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Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is often divided into structural MRI and functional MRI (fMRI). The former is a widely used imaging technique in research as well as in clinical practice  for examining the anatomy and pathology of the brain. On the other hand the latter  measures brain activity by detecting changes associated with blood flow. Moreover in the last decade had been developed Perfusion weighted imaginga variety of MRI technique able to give insight into the perfusion tissues by blood

Structural Magnetic Resonance Imaging

Structural Magnetic Resonance Imaging provides static anatomical information of the brain and has become a widely used approach to investigate neuroanatomical correlates of both normal brain development and neurological disorders.  The cerebral cortex is a highly folded sheet of gray matter (GM) that lies inside the cerebrospinal fluid (CSF) and surrounds a core of white matter (WM). To analyse cortical structures, it is necessary to remove non-brain tissue and to classify the remaining tissue into GM, WM, and CSF.  Most typically, changes in grey matter have been assessed using T1-weighted images while changes in white matter are more typically explored using diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI) (Smith et al., 2006). Some of the most commonly used methods for investigating cortical grey matter in T1-weighted images include volumetric comparisons of manually, semi-automatically or automatically delineated neuroanatomical regions of interest, whole-brain voxel-based comparisons of grey matter and cortical surface-based comparisons of cortical thickness.

A commonly used method for performing voxel-based comparisons of grey matter is known as voxel-based morphometry or VBM (Ashburner and Friston, 2000; Wright et al., 1995). An alternative to volumetric measurements for assessment of subtle cortical changes in the human brain is represented by cortical thickness. (Fischl and Dale, 2000; Jones et al., 2000).  From a structural point of view, it also possible to estimate the location, orientation as well as anisotropy of the brain’s white matter tracts by means of Diffusion Tensor Imaging (Merboldt et al., 1985). It based on mapping the diffusion process of molecules (mainly water), in the brain. Indeed, water molecule diffusion patterns can reveal microscopic details about tissue architecture, either normal or in a diseased state. A special kind of DWI, diffusion tensor imaging (DTI), has been used extensively to map white matter tractography in the brain (Catani & Thiebaut de Schotten)

Functional Magnetic Resonance Imaging (fMRI)

Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow (Huettel et al., 2009). This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases (Logothetis et al., 2001). It is being used in many studies to better understand how the healthy brain works, and in a growing number of studies it is being applied to understand how that normal function is disrupted in disease. The metabolic changes studied by means of fMRI can be consequent to task-induced cognitive state changes or the result of unregulated processes in the resting brain. In the first case a subject alternates between periods of doing a particular task and a control state, such as 30 second blocks looking at a visual stimulus alternating with 30 second blocks with eyes closed.

The fMRI data is analyzed to identify brain areas in which the MR signal has a matching pattern of changes, and these areas are taken to be activated by the stimulus (Worsley & Friston, 1995). The second one is the case of Resting state fMRI (rsfMRI or R-fMRI) which is a method that can be used to evaluate regional interactions that occur when a subject is not performing an explicit task (Biswal, 2012).

Perfusion Weighted Imaging

Perfusion weight Imaging (e.g. Arterial Spin Labelling) is an alternative MRI method for measuring blood flow changes directly. One of the limitations of the BOLD signal is that it is always a signal change between two conditions, such as tapping your fingers compared to resting. For this reason, BOLD imaging can tell us nothing about the actual level of blood flow before the task started. With ASL it is possible to measure the absolute level of blood flow in any condition. For example, if blood flow decreases as Alzheimer’s disease develops, this could be detected with ASL methods but not with BOLD imaging. ASL works by manipulating the MR signal of arterial blood before it is delivered to different areas of the brain. By subtracting two images in which the arterial blood is manipulated differently, the static signal from all the hydrogen nuclei in the rest of the tissue subtracts out, leaving just the signal arising from the delivered arterial blood. ASL and BOLD imaging can be used together to provide a more quantitative probe of brain function, including assessment of oxygen metabolism changes, and this potential synergy is a primary motivation for ongoing research at the CFMRI in developing the next generation of fMRI methods [see Aslop et al., 2015]

References

Alsop DC, Detre JA, Golay X, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 2015; 73: 102–116

Ashburner J., Friston K.J. Voxel-based morphometry—the methods. NeuroImage. 2000;11:805–821.

Biswal BB. Resting state fMRI: a personal history. Neuroimage 2012; 62: 938–944

Catani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 2008 Sep;44(8):1105-32. doi: 10.1016/j.cortex.2008.05.004.

Fischl B., Dale A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U. S. A. 2000;97:11050–11055.

Huettel, S. A.; Song, A. W.; McCarthy, G. (2009), Functional Magnetic Resonance Imaging (2 ed.), Massachusetts: Sinauer, ISBN 978-0-87893-286-3

Jones S.E., Buchbinder B.R., Aharon I. Three-dimensional mapping of cortical thickness using Laplace’s equation. Hum. Brain Mapp. 2000;11:12–32.

Logothetis, N. K.; Pauls, Jon; Auguth, M.; Trinath, T.; Oeltermann, A. “A neurophysiological investigation of the basis of the BOLD signal in fMRI”. Nature. 2001. 412 (6843): 150–157. doi:10.1038/35084005. PMID 11449264. Our results show unequivocally that a spatially localized increase in the BOLD contrast directly and monotonically reflects an increase in neural activity.

Merboldt, K; Hanicke, W; Frahm, J (1985). “Self-diffusion NMR imaging using stimulated echoes”. Journal of Magnetic Resonance. 64 (3): 479–486. Bibcode:1985JMagR..64..479M. doi:10.1016/0022-2364(85)90111-8

Smith S.M., Jenkinson M., Johansen-Berg H., Rueckert D., Nichols T.E., Mackay C.E., Watkins K.E., Ciccarelli O., Cader M.Z., Matthews P.M., Behrens T.E. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage. 2006;31:1487–1505.

Worsley KJ, Friston KJ. Analysis of fMRI time series revisited Again. NeuroImage. 1995;2:173–181

Wright I.C., McGuire P.K., Poline J.B., Travere J.M., Murray R.M., Frith C.D., Frackowiak R.S., Friston K.J. A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. NeuroImage. 1995;2:244–252.

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