Center for Functional MRI In the Department of Radiology

UCSD fMRI Signal Processing and Dynamics Group

Overview

Our research group is based at the UCSD Center for Functional MRI. We are interested in
  1. Understanding the mechanisms of brain activity using an integrated approach involving fMRI, EEG, and MEG measures.
  2. The development of novel approaches for the design and integrated analysis of fMRI, EEG, and MEG experiments.
  3. Experimental and modeling work to elucidate the fundamental physiology and biomechanics of the hemodynamic response to neural stimulus.
  4. The development and application of arterial spin labeling methods to measure cerebral blood flow and other aspects of neurovascular function.
Because of the interdisciplinary nature of our research, we routinely use methods from statistics, signal processing, deep learning, biomechanics, physiology, and magnetic resonance physics. A full list of our publications can be found here. Papers describing some of our earlier work are provided below.

Experimental Design and Analysis

Our work in this area has focused on understanding and characterizing the trade-offs inherent in fMRI experiments
  1. Liu TT, Wong EC, Frank LR.,Buxton RB. Detection Power,Estimation Efficiency, and Predictability in Event-Related fMRI. NeuroImage, 13: 759-773, 2001.[PDF]

  2. Liu TT and Frank LR  Efficiency, power, and entropy in event-related FMRI with multiple trial types. Part I: theory. Neuroimage. 2004; 21(1): 387-400. [PDF] [Erratum]

  3. Liu TT. Efficiency, power, and entropy in event-related fMRI with multiple trial types. Part II: design of experiments. Neuroimage. 2004; 21(1): 401-13. [PDF]

Arterial Spin Labeling: Methods and Appplications

Our work in this area has focused on (1) the development of new arterial spin labeling (ASL) methods for the non-invasive measure of functional changes in cerebral blood flow (CBF) and (2) the application of ASL methods to measure functional CBF changes in brain regions such as the medial temporal lobe.
  1. Liu TT, Wong EC, Frank LR, Buxton RB. Analysis and Design of Perfusion Based Event-Related fMRI Experiments. NeuroImage.  2002;16:269-282. [PDF]

  2. Liu TT and Wong EC. A signal processing model for arterial spin labeling functional MRI. NeuroImage, 2005; 24:207-15. [PDF]

  3. Restom K, Behzadi Y, Liu TT.   Physiological noise correction for arterial spin labeling functional MRI. NeuroImage, 31:1104-1115, 2006. [PDF]

  4. Woolrich, M.W., Chiarelli, P., Gallichan, D., Perthen, J.E., Liu, TT.   Bayesian Inference of Hemodynamic Changes in Functional ASL Data, Magnetic Resonance in Medicine, 58:891-906, 2006. [PDF]

  5. Liu, T.T., and Brown, G.G.   Measurement of Cerebral Perfusion with Arterial Spin Labeling: Part 1. Methods, Journal of the International Neuropsychological Society, 13:517-25, 2007. [PDF]

  6. Brown, G.G., Clark C., Liu T.T.   Measurement of Cerebral Perfusion with Arterial Spin Labeling: Part 2. Clinical Applications, Journal of the International Neuropsychological Society, 13:527-38, 2007. [PDF]

  7. Behzadi, Y. and Liu, T.T.   A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI. NeuroImage (In Press).

  8. Restom, K., Bangen, K.J., Bondi, M.W., Perthen, J.E., Liu, T.T.,   Cerebral Blood Flow and BOLD Responses to a Memory Encoding Task: A Comparison Between Healthy Young and Elderly Adults. NeuroImage (In Press).

  9. Lu, K., Perthen, J.E., Duncan, R.O., Zangwill, L.M., Liu, T.T.   Noninvasive Measurement of the Cerebral Blood Flow Response in Human Lateral Geniculate Nucleus with Arterial Spin Labeling fMRI. Human Brain Mapping (Revision In Review).

fMRI Dynamics and Modeling

We use experimental and modeling approaches to better understand the link between neural activity and the hemodynamic response. In recent work, we have used caffeine to show that a decrease in baseline cerebral blood flow leads to significant changes in the hemodynamic response (Liu at al 2004). To explain these findings, we have developed a nonlinear dynamic model described in (Behzadi and Liu 2005). We are currently investigating extensions to the model and developing methods to obtain optimal estimates of model parameters.
  1. Miller KL, Luh WM, Liu TT, Martinez A, Obata T, Wong EC, Frank LR, Buxton RB.  Nonlinear temporal dynamics of the cerebral blood flow response. Human Brain Mapping.  2001;13:1-12 [PDF]

  2. Obata T, Liu TT, Miller, KL, Luh WM, Wong EC, Frank LR, Buxton RB. Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the balloon model to the interpretation of BOLD transients, NeuroImage.  2004; 21:144-153. [PDF]

  3. Uludag K,  Dubowitz DJ, Yoder EJ, Restom K,  Liu TT,  Buxton RB.  Coupling of cerebral blood flow and oxygen  consumption during physiological activation and  deactivation measured with fMRI. NeuroImage, 2004; 23: 148-155. [PDF]

  4. Buxton RB, Uludag K,  Dubowitz DJ,  Liu TT. Modeling the Hemodynamic Response to Brain Activation. NeuroImage, 2004; 23:S220-33. [PDF]

  5. Liu TT, Behzadi Y, Restom K, Uludag K, Lu K, Buracas GT, Dubowitz DJ, Buxton RB.  Caffeine Alters the Temporal Dynamics of the Visual BOLD Response.  NeuroImage, 2004; 23:1402-13. [PDF]

  6. Behzadi Y and Liu TT.  An arteriolar compliance model of the cerebral blood flow response to neural stimulus. NeuroImage, 2005; 25:1100-11. [PDF]

  7. Behzadi, Y and Liu TT.   Caffeine reduces the initial dip in the visual BOLD response. NeuroImage, 32:9-15, 2006. [PDF]