Cortex-based independent component analysis of fMRI time series

The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies. Since only about 20% of the voxels of a typical fMRI data set lie within the cortex, statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. While such restriction does not influence conventional univariate statistical tests, it may have a substantial effect on the performance of multivariate methods.

Here, we describe a novel approach for data-driven analysis of single-subject fMRI time series that combines techniques for the segmentation and reconstruction of the cortical surface of the brain and the spatial independent component analysis (sICA) of the functional time courses (TCs). We use the mesh of the white matter/gray matter boundary, automatically reconstructed from high-spatial-resolution anatomical MR images, to limit the sICA decomposition of a coregistered functional time series to those voxels which are within a specified region with respect to the cortical sheet (cortex-based ICA, or cbICA). We illustrate our analysis method in the context of fMRI blocked and event-related experimental designs and in an fMRI experiment with perceptually ambiguous stimulation, in which an a priori specification of the stimulation protocol is not possible.

A comparison between cbICA and conventional hypothesis-driven statistical methods shows that cortical surface maps and component TCs blindly obtained with cbICA reliably reflect task-related spatiotemporal activation patterns. Furthermore, the advantages of using cbICA when the specification of a temporal model of the expected hemodynamic response is not straightforward are illustrated and discussed. A comparison between cbICA and anatomically unconstrained ICA reveals that — beside reducing computational demand — the cortex-based approach improves the fitting of the ICA model in the gray matter voxels, the separation of cortical components and the estimation of their TCs, particularly in the case of fMRI data sets with a complex spatiotemporal statistical structure.     

Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI

Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique used to investigate the spontaneous correlations of blood-oxygen-level-dependent signals across different regions of the brain. Using functional connectivity tools, it is possible to investigate a specific RS-fMRI network, referred to as "default-mode" (DM) network, that involves cortical regions deactivated in fMRI experiments with cognitive tasks. Previous works have reported a significant effect of aging on DM regions activity. Independent component analysis (ICA) is often used for generating spatially distributed DM functional connectivity patterns from RS-fMRI data without the need for a reference region. This aspect and the relatively easy setup of an RS-fMRI experiment even in clinical trials have boosted the combined use of RS-fMRI and ICA-based DM analysis for noninvasive research of brain disorders. In this work, we considered different strategies for combining ICA results from individual-level and population-level analyses and used them to evaluate and predict the effect of aging on the DM component. Using RS-fMRI data from 20 normal subjects and a previously developed group-level ICA methodology, we generated group DM maps and showed that the overall ICA-DM connectivity is negatively correlated with age. A negative correlation of the ICA voxel weights with age existed in all DM regions at a variable degree. As an alternative approach, we generated a distributed DM spatial template and evaluated the correlation of each individual DM component fit to this template with age. Using a "leave-one-out" procedure, we discuss the importance of removing the bias from the DM template-generation process.     

Fractional amplitude of low-frequency fluctuation changes in functional dyspepsia: A resting-state fMRI study

Recently, there is an increasing interest in the study of the role of brain dysfunction in the pathogenesis of symptoms of functional dyspepsia (FD). More specifically, abnormal brain activities in patients with FD during the resting state have been proven by several positron emission tomography (PET) studies. Resting-state functional magnetic resonance imaging (fMRI) is also a valuable tool in investigating spontaneous brain activity abnormalities in pathological conditions. In the present study, we examined the amplitude of low-frequency fluctuations (ALFF) and fractional (f)ALFF changes in patients with FD by using fMRI. Twenty-nine patients with FD and sixteen healthy controls participated in this study. Between-group differences in ALFF/fALFF were examined using a permutation-based nonparametric test after accounting for the gender and age effects. The results revealed a significant between-group difference in fALFF but not in ALFF in multiple brain regions including the right insula, brainstem and cerebellum. Seed-based resting-state functional connectivity analysis revealed that FD patients have increased correlations between the right cerebellum and multiple brain regions including the bilateral brainstem, bilateral cerebellum, bilateral thalamus, left para-/hippocampus, left pallidum and left putamen. Furthermore, fLAFF values in the right insula were positively correlated with the severity of the disease. These findings have provided further evidence of spontaneous brain activity abnormalities in FD patients which might contribute to our understanding of the pathophysiology of the disease.     

