Functional activation using apparent diffusion coefficient-dependent contrast allows better spatial localization to the neuronal activity: evidence using diffusion tensor imaging and fiber tracking

Recent studies suggested that functional activation using apparent diffusion coefficient (ADC) contrast can be used to detect synchronized functional MRI (fMRI) signal changes during brain activation. Such changes may reflect better spatial localization to the smaller vessels, which are closely coupled to the true neuronal activation. Since it is generally believed that there are neural pathways among neuronally relevant areas, methods that would allow clear delineation of such pathways could help validate the neuronal relevance of the activated functional areas. The development of diffusion tensor imaging (DTI) has shown promise in detailed nerve fiber tracking. In this report, DTI was adopted to track the fiber connections among the discrete areas determined using the ADC contrast, in an effort to confirm the neuronal origin of these activated areas. As a comparison, activated areas using blood oxygenation level-dependent (BOLD) contrast were also obtained. Our results showed that the areas determined by the ADC contrast consistently allowed better fiber tracking within, while the BOLD-activated areas were more spatially diffused due to the smearing effect of brain vasculature, rendering the task of fiber tracking more difficult. This observation provides converging evidence that the activated areas using ADC contrast are more closely coupled to the neuronal activity than those using BOLD contrast.

[1]  Ravi S. Menon,et al.  Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. , 1993, Biophysical journal.

[2]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[3]  Allen W Song,et al.  On the timing characteristics of the apparent diffusion coefficient contrast in fMRI , 2002, Magnetic resonance in medicine.

[4]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[5]  J. R. Baker,et al.  The intravascular contribution to fmri signal change: monte carlo modeling and diffusion‐weighted studies in vivo , 1995, Magnetic resonance in medicine.

[6]  C. Poupon,et al.  Regularization of Diffusion-Based Direction Maps for the Tracking of Brain White Matter Fascicles , 2000, NeuroImage.

[7]  I. Loubinoux,et al.  Spreading of vasogenic edema and cytotoxic edema assessed by quantitative diffusion and T2 magnetic resonance imaging. , 1997, Stroke.

[8]  J. Detre,et al.  Technical aspects and utility of fMRI using BOLD and ASL , 2002, Clinical Neurophysiology.

[9]  Dae-Shik Kim,et al.  Localized cerebral blood flow response at submillimeter columnar resolution , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  W. T. Dixon,et al.  The effect of inhomogeneous sample susceptibility on measured diffusion anisotropy using NMR imaging. , 1995, Journal of magnetic resonance. Series B.

[11]  J. Gore,et al.  In vivo measurement of ADC change due to intravascular susceptibility variation , 1999, Magnetic resonance in medicine.

[12]  A. Song,et al.  Diffusion weighted fMRI at 1.5 T , 1996, Magnetic resonance in medicine.

[13]  Gregory McCarthy,et al.  Enhanced Spatial Localization of Neuronal Activation Using Simultaneous Apparent-Diffusion-Coefficient and Blood-Oxygenation Functional Magnetic Resonance Imaging , 2002, NeuroImage.

[14]  P. Basser,et al.  A simplified method to measure the diffusion tensor from seven MR images , 1998, Magnetic resonance in medicine.

[15]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[16]  E C Wong,et al.  Comparison of simultaneously measured perfusion and BOLD signal increases during brain activation with T1‐based tissue identification , 2000, Magnetic resonance in medicine.

[17]  J C Gore,et al.  Quantification of intravascular and extravascular contributions to BOLD effects induced by alteration in oxygenation or intravascular contrast agents , 1998, Magnetic resonance in medicine.

[18]  D. Tank,et al.  4 Tesla gradient recalled echo characteristics of photic stimulation‐induced signal changes in the human primary visual cortex , 1993 .

[19]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[20]  J B Poline,et al.  Transient decrease in water diffusion observed in human occipital cortex during visual stimulation , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[21]  R. Turner,et al.  Functional mapping of the human visual cortex at 4 and 1.5 tesla using deoxygenation contrast EPI , 1993, Magnetic resonance in medicine.

[22]  Robert Turner,et al.  The capillary network: a link between ivim and classical perfusion , 1992, Magnetic resonance in medicine.

[23]  M. Solaiyappan,et al.  In vivo three‐dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging , 1999, Magnetic resonance in medicine.

[24]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[25]  A. Song,et al.  Optimized isotropic diffusion weighting , 1995, Magnetic resonance in medicine.

[26]  R. S. Hinks,et al.  Time course EPI of human brain function during task activation , 1992, Magnetic resonance in medicine.

[27]  Wolfgang Engelien,et al.  A CBF-Based Event-Related Brain Activation Paradigm: Characterization of Impulse–Response Function and Comparison to BOLD , 2000, NeuroImage.