Comparing the Microvascular Specificity of the 3- and 7-T BOLD Response Using ICA and Susceptibility-Weighted Imaging

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.

Advances in translational imaging of the microcirculation

The past few decades have seen an explosion in the development and use of methods for imaging the human microcirculation during health and disease. The confluence of innovative imaging technologies, affordable computing power, and economies of scale have ushered in a new era of "translational" imaging that permit us to peer into blood vessels of various organs in the human body. These imaging techniques include near-infrared spectroscopy (NIRS), positron emission tomography (PET), and magnetic resonance imaging (MRI) that are sensitive to microvascular-derived signals, as well as computed tomography (CT), optical imaging, and ultrasound (US) imaging that are capable of directly acquiring images at, or close to microvascular spatial resolution. Collectively, these imaging modalities enable us to characterize the morphological and functional changes in a tissue's microcirculation that are known to accompany the initiation and progression of numerous pathologies. Although there have been significant advances for imaging the microcirculation in preclinical models, this review focuses on developments in the assessment of the microcirculation in patients with optical imaging, NIRS, PET, US, MRI, and CT, to name a few. The goal of this review is to serve as a springboard for exploring the burgeoning role of translational imaging technologies for interrogating the structural and functional status of the microcirculation in humans, and highlight the breadth of current clinical applications. Making the human microcirculation "visible" in vivo to clinicians and researchers alike will facilitate bench-to-bedside discoveries and enhance the diagnosis and management of disease.

Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant's decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant's decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.

Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals

Performing voluntary movements involves many regions of the brain, but it is unknown how they work together to plan and execute specific movements. We recorded high-resolution ultra-high-field blood-oxygen-level-dependent signal during a cued ankle-dorsiflexion task. The spatiotemporal dynamics and the patterns of task-relevant information flow across the dorsal motor network were investigated. We show that task-relevant information appears and decays earlier in the higher order areas of the dorsal motor network then in the primary motor cortex. Furthermore, the results show that task-relevant information is encoded in general initially, and then selective goals are subsequently encoded in specifics subregions across the network. Importantly, the patterns of recurrent information flow across the network vary across different subregions depending on the goal. Recurrent information flow was observed across all higher order areas of the dorsal motor network in the subregions encoding for the current goal. In contrast, only the top-down information flow from the supplementary motor cortex to the frontoparietal regions, with weakened recurrent information flow between the frontoparietal regions and bottom-up information flow from the frontoparietal regions to the supplementary cortex were observed in the subregions encoding for the opposing goal. We conclude that selective motor goal encoding and execution rely on goal-dependent differences in subregional recurrent information flow patterns across the long-range dorsal motor network areas that exhibit graded functional specialization.

Identification of Voxels Confounded by Venous Signals Using Resting-State fMRI Functional Connectivity Graph Community Identification

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6±5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remaining differences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needing any additional data beyond what is usually acquired (and exported) in standard fMRI experiments.

Using a novel source-localized phase regressor technique for evaluation of the vascular contribution to semantic category area localization in BOLD fMRI

Numerous studies have shown that gradient-echo blood oxygen level dependent (BOLD) fMRI is biased toward large draining veins. However, the impact of this large vein bias on the localization and characterization of semantic category areas has not been examined. Here we address this issue by comparing standard magnitude measures of BOLD activity in the Fusiform Face Area (FFA) and Parahippocampal Place Area (PPA) to those obtained using a novel method that suppresses the contribution of large draining veins: source-localized phase regressor (sPR). Unlike previous suppression methods that utilize the phase component of the BOLD signal, sPR yields robust and unbiased suppression of large draining veins even in voxels with no task-related phase changes. This is confirmed in ideal simulated data as well as in FFA/PPA localization data from four subjects. It was found that approximately 38% of right PPA, 14% of left PPA, 16% of right FFA, and 6% of left FFA voxels predominantly reflect signal from large draining veins. Surprisingly, with the contributions from large veins suppressed, semantic category representation in PPA actually tends to be lateralized to the left rather than the right hemisphere. Furthermore, semantic category areas larger in volume and higher in fSNR were found to have more contributions from large veins. These results suggest that previous studies using gradient-echo BOLD fMRI were biased toward semantic category areas that receive relatively greater contributions from large veins.

Functional Magnetic Resonance Imaging Methods

Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.

Differential functional benefits of ultra highfield MR systems within the language network

Several investigations have shown limitations of fMRI reliability with the current standard field strengths. Improvement is expected from ultra highfield systems but studies on possible benefits for cognitive networks are lacking. Here we provide an initial investigation on a prominent and clinically highly-relevant cognitive function: language processing in individual brains. 26 patients evaluated for presurgical language localization were investigated with a standardized overt language fMRI paradigm on both 3T and 7T MR scanners. During data acquisition and analysis we made particular efforts to minimize effects not related to static magnetic field strength differences. Six measures relevant for functional activation showed a large dissociation between essential language network nodes: although in Wernicke's area 5/6 measures indicated a benefit of ultra highfield, in Broca's area no comparison was significant. The most important reason for this discrepancy was identified as being an increase in susceptibility-related artifacts in inferior frontal brain areas at ultra high field. We conclude that functional UHF benefits are evident, however these depend crucially on the brain region investigated and the ability to control local artifacts.

Special considerations/technical limitations of blood-oxygen-level-dependent functional magnetic resonance imaging

In this review, limitations affecting the results of presurgical mapping with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) are discussed. There is a great need to standardize fMRI acquisition and analysis methods and establish guidelines to address quality control issues. Several national and international organizations are formulating guidelines and standards for both clinical and research applications of BOLD fMRI. Consensus regarding management of these issues will likely both improve the clinical standard of care and enhance future research applications of fMRI.