Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.     

New fMRI analysis method for multiple stimuli using reference estimation

The stimulation paradigms of a functional MRI (fMRI) usually consist of one or more stimulations and a resting state in the block‐based and event‐related designs. To localize the activation areas in the human brain, each voxel is statistically analyzed using the fMRI data measured with the stimulation. The conventional method can be inefficient for experiments with multiple stimuli because of measuring the resting‐state signals repeatedly, causing redundancy in the scanning process. Although the phase mapping method can be applied to reduce the redundant resting‐state measurements, there are still limitations in the detection of regions activated by multiple stimuli and the periodic sequence of the multiple stimuli. In this article, a new fMRI data analysis method is presented that enables the detection of functional activations without the resting‐state images. This method estimates the reference signal from the signals acquired during multiple stimuli, and a random sequence and various durations of the multiple stimuli can be applied. Therefore, it can be used in the event‐related design as well as the block‐based design. The results of simulation and fMRI experiments show that the proposed method can correctly detect the activation regions of multiple stimuli, even for overlap regions, and can reduce the imaging time by skipping the resting‐state imaging. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 315–322, 2011     

Neural substrates in secondary somatosensory area for the perception of different tactile sensations

This functional magnetic resonance imaging (fMRI) study investigated the secondary somatosensory area (SII) in humans, especially regarding the presence of distinctive spatial arrangements of neural subunits responding to different types of tactile stimuli. Ten healthy volunteers received four different sensory stimuli on the palmar side of the right hand—vibrotactile, pressure, warmth, and coolness. Based on group‐level analysis of the fMRI data, all four of the somatosensory stimuli resulted in activations in the bilateral SII and insula as well as the SI contralateral to the stimulation. The spatial distribution of neural activations corresponding to each stimulus, as examined by cortical inflation technique, revealed significant overlaps, but also showed the differences in terms of its overall topology. The local maxima of the activation within the SII, corresponding to these stimuli, manifested distinctive spatial location. These results suggest that differential networks of the SII subregions are involved in the processing of the different nature of the tactile stimuli, while sharing common regions in the parietal operculum.     

Tiny Signals from the Human Brain: Acquisition and Processing of Biomagnetic Fields in Magnetoencephalography

Low-temperature superconductivity plays an important role in some specific biomedical applications, and, in particular, in non-invasive imaging methods of human brain activity. Superconducting magnets are indispensable for functional magnetic resonance imaging (fMRI) which allows functional imaging of the brain with high spatial but poor temporal resolution. Superconducting quantum interference devices (SQUIDs) are the most sensitive magnetic field detectors. Up to a few hundreds of SQUIDs are nowdays used in modern whole-head magnetoencephalography (MEG) systems. They allow tracking brain activation with a superior temporal resolution of milliseconds, which is a quintessential condition for the monitoring of brain dynamics and the understanding of information processing in the human brain. We introduce the prerequisites of MEG data acquisition and briefly review two established methods of biomagnetic signal processing: The concept of signal averaging, and the subsequent source identification as a solution of the biomagnetic inverse problem. Beside these standard techniques, we discuss advanced methods for signal processing in MEG, which take into account the frequency content of the recorded signal. We briefly refer to the prospects of Fourier analysis and wavelet transform in MEG data analysis, and suggest matching pursuit as a promising tool for signal decomposition and reconstruction with high resolution in time-frequency plane.     

A study of long‐term fMRI reproducibility using data‐driven analysis methods

The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI‐based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques. Particularly, the assessment of reproducibility is complicated by a fact that fMRI results may depend on data analysis techniques used in reproducibility studies. In this work, the long‐term fMRI reproducibility was investigated with a focus on the data analysis methods. Two spatial smoothing techniques, including a wavelet‐domain Bayesian method and the Gaussian smoothing, were evaluated in terms of their effects on the long‐term reproducibility. A multivariate support vector machine (SVM)‐based method was used to identify active voxels, and compared to a widely used general linear model (GLM)‐based method at the group level. The reproducibility study was performed using multisession fMRI data acquired from eight healthy adults over 1.5 years' period of time. Three regions‐of‐interest (ROI) related to a motor task were defined based upon which the long‐term reproducibility were examined. Experimental results indicate that different spatial smoothing techniques may lead to different reproducibility measures, and the wavelet‐based spatial smoothing and SVM‐based activation detection is a good combination for reproducibility studies. On the basis of the ROIs and multiple numerical criteria, we observed a moderate to substantial within‐subject long‐term reproducibility. A reasonable long‐term reproducibility was also observed from the inter‐subject study. It was found that the short‐term reproducibility is usually higher than the long‐term reproducibility. Furthermore, the results indicate that brain regions with high contrast‐to‐noise ratio do not necessarily exhibit high reproducibility. These findings may provide supportive information for optimal design/implementation of fMRI studies and data interpretation.     

