President, Milwaukee Chapter of the American Statistical Association (2006-2007)
Editorial Board: Journal of Neuroscience Methods www.elsevier.com/locate/jneumeth
Education
BS (Physics, Math minor) University of California, Irvine 1992
BS (Statistics) University of California, Riverside 1993
MS (Statistics) University of California, Riverside 1995
PhD (Statistics, Imaging Conc.) University of California, Riverside 1998
PostDoc (fMRI) California Institute of Technology 2001
Group Members
Research Description
Abstract: The long term goal of the Rowe Lab research efforts is to develop a unified mathematical model for functional magnetic resonance imaging (fMRI) that includes the fundamental physics of the nuclear magnetic resonance signal and the statistically significant determination of focal brain activation. The mathematical model will extract the most information from the acquired data in the most efficient way possible. This unified model will contain important physiological information that may not be available by other means or is only available by more time consuming elaborate means. This model will allow us to address important fundamental neuroscience questions with fMRI. The Rowe Lab's primary research efforts are focused on working deeper in the data acquisition and processing stream while expanding the unified model at each step.
Accomplishments: The fMRI technical process can be categorized into the five main research areas of hardware improvements, pulse sequence development, image reconstruction, time series modeling to determine activation, and activation map thresholding. The Rowe Lab efforts have involved working deeper into the processing stream. Thresholding: Initial and recent research efforts have examined the thresholding of statistical activation maps (Logan & Rowe, 2004; Logan, Geliazkova, & Rowe, 2007). Thresholding allows the objective statistical separation of voxels that contain signal (active) and those that contain noise (not active). Thresholding is important for the neurological interpretation of fMRI results. Activation: Further efforts have been aimed at a complete model of the true complex-valued (magnitude-phase or real-imaginary) voxel time series to produce statistical activation maps (Rowe & Logan, 2004; Rowe & Logan, 2005; Rowe, 2005a; Rowe, 2005b) in contrast to the commonly used magnitude-only model. Additionally, phase-only activation has been examined with an angular regression model (Rowe, Meller, & Hoffmann, 2007) as has more precise magnitude-only activation (Zhu et al., 2007). These models yield statistical activation maps that can be thresholded with the methods examined in the Rowe lab. The mathematics for Dr. Rowe’s activation models convincingly demonstrate that more biological information, such as vascularity within voxels, can be extracted with the use of the phase portion of the complex-valued time series (Nencka & Rowe, 2007a) and that increased statistical detection power can be achieved (Rowe, 2005a). Reconstruction: Continuing research efforts have connected complex-valued voxel measurements to the original pre-reconstruction (inverse Fourier transformation) complex-valued k-space (spatial frequency) measurements (Rowe, Nencka, & Hoffmann, 2007). Voxel measurements can be written as a linear combination of k-space measurements. This relationship has allowed the connection of Dr. Rowe’s complex-valued time series activation models to k-space and thus has led to a brain activation model that determines statistical activation in terms of the original complex-valued k-space (spatial frequency) measurements (Rowe, 2007a; Rowe, 2007b). With the linear relationship between complex-valued k-space and voxel measurements, it has made possible the examination of modified and potentially induced correlation between voxels due to pre-reconstruction pre-processing methods (Nencka & Rowe, 2007b; Nencka & Rowe, 2008a; Nencka & Rowe, 2008b).
