Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI.

Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit

Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set. In this article, we propose to apply RBM to fMRI time courses instead of volumes for BSS. The proposed method not only interprets fMRI time courses explicitly to take advantages of deep learning models in latent feature learning but also substantially reduces model complexity and increases the scale of training set to improve training efficiency. Our experimental results based on Human Connectome Project (HCP) datasets demonstrated the superiority of the proposed method over ICA and the one that applied RBM to fMRI volumes in identifying task-related components, resulted in more accurate and specific representations of task-related activations. Moreover, our method separated out components representing intermixed effects between task events, which could reflect inherent interactions among functionally connected brain regions. Our study demonstrates the value of RBM in mining complex structures embedded in large-scale fMRI data and its potential as a building block for deeper models in fMRI data analysis.

A new adaptive signal-preserving technique for noise suppression in event-related functional magnetic resonance imaging (fMRI) data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. This model is subsequently used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data from event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Moreover, we demonstrate that further analysis using principal component analysis and independent component analysis shows a significant improvement in both convergence and clarity of results when the new technique is used. Given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for fMRI data analysis.

SummaryHumans show a remarkable ability to discriminate others' gaze direction, even though a given direction can be conveyed by many physically dissimilar configurations of different eye positions and head views. For example, eye contact can be signaled by a rightward glance in a left-turned head or by direct gaze in a front-facing head. Such acute gaze discrimination implies considerable perceptual invariance. Previous human research found that superior temporal sulcus (STS) responds preferentially to gaze shifts [1], but the underlying representation that supports such general responsiveness remains poorly understood. Using multivariate pattern analysis (MVPA) of human functional magnetic resonance imaging (fMRI) data, we tested whether STS contains a higher-order, head view-invariant code for gaze direction. The results revealed a finely graded gaze direction code in right anterior STS that was invariant to head view and physical image features. Further analyses revealed similar gaze effects in left anterior STS and precuneus. Our results suggest that anterior STS codes the direction of another's attention regardless of how this information is conveyed and demonstrate how high-level face areas carry out fine-grained, perceptually relevant discrimination through invariance to other face features.

Temporal spatial differences observed by functional MRI and human intraoperative optical imaging

Event Abstract Back to Event A combination model of ICA and sparsity prior with respect to fMRI signal analysis Nizhuan Wang1* and Weiming Zeng1 1 Shanghai maritime University, College of Information Engineering, China The blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data effective analysis is a challenging task because of the complexity of neuron activity in human brain. Independent component analysis (ICA) has been widely used to investigate the functional connectivity of fMRI data, which assumes that the sources of functional regions are independent statistically. However, in the method, the spatial sparsity feature has usually been ignored, which is intrinsic property of the fMRI sources. In 2001, Alexander et al. demonstrated that the optimal sparse representation could significantly benefit the de-noising of fMRI time courses [1]. Recently, Daubechies et al. also demonstrated that the sparsity was more general, intuitive and promising assumption for BOLD fMRI signals [2], whose sources were highly clustering and centering located. In this study, a combination model of FastICA and sparsity prior with respect to fMRI signal analysis, named SFICA, is presented. In this model, the sparse decomposition is performed on the fMRI signal using the wavelet packet decomposition, which yields wavelet tree nodes with different degree of sparsity [3]. Based on these wavelet tree nodes, a common fuzzy C-means clustering is applied to form the optimal sparse representation set of fMRI signal, which can be as an input of FastICA. Compared with FastICA, hybrid data experiment in our study demonstrated this combination model had better spatial source recovery performance on the ground of receiver operating characteristic (ROC) analysis (shown in Fig.1A). For task-related and resting state experimental tests, according to the skewness analysis and correlation variance analysis, almost all the functional networks discovered by this combination model were more symmetric with less skewness values in spatial domain and more consistent in temporal domain than the ones by FastICA. For simplicity, the partial experimental results corresponding to task-related experimental tests were showed in the Fig.1B. Fig. 1: (A) Hybrid data experimental results: ROC curves of simulated signals S1 and S2 corresponding to anterior regions and the more posterior regions respectively, where the z-score ranges from 0.5 to 2.0; (B) Task-related data experimental results: the visual networks of one subject detected by the FastICA and SFICA methods corresponding to the task-related visual stimulus. The corresponding skewness values are labeled and the spatial differences of the visual networks corresponding to the two method are marked by circle shapes with distinct colors in this figure. Figure 1 Acknowledgements Research supported by the National Natural Science Foundation of China (Grant No.31170952), the Innovation Program of Shanghai Municipal Education Commission (Grant No.11ZZ143) , the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning and the Program for Graduate Special Endowment Fund for Innovative developing of Shanghai Maritime University (Grant No. yc2011021).

The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm

Generalized relative entropy in functional magnetic resonance imaging

A multivariate approach for processing magnetization effects in triggered event-related functional magnetic resonance imaging time series

A new adaptive signal-preserving technique for noise suppression in functional magnetic resonance imaging (fMRI) data is proposed based on spectrum subtraction. The proposed technique estimates a model for the power spectrum of random noise from the acquired data. This model is used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data for event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components. Moreover, further analysis using principal component analysis (PCA) and independent component analysis (ICA) shows a significant improvement in both convergence and clarity of results when the new technique is used. This suggests the value of the new technique as a useful preprocessing step for this type of signal.

Functional MRI evidence for disparate developmental processes underlying intelligence in boys and girls

Analysis of functional MRI data based on an estimation of the hemodynamic response in the human brain

We propose a technique that enables robust use of blind source separation techniques in fMRI data analysis. The fMRI temporal signal is modeled as the summation of the true activation signal, a physiological baseline fluctuation component, and a random noise component. A preprocessing denoising is used to reduce the dimensionality of the random noise component in this mixture before applying the principal/independent component analysis (PCA/ICA) methods. The set of denoised time courses from a localized region are utilized to capture the region-specific activation patterns. We show a significant improvement in the convergence properties of the ICA iteration when the denoised time courses are used. We also demonstrate the advantage of using ICA over PCA to separate components due to physiological signals from those corresponding to actual activation. Moreover, we propose the use of ICA to analyze the magnitude of the Fourier domain of the time courses. This allows ICA to group signals with similar patterns and different delays together, which makes the iteration even more efficient. The proposed technique is verified using computer simulations as well as actual data from a healthy human volunteer. The results confirm the robustness of the new strategy and demonstrate its value for clinical use.

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that is widely available and can be used to determine the spatial relationships between tumor tissue and eloquent brain areas. Within certain limits, this functional information can be applied in the field of neurosurgery as a pre-operative mapping tool to minimize damage to eloquent brain areas. In this article, we review the literature on the use of fMRI for neurosurgical planning. The issues addressed are: (1) stimulation paradigms, (2) the influence of tumors on the blood oxygenation level-dependent (BOLD) signal, (3) post-processing the fMRI time course, (4) integration of fMRI results into neuronavigation systems, (5) the accuracy of fMRI and (6) fMRI compared to intra-operative mapping (IOM).

Error-related processing following severe traumatic brain injury: An event-related functional magnetic resonance imaging (fMRI) study