Magnetic Resonance Inverse Imaging Research Articles

Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals

Granger causality analysis has been suggested as a method of estimating causal modulation without specifying the direction of information flow a priori. Using BOLD-contrast functional MRI (fMRI) data, such analysis has been typically implemented in the time domain. In this study, we used magnetic resonance inverse imaging, a method of fast fMRI enabled by massively parallel detection allowing up to 10Hz sampling rate, to investigate the causal modulation at different frequencies up to 5Hz. Using a visuomotor two-choice reaction-time task, both the spectral decomposition of Granger causality and isolated effective coherence revealed that the BOLD signal at frequency up to 3Hz can still be used to estimate significant dominant directions of information flow consistent with results from the time-domain Granger causality analysis. We showed the specificity of estimated dominant directions of information flow at high frequencies by contrasting causality estimates using data collected during the visuomotor task and resting state. Our data suggest that hemodynamic responses carry physiological information related to inter-regional modulation at frequency higher than what has been commonly considered.

Improving the spatial resolution of magnetic resonance inverse imaging via the blipped-CAIPI acquisition scheme

Using simultaneous acquisition from multiple channels of a radio-frequency (RF) coil array, magnetic resonance inverse imaging (InI) achieves functional MRI acquisitions at a rate of 100ms per whole-brain volume. InI accelerates the scan by leaving out partition encoding steps and reconstructs images by solving under-determined inverse problems using RF coil sensitivity information. Hence, the correlated spatial information available in the coil array causes spatial blurring in the InI reconstruction. Here, we propose a method that employs gradient blips in the partition encoding direction during the acquisition to provide extra spatial encoding in order to better differentiate signals from different partitions. According to our simulations, this blipped-InI (bInI) method can increase the average spatial resolution by 15.1% (1.3mm) across the whole brain and from 32.6% (4.2mm) in subcortical regions, as compared to the InI method. In a visual fMRI experiment, we demonstrate that, compared to InI, the spatial distribution of bInI BOLD response is more consistent with that of a conventional echo-planar imaging (EPI) at the level of individual subjects. With the improved spatial resolution, especially in subcortical regions, bInI can be a useful fMRI tool for obtaining high spatiotemporal information for clinical and cognitive neuroscience studies.

Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging.

The acquisition time of BOLD contrast functional MRI (fMRI) data with whole-brain coverage typically requires a sampling rate of one volume in 1-3s. Although the volumetric sampling time of a few seconds is adequate for measuring the sluggish hemodynamic response (HDR) to neuronal activation, faster sampling of fMRI might allow for monitoring of rapid physiological fluctuations and detection of subtle neuronal activation timing information embedded in BOLD signals. Previous studies utilizing a highly accelerated volumetric MR inverse imaging (InI) technique have provided a sampling rate of one volume per 100 ms with 5mm spatial resolution. Here, we propose a novel modification of this technique, the echo-shifted InI, which allows TE to be longer than TR, to measure BOLD fMRI at an even faster sampling rate of one volume per 25 ms with whole-brain coverage. Compared with conventional EPI, echo-shifted InI provided an 80-fold speedup with similar spatial resolution and less than 2-fold temporal SNR loss. The capability of echo-shifted InI to detect HDR timing differences was tested empirically. At the group level (n=6), echo-spaced InI was able to detect statistically significant HDR timing differences of as low as 50 ms in visual stimulus presentation. At the level of individual subjects, significant differences in HDR timing were detected for 400 ms stimulus-onset differences. Our results also show that the temporal resolution of 25 ms is necessary for maintaining the temporal detecting capability at this level. With the capabilities of being able to distinguish the timing differences in the millisecond scale, echo-shifted InI could be a useful fMRI tool for obtaining temporal information at a time scale closer to that of neuronal dynamics.

Corrigendum to “Functional magnetic resonance inverse imaging of human visuomotor systems using eigenspace linearly constrained minimum amplitude (eLCMA)” Beamformer [NeuroImage 55/1 (March 2011) 87–100

Corrigendum to “Functional magnetic resonance inverse imaging of human visuomotor systems using eigenspace linearly constrained minimum amplitude (eLCMA)” Beamformer [NeuroImage 55/1 (March 2011) 87–100

Multi-projection magnetic resonance inverse imaging of the human visuomotor system

Using highly parallel radiofrequency (RF) detection, magnetic resonance inverse imaging (InI) can achieve 100 ms temporal resolution with whole brain coverage. This is achieved by trading off partition encoding steps and thus spatial resolution for a higher acquisition rate. The reduced spatial information is estimated by solving under-determined inverse problems using RF coil sensitivity information. Here we propose multi projection inverse imaging (mInI) to combine different projection images to improve the spatial resolution of InI. Specifically, coronal, sagittal, and transverse projection images were acquired from different runs of the fMRI acquisitions using a 32-channel head coil array. Simulations show that mInI improves the quality of the instantaneous image reconstruction significantly. Going from one projection to three projections, the spatial resolution quantified by the full width at half maximum of the point-spread function (PSF) is improved from 2.6 pixels to 1.4 pixels (4 mm nominal resolution per pixel). Considering the shape of the PSF, the effective spatial resolution is improved from 16.9 pixels to 4.7 pixels. In vivo fMRI experiments using a two-choice reaction time tasks show visual and sensorimotor cortical activities spatially consistent with typical EPI data, yet mInI offers 100 ms temporal resolution with the whole brain coverage. The mInI data with three projections revealed that the sensorimotor cortex was activated 700 ms after the visual cortex. mInI can be applied to BOLD-contrast fMRI experiments to characterize the dynamics of the activated brain areas with a high spatiotemporal resolution.