Fixed-point algorithms for constrained ICA and their applications in fMRI data analysis

Constrained independent component analysis (CICA) eliminates the order ambiguity of standard ICA by incorporating prior information into the learning process to sort the components intrinsically. However, the original CICA (OCICA) and its variants depend on a learning rate, which is not easy to be tuned for various applications. To solve this problem, two learning-rate-free CICA algorithms were derived in this paper using the fixed-point learning concept. A complete stability analysis was provided for the proposed methods, which also made a correction to the stability analysis given to OCICA. Variations for adding constraints either to the components or to the associated time courses were derived too. Using synthetic data, the proposed methods yielded a better stability and a better source separation quality in terms of higher signal-to-noise-ratio and smaller performance index than OCICA. For the artificially generated brain activations, the new CICAs demonstrated a better sensitivity/specificity performance than standard univariate general linear model (GLM) and standard ICA. Original CICA showed a similar sensitivity/specificity gain but failed to converge for several times. Using functional magnetic resonance imaging (fMRI) data acquired with a well-characterized sensorimotor task, the proposed CICAs yielded better sensitivity than OCICA, standard ICA and GLM in all the target functional regions in terms of either higher t values or larger suprathreshold cluster extensions using the same significance threshold. In addition, they were more stable than OCICA and standard ICA for analyzing the sensorimotor fMRI data.     

Performance of blind source separation algorithms for fMRI analysis using a group ICA method

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.     

Broadband shock noise reduction in turbulent jets by water injection

The concept of effective jet properties introduced by the author (Kandula M. Prediction of turbulent jet mixing noise reduction by water injection. AIAA J 2008;46(11):2714-22) has been extended to the estimation of broadband shock noise reduction by water injection in supersonic jets. Comparison of the predictions with the test data for cold and hot underexpanded supersonic nozzles shows a satisfactory agreement. The results also reveal the range of water mass flow rates over which saturation of mixing noise reduction and existence of parasitic noise are manifest.     

A three-dimensional physical model of MRI noise based on current noise sources in a conductor

Magnetic flux noise is generated by any conductor in equilibrium with a bath as a result of random fluctuating currents. A physical model of this flux noise is proposed, based on allowable current patterns in the conductor, which we describe as natural current modes. This model gives insight into the spatial characteristics of the magnetic noise which is encountered in a variety of magnetic measurements and imaging modalities such as magnetic resonance imaging (MRI).     

Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is currently the dominant technique for non-invasive investigation of brain functions. One of the challenges with BOLD fMRI, particularly at high fields, is compensation for the effects of spatiotemporally varying magnetic field inhomogeneities (ΔB₀) caused by normal subject respiration and, in some studies, movement of the subject during the scan to perform tasks related to the functional paradigm. The presence of ΔB₀ during data acquisition distorts reconstructed images and introduces extraneous fluctuations in the fMRI time series that decrease the BOLD contrast-to-noise ratio. Optimization of the fMRI data-processing pipeline to compensate for geometric distortions is of paramount importance to ensure high quality of fMRI data. To investigate ΔB₀ caused by subject movement, echo-planar imaging scans were collected with and without concurrent motion of a phantom arm. The phantom arm was constructed and moved by the experimenter to emulate forearm motions while subjects remained still and observed a visual stimulation paradigm. These data were then subjected to eight different combinations of preprocessing steps. The best preprocessing pipeline included navigator correction, a complex phase regressor and spatial smoothing. The synergy between navigator correction and phase regression reduced geometric distortions better than either step in isolation and preconditioned the data to make them more amenable to the benefits of spatial smoothing. The combination of these steps provided a 10% increase in t-statistics compared to only navigator correction and spatial smoothing and reduced the noise and false activations in regions where no legitimate effects would occur.     

基于独立成分分析的全波核磁共振信号噪声滤除方法研究

核磁共振测深(MRS)探水仪探测到的纳伏级微弱信号极易受到各种环境噪声的干扰, 严重影响信号特征参数的准确提取, 导致后续反演解释错误率增高. 针对这一难题, 提出了基于独立成分分析的快速固定点算法进行信噪分离. 首先分析了该算法用于全波MRS信号消噪的适用性; 其次, 采用数字正交法解决欠定盲源分离问题, 提出了频谱校正法实现分离信号幅值的有效恢复. 仿真结果表明, 该算法能够有效地实现全波MRS信号的信噪分离, 且数据拟合后初始振幅和弛豫时间的相对误差小于± 5.00%; 通过与其他经典算法的对比分析, 进一步证明了该算法消噪性能的优越性. 将该算法应用到野外实测信号处理, 结果证明其能有效滤除环境噪声.     

