Brain Connectivity Mapping in Cerebrovascular Diseases

Amplitude and delays of BOLD spontaneous fluctuations

(T. Christen, H. Jahanian, W.W. Ni

Spontaneous fluctuations of the MR BOLD signal are usually acquired to explore the brain’s functional organization in resting-state BOLD (rsBOLD) fMRI studies. However, a few recent reports have suggested that rsBOLD could also be used for perfusion measurements. The amplitude of BOLD fluctuations could be related to blood volume/flow/oxygenation [1-2], while delays in the BOLD signal may reflect arterial arrival time [3-4].

In this project, we compare perfusion maps obtained with rsBOLD (no contrast agent used) to perfusion maps obtained with Dynamic Susceptibility Contrast (using Gadolinium contrast agent) in patients with cerebrovascular diseases.

From a standard echo EPI acquisition, we create maps of rsBOLD Standard deviation by transforming the time series to the frequency domain, and computing the amplitude of signal fluctuations as the squared root of the average of the power spectral density for each voxel. rsBOLD time lag maps are created by manually delineating a region of interest over the superior sagittal sinus vein. A cross correlation analysis is performed between this ‘seed’ signal and all other brain voxels. The analysis is also performed with the reference signal shifted from +/-5TR to account for possible time delays. The rsBOLD lag maps are eventually derived by taking the maximum of the correlation coefficient over the time lag.

Resting-State BOLD functional connectivity mapping in Moyamoya and Stroke patients

(H. Jahanian, T. Christen)

Resting state fMRI has been increasingly used to probe alterations of functional organization in neurological or psychiatric diseases [5]. In rsBOLD fMRI, functional networks are assessed using the temporal correlation between spontaneous BOLD signal fluctuations of spatially remote areas of the brain. We have created a pipeline to acquire resting-state fMRI data and create automatically maps of the 10 most common resting-state networks.

In neurological diseases where there are significant delays in different areas of the brain, standard rsBOLD fMRI analysis, both seed-based and using independent component analysis (ICA), may lead to erroneous identification of functional connectivity networks. In an effort to investigate the effects of these transit delays on rsBOLD fMRI, we study networks (see default mode network (DMN) result in Fig1) in Moyamoya and stroke patients and compare it with normal healthy volunteers. We also proposed for the first time a functional connectivity analysis method that accounts for transit delay.

Structural connectivity mapping with Diffusion Tensor Imaging

(S. Soman)

While carotid endarterectomy (CEA) has been noted to reduce the risk of future stroke in patients with high-grade stenosis, approximately 25% of CEA patients experience decline in postoperative neurocognitive function as measured on neuropsychological testing [6]. However, no imaging biomarkers have been established for identifying these patients. In this project, we apply structural connectivity graph analysis to identify patients at increased risk for cognitive decline after CEA using only preoperative T1 and Diffusion Tensor Imaging (DTI).


References: [1] Liu et al., JMRI 2007. [2] Wang et al, JMRI, 2008. [3] Lv et al., Ann Neurol, 2013. [4] Christen et al, JMRI, 2014. [5] Vargas et al. J Affect Disord 3:727-35, 2013. [6] Mocco et al. Neurosurgery, 2006; 58(5): 844–850.

Greg Zaharchuk, MD., PhD.

Associate Professor of Radiology
Office: Lucas Center, PS-04
Phone: (650) 735-6172
Email: gregz@stanford.edu

Michael E. Moseley, PhD.

Professor, Radiology
Office: Lucas Center, Rm PS-062
Phone: (650) 723-8697
email: moseley@stanford.edu