Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI

An efficient multi-slice inversion–recovery EPI (MS-IR-EPI) sequence for fast, high spatial resolution, quantitative T1 mapping is presented, using a segmented simultaneous multi-slice acquisition, combined with slice order shifting across multiple acquisitions. The segmented acquisition minimises the effective TE and readout duration compared to a single-shot EPI scheme, reducing geometric distortions to provide high quality T1 maps with a narrow point-spread function. The precision and repeatability of MS-IR-EPI T1 measurements are assessed using both T1-calibrated and T2-calibrated ISMRM/NIST phantom spheres at 3 and 7 T and compared with single slice IR and MP2RAGE methods. Magnetization transfer (MT) effects of the spectrally-selective fat-suppression (FS) pulses required for in vivo imaging are shown to shorten the measured in-vivo T1 values. We model the effect of these fat suppression pulses on T1 measurements and show that the model can remove their MT contribution from the measured T1, thus providing accurate T1 quantification. High spatial resolution T1 maps of the human brain generated with MS-IR-EPI at 7 T are compared with those generated with the widely implemented MP2RAGE sequence. Our MS-IR-EPI sequence provides high SNR per unit time and sharper T1 maps than MP2RAGE, demonstrating the potential for ultra-high resolution T1 mapping and the improved discrimination of functionally relevant cortical areas in the human brain

An intra-neural microstimulation system for ultra-high field magnetic resonance imaging and magnetoencephalography.

BACKGROUND: Intra-neural microstimulation (INMS) is a technique that allows the precise delivery of low-current electrical pulses into human peripheral nerves. Single unit INMS can be used to stimulate individual afferent nerve fibres during microneurography. Combining this with neuroimaging allows the unique monitoring of central nervous system activation in response to unitary, controlled tactile input, with functional magnetic resonance imaging (fMRI) providing exquisite spatial localisation of brain activity and magnetoencephalography (MEG) high temporal resolution. NEW METHOD: INMS systems suitable for use within electrophysiology laboratories have been available for many years. We describe an INMS system specifically designed to provide compatibility with both ultra-high field (7T) fMRI and MEG. Numerous technical and safety issues are addressed. The system is fully analogue, allowing for arbitrary frequency and amplitude INMS stimulation. RESULTS: Unitary recordings obtained within both the MRI and MEG screened-room environments are comparable with those obtained in 'clean' electrophysiology recording environments. Single unit INMS (current <7μA, 200μs pulses) of individual mechanoreceptive afferents produces appropriate and robust responses during fMRI and MEG. COMPARISON WITH EXISTING METHOD(S): This custom-built MRI- and MEG-compatible stimulator overcomes issues with existing INMS approaches; it allows well-controlled switching between recording and stimulus mode, prevents electrical shocks because of long cable lengths, permits unlimited patterns of stimulation, and provides a system with improved work-flow and participant comfort. CONCLUSIONS: We demonstrate that the requirements for an INMS-integrated system, which can be used with both fMRI and MEG imaging systems, have been fully met

Somatotopic map and inter- and intra-digit distance in Brodmann area 2 by pressure stimulation

The somatotopic representation of the tactile stimulation on the finger in the brain is an essential part of understanding the human somatosensory system as well as rehabilitation and other clinical therapies. Many studies have used vibrotactile stimulations and reported finger somatotopic representations in the Brodmann area 3 (BA 3). On the contrary, few studies investigated finger somatotopic representation using pressure stimulations. Therefore, the present study aimed to find a comprehensive somatotopic representation (somatotopic map and inter- and intra-digit distance) within BA 2 of humans that could describe tactile stimulations on different joints across the fingers by applying pressure stimulation to three joints-the first (p1), second (p2), and third (p3) joints-of four fingers (index, middle, ring, and little finger). Significant differences were observed in the inter-digit distance between the first joints (p1) of the index and little fingers, and between the third joints (p3) of the index and little fingers. In addition, a significant difference was observed in the intra-digit distance between p1 and p3 of the little finger. This study suggests that a somatotopic map and inter- and intra-digit distance could be found in BA 2 in response to pressure stimulation on finger joints.ope

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time–frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time–frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas

Functional Quantitative Susceptibility Mapping (fQSM)

Quantitative susceptibility mapping (QSM) was performed on fMRI time-series to reconstruct maps with activation induced local susceptibility changes. A multi-step image processing algorithm was developed for filtering out unwanted temporal and spatial signal variations. The method was validated on GE-EPI BOLD-fMRI datasets acquired at 7T with 1mm isotropic resolution during the application of visual, motor and somatosensory activation paradigms. Series of high-quality phase and susceptibility maps with minimal temporal noise were reconstructed, which resulted in activation maps corresponding to estimations from the modulus data. Functional QSM (fQSM) allows for quantification of the BOLD-effect for any time-series with phase information

Experimental investigation of the relation between gradient echo BOLD fMRI contrast and underlying susceptibility changes at 7T

Gradient echo BOLD fMRI contrast was compared to the underlying local susceptibility changes, calculated from the same time-series using functional quantitative susceptibility mapping (fQSM). Comparing voxels coincidentally activated in both fQSM and GE-fMRI, differences were found between motor, visual and somatosensory paradigms in the respective brain areas. In most of these common voxels the signs of susceptibility and magnitude change were opposite confirming expectations. Yet, in a significant number of voxels the signs matched, suggesting strong non-local contributions. Experiments with the motor paradigm showed positive susceptibility shifts and magnitude signal changes in deep cortical layers

Contribution of large scale biases in decoding of direction-of-motion from high-resolution fMRI data in human early visual cortex

Previous studies have demonstrated that the perceived direction of motion of a visual stimulus can be decoded from the pattern of functional magnetic resonance imaging (fMRI) responses in occipital cortex using multivariate analysis methods (Kamitani and Tong, 2006). One possible mechanism for this is a difference in the sampling of direction selective cortical columns between voxels, implying that information at a level smaller than the voxel size might be accessible with fMRI. Alternatively, multivariate analysis methods might be driven by the organization of neurons into clusters or even orderly maps at a much larger scale. To assess the possible sources of the direction selectivity observed in fMRI data, we tested how classification accuracy varied across different visual areas and subsets of voxels for classification of motion-direction. To enable high spatial resolution functional MRI measurements (1.5 mm isotropic voxels), data were collected at 7 T. To test whether information about the direction of motion is represented at the scale of retinotopic maps, we looked at classification performance after combining data across different voxels within visual areas (V1–3 and MT +/V5) before training the multivariate classifier. A recent study has shown that orientation biases in V1 are both necessary and sufficient to explain classification of stimulus orientation (Freeman et al., 2011). Here, we combined voxels with similar visual field preference as determined in separate retinotopy measurements and observed that classification accuracy was preserved when averaging in this ‘retinotopically restricted’ way, compared to random averaging of voxels. This insensitivity to averaging of voxels (with similar visual angle preference) across substantial distances in cortical space suggests that there are large-scale biases at the level of retinotopic maps underlying our ability to classify direction of motion