Fractional amplitude of low-frequency fluctuation changes in monkeys with spinal cord injury: A resting-state fMRI study

Purpose

Although functional magnetic resonance imaging (fMRI) has revealed that spinal cord injury (SCI) causes anomalous changes in task-induced brain activation, its effect during the resting state remains unclear. The aim of this study is to explore the changes of the brain resting-state function in non-human primates with unilateral SCI.

Materials and methods

Eleven adult female rhesus monkeys were subjected to resting-state fMRI: five with unilateral thoracic SCI and six healthy monkeys, to obtain the fractional amplitude of low-frequency fluctuations (fALFF) of the blood oxygenation level-dependent (BOLD) contrast signal to determine the influence of SCI on the cerebral resting-state function.

Results

The SCI-induced fALFF vary significantly in several encephalic regions, including the left cerebellum, the left thalamus, the right lateral geniculate nucleus, the right superior parietal lobule, and the posterior cingulate gyrus.

Conclusion

Analysis of the resting-state fMRI provides evidence of abnormal spontaneous brain activations in primates with SCI, which may help us understand the pathophysiologic mechanisms underlying the changes in neural plasticity in the central nervous system after SCI.     

On the nature of the BOLD fMRI contrast mechanism

Since its development about 15 years ago, functional magnetic resonance imaging (fMRI) has become the leading research tool for mapping brain activity. The technique works by detecting the levels of oxygen in the blood, point by point, throughout the brain. In other words, it relies on a surrogate signal, resulting from changes in oxygenation, blood volume and flow, and does not directly measure neural activity. Although a relationship between changes in brain activity and blood flow has long been speculated, indirectly examined and suggested and surely anticipated and expected, the neural basis of the fMRI signal was only recently demonstrated directly in experiments using combined imaging and intracortical recordings. In the present paper, we discuss the results obtained from such combined experiments. We also discuss our current knowledge of the extracellularly measured signals of the neural processes that they represent and of the structural and functional neurovascular coupling, which links such processes with the hemodynamic changes that offer the surrogate signal that we use to map brain activity. We conclude by considering applications of invasive MRI, including injections of paramagnetic tracers for the study of connectivity in the living animal and simultaneous imaging and electrical microstimulation.     

Effects of urban morphology on the traffic noise distribution through noise mapping: A comparative study between UK and China

Due to the rapid urban development and massive population increase in many eastern cities, the difference in urban density and morphology between typical western and eastern cities is becoming significant. This consequently makes the noise distribution in the eastern cities rather different from typical low density European cities. In this research, two representative cities with different urban densities, Greater Manchester in the UK and Wuhan in China, were selected, which have low and high average urban density respectively, and also have considerable differences in building form and traffic pattern. In the mean time, these two cities have similar urban scale and traffic amount. In each city, based on the urban morphological analyses considering urban land-use, building and road density, and noise source distribution, a number of typical urban areas, 500 * 500 m² each, were sampled. A noise-mapping software package was then used to generate generic noise maps, based on existing digital vector maps for terrain and building, and traffic data obtained by on-site measurements. The comparison results show that the average and minimum noise level in Greater Manchester samples is generally higher than that in Wuhan samples, while the maximum noise level in Wuhan samples is mostly higher. By developing a Matlab program, correlations have been analysed between noise distributions and the urban characteristics relating to urban density, such as the road and building coverage ratio. Overall, comparisons between these two typical cities have shown significant effects of urban morphology on the traffic noise distribution.     

