an initial neurophysiological basis for interpreting studies of DMN FC alterations.

The cortical processing of auditory-alone, visual-alone, and audiovisual speech information is temporally and spatially distributed, and functional magnetic resonance imaging (fMRI) cannot adequately resolve its temporal dynamics. In order to investigate a hypothesized spatiotemporal organization for audiovisual speech processing circuits, event-related potentials (ERPs) were recorded using electroencephalography (EEG). Stimuli were congruent audiovisual/ba/, incongruent auditory/ba/synchronized with visual/ga/, auditory-only/ba/, and visual-only/ba/and/ga/. Current density reconstructions (CDRs) of the ERP data were computed across the latency interval of 50-250 ms. The CDRs demonstrated complex spatiotemporal activation patterns that differed across stimulus conditions. The hypothesized circuit that was investigated here comprised initial integration of audiovisual speech by the middle superior temporal sulcus (STS), followed by recruitment of the intraparietal sulcus (IPS), followed by activation of Broca's area [Miller, L.M., d'Esposito, M., 2005. Perceptual fusion and stimulus coincidence in the cross-modal integration of speech. Journal of Neuroscience 25, 5884-5893]. The importance of spatiotemporally sensitive measures in evaluating processing pathways was demonstrated. Results showed, strikingly, early (100 ms) and simultaneous activations in areas of the supramarginal and angular gyrus (SMG/AG), the IPS, the inferior frontal gyrus, and the dorsolateral prefrontal cortex. Also, emergent left hemisphere SMG/AG activation, not predicted based on the unisensory stimulus conditions was observed at approximately 160 to 220 ms. The STS was neither the earliest nor most prominent activation site, although it is frequently considered the sine qua non of audiovisual speech integration. As discussed here, the relatively late activity of the SMG/AG solely under audiovisual conditions is a possible candidate audiovisual speech integration response.

The notion of fractal has been largely used to describe geometrical properties of complex objects in biology and medicine. In the present study the question is addressed whether the human cerebral cortex is self-similar in a statistical sense, which is commonly referred to as being a fractal. A new calculational method is presented, which is volumetric and based on the fast Fourier transform (FFT) of segmented three-dimensional high-resolution magnetic resonance images. The analysis covers a wide range of spatial scales from the size of the whole cortex to the ultimate pixel size. Results obtained in six subjects confirm the fractal nature of the human cerebral cortex down to a spatial scale of 3 mm. The obtained fractal dimension is D = 2.80 +/- 0.05, which is in reasonable agreement with previously reported results. Deployment of FFT enables a simple interpretation of the results and yields a high performance, which is necessary to analyze the entire cortex. Thus the FFT-based analysis of segmented MR images offers a comprehensive approach to study neurodevelopmental and neurodegenerative changes in the fractal geometry of the cerebral cortex.

In functional magnetic resonance imaging (fMRI), modeling the complex link between neuronal activity and its hemodynamic response via the neurovascular coupling requires an elaborate and sensitive response model. Methods based on physiologic assumptions as well as direct, descriptive models have been proposed. The focus of this study is placed on such a direct approach that allows for a robust pixelwise determination of hemodynamic characteristics, such as time to peak or the poststimulus undershoot. A Bayesian procedure is presented that can easily be adapted to different hemodynamic properties in question and can be estimated without numerical problems known from nonlinear optimization algorithms. The usefulness of the model is demonstrated by thorough analyzes of the poststimulus undershoot in visual and acoustic stimulation paradigms. Further, we show the capability of this approach to improve analysis of fMRI data in altered hemodynamic conditions.

Diffusiontensor imaging (DTI) is an emerging and promising tool to provide information about the course of white matter fiber tracts in the human brain. Based on specific acquisition schemes, diffusion tensor data resemble local fiber orientations allowing for a reconstruction of the fiber bundles. Current techniques to calculate fascicles range from simple heuristic tracking solutions to Bayesian and differential equations approaches. Most methods are based only on local diffusion information, often resulting in bending or kinking fiber paths in voxels with reduced diffusion properties. In this article we present a new tracking approach based on linear state space models encompassing an inherent smoothness criterion to avoid too wiggly tracked fiber bundles. The new technique will be described formally and tested on simulated and real data. The performance tests are focused on the pyramidal tract, where we employed a test–retest study and a group comparison in healthy subjects. Anatomical course was confirmed in a patient with selective degeneration of the pyramidal tract. The potential of the presented technique for improved neurosurgical planning is demonstrated by visualization of a tumor-induced displacement of the motor pathways. The paper closes with a thorough discussion of perspectives and limitations of the new tracking approach.