Biophysical Simulation of the Functional Magnetic Resonance Signal Formation in Realistic Neurovascular Networks

The human brain is one of the most complex living systems. The scientific study of the brain’s anatomy and neurophysiology are fundamental to understand the basic principles of mental processes such as cognition and behavior. For this reason, in the endeavor to investigate the underlying neural mechanisms that drive these processes, the neuroscientific research has employed all the available technological resources and methodologies from different fields like anatomy, histology, electrophysiology, neurobiology, etc. Likewise, great advances have been provided by neuroimaging techniques such as PET and MRI, in order to comprehend the neural activity and the metabolic reactions that occur in the central nervous system. In particular, functional MRI provides an indirect measurement of the neural activity throughout local hemodynamic changes, thus related to the neurovascular coupling, as a response to a particular task-evoked stimulus. This MR signal behavior modulated by oxygenation level changes is better known as the BOLD signal. Although progress has been done in order to understand the BOLD signal change under well-defined nonrealistic vascular geometries, on the other hand, realistic neurovascular networks might give valuable information to resolve the influence on the BOLD signal evolution from a particular vascular tissue and specific hemodynamic responses. In order to extend the analysis of the BOLD signal change obtained by randomly oriented cylinders and spheres, throughout this thesis, the geometrical features of a realistic neurovascular network as well as the biophysical effects related to the hemodynamic response and thermal motion were investigated by means of Monte Carlo simulations in pursuance to resolve the functional MR signal formation. In the Introduction of this thesis, I made a small recapitulation on the MR physics and spin dynamics; magnetic susceptibility and thermal motion as crucial modulators of the BOLD signal behavior. In addition, a summary of the problem and the aims of the project. Therefore, I described the importance of the use of the Monte Carlo method to calculate the MR signal under nonrealistic vascular models. I summarized the seminal analytical and numerical results that provide important insights to characterize the main parameters that influence the MR signal formation. Finally, I described the importance of the use of realistic neurovascular structures in order to disentangle the specific tissue contribution and the direct impact on the BOLD signal change

Dependence of the magnetic resonance signal on the magnetic susceptibility of blood studied with models based on real microvascular networks

PURPOSE: The primary goal of this study was to estimate the value of beta , the exponent in the power law relating changes of the transverse relaxation rate and intra-extravascular local magnetic susceptibility differences as Delta R 2 * proportional, variant ( Delta chi ) beta. The secondary objective was to evaluate any differences that might exist in the value of beta obtained using a deoxyhemoglobin-weighted Delta chi distribution versus a constant Delta chi distribution assumed in earlier computations. The third objective was to estimate the value of beta that is relevant for methods based on susceptibility contrast agents with a concentration of Delta chi higher than that used for BOLD fMRI calculations. METHODS: Our recently developed model of real microvascular anatomical networks is used to extend the original simplified Monte-Carlo simulations to compute beta from the first principles. RESULTS: Our results show that beta = 1 for most BOLD fMRI measurements of real vascular networks, as opposed to earlier predictions of beta = 1 .5 using uniform Delta chi distributions. For perfusion or fMRI methods based on contrast agents, which generate larger values for Delta chi , beta = 1 for B 0 </= 9.4 T, whereas at 14 T beta can drop below 1 and the variation across subjects is large, indicating that a lower concentration of contrast agent with a lower value of Delta chi is desired for experiments at high B0. CONCLUSION: These results improve our understanding of the relationship between R2 (*) and the underlying microvascular properties. The findings will help to infer the cerebral metabolic rate of oxygen and cerebral blood volume from BOLD and perfusion MRI, respectively.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435380/Accepted manuscrip

Effects of phase regression on high-resolution functional MRI of the primary visual cortex

High-resolution functional MRI studies have become a powerful tool to non-invasively probe the sub-millimeter functional organization of the human cortex. Advances in MR hardware, imaging techniques and sophisticated post-processing methods have allowed high resolution fMRI to be used in both the clinical and academic neurosciences. However, consensus within the community regarding the use of gradient echo (GE) or spin echo (SE) based acquisition remains largely divided. On one hand, GE provides a high temporal signal-to-noise ratio (tSNR) technique sensitive to both the macro- and micro-vascular signal while SE based methods are more specific to microvasculature but suffer from lower tSNR and specific absorption rate limitations, especially at high field and with short repetition times. Fortunately, the phase of the GE-EPI signal is sensitive to vessel size and this provides a potential avenue to reduce the macrovascular weighting of the signal (phase regression, Menon 2002). In order to determine the efficacy of this technique at high-resolution, phase regression was applied to GE-EPI timeseries and compared to SE-EPI to determine if GE-EPI\u27s specificity to the microvascular compartment improved. To do this, functional data was collected from seven subjects on a neuro-optimized 7 T system at 800 μm isotropic resolution with both GE-EPI and SE-EPI while observing an 8 Hz contrast reversing checkerboard. Phase data from the GE-EPI was used to create a microvasculature-weighted time series (GE-EPI-PR). Anatomical imaging (MP2RAGE) was also collected to allow for surface segmentation so that the functional results could be projected onto a surface. A multi-echo gradient echo sequence was collected and used to identify venous vasculature. The GE-EPI-PR surface activation maps showed a high qualitative similarity with SE-EPI and also produced laminar activity profiles similar to SE-EPI. When the GE-EPI and GE-EPI-PR distributions were compared to SE-EPI it was shown that GE-EPI-PR had similar distribution characteristics to SE-EPI (p \u3c 0.05) across the top 60% of cortex. Furthermore, it was shown that GE-EPI-PR has a higher contrast-to-noise ratio (0.5 ± 0.2, mean ± std. dev. across layers) than SE-EPI (0.27 ± 0.07) demonstrating the technique has higher sensitivity than SE-EPI. Taken together this evidence suggests phase regression is a useful method in low SNR studies such as high-resolution fMRI

