N170 is an important neurophysiological index to study the underlying mechanisms of face and object perception. In this
study, we proposed a mean-sensitive spatial filtering (MSF) method for linear transformation of event-related potentials
(ERP) that is sensitive to mean differences between stimuli conditions and applied it to N170 component to extract
category-specific spatio-temporal features contained in EEG. MSF method estimated a set of optimal projecting vectors
according to the spatial distribution patterns of N170 means. Then, we applied these spatial filters to single-trial ERP
data and perform classification on the extracted features. In this way, the presence of a larger negative component in
EEG time courses evoked by faces can be detected robustly in single trial EEG, and hereby we can infer the category of
every presented stimulus from faces and objects. Furthermore, we also successfully extracted the unobvious distinct
spatial patterns between cars and cats with MSF and separated them correctly. Our remarkable and robust classification
performances suggest that MSF works well in extracting stable spatial patterns from N170. Therefore, MSF provides a
promising solution for decoding presented visual information basing on single-trial N170 component.
KEYWORDS: Diffusion, Data modeling, Diffusion tensor imaging, Magnetic resonance imaging, 3D modeling, Error analysis, Signal attenuation, Anisotropy, Signal to noise ratio, Computer simulations
In conventional diffusion tensor imaging (DTI) based on magnetic resonance data, each voxel is assumed to contain a single component having diffusion properties that can be fully represented by a single tensor. In spite of its apparent lack of generality, this assumption has been widely used in clinical and research purpose. This resulted in situations where correct interpretation of data was hampered by mixing of components and/or tractography. Even though this assumption can be valid in some cases, the general case involves mixing of components resulting in significant deviation from the single tensor model. Hence, a strategy that allows the decomposition of data based on a mixture model has the potential of enhancing the diagnostic value of DTI. This work aims at developing a stable solution for the most general problem of multi-component modeling of diffusion tensor imaging data. This model does not include any assumptions about the nature or volume ratio of any of the components and utilizes a projection pursuit based strategy whereby a combination of exhaustive search and least-squares estimation is used to estimate 1D projections of the solution. Then, such solutions are combined to compute the multidimensional components in a fast and robust manner. The new method is demonstrated by both computer simulations and real diffusion-weighted data. The preliminary results indicate the success of the new method and its potential to enhance the interpretation of DTI data sets.
Synchronized oscillations in resting state timecourses have been detected in recent fMRI studies. These oscillations are low frequency in nature (< 0.08 Hz), and seem to be a property of symmetric cortices. These fluctuations are important as a potential signal of interest, which could indicate connectivity between functionally related areas of the brain. It has also been shown that the synchronized oscillations decrease in some spontaneous pathological states. Thus, detection of these functional connectivity patterns may help to serve as a gauge of normal brain activity. The cognitive effects of muscle fatigue are not well characterized. Sustained fatigue has the potential to dynamically alter activity in brain networks. In this work, we examined the interhemispheric correlations in the left and right primary motor cortices and how they change with muscle fatigue. Resting-state functional MRI imaging was done before and after a repetitive unilateral fatigue task. We find that the number of significant correlations in the bilateral motor network decreases with fatigue. These results suggest that resting-state interhemispheric motor cortex functional connectivity is affected by muscle fatigue.
In this paper, we have investigated local spatial couplings in the human brain by applying nonlinear dynamical techniques on fMRI data. We have recorded BOLD-contrast echo-planar fMRI data along with high-resolution T1-weighted anatomical images from the resting brain of healthy human subjects and performed physiological correction on the functional data. The corrected data from resting subjects is spatially embedded into its phase space and the largest Lyapunov exponent of the resulting attractor is calculated and whole slice maps are obtained. In addition, we segment the high-resolution anatomical image and obtain a down sampled mask corresponding to gray and white matter, which is used to obtain mean indices of the exponents for both the tissues separately. The results show the existence of local couplings, its tissue specificity (more local coupling in gray matter than white matter) and dependence on the size of the neighborhood (larger the neighborhood, lesser the coupling). We believe that these techniques capture the information of a nonlinear and evolving system like the brain that may not be evident from static linear methods. The results show that there is evidence of spatio-temporal chaos in the brain, which is a significant finding hitherto not reported in literature to the best of our knowledge. We try to interpret our results from healthy resting subjects based on our knowledge of the native low frequency fluctuations in the resting brain and obtain a better understanding of the local spatial behavior of fMRI. This exploratory study has demonstrated the utility of nonlinear dynamical techniques like spatial embedding in analyzing fMRI data to gain meaningful insights into the working of human brain.