A modified POCS‐based reconstruction method for compressively sampled MR imaging

One of the challenging tasks in the application of compressed sensing to magnetic resonance imaging is the reconstruction algorithm that can faithfully recover the MR image from randomly undersampled k‐space data. The nonlinear recovery algorithms based on iterative shrinkage start with a single initial guess and use soft‐thresholding to recover the original MR image from the partial Fourier data. This article presents a novel method based on projection onto convex set (POCS) algorithm but it takes two images and then randomly combines them at each iteration to estimate the original MR image. The performance of the proposed method is validated using the original data taken from the MRI scanner at St. Mary's Hospital, London. The experimental results show that the proposed method can reconstruct the original MR image from variable density undersampling scheme in less number of iterations and exhibits better performance in terms of improved signal‐to‐noise ratio, artifact power, and correlation as compared to the reconstruction through low‐resolution and POCS algorithms. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 203–207, 2014     

Decoding visual network-related dynamic functional connectivity for eyes-open and eyes-closed using machine learning

Eyes-open (EO) and eyes-closed (EC) are the two experimental conditions during resting state functional magnetic resonance imaging (fMRI) scan sessions. However, the dynamic neural mechanisms of EO/EC based on intrinsic connectivity networks (ICNs) remains largely unexplored. This paper aimed to decode the dynamic internetwork neural mechanisms for EO/EC using data mining and to identify EO/EC resting state fMRI scans based on machine learning. To achieve these goals, the two states were analyzed using the discriminative models, resulting in total accuracy of 85.87%, a sensitivity of 91.3%, and a specificity of 80.43%. In addition, the discriminative features discovered using data mining were related to previous findings. In summary, we applied visual network-related inter-ICN features to decode the neural mechanisms of EO/EC. The reproducible results suggested that visual network-related inter-ICN dynamic features could be beneficial for decoding visual attentions, and had potential as neuroimaging-markers to identify EO/EC resting state fMRI scans.     

多通道图像色彩重建的神经网络算法

为了解决目前多光谱成像设备输出的应用问题,将神经网络算法应用于多通道图像色彩重建。CCD相机加LED光源组成多光谱成像系统,研究其输出的多通道图像中色彩信息的高效转换方法,建立了D50光源下的神经网络转换模型,利用BP神经网络和GRNN对多光谱成像系统进行色彩重建实验。研究结果表明：采用搭建的模型可以得到较高的色度重建精度,更换目标样本色彩重建性能保持稳定。     

A shrinkage method for causal network detection of brain regions

We present a computationally as well as statistically efficient method of inferring causal networks for the brain regions. It is based on James‐Stein‐type shrinkage estimation of covariance matrix, suggested by (Opgen‐Rhein and Strimmer, BMC Syst Biol 1 ( 2007 ), 37‐40), among different brain regions of interest of the functional magnetic resonance imaging (fMRI) experiment, that enhance the accuracy of vector autoregressive (VAR) model coefficient estimates. We have shown that this approach is well suited for the small number of samples in time and large number of brain regions encountered in real fMRI experiments of seventeen healthy individuals. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 140–146, 2013     

Variations in BOLD response latency estimated from event‐related fMRI at 3T: Comparisons between gradient‐echo and Spin‐echo

Functional magnetic resonance imaging (fMRI) commonly uses gradient‐recalled echo (GRE) signals to detect regional hemodynamic variations originating from neural activities. While the spatial localization of activation shows promising applications, indexing temporal response remains a poor mechanism for detecting the timing of neural activity. Particularly, the hemodynamic response may fail to resolve sub‐second temporal differences between brain regions because of its signal origin or noise in data, or both. This study aimed at evaluating the performance of latency estimation using different fMRI techniques, with two event‐related experiments at 3T. Experiment I evaluated latency variations within the visual cortex and their relationship with contrast‐to‐noise ratios (CNRs) for GRE, spin echo (SE), and diffusion‐weighted SE (DWSE). Experiment II used delayed visual stimuli between two hemifields (delay time = 0, 250, and 500 ms, respectively) to assess the temporal resolving power of three protocols: GRE_TR1000, GRE_TR500, and SE_TR1000. The results of experiment I showed the earliest latency with DWSE, followed by SE, and then GRE. Latency variations decreased as CNR increased. However, similar variations were found between GRE and SE, when the latter had lower CNR. In experiment II, measured stimulus delays from all conditions were significantly correlated with preset stimulus delays. Inter‐subject variation in the measured delay was found to be greatest with GRE_TR1000, followed by GRE_TR500, and the least with SE_TR1000. Conclusively, blood oxygenation level‐dependent responses obtained from GRE exhibit greater CNR but no compromised latency variations in the visual cortex. SE is potentially capable of improving the performance of latency estimation, especially for group analysis. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 215–221, 2013     

