Applications of Functional Magnetic Resonance Imaging

Introduction

MRI refers to a noninvasive modality which renders physiologic and anatomic information of the human body. It has been continually and increasingly used in understanding functional activity in the human’s brain and diagnosing pathophysiologic conditions. The measurement of brain activity is done through functional Magnetic Resonance imaging (fMRI). FMRI measures the activity of the brain via the detection of changes associated with the flow of blood in the brain (Huttel et al, 2009). This measure usually relies on two main things; the neuronal activation and the cerebral flow of blood. Usage of a certain area of the brain basically means that part of the brain would receive a higher blood flow. Functional MR imaging techniques have been primarily engineered to investigate the activity of the brain. fMRI applies to measurement of the hemodynamic response associate with the neural activity in the brain (Chen & Li, 2012). The increase in blood flow to the local vasculature is the basis of fMRI, in regards to the neural activity, making it a relevant topic for those seeking healthcare dissertation help in advanced imaging techniques.

FMRI primarily uses the BOLD contrast, which was discovered in 1990 by Ogawa Seiji (Huttel et al, 2009); this refers to the Blood- oxygen- level dependent contrast. This form involves the mapping of neural activity in the brain using the hemodynamic response (changes in blood flow) compared to the energy the brain cells use. Some studies on the use of BOLD signals have shown an increase of these signals as the age of the participant in regards to brain development rises. BOLD MR has depicted evolutions of the brain’s architecture from a local to a more distributed one. However, the results’ interpretation highlights the fact that the BOLD signal coincides directly with blood oxygenation, which is affected by changes in the flow of blood and neural activity. This, summarily, shows that the BOLD signal may be affected by change in the neuronal- vascular coupling (Shmithorst et al, 2015).

For the noninvasive imaging of the brain, the high resolution and good coverage that is offered by functional MRI enables it to be a perfect tool. In a much more effective manner, functional MRI that is highly spatiotemporally resolved has the capability of revealing the organization of neural networks. The accompanying recordings derived from the process can even further depict assemblies of small neurons, contributing to the organization. Through the application of the BOLD functional MRI, investigations as to the levels of neural organization have been made even easier (Logothetis, 2003).

The interpretation of fMRI studies have been aided significantly by the quantitative measures of the functional cerebral blood flow (CBF) response. CBF response offers an important complement to the measures of the BOLD signal. The measures analyzed from functional CBF responses potentially provide more accurate reflections of the neural activity than BOLD signal measures; the reason for this is that the quantitative measures of functional CBF responses clearly describes how the neural stimulus responds to a well-defined quantity in physiology. Active Cerebral blood flow, continuous delivery of oxygen and metabolic nutrients are crucial factors in the cerebral function of human beings. Consequently, multiple physiological mechanisms titrate the cerebrovascular function (Smith & Ainslie, 2017). According to the results from Shen et al’s (2008) experiment, the signals portrayed by BOLD were peaked last and showed the slowest onset, these were statistically different from those of CBF. There are different vascular components present in BOLD and CBF signals; such as veins, capillary, venules and arteries. These temporal dynamics could be a basis for the different qualitative insights in regards to vascular contributions. The detection of very small differences in the flow of blood is also a factor to consider. Ideally, a measurable increase in oxygen in the blood is recorded in areas metabolically active; this is as a result of vasodilatation in certain regions which upregulate flow therefore increasing the extraction of oxygen in relation to the metabolic activity. Evidently, as stated by Ma et al (2020), qualitative measures recorded though responses in CBF potentially provide better results compared to BOLD measures. These responses record clearly described responses of specifically defined physiological quantities to the neural stimuli.

Throughout its use, Blood Oxygen Level Dependent technique contextualizes the continuous change of diamagnetic oxyhemoglobin to paramagnetic deoxyhemoglobin. This, according to Forster et al (1998), occurs with the decreasing signal MRI intensity and the results of brain activation.

Arterial spin labeling (ASL) refers to a method of MRI that is foundationally non- invasive in nature, specifically used in the measurement of CBF. A label image is captured in arteria spin labelling where there is utilization of water molecules circulating with the brain. While using radiofrequency pulse, in utilizing these water molecules, this technique tracks the blood water in its circulation in the brain. The process also involves a subtraction technique which provides for perfusion measurements (Petcharunpaisan et al, 2010). This technique uses intrinsic tracers which are freely diffusible. In terms of its ideality for clinical and research studies, the technique’s noninvasive ability and nature to quantitatively provide measurements of perfusion in the tissues is a significant factor. Nutrients and oxygen are, in other circumstances, delivered to issues through blood flow; this process is referred to as perfusion.