KEYWORDS: Linear filtering, Magnetic resonance imaging, Brain, Statistical analysis, Data acquisition, Functional magnetic resonance imaging, Algorithm development, Scanners, Medical imaging, Physiology
Synchronized oscillations in resting state timecourses have been detected in recent fMRI studies. These oscillations are low frequency in nature (<0.08 Hz), and seem to be a property of symmetric cortices. These fluctuations are important as a pontential signal of interest, which could indicate connectivity between functionally related areas of the brain. It has also been shown that the synchronized oscillations decrease in some spontaneous pathological states (such as cocaine injection). Thus, detection of these functional connectivity patterns may help to serve as a guage of normal brain activity. Currently, functional connectivity detection is applied only in offline post-processing analysis. Online detection methods have been applied to detect task activation in functional MRI. This allows real-time analysis of fMRI results, and could be important in detecting short-term changes in functional states. In this work, we develop an outline algorithm to detect low frequency resting state functional connectivity in real time. This will extend connectivity analysis to allow online detection of changes in "resting state" brain networks.
KEYWORDS: Independent component analysis, Denoising, Data modeling, Magnetic resonance imaging, Interference (communication), Functional magnetic resonance imaging, Data acquisition, Brain, Principal component analysis, Signal to noise ratio
Resting state oscillations have been detected in functional MRI studies, and appear to be synchronized between functionally related areas. It has also been shown that these synchronized oscillations decrease in some pathological states. Thus, these fluctuations are important as a potential signal of interest, which could indicate connectivity between functionally related areas of the brain. A current challenge is to detect these patterns without using an external reference. ICA analysis is a promising model-free technique that finds the independent components in a data set. A drawback to using ICA is the possibility of convergence problems in the presence of noise, and signal mixing across components. This work utilizes a recently developed denoising method as a preprocessing step to condition task and resting state functional MRI data for ICA analysis. The advantages of this approach include increased reliability of ICA results and allowing region specific signal patterns to be separated using a model-free analysis.
KEYWORDS: Functional magnetic resonance imaging, Brain, Physiology, Nonlinear dynamics, Signal to noise ratio, Brain mapping, Dynamical systems, Time metrology, Magnetic resonance imaging, Neuroimaging
Functional magnetic resonance imaging (fMRI) is a technique that is sensitive to correlates of neuronal activity. The application of fMRI to measure functional connectivity of related brain regions across hemispheres (e.g. left and right motor cortices) has great potential for revealing fundamental physiological brain processes. Primarily, functional connectivity has been characterized by linear correlations in resting-state data, which may not provide a complete description of its temporal properties. In this work, we broaden the measure of functional connectivity to study not only linear correlations, but also those arising from deterministic, non-linear dynamics. Here the delta-epsilon approach is extended and applied to fMRI time series. The method of delays is used to reconstruct the joint system defined by a reference pixel and a candidate pixel. The crux of this technique relies on determining whether the candidate pixel provides additional information concerning the time evolution of the reference. As in many correlation-based connectivity studies, we fix the reference pixel. Every brain location is then used as a candidate pixel to estimate the spatial pattern of deterministic coupling with the reference. Our results indicate that measured connectivity is often emphasized in the motor cortex contra-lateral to the reference pixel, demonstrating the suitability of this approach for functional connectivity studies. In addition, discrepancies with traditional correlation analysis provide initial evidence for non-linear dynamical properties of resting-state fMRI data. Consequently, the non-linear characterization provided from our approach may provide a more complete description of the underlying physiology and brain function measured by this type of data.