Current Efforts: Challenges have existed experimentally with humans (not with phantoms) to robustly demonstrate an improvement with the activation models that Dr. Rowe have developed. However these challenges have recently been overcome. In humans ancillary temporally varying physiological processes such as respiration produce dynamic magnetic field changes that manifest as temporally varying signals that are not of interest (and geometric warping of images). These physiologic signals that are not of interest manifest largely in the phase portion of the complex-valued time series and are very difficult to mathematically model. The unmodeled physiologic signals increase the models’ residual variance (while leaving the estimated signal of interest nearly unchanged) and decrease detection power. Pulse Sequence: This challenge has been overcome with the correction of magnetic field inhomogeneities through time to remove the physiological signal that is not of interest (Hahn, Nencka, & Rowe, 2008a, Hahn, Nencka, & Rowe, 2008b). Preliminary results demonstrate that the correction for the dynamic magnetic field changes removes nearly all of the ancillary signals (and geometrically unwarps the images). This allows Dr. Rowe’s statistical activation models to be utilized for the extraction of more biological information such as vascularity within voxels and increased detection power. For fMRI to address fundamental neuroscience questions such as the diagnosis of human brain disease and the precise localization of brain function for clinical presurgical mapping, the signal needs to be considered in light the advances in the previously described research.
Future Efforts: Current efforts are aimed at extending the brain activation model to include and estimate both the proton spin density and the transverse relaxation (Nencka, Hahn, & Rowe, 2008a; Nencka, Hahn, & Rowe, 2008b). Estimating proton spin density and transverse relaxation will allow the extraction of more biological information within voxels in terms of tangible physiologic parameters. This may allow the extraction of sub-voxel information such as vascularity or possibly direct detection of neuronal firing via local magnetic field changes. Future plans include the examination of parallel imaging methods and the linking of them to the aforementioned body of work. The Rowe Lab’s research efforts involve the theoretical development of new mathematical methods, their statistical characterization by computer simulation, and validation by human experiments. This will enable better understanding of brain function and better understanding of how it is affected by neurodegenerative diseases, mental illnesses, and brain injuries.
Annotated Relevant References:
Hahn, A.D., Nencka, A.S., and Rowe, D.B.: Dynamic Compensation of B0 Field Inhomogeneities Restores Complex fMRI Time Series Activation Power. Proc. Intl. Soc. Magn. Reson. Med., 16, 2008. (Contribution: Estimate and adjust for dynamic magnetic field changes. Significance: Removes nearly all temporally varying ancillary signals such as respiration to reduce the residual variance of fMRI activation models that use phase information. Allows statistical activation models that utilize phase information to be used for the extraction of more biological information and for increased detection power.)
Hahn, A., Nencka, A.S., Rowe, D.B.: Dynamic compensation for temporal variations in the homogeneity of the main magnetic field (B0) with application to fMRI. In submission, 2008b. (Contribution: Formal model for the estimation and adjustment for dynamic magnetic field changes both in simulated and real data. Significance: Removes nearly all temporally varying ancillary signals such as respiration to reduce the residual variance of fMRI activation models that use phase information. Allows complex-valued statistical activation models that utilize phase information to be used for the extraction of more biological information and for increased detection power.)
Logan, B.R. and Rowe, D.B.: An evaluation of thresholding techniques in fMRI analysis. NeuroImage 22(1):95-108, 2004. (Contribution: Evaluated the operating characteristics of voxel thresholding methods. Significance: In practice computationally intensive permutation resampling methods that account for spatial correlation do not need to be utilized.)
Logan, B.R., Geliazkova, M.P., and Rowe, D.B.: An Evaluation of spatial thresholding techniques in fMRI analysis. Hum. Brain Mapp., Status: In Press, 2007. (Contribution: Operating characteristics and properties of thresholding methods that utilize the activation status of neighboring voxels are examined. Significance: The Bayesian spatial mixture model performs optimally among the thresholding methods that were considered.)
Nencka, A.S., Hahn, A.D., and Rowe, D.B.: Redundant Spatial Harmonic Information in Zeugmatography with Linear Encoding (R-SHIZLE) Theoretically Encodes Intra-Acquisition Decay. Submitted to Proc. Intl. Soc. Magn. Reson. Med., 16, 2008. (Contribution: Introduces a method of estimating a T2* or T2 map within a single EPI acquisition, even under the circumstance of nonnegligible magnetic field inhomogeneity. Significance: Provides theoretical work for making quantitative measurements of T2* or T2 from a single image acquisition following a single excitation pulse by utilizing the expected symmetry of k-space observations.)
