Complex-valued Time Series Modeling for Improved Activation Detection in fMRI Studies

A complex-valued data-based model with th order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model. Such improved accuracy in mapping the left functional central sulcus has important implications in neurosurgical planning for tumor and epilepsy patients. Additionally, we develop magnitude and phase detrending procedures for complex-valued time series and examine the effect of spatial smoothing. These methods improve the power of complex-valued data-based activation statistics. Our results advocate for the use of the complex-valued data and the modeling of its dependence structures as a more efficient and reliable tool in fMRI experiments over the current practice of using only magnitude-valued datasets

A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNRs). We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. We develop models with complex-valued spike-and-slab priors on the activation parameters that are able to combine the magnitude and phase information. We present a complex-valued EM variable selection algorithm that leads to fast detection at the voxel level in CV-fMRI slices and also consider full posterior inference via Markov chain Monte Carlo (MCMC). Model performance is illustrated through extensive simulation studies, including the analysis of physically based simulated CV-fMRI slices. Finally, we use the complex-valued Bayesian approach to detect active voxels in human CV-fMRI from a healthy individual who performed unilateral finger tapping in a designed experiment. The proposed approach leads to improved detection of activation in the expected motor-related brain regions and produces fewer false positive results than other methods for CV-fMRI. Supplementary materials for this article are available online

Analysing multi-person timing in music and movement : event based methods

Accurate timing of movement in the hundreds of milliseconds range is a hallmark of human activities such as music and dance. Its study requires accurate measurement of the times of events (often called responses) based on the movement or acoustic record. This chapter provides a comprehensive over - view of methods developed to capture, process, analyse, and model individual and group timing [...] This chapter is structured in five main sections, as follows. We start with a review of data capture methods, working, in turn, through a low cost system to research simple tapping, complex movements, use of video, inertial measurement units, and dedicated sensorimotor synchronisation software. This is followed by a section on music performance, which includes topics on the selection of music materials, sound recording, and system latency. The identification of events in the data stream can be challenging and this topic is treated in the next section, first for movement then for music. Finally, we cover methods of analysis, including alignment of the channels, computation of between channel asynchrony errors and modelling of the data set

Cortical lamina-dependent blood volume changes in human brain at 7T

Cortical layer-dependent high (sub-millimeter) resolution functional magnetic resonance imaging (fMRI) in human or animal brain can be used to address questions regarding the functioning of cortical circuits, such as the effect of different afferent and efferent connectivities on activity in specific cortical layers. The sensitivity of gradient echo (GE) blood oxygenation level-dependent (BOLD) responses to large draining veins reduces its local specificity and can render the interpretation of the underlying laminar neural activity impossible. The application of the more spatially specific cerebral blood volume (CBV)-based fMRI in humans has been hindered by the low sensitivity of the noninvasive modalities available. Here, a vascular space occupancy (VASO) variant, adapted for use at high field, is further optimized to capture layer-dependent activity changes in human motor cortex at sub-millimeter resolution. Acquired activation maps and cortical profiles show that the VASO signal peaks in gray matter at 0.8–1.6 mm depth, and deeper compared to the superficial and vein-dominated GE-BOLD responses. Validation of the VASO signal change versus well-established iron-oxide contrast agent based fMRI methods in animals showed the same cortical profiles of CBV change, after normalization for lamina-dependent baseline CBV. In order to evaluate its potential of revealing small lamina-dependent signal differences due to modulations of the input-output characteristics, layer-dependent VASO responses were investigated in the ipsilateral hemisphere during unilateral finger tapping. Positive activation in ipsilateral primary motor cortex and negative activation in ipsilateral primary sensory cortex were observed. This feature is only visible in high-resolution fMRI where opposing sides of a sulcus can be investigated independently because of a lack of partial volume effects. Based on the results presented here, we conclude that VASO offers good reproducibility, high sensitivity and lower sensitivity than GE-BOLD to changes in larger vessels, making it a valuable tool for layer-dependent fMRI studies in humans

Complex-valued analysis of arterial spin labeling–based functional magnetic resonance imaging signals

Cerebral blood flow-dependent phase differences between tagged and control arterial spin labeling images are reported. A biophysical model is presented to explain the vascular origin of this difference. Arterial spin labeling data indicated that the phase difference is largest when the arterial component of the signals is preserved but is greatly reduced as the arterial contribution is suppressed by postinversion delays or flow-crushing gradients. Arterial vasculature imaging by saturation data of activation and hypercapnia conditions showed increases in phase accompanying blood flow increases. An arterial spin labeling functional magnetic resonance imaging study yielded significant activation by magnitude-only, phase-only, and complex analyses when preserving the whole arterial spin labeling signal. After suppression of the arterial signal by postinversion delays, magnitude-only and complex models yielded similar activation levels, but the phase-only model detected nearly no activation. When flow crushers were used for arterial suppression, magnitude-only activation was slightly lower and fluctuations in phase were dramatically higher than when postinversion delays were used. Although the complex analysis performed did not improve detection, a simulation study indicated that the complex-valued activation model exhibits combined magnitude and phase detection power and thus maximizes sensitivity under ideal conditions. This suggests that, as arterial spin labeling imaging and image correction methods develop, the complex-valued detection model may become helpful in signal detection. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64527/1/22106_ftp.pd