Some of the major breakthroughs of BOLD signal applications in functional MRI involve easier investigations into the levels of organization of neurons that are impossible to be studied by electrodes. Some of the fields touched on are interactions in long- range between different structures of the brain, reorganization and plasticity after focal lesions have been experimentally placed, contrast agents labelling techniques that cause dynamic patterns of connectivity and the neurochemical dynamics resulting from localized Magnetic Resonance spectroscopic imaging or in vivo spectroscopy (Logothetis, 2003). In reiteration, the BOLD functional MRI signal was first discovered in the 1990s. A contrast mechanism reflecting the blood oxygen level came about from accentuating the effects on susceptibility of the gradient- echo methods with the deoxyhemoglobin in the venous blood.

MRI can be regarded as quantitative when meaningful chemical or physical map variables obtained can be compared among subjects and tissue regions, in addition to them being measurable in physical units (Pierpaoli, 2010). Undoubtedly, a more widespread use of quantitative methods in MRI would prove to be more advantageous than its conventional use. Better quantitative measurements increase clinical brain sensitivity; this happens through the possible comparison of measurements acquired in healthy populations in individual subjects with normative values. Another advantage would be the provision of biological specificity. According to Pierpaoli (2010), various authors have provided important scientific and theoretical contributions towards the implementation of certain techniques that would improve quantitative measurement in MRIs. These cover using ASL in perfusion MRI, using dynamic- susceptibility contrast in MRI, diffusion MRI, spectroscopic imaging and proton magnetic resonance spectroscopy and proton relaxometry in MRI.

Quantitative Susceptibility Mapping (QSM) has increasingly gained acceptance in clinical practice. Particularly, QSM has provided new insights into the composition in tissues and such organization because of its more direct relationship with the actual magnetic properties of the physical tissue (Reinchenbach et al, 2015). A novel contrast mechanism is provided QSM. The underlying tissue apparent magnetic susceptibility is linearly proportional to the voxel intensity in QSM. This proportion is useful in the quantification of biomarkers which are specific and chemical identification; these specific biomarkers include gadolinium, calcium super paramagnetic iron oxide nano- particles. Based on its improved specificity in the identification of the lesions’ magnetic signatures, the QSM field has rapidly progressed in terms of algorithms as well as optimization of post- processing strategies. In QSM, the phase images are utilized, susceptibility source to magnetic field inverse problem is solved and susceptibility distribution in three dimensions is generated. QSM measurements are largely affected by concentration of iron; high concentrations largely reduce the signal decay time and the measurements, according to Lu et al (2018), were only accurate if there was a large dynamic range of iron concentrations. Both BOLD and QSM measure quantitative magnetic susceptibility units. Similar variability is seen from the plots on comparison of measurements and correlation coefficients; on statistical maps, they however portray less sensitivity (McDonald et al, 2015).

In the order of regulatory importance, the following factors regulate the cerebral blood flow through reflexive responses: cardiac output, activity of the neurons, arteria blood pressure, cerebral metabolism and the fluctuating arterial blood gases (Smith & Ainslie, 2017). OEF is notably an important brain metabolism parameter of measurement. Its measurement provides information on misery perfusion, which is about the deficiencies in the cerebral supply of blood in relation to oxygen. The corresponding values of CBF, OEF and CMRO2 are better in clinical trials. In positron emission tomography (PET) scans, excellent results on variability should be observed for CBF, CMRO2 and OEF (Rodgers et al, 2016). The physiological everyday variability of CBF may affect the variability of absolute OEF and CBF measurements. The gold standard approach for measurements of OEF is considered to be Positron emission tomography. PET has a number of disadvantages, for instance in relation to exposure to radiation and its limited availability. A single MRI phase image acquisition is yielded from QSM. This technique, in regards to the measurement of OEF, only allows for the estimation of the relative difference or change in OEF (Rodgers et al, 2016).

CMRO2 portrays a significant parameter of measurement, this is due to the reason that the brain ultimately depends on oxidative metabolism to meet its requirements (Rodgers et al, 2016). From recent studies, different CMRO2 quantification methods based on MRI have emerged. Traditionally, changes in CMRO2 were measured using PET scans. This was generally done with either hypocapnia or hypercapnia gas challenge; during these circumstances they would assume that there would also be change in metabolism (Wise et al, 2013). Other factors that directly affect CMRO2 would then be generated, thereof enabling the calculation of the changes. However, there is some work suggesting that measuring changes in CMRO2 can be done without gas-challenges (Liu et al., 2020). Therefore, this study opens up the possibility of being able to examine the brain in different conditions to measure CMRO2 changes. One condition we are interested in is hypoxia. According to Wise et al (2013), in their interleaved point of view towards CMRO2 and OEF changes in hypercapnia and hyperoxia, displayed notable increase in regional cerebral blood flow and decrease in OEF in the form of venous oxygen saturation which demonstrates a much bigger potential in the measurement of long term changes in CMRO2.