基于功能磁共振成像方法的单相抑郁症情绪加工特点研究

运用功能磁共振成像(fMRI)技术,采用适合中国人情绪加工特点的表情图片(愉快、悲伤和中性表情)作为刺激材料,选取15名严格入组的未服药单相抑郁症女性患者与15名条件匹配的正常志愿者,对其情绪加工的大脑活动进行了对比研究。结果发现,与中性表情相比,在加工愉快表情时,抑郁症患者激活了右侧前额叶,而正常对照组激活的是左侧前额叶;在悲伤表情条件下,抑郁症患者激活了双侧颞下回,而正常对照组激活的是双侧前额叶。结果还发现,与正常对照组相比,抑郁症患者在双侧额下回三角区、右侧眶额下回、右侧颞下回等脑区的激活范围和强度均有所减少,尤其在加工悲伤表情时表现得更明显。该结果表明,单相抑郁症病人不仅在情绪加工相关脑区的功能上出现异常,而且加工情绪的脑区也可能出现了转移。     

fMRI analysis of excessive binocular disparity on the human brain

The aim of this study is to evaluate brain regions related with excessive binocular disparity that may be linked to stereoscopic visual fatigue. In stereoscopic displays, excessive binocular disparity may generate blurring or double vision in the stereovision and induce unnatural oscillations in accommodation and vergence. These phenomena may lead to visual fatigue and activation (or deactivation) of human brain related with sensory and eye movement functions. A functional magnetic resonance imaging (fMRI) method is used to investigate the effect of excessive binocular disparity on human brain. Subjective assessments of visual fatigue are also conducted with the same stimuli as the fMRI experiment. Based on the subjective assessment results, participants are classified into low‐ and high‐fatigue groups. From the fMRI experiments, the high‐fatigue group showed more activation at the intraparietal sulcus (IPS) than the low‐fatigue group, when viewing an excessive disparity stimulus. The results showed that the excessive binocular disparity stimulus may induce overload to the IPS region, which is related with stereo processing and saccadic eye movement. In addition, it could be possible to use fMRI as an objective measurement method for understanding the stereoscopic visual fatigue when stimuli with excessive binocular disparity are applied.     

Range accuracy limitation of pulse ranging systems based on Geiger mode single-photon detectors

Geiger mode single-photon detectors have been used in pulse ranging system and three-dimensional imaging systems due to high sensitivity and easy integration. The ranging accuracy and precision is influenced by many factors. Based on statistical theory, five main factors are discussed in this article, namely, echo signal intensity, pulse width, detector quantum efficiency, target position, and background noise. An analytical relationship among the ranging accuracy, precision, and these factors is obtained for a Q-switched laser pulse. Through this relationship, it is shown that the echo signal intensity and pulse width are more important than other factors and higher echo signal intensity and narrower pulse width can result in better accuracy and precision.     

PET image reconstruction using ANN

The aim of this study is to improve the positron emission tomography (PET) image quality for medical diagnosis. The statistical reconstructions on the maximum a posteriori (MAP) algorithm often results in a blurring effect, which fails to determine the toughness class in the reconstructed image. The development of new reconstruction algorithms for PET is an active field of research. In this article, artificial neural network (ANN) is proposed for replicating the output image, which is generated from the acquired projection data with the corresponding angles using the PET images. This article proposes the advantage of arranging the neural network to stock up the information of the continuous capacity. This reduces the storage space and recuperates as much sequence of the continuous quantity as possible. The performance of image quality parameters using ANN is better when compared with MAP, FBP‐NN (filtered back projection with nearest neighbor interpolation). Thus ANN provides 63% better peak signal to noise ratio (PSNR) when compared with FBP‐NN and 47% better when compared to MAP. Thus, ANN is better than FBP and MAP algorithm, by providing better PSNR. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 249–255, 2014     

Real‐time ballistocardiographic artifact reduction using the k‐teager energy operator detector and multi‐channel referenced adaptive noise cancelling

The simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) recording technique has recently received considerable attention and has been used in many studies on cognition and neurological disease. EEG‐fMRI simultaneous recording has the advantage of enabling the monitoring of brain activity with both high temporal resolution and high spatial resolution in real time. The successful removal of the ballistocardiographic (BCG) artifact from the EEG signal recorded during an MRI is an important prerequisite for real‐time EEG‐fMRI joint analysis. We have developed a new framework dedicated to BCG artifact removal in real‐time. This framework includes a new real‐time R‐peak detection method combining a k‐Teager energy operator, a thresholding detector, and a correlation detector, as well as a real‐time BCG artifact reduction procedure combining average artifact template subtraction and a new multi‐channel referenced adaptive noise cancelling method. Our results demonstrate that this new framework is efficient in the real‐time removal of the BCG artifact. The multi‐channel adaptive noise cancellation (mANC) method performs better than the traditional ANC method in eliminating the BCG residual artifact. In addition, the computational speed of the mANC method fulfills the requirements of real‐time EEG‐fMRI analysis. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 209–215, 2016     