Complex-Valued Time-Series Correlation Increases Sensitivity in FMRI Analysis

Purpose To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis. Materials and Methods The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform of MO and CV correlations for varying degrees of task related magnitude and phase amplitude change in the time-series. The increased sensitivity of the complex-valued Fourier representation of correlation is also demonstrated with experimental human data. Since the correlation description in the temporal frequency domain is represented as a summation of second order temporal frequencies, the correlation is easily divided into experimentally relevant frequency bands for each voxel\u27s temporal frequency spectrum. The MO and CV correlations for the experimental human data are analyzed for four voxels of interest (VOIs) to show the framework with high and low contrast-to-noise ratios in the motor cortex and the supplementary motor cortex. Results The simulation demonstrates the increased strength of CV correlations over MO correlations for low magnitude contrast-to-noise time-series. In the experimental human data, the MO correlation maps are noisier than the CV maps, and it is more difficult to distinguish the motor cortex in the MO correlation maps after spatial processing. Conclusions Including both magnitude and phase in the spatial correlation computations more accurately defines the correlated left and right motor cortices. Sensitivity in correlation analysis is important to preserve the signal of interest in fMRI data sets with high noise variance, and avoid excessive processing induced correlation

Dynamics of quantum materials at the nanoscale

Programming the properties of quantum materials on demand is a central goal of condensed matter physics with the potential to usher in a new technological era. Photoexcitation has proven to be an exceptionally capable means of resonant and non-resonant control over matter offering coveted routes to selectively control the electronic, lattice, interband or valley optical and excitonic properties of quantum materials. One major limitation of probing the rich class of phenomena enabled by photoexcitation is the diffraction limit. The properties of quantum materials are often sensitive to the microscopic details of the environment at phase transition boundaries: which naturally leads drastic inhomogeneity at the nanoscale. In other cases, the media may transiently support high-momentum “nano-light” or host topologically protected conductive channels that are localized to one-dimensional physical edges. All of these phenomena demand a probe with the spatial resolution that is commensurate with the emergent behavior. To address these demands the author contributed to the development of time-resolved scattering near-field optical microscopy (Tr-SNOM). Utilizing the principles developed as part of this thesis amplified laser technology was combined with a commercial near-field optical microscope to produce a state-of-the-art time-resolved nanoscope. The custom apparatus operates with twenty nanometer spatial resolution with unprecedented spectral coverage spanning visible to mid-infrared all with (30-300) femtosecond temporal resolution. The experimental apparatus was, first, applied to investigate the photo-induced insulator-to-metal transition in Vanadium Dioxide. We observe nanoscale inhomogeneity of the transient conductivity. Our data reveals that local nanoscopic variations of the strain exist in our particular VO2 thin film at equilibrium. Regions of compressive strain are, furthermore, found to correlate with regions where a high degree of transient conductivity is attained. Our systematic study of the local fluence dependence and dynamics reveal that the fluence threshold, Fc, for the monoclinic-insulator to rutile-metal transition is inhomogeneous in real-space. A second growth process is identified, even at excitations fluences well below Fc, which operates on a longer timescale with an inhomogeneous rise time, tau-1. Together Fc and tau-1 govern the inhomogeneous nano-texturing of the transient conductivity. Secondly, we uncover that crystals of van-der Waals (vdW) semiconductors behave as optical waveguides with broadly tunable properties at femto-second time scales. We detect giant optical phase shifts of waveguided photons under strong photo-excitation devoid of any unwanted added losses in the vdW crystal, WSe2. Our results firmly implicate bound excitons in the observed behavior. Our transient spatio-temporal maps reveal two concomitant effects: i) photo-generation of electron-hole plasma that drives the WSe2 crystal towards a Mott transition where excitons dissociate and ii) a coherent interaction between the waveguide material and pump light, known as the optical Stark effect, that alters the phase velocity of guided photons on the femtosecond timescale

Incorporation of phase changes in functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) data is acquired as a complex image pair including magnitude and phase information. The vast majority of fMRI experiments do not attempt to take advantage of the time varying phase information. The phase of the MRI signal is related to the local magnetic field changes, suggesting it may contain useful information about the source of hemodynamic activity. Analysis of phase data acquired from different fMRI experiments has shown the presence of activity in response to various stimuli. However, there have been no studies which have examined phase data in a larger group of subjects for multiple types of fMRI tasks, nor have studies examined phase changes due to event-related stimuli. In this thesis, we examine the magnitude and phase changes in group data in a block-design motor tapping task and in an event-related auditory oddball task. We also look at any additional processing steps that might be required for phase. The results for both block-design and event-related tasks indicate the presence of task related information in the phase data with phase only and magnitude only approaches showing signal changes in the expected brain regions. Techniques like temporal smoothing and Gaussian smoothing seem to help improve the results. Although there is more overall activity detected with magnitude data, the phase only analysis also reveals activity in regions expected to be involved in the task, but not significantly activated in the magnitude only analysis, suggesting that the phase might provide some unique information. In addition, the phase can potentially increase sensitivity within regions also showing magnitude changes. Future work should focus on additional methods for combining the magnitude and phase data