Nencka, A.S., Hahn, A.D., Rowe, D.B.: Estimation of magnetic resonance transverse relaxation and proton density from broken k-space symmetry. In submission, 2008b. (Contribution: Estimates T2* or T2 map within a single EPI acquisition, even under the circumstance of nonnegligible magnetic field inhomogeneity using both simulated and real data. Significance: Provides theoretical work for making quantitative measurements of T2* or T2 from a single image acquisition following a single excitation pulse by utilizing the expected symmetry of k-space observations.)
Nencka, A.S. and Rowe, D.B.: Reducing the unwanted draining vein BOLD contribution in fMRI with statistical post-processing methods. NeuroImage 37(1):177-188, 2007a. (Contribution: Developed Monte Carlo simulations and examined Human echo planar imaging data with two activation methods. Significance: We found that the complex-valued model (Rowe and Logan, 2005) exhibits a strong bias against detecting magnitude signal changes in voxels that have task related phase changes (characteristic of unwanted signal from draining veins). Thus the complex model yields grey matter voxels that are a subset of those from the magnitude-only model.)
Nencka, A.S. and Rowe, D.B.: Image space correlations induced by k-space processes. Proc. Organization for Hum. Brain Mapp. S55:284, 2007b. (Contribution: Uses relationship between complex-valued k-space and complex-valued voxel measurements to characterize voxel correlation induced by preprocessing. Significance: These adjustments produce or modify the spatial correlation between voxel measurements. Possible k-space adjustments include the shifting of alternating lines to correct the signal (but also the noise) to eliminate ghosting, apodization of k-space measurements, and partial k-space acquisition since many spatial frequencies are identically the same numbers. The true correlation (connectivity) between voxels may be less than (or greater than) previously thought.)
Nencka, A.S., Rowe, D.B.: Apodization and Smoothing Alter Voxel Time Series Correlations. Accepted to Proc. Intl. Soc. Magn. Reson. Med., 16, 2008. (Contribution: Uses relationship between complex-valued k-space and voxel measurements to characterize the correlation between voxels induced by the consistent temporal application of apodization and smoothing of k-space data. Significance: This consistently added spatial correlation over a time series leads to correlation between voxel time series and thus can affect connectivity measurements. The effects of apodization on connectivity measurements are shown to be non-negligible.)
Nencka, A.S., Rowe, D.B.: K-space preprocessing before magnitude fMRI time series formation modifies and could induce voxel correlation and ROI connectivity. In submission, 2008b. (Contribution: Extends and examines previous work (Nencka and Rowe, 2007b) to correlation between magnitude time series to characterize voxel correlation induced by preprocessing. Significance: The pre-reconstruction pre-magnitude time series formation pre-processing adjustments produce or modify the spatial correlation between voxels. The true correlation (connectivity) between voxels may be greater than or less than previously thought.)
Rowe, D.B. and Logan, B.R.: A complex way to compute fMRI activation. NeuroImage 23(3):1078-1092, 2004. (Contribution: Determined magnitude fMRI activation in complex-valued voxel time series data while specifying the traditionally believed voxel-wise temporally constant but spatially varying phase. Significance: 1) Uses the correct thermal noise statistical distribution of bivariate normality with phase coupled means and not the incorrect normal assumption for the magnitudes. 2) Possesses increased detection power at all SNRs by inclusion of all 2n real-imaginary observations instead of n magnitude quantities. 3) Produces more highly-focused activation regions that are better localized to grey matter which is exactly where firing neurons and functional activation should be localized.)
