KEYWORDS: Functional magnetic resonance imaging, Data modeling, Data analysis, Independent component analysis, Brain activation, Detection and tracking algorithms, Algorithms, Medical imaging, Brain, Principal component analysis
This paper introduces a framework for the application of constrained non-negative matrix factorization (cNMF) to estimate the statistically distinct neural responses in a sequence of functional magnetic resonance images (fMRI). While an improved objective function has been defined to make the representation suitable for task-related brain activation detection, in this paper we explore various methods for better detection and efficient computation, placing particular emphasis on the initialization of the constrained NMF algorithm. The K-means algorithm performs this structured initialization and the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. We illustrate the method by a set of functional neuroimages from a motor activation study.
The comprehensive understanding of human emotion processing needs consideration both in the spatial distribution and the temporal sequencing of neural activity. The aim of our work is to identify brain regions involved in emotional recognition as well as to follow the time sequence in the millisecond-range resolution. The effect of activation upon visual stimuli in different gender by International Affective Picture System (IAPS) has been examined. Hemodynamic and electrophysiological responses were measured in the same subjects. Both fMRI and ERP study were employed in an event-related study. fMRI have been obtained with 3.0 T Siemens Magnetom whole-body MRI scanner. 128-channel ERP data were recorded using an EGI system. ERP is sensitive to millisecond changes in mental activity, but the source localization and timing is limited by the ill-posed 'inversed' problem. We try to investigate the ERP source reconstruction problem in this study using fMRI constraint. We chose ICA as a pre-processing step of ERP source reconstruction to exclude the artifacts and provide a prior estimate of the number of dipoles. The results indicate that male and female show differences in neural mechanism
during emotion visual stimuli.
KEYWORDS: Functional magnetic resonance imaging, Complex systems, Hemodynamics, Systems modeling, Image processing, Medical imaging, System identification, Fourier transforms, Wavelets, Data modeling
This study presents a fast orthogonal search (FOS) method for modeling fMRI time series. Based on the system identification theory, an orthogonalization procedure to model the fMRI time series is described. FOS method does not require equally space data, and can resolve sinusoidal frequencies much more closely than Fourier transform method. After the time series are modeled by means of the FOS, F-test is employed to detect the activation regions. Eight volunteers' data were collected to validate the proposed method. The results demonstrate the feasibility of the proposed method.
Non-negative Matrix Factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. In this paper, we introduce this new technique to the field of fMRI data analysis. In order to make the representation suitable for task-related brain activation detection, we imposed some additional constraints, and defined an improved contrast function. We deduced the update rules and proved the convergence of the algorithm. In the procedure, the number of factors was determined by visual assessment. We studied 8 healthy right-handed adult volunteers by a 3.0T GE Signa scanner. A block design motor paradigm (bilateral finger tapping) stimulated the blood oxygenation level-dependent (BOLD) response. Gradient Echo EPI sequence was utilized to acquire BOLD contrast functional images. With this constrained NMF (cNMF) we could obtain major activation components and the corresponding time courses, which showed high correlation with the reference function (r>0.7). The results showed that our method would be feasible for detection brain activations from task-related fMRI series.
The aim of this study was to assess the validation of the local density random walk (LDRW) function to correct the delayed and dispersed arterial input function (AIF) data derived from dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). Instead of using the gamma-variate function to smooth and extrapolate the AIF curves, we suggested a method which was based on diffusion with drift approach. Forty-seven AIF curves from ten patients were segmented to test the effectiveness of the proposed method. The results of the comparisons with the gamma-variate function showed that the LDRW distribution function may provide a new means for more accurate correction of AIF curves.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.