Hypoxia refers to a condition where there is deprivation of adequate levels of oxygen supply in the body or region of the body at the tissue level (Vastergaard et al, 2016). This leads the condition to two main classifications; generalized or local. According to Vastergaard et al (2017), although hypoxia may result from a number of reasons, normal physiology would also entail variations in the arterial oxygen concentrations, for instance strenuous exercise or hypoventilation training. There are certain changes that occur in the homeostasis of the brain during exposure to acute hypoxia in the normal human brain. Hypoxia, on the same note, also increases perfusion and the rate of metabolism, which in turn would portray an increase in neuronal activity.

References

Chen, S & Li, X. (2012) Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress. Computational and Mathematical Methods in Medicine

Ekstrom, A. (2010) How and when the FMRI BOLD signal relates to underlying neural activity: The danger in dissociation. Brain research reviews.

Hare, R. D., Smith, A. M., Forster, B. B., MacKay, A. L., Whittall, K. P., Kiehl, K. A., & Liddle, P. F. (1998). Functional magnetic resonance imaging: the basics of blood-oxygen-level dependent (BOLD) imaging. Canadian Association of Radiologists Journal, 49(5), 320.

Huttel, S., Song, A., McCarthy, G. (2009) Functional Magnetic Resonance Imaging. Massachusetts. Sinauer.

Liu, E. Y., Guo, J., Simon, A. B., Haist, F., Dubowitz, D. J., & Buxton, R. B. (2020) The potential for gas-free measurements of absolute oxygen metabolism during both baseline and activation states in the human brain. NeuroImage, 207, 116342

Logothetis, N. (2003) The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal. The Journal of Neuroscience

Lu, X., Ma, Y., Chang, E., He, Q., Searleman, A., Drygalski, A., Du, J. (2018) Simultaneous Quantitative Susceptibility Mapping (QSM) and R2 for High Iron Concentration Quantification with 3D Ultrashort Echo Time Sequences: An Echo Dependence Study. Magnetic Resonance in Medicine

Ma, Y., Sun, H., Cho, J., Mazerolle, E. L., Wang, Y., & Pike, G. B. (2020) Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging. Magnetic resonance in medicine, 83(1), 68-82.

McDonald, M., Williams, R., Berman, A., Mazerolle, E. (2015) Bold Oxygenation Level Dependence (BOLD) Quantitative Susceptibility Mapping (QSM) at Different Head Orientations. University of Calgary

Pierpaoli, C. (2010). Quantitative brain MRI.

Petcharunpaisan, S., Ramalho, J., Castillo, M. (2010) Arterial spin labeling in neuroimaging. World Journal of Radiology.

Rodgers, Z. B., Detre, J. A., & Wehrli, F. W. (2016). MRI-based methods for quantification of the cerebral metabolic rate of oxygen. Journal of Cerebral Blood Flow & Metabolism, 36(7), 1165-1185.

Reinchenbach, J., Schweser, F., Serres, B., Deistung, A. (2015) Quantitative Susceptability Mapping: Concepts and Applications. Clin Neuroradiol

Schmithorst, V., Vannes, J., Lee, G., Hernandez- Garcia, L., Plante, E., Rajagopal, A., Holland, S., CMIND Authorship Consortium. (2015) Evidence that neurovascular coupling underlying the BOLD effect increases with age during childhood. Hum Brain Mapp.

Shen, Q., Ren, H., Duong, T. (2008) CBF, BOLD, CBV, and CMRO2 fMRI signal Temporal Dynamics at 500- msec Resolution. J Magn Reson Imaging

Smith, K & Ainslie, P (2017) Regulation of cerebral blood flow and metabolism during exercise. Exp Physiol

Textor, S. (2018) Hypertension: A Companion to Braunwald’s Heart Disease. Renovascular Hypertension and Ischemic Nephropathy. 3rd ed.

Vestergaard, M. B., Lindberg, U., Aachmann-Andersen, N. J., Lisbjerg, K., Christensen, S. J., Law, I., et al. (2016) Acute hypoxia increases the cerebral metabolic rate - a magnetic resonance imaging study. Journal of Cerebral Blood Flow Metabolism, 36(6), 1046–1058.

Vestergaard, M. B., & Larsson, H. B. (2017) Cerebral metabolism and vascular reactivity during breath-hold and hypoxic challenge in freedivers and healthy controls. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 38, 0271678X1773790–15.

Wise, R. G., Harris, A. D., Stone, A. J., & Murphy, K. (2013) Measurement of OEF and absolute CMRO2: MRI-based methods using interleaved and combined hypercapnia and hyperoxia. Neuroimage, 83, 135-147.

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