Rowe, D.B. and Logan, B.R.: Complex fMRI analysis with unrestricted phase is equivalent to a magnitude-only model. NeuroImage 24(2):603-606, 2005. (Contribution: Outlined a more general magnitude fMRI activation based on complex-valued data that assumed unrestricted or unique temporal phase values. Significance: 1) Derived the same regression coefficients and activation statistics as used for the usual magnitude-only data model. 2) By deriving these statistics we now understand the complex-valued assumptions inherent in the commonly used magnitude-only fMRI activation model.)
Rowe, D.B.: Parameter estimation in the complex fMRI model. NeuroImage, 25(4):1124-1132, 2005a. (Contribution: Two models were evaluated in terms of parameter estimation and brain activation statistics (Rowe, 2005a). Significance: 1) Showed that the unrestricted phase or magnitude-only parameter estimates become increasingly biased as the SNR decreases whereas the complex-valued model is unbiased at all SNR levels. 2) The parameter estimates achieved their Cramer-Rao variance lower bound for the complex-valued model regardless of SNR while the magnitude-only model did not. 3) The complex-valued activation statistic was uniformly higher than the magnitude-only model.)
Rowe, D.B.: Modeling both the magnitude and phase of complex-valued fMRI data. NeuroImage, 25(4):1310-1324, 2005b. (Contribution: Developed a more general fMRI model that simultaneously describes both the magnitude and phase of complex-valued fMRI data, thus allowing the observed data to be fully utilized in answering important biological questions (Rowe, 2005b). Significance: 1) Can determine signal changes corresponding to true activation close to the activation site via blood oxygenation to the highly-localized capillary bed. 2) These activation maps have drastically reduced contamination by unwanted draining veins carrying away the blood for long distances from the activation site that also exhibit task related phase changes (TRPCs).)
Rowe, D.B., Nencka, A.S., and Hoffmann, R.G.: Signal and noise of Fourier reconstructed fMRI data. J. Neurosci. Methods, 159(2):361-369, 2007. (Contribution: Related measured complex-valued k-space spatial frequencies and complex-valued images. Significance: 1) This allows the computation of fMRI brain activation directly from unreconstructed originally observed k-space measurements (Rowe, 2007). 2) This allows modeling of covariation between the originally acquired data in its more natural setting rather than a very nonintuitive complicated transformed version between voxels.)
Rowe, D.B., Meller, C.P., and Hoffmann, R.G.: Characterizing phase-only fMRI data with an angular regression model. J. Neurosci. Methods, 161(2):331-341, 2007. (Contribution: Phase-only fMRI activation is examined using an angular regression model, linear independent variable (x) and angular dependent variable (y). Significance: 1) No longer need to unwrap phase time series. 2) Accurate regression coefficient and variance estimates.)
Rowe, D.B.: FMRI Activation in image space from k-space data. Proc. Organization for Hum. Brain Mapp., S114:377, Chicago, USA, 2007a. (Contribution: Computes brain activation in image space in terms of k-space measurements. Significance: 1) Activation is one step closer to the original data. 2) The relationship between k-space measurements and correlation can be incorporated instead of trying to model correlation between voxels that are more complicatedly related.)
Rowe, D.B.: FMRI statistical brain activation from k-space data. Proc. Am. Stat. Assoc. Biometrics Section, 12:107-114, 2007b. (Contribution: Computes brain activation in image space in terms of k-space measurements. The correlation between voxel measurements can also be written in terms of correlation between k-space measurements. Significance: 1) Activation and association is one step closer to the original data. 2) The relationship between k-space measurements and correlation can be incorporated instead of trying to model correlation between voxels that are more complicatedly related.)
Zhu, H., Li, Y., Ibrahim, J.G., Shi, X., An, H., Chen, Y., Lin, W., Rowe, D.B., Peterson, B.S.: Rician Regression Models for Magnetic Resonance Images. In submission, 2008. (Contribution: Introduce a more precise magnitude-only Rician regression model to characterize background noise to properly estimate model parameters in various MRI techniques such as diffusion weighted images and functional MRI. Significance: Model parameters are able to be accurately estimated for lower SNRs.)