KEYWORDS: Magnetic resonance imaging, Motion estimation, Computer programming, Signal to noise ratio, Data acquisition, Fourier transforms, Reconstruction algorithms, Biomedical engineering, Control systems, Image visualization
We propose a technique for suppression of both intra-slice and inter-slice types of motion artifacts simultaneously. Starting from the general assumption of rigid body motion, we consider the case when the acquisition of the k-space is in the form of bands of finite number of lines arranged in a rectilinear fashion to cover the k-space area of interest. We also assume that an averaging factor of at least 2 is desired. Instead of acquiring a full k-space of each image and then average the result, we propose a new acquisition strategy based on acquiring the k-space in consecutive bands having 50% overlap going from one end of the phase encoding direction to the other end. In case of no motion, this overlap can be used as the second acquisition (NEX=2). When motion is encountered, both types motion are reduced to the same form under this acquisition strategy. In particular, detection and correction of motion between consecutive bands result in suppression of both motion types. In this work, this is achieved by utilizing the overlap area to estimate the motion, which is then taken into consideration in further reconstruction (or even acquisition if real-time control is available on the MR system). We demonstrate the accuracy and computational efficiency of this motion estimation approach. Once the motion is estimated, we propose a simple strategy to reconstruct artifact-free images from the acquired data that take into account the possible voids in the acquired k-space as a result of rotational motion between blades.
KEYWORDS: Functional magnetic resonance imaging, Signal to noise ratio, Interference (communication), Brain, Signal processing, Data modeling, Data acquisition, Denoising, Optical filters, Fourier transforms
The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a new non-parametric technique for noise suppression in event related fMRI data is proposed based on spectrum subtraction. The new technique is based on generalized spectral subtraction that allows correlated noise components to be treated robustly. Moreover, it adaptively estimates a nonparametric model for random and physiological components of noise from the acquired data in a simple and computationally efficient manner. This allows the new method to overcome the limitations of previous methods while maintaining a robust performance given its fewer assumptions and suggests its value as a useful preprocessing step for fMRI data analysis.
KEYWORDS: Independent component analysis, Functional magnetic resonance imaging, Canonical correlation analysis, Principal component analysis, Simulation of CCA and DLA aggregates, Brain, Statistical analysis, Model-based design, Interference (communication), Signal to noise ratio
The application of independent components analysis (ICA) to functional magnetic resonance imaging data has been proven useful to decompose the signal in terms of its basic sources. The main advantage is that ICA requires no prior assumption about the neuronal activity or the noise structure, which are usually unknown in fMRI. This enables the detection of true activation components free of random and physiological noise. Hence, this technique is superior to other techniques such as subspace modeling or canonical correlation analysis, which have underlying assumptions about the signal components. Nevertheless, this technique suffers from a fundamental limitation of not providing a consistent ordering of the signal components as a result of the whitening step involved in ICA. This mandates human intervention to pick out the relevant activation components from the outcome of ICA, which poses a significant obstacle to the practicality of this technique. In this work, a simple yet robust technique is proposed for ranking the resultant independent components. This technique adds a second step to ICA based on canonical correlation analysis and the prior information about the activation paradigm. This enables the proposed technique to provide a consistent and reproducible ordering of independent components. The proposed technique was applied to real event-related functional magnetic resonance imaging data and the results confirm the practicality and robustness of the proposed method.
KEYWORDS: Signal detection, Feature extraction, Sensors, Evolutionary algorithms, Artificial intelligence, Image processing, Detection and tracking algorithms, Distance measurement, Signal processing, Signal to noise ratio
General aspects of feature extraction and matching are addressed, which include optimal principles, similarity measures, constraints, and heuristics. The common characteristics of feature extraction and matching are summarized which show that they can be considered as special cases of signal detection. However, the existing signal detection theories do not solve these problems readily. Therefore, a general formulation of feature extraction and matching as a problem of signal detection is desired and presented. This formulation considers feature extraction and matching as similar, subsequent processes, which well integrates the two different processes together to form an automatic system for image matching or object recognition. Guidelines for designing algorithms for detection or matching of arbitrary image features or patterns are derived which can be easily reconfigured for many practical applications. Typical methods and the associated experiments with real image data are provided which demonstrate the superb performance of the methods.
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