Physiological noise reduction using volumetric functional magnetic resonance inverse imaging

Physiological noise arising from a variety of sources can significantly degrade the detection of task-related activity in BOLD-contrast fMRI experiments. If whole head spatial coverage is desired, effective suppression of oscillatory physiological noise from cardiac and respiratory fluctuations is quite difficult without external monitoring, since traditional EPI acquisition methods cannot sample the signal rapidly enough to satisfy the Nyquist sampling theorem, leading to temporal aliasing of noise. Using a combination of high speed magnetic resonance inverse imaging (InI) and digital filtering, we demonstrate that it is possible to suppress cardiac and respiratory noise without auxiliary monitoring, while achieving whole head spatial coverage and reasonable spatial resolution. Our systematic study of the effects of different moving average (MA) digital filters demonstrates that a MA filter with a 2 s window can effectively reduce the variance in the hemodynamic baseline signal, thereby achieving 57%-58% improvements in peak z-statistic values compared to unfiltered InI or spatially smoothed EPI data (FWHM = 8.6 mm). In conclusion, the high temporal sampling rates achievable with InI permit significant reductions in physiological noise using standard temporal filtering techniques that result in significant improvements in hemodynamic response estimation.

K-space reconstruction of magnetic resonance inverse imaging (K-InI) of human visuomotor systems

Using simultaneous measurements from multiple channels of a radio-frequency coil array, magnetic resonance inverse imaging (InI) can achieve ultra-fast dynamic functional imaging of the human with whole-brain coverage and a good spatial resolution. Mathematically, the InI reconstruction is a generalization of parallel MRI (pMRI), which includes image space and k-space reconstructions. Because of the auto-calibration technique, the pMRI k-space reconstruction offers more robust and adaptive reconstructions compared to the image space algorithm. Here we present the k-space InI (K-InI) reconstructions to reconstruct the highly accelerated BOLD-contrast fMRI data of the human brain to achieve 100 ms temporal resolution. Simulations show that K-InI reconstructions can offer 3D image reconstructions at each time frame with reasonable spatial resolution, which cannot be obtained using the previously proposed image space minimum-norm estimates (MNE) or linear constraint minimum variance (LCMV) spatial filtering reconstructions. The InI reconstructions of in vivo BOLD-contrast fMRI data during a visuomotor task show that K-InI offer 3 to 5 fold more sensitive detection of the brain activation than MNE and a comparable detection sensitivity to the LCMV reconstructions. The group average of the high temporal resolution K-InI reconstructions of the hemodynamic response also shows a relative onset timing difference between the visual (first) and somatomotor (second) cortices by 400 ms (600 ms time-to-peak timing difference). This robust and sensitive K-InI reconstruction can be applied to dynamic MRI acquisitions using a large-n coil array to improve the spatiotemporal resolution.

Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging

Accurate estimation of the timing of neural activity is required to fully model the information flow among functionally specialized regions whose joint activity underlies perception, cognition and action. Attempts to detect the fine temporal structure of task-related activity would benefit from functional imaging methods allowing higher sampling rates. Spatial filtering techniques have been used in magnetoencephalography source imaging applications. In this work, we use the linear constraint minimal variance (LCMV) beamformer localization method to reconstruct single-shot volumetric functional magnetic resonance imaging (fMRI) data using signals acquired simultaneously from all channels of a high density radio-frequency (RF) coil array. The LCMV beamformer method generalizes the existing volumetric magnetic resonance inverse imaging (InI) technique, achieving higher detection sensitivity while maintaining whole-brain spatial coverage and 100 ms temporal resolution. In this paper, we begin by introducing the LCMV reconstruction formulation and then quantitatively assess its performance using both simulated and empirical data. To demonstrate the sensitivity and inter-subject reliability of volumetric LCMV InI, we employ an event-related design to probe the spatial and temporal properties of task-related hemodynamic signal modulations in primary visual cortex. Compared to minimum-norm estimate (MNE) reconstructions, LCMV offers better localization accuracy and superior detection sensitivity. Robust results from both single subject and group analyses demonstrate the excellent sensitivity and specificity of volumetric InI in detecting the spatial and temporal structure of task-related brain activity.

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

Developments in multi-channel radio-frequency (RF) coil array technology have enabled functional magnetic resonance imaging (fMRI) with higher degrees of spatial and temporal resolution. While modest improvement in temporal acceleration has been achieved by increasing the number of RF coils, the maximum attainable acceleration in parallel MRI acqisition is intrinsically limited only by the amount of independent spatial information in the combined array channels. Since the geometric configuration of a large- n MRI head coil array is similar to that used in EEG electrode or MEG SQUID sensor arrays, the source localization algorithms used in MEG or EEG source imaging can be extended to also process MRI coil array data, resulting in greatly improved temporal resolution by minimizing k-space traversal during signal acquisition. Using a novel approach, we acquire multi-channel MRI head coil array data and then apply inverse reconstruction methods to obtain volumetric fMRI estimates of blood oxygenation level dependent (BOLD) contrast at unprecedented whole-brain acquisition rates of 100 ms. We call this combination of techniques magnetic resonance Inverse Imaging (InI), a method that provides estimates of dynamic spatially-resolved signal change that can be used to construct statistical maps of task-related brain activity. We demonstrate the sensitivity and inter-subject reliability of volumetric InI using an event-related design to probe the hemodynamic signal modulations in primary visual cortex. Robust results from both single subject and group analyses demonstrate the sensitivity and feasibility of using volumetric InI in high temporal resolution investigations of human brain function.