The impact of vessel size, orientation and intravascular contribution on the neurovascular fingerprint of BOLD bSSFP fMRI

Monte Carlo simulations have been used to analyze oxygenation-related signal changes in pass-band balanced steady state free precession (bSSFP) as well as in gradient echo (GE) and spin echo (SE) sequences. Signal changes were calculated for artificial cylinders and neurovascular networks acquired from the mouse parietal cortex by two-photon laser scanning microscopy at 1 μm isotropic resolution. Signal changes as a function of vessel size, blood volume, vessel orientation to the main magnetic field B0 as well as relations of intra- and extravascular and of micro- and macrovascular contributions have been analyzed. The results show that bSSFP is highly sensitive to extravascular and microvascular components. Furthermore, GE and bSSFP, and to a lesser extent SE, exhibit a strong dependence of their signal change on the orientation of the vessel network to B0

Phase imaging for reducing macrovascular signal contributions in high-resolution fMRI

High resolution functional MRI allows for the investigation of neural activity within the cortical sheet. One consideration in high resolution fMRI is the choice of which sequence to use during imaging, as all methods come with sensitivity and specificity tradeoffs. The most used fMRI sequence is gradient-echo echo planar imaging (GE-EPI) which has the highest sensitivity but is not specific to microvasculature. GE-EPI results in a signal with pial vessel bias which increases complexity of performing studies targeted at structures within the cortex. This work seeks to explore the use of MRI phase signal as a macrovascular filter to correct this bias. First, an in-house phase combination method was designed and tested on the 7T MRI system. This method, the fitted SVD method, uses a low-resolution singular value decomposition and fitting to a polynomial basis to provide computationally efficient, phase sensitive, coil combination that is insensitive to motion. Second, a direct comparison of GE-EPI, GE-EPI with phase regression (GE-EPI-PR), and spin echo EPI (SE-EPI) was performed in humans completing a visual task. The GE-EPI-PR activation showed higher spatial similarity with SE-EPI than GE-EPI across the cortical surface. GE-EPI-PR produced a similar laminar profile to SE-EPI while maintaining a higher contrast-to-noise ratio across layers, making it a useful method in low SNR studies such as high-resolution fMRI. The final study extended this work to a resting state macaque experiment. Macaques are a common model for laminar fMRI as they allow for simultaneous imaging and electrophysiology. We hypothesized that phase regression could improve spatial specificity of the resting state data. Further analysis showed the phase data contained both system and respiratory artifacts which prevented the technique performing as expected under two physiological cleaning strategies. Future work will have to examine on-scanner physiology correction to obtain a phase timeseries without artifacts to allow for the phase regression technique to be used in macaques. This work demonstrates that phase regression reduces signal contributions from pial vessels and will improve specificity in human layer fMRI studies. This method can be completed easily with complex fMRI data which can be created using our fitted SVD method

Neural Representations of Visual Motion Processing in the Human Brain Using Laminar Imaging at 9.4 Tesla

During natural behavior, much of the motion signal falling into our eyes is due to our own movements. Therefore, in order to correctly perceive motion in our environment, it is important to parse visual motion signals into those caused by self-motion such as eye- or head-movements and those caused by external motion. Neural mechanisms underlying this task, which are also required to allow for a stable perception of the world during pursuit eye movements, are not fully understood. Both, perceptual stability as well as perception of real-world (i.e. objective) motion are the product of integration between motion signals on the retina and efference copies of eye movements. The central aim of this thesis is to examine whether different levels of cortical depth or distinct columnar structures of visual motion regions are differentially involved in disentangling signals related to self-motion, objective, or object motion. Based on previous studies reporting segregated populations of voxels in high level visual areas such as V3A, V6, and MST responding predominantly to either retinal or extra- retinal (‘real’) motion, we speculated such voxels to reside within laminar or columnar functional units. We used ultra-high field (9.4T) fMRI along with an experimental paradigm that independently manipulated retinal and extra-retinal motion signals (smooth pursuit) while controlling for effects of eye-movements, to investigate whether processing of real world motion in human V5/MT, putative MST (pMST), and V1 is associated to differential laminar signal intensities. We also examined motion integration across cortical depths in human motion areas V3A and V6 that have strong objective motion responses. We found a unique, condition specific laminar profile in human area V6, showing reduced mid-layer responses for retinal motion only, suggestive of an inhibitory retinal contribution to motion integration in mid layers or alternatively an excitatory contribution in deep and superficial layers. We also found evidence indicating that in V5/MT and pMST, processing related to retinal, objective, and pursuit motion are either integrated or colocalized at the scale of our resolution. In contrast, in V1, independent functional processes seem to be driving the response to retinal and objective motion on the one hand, and to pursuit signals on the other. The lack of differential signals across depth in these regions suggests either that a columnar rather than laminar segregation governs these functions in these areas, or that the methods used were unable to detect differential neural laminar processing. Furthermore, the thesis provides a thorough analysis of the relevant technical modalities used for data acquisition and data analysis at ultra-high field in the context of laminar fMRI. Relying on our technical implementations we were able to conduct two high-resolution fMRI experiments that helped us to further investigate the laminar organization of self-induced and externally induced motion cues in human high-level visual areas and to form speculations about the site and the mechanisms of their integration