Georgia Tech College of Engineering Georgia Tech College of Engineering Emory University School of Medicine

Biomedical Imaging Technology Center







fMRI Research




Functional MRI (fMRI) | Diffusion Tensor Imaging | Sequence Development | Molecular Imaging | Animal Imaging | Spectroscopy

Introduction

Functional Magnetic Resonance Imaging (fMRI) as an outgrowth of nuclear magnetic resonance (NMR) has been used for more than a decade now. Yet it continues to gain higher momentum every year as technology advances and our understanding of the principles underlying fMRI continues to grow and be utilized in a multitude of its clinical applications.

Although the first fMRI experiments used an exogenous gadolinium-based contrast agent, this technique was rapidly superseded by the discovery in 1990 that deoxyhemoglobin molecule is paramagnetic unlike its oxyhemoglobin counterpart and therefore could be used as an endogenous contrast agent. This so-called blood oxygenation level dependent (BOLD) contrast has proved to be a very sensitive MRI marker of neuronal activity based on a magnitude of oxygen extraction from capillary blood by neurons directly involved in an examined task. The sensitivity of BOLD contrast is such that experiments can be performed in individual subjects with temporal resolution beyond tens of milliseconds and spatial resolution of 3 mm or less in almost all cortical and subcortical structures of the brain. And since fMRI can measure neuronal activity in real time, it is capable of non-invasive investigation of functional attributes of the brain while performing a certain task.

The potential of fMRI utilization in clinical and research medicine as well as neuroscience is enormous and continues to grow every year. This technique is capable of generating maps of neuronal activity in the human brain in normal and diseased states. Functional neuroimaging is a superb tool for studying both acquired damage of neurons such as in cerebrovascular accidents and congenital abnormalities including but not limited to genetic disorders affecting cerebral function as well as psychiatric and neurodegenerative diseases which could be investigated very poorly prior to invention of fMRI.


Functional Connectivity

Recent studies in functional MRI have shown slowly varying timecourse fluctuations that are temporally correlated between functionally related areas. These low frequency oscillations (<0.08 Hz) seem to be a general property of symmetric cortices, and have been shown to exist in the motor, auditory, visual, and sensorimotor systems, among others [1-6]. Thus, these fluctuations agree with the concept of functional connectivity: a descriptive measure of spatio-temporal correlations between spatially distinct regions of cerebral cortex [7]. Several recent studies have further shown decreased low-frequency correlations for patients in pathological states [8-9]. Thus, low frequency functional connectivity is potentially important as an indicator of regular neuronal activity within the brain. Our current work involves investigating and characterizing these resting state functional connectivity patterns in normal and pathological subjects, including detection using model-free analysis methods.

Figure 1. Comparison of the significant activity in a motor task activation study (top) and the corresponding resting state functional connectivity study (bottom). Significant voxels are seen in the contralateral motor cortex and the SMA in task activation study, while both the contralateral and ipsilateral motor cortices are seen in the resting state functional connectivity pattern.

References
[1] B. Biswal, F.Z. Yetkin, V.M. Haughton, J.S. Hyde. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537-541 (1995).
[2] J. Hyde, B. Biswal. Functionally related correlation in the noise. In Functional MRI (C.T. Moonen, P.A. Bandettini, Eds.), pp. 263-275. Springer-Verlag, Berlin, 2000.
[3] Hampson, B.S. Peterson, P. Skudlarski, J.C. Gatenby, J.C. Gore. Detection of functional connectivity using temporal correlations in MR images. Human Brain Mapping 15:247-262 (2002).
[4] M.J. Lowe, B. Mock, J.A. Sorenson. Functional connectivity in single and multislice echoplanar imaging using resting state fluctuations. Neuroimage 7:119-132 (1998).
[5] D. Cordes, V. Haughton, K. Arfanakis, G. Wendt, P. Turski, C. Moritz, M. Quigley, M.E. Meyerand. Mapping functionally related regions of brain with functional connectivity MR imaging. Am J Neuroradiol 21:1636-1644 (2000).
[6] J. Xiong, L. Parsons, J. Gao, P. Fox. Interregional connectivity to primary motor cortex revealed using MRI resting state images. Human Brain Mapping 8:151-156 (1999).
[7] K.J. Friston, C. Frith, P. Liddle, R. Frickowiak. Functional connectivity: the principal components analysis of large (PET) data sets. J Cereb Blood Flow Metab 13:5-14 (1993).
[8] S.J. Li, B. Biswal, Z. Li, R. Risinger, C. Rainey, J. Cho, B. Salmeron, E. Stein. Cocaine administration decreases functional connectivity in human primary visual and motor cortex as detected by functional MRI. Magn Reson Med 43:45-51 (2000).
[9] M.J. Lowe, M.D. Phillips, D. Mattson, M. Dzemidzic, V.P. Matthews. Multiple sclerosis: Low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity-initial results. Radiology 224:184-192 (2002).

Publications
S.J. Peltier, D.C. Noll. T2* dependence of low frequency functional connectivity. Neuroimage 16:985-992 (2002).
S.J. Peltier, T.A. Polk, D.C. Noll. Model-free functional connectivity detection using self-organizing maps, in "Proc., ISMRM, 10th Annual Meeting, Honolulu, 2002", p. 1452.





Return to BITC Research

Return to BITC Intro Page