Stereoscopic 3D objects evoke stronger saliency for nonverbal working memory: An fMRI study

Effective working memory (WM) training is often desired to improve WM. Recent studies have suggested that WM training is more successful when participants monitor scenes in three‐dimensional (3D) environments. Although previous neuroimaging studies have examined visuospatial WM in relation to a 3D scene or object, these studies did not investigate WM using stereoscopic 3D object stimuli. We used functional magnetic resonance imaging (fMRI) to identify brain activation during an N‐back task with 3D object stimuli, and determined the difference in activation pattern between stereoscopic versus shaded 3D objects. We found that the anterior insula, ventral striatum, and posterior orbitofrontal cortex showed greater activation during the 2‐back task with stereoscopic 3D objects than with shaded 3D objects. These regions have previously been associated with a salience network.     

An optimal RF shielding method for MR‐PET fusion system with insertable PET

An RF shielding method is proposed to preserve the resonance frequency of an RF coil regardless of the existence of a positron emission tomography (PET) detector module, so that the RF coil can effectively operate for both simultaneous MR‐PET imaging and MR stand‐alone imaging. The RF shield between the RF coil and the PET detector module was manufactured in the form of a hollow acryl cylinder wrapped with gold‐taffeta woven tape. As we adopted a double‐layer RF shield between the RF coil and the PET detector module, it was possible to maintain the resonance frequency of the RF coil and the MR image quality was similar in both cases, with and without the insertable PET detector module. Using the insertable concept of the PET system and the RF coil with an additional double‐layer RF shield, both an MR‐PET fusion system and an MR stand‐alone system were realized. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 263–269, 2014     

A novel method for detection of voxels for decision making: An fMRI study

This study explores the patterns of activation in brain regions toward classifying decision making voxels from among four major Brodmann areas (BAs) upon stimulus of visual tasks. Toward this goal, a well‐known clustering analysis has been performed on real‐time data of the human brain obtained using functional magnetic resonance imaging (fMRI). The functional connectivity among various brain regions was detected by leveraging a distance correlation graph. Graphical methods have been employed to visualize the clusters elicited in the process. The analysis of the results sheds new light on how four significantly activated BAs of the brain exhibit effective connectively to perform a visual task in the context of decision making.     

Image‐guided navigation of single‐element focused ultrasound transducer

The spatial specificity and controllability of focused ultrasound (FUS), in addition to its ability to modify the excitability of neural tissue, allows for the selective and reversible neuromodulation of the brain function, with great potential in neurotherapeutics. Intraoperative magnetic resonance imaging (MRI) guidance has limitations due to its complicated examination logistics, such as fixation through skull screws to mount the stereotactic frame, simultaneous sonication in the MRI environment, and restrictions in choosing MR‐compatible materials. To overcome these limitations, an image‐guidance system based on optical tracking and preoperative imaging data is developed, separating the imaging acquisition for guidance and sonication procedure for treatment. Techniques to define the local coordinates of the focal point of sonication are presented. First, mechanical calibration detects the concentric rotational motion of a rigid‐body optical tracker, attached to a straight rod mimicking the sonication path, pivoted at the virtual FUS focus. The spatial error presented in the mechanical calibration was compensated further by MRI‐based calibration, which estimates the spatial offset between the navigated focal point and the ground‐truth location of the sonication focus obtained from a temperature‐sensitive MR sequence. MRI‐based calibration offered a significant decrease in spatial errors (1.9 ± 0.8 mm; 57% reduction) compared to the mechanical calibration method alone (4.4 ± 0.9 mm). Using the presented method, pulse‐mode FUS was applied to the motor area of the rat brain, and successfully stimulated the motor cortex. The presented techniques can be readily adapted for the transcranial application of FUS to intact human brain. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 177–184, 2012     

Iterative adaptive spatial filtering for noise‐suppression in functional magnetic resonance imaging time‐series

We present an iterative scheme for adaptive smoothing of functional magnetic resonance images. We propose a novel similarity measure to estimate the weights of the smoothing filter based on the functional similarity of the voxels under the smoothing kernel with the voxel under consideration as well as their similarity with a reference time‐course representing the expected BOLD response. We demonstrate the performance of the proposed method by applying the method to preprocess both simulated and real fMRI data. The method improves the functional SNR of the data while preserving the shapes of the functionally active region and its performance is not compromised when structured noise is the dominant noise source. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 260‐270, 2011;