A quality control method for detecting and suppressing uncorrected residual motion in fMRI studies

Motion correction is an important step in the functional magnetic resonance imaging (fMRI) analysis pipeline. While many studies simply exclude subjects who are estimated to have moved beyond an arbitrary threshold, there exists no objective method for determining an appropriate threshold. Furthermore, any criterion based only upon motion estimation ignores the potential for proper realignment. The method proposed here uses unsupervised learning (specifically k-means clustering) on features derived from the mean square derivative (MSD) of the signal before and after realignment to identify problem data. These classifications are refined through analysis of correlation between subject activation maps and the mean activation map, as well as the relationship between tasking and motion as measured through regression of the canonical hemodynamic response functions to fit both estimated motion parameters and MSD. The MSD is further used to identify specific scans containing residual motion, data which is suppressed by adding nuisance regressors to the general linear model; this statistical suppression is performed for identified problem subjects, but has potential for use over all subjects. For problem subjects, our results show increased hemodynamic activity more consistent with group results; that is, the addition of nuisance regressors resulted in a doubling of the correlation between the activation map for the problem subjects and the activation map for all subjects. The proposed method should be useful in helping fMRI researchers make more efficient use of their data by reducing the need to exclude entire subjects from studies and thus collect new data to replace excluded subjects.     

Surface-based functional magnetic resonance imaging analysis of partial brain echo planar imaging data at 1.5 T

Surface-based functional magnetic resonance imaging (fMRI) analysis is more sensitive and accurate than volume-based analysis for detecting neural activation. However, these advantages are less important in practical fMRI experiments with commonly used 1.5-T magnetic resonance devices because of the resolution gap between the echo planar imaging data and the cortical surface models. We expected high-resolution segmented partial brain echo planar imaging (EPI) data to overcome this problem, and the activation patterns of the high-resolution data could be different from the low-resolution data. For the practical applications of surface-based fMRI analysis using segmented EPI techniques, the effects of some important factors (e.g., activation patterns, registration and local distortions) should be intensively evaluated because the results of surface-based fMRI analyses could be influenced by them. In this study, we demonstrated the difference between activations detected from low-resolution EPI data, which were covering whole brain, and high-resolution segmented EPI data covering partial brain by volume- and surface-based analysis methods. First, we compared the activation maps of low- and high-resolution EPI datasets detected by volume- and surface-based analyses, with the spatial patterns of activation clusters, and analyzed the distributions of activations in occipital lobes. We also analyzed the high-resolution EPI data covering motor areas and fusiform gyri of human brain, and presented the differences of activations detected by volume- and surface-based methods.     

Phase vs. magnitude information in functional magnetic resonance imaging time series: toward understanding the noise

Although it has been shown that the phase of the MR signal from the brain is particularly prone to variation due to respiration, the overall physiological information contained in phase time series is not well understood. Here, we explore the different physiological processes contributing to the phase time series noise, identify their spatiotemporal characteristics and examine their relationship to BOLD-related and non-BOLD-related physiological noise in the magnitude time series. This was performed by manipulating the contribution of physiological noise to the total signal variance by modulating the TE and voxel volume, and using a short TR in order to adequately sample physiological signal fluctuations. The phase and magnitude signals were compared both before and after removal of signal fluctuations at the primary respiratory and cardiac frequencies with RETROICOR. We found that the temporal phase noise increased with TE at a faster rate than predicted by 1/TSNR as a result of physiological noise. As suggested by previous studies, the primary contributor to phase physiological noise was respiration-related effects which were manifested at a large scale (>1 cm). Notably, RETROICOR removed respiration-related large-scale artifacts and this resulted in considerable improvements in the temporal phase stability (7–90%). Physiological noise in the magnitude time series after RETROICOR consisted of low-frequency BOLD-related fluctuations (<0.13 Hz) localized to gray matter and the vasculature, and fluctuations in the vasculature correlated with slow (<0.1 Hz) variations in respiration volume and cardiac rhythm. Physiological noise in the phase signal after RETROICOR also occurred in frequencies below 0.13 Hz and was consistent with (1) residual large-scale magneto-mechanical effects correlated with slow variations in respiration volume and cardiac rhythm over time, and (2) local scale (<1 cm) effects localized in gray matter and vasculature most likely due to vascular dephasing mediated by a BOLD susceptibility change. While BOLD-related magnitude noise exhibited a TE dependence similar to BOLD, the ‘BOLD-related’ noise in the phase data increased with increasing TE and thus caused the overall phase noise to increase at a faster rate with TE than predicted by 1/TSNR. Interestingly, the spatial specificity of this effect was more evident for the higher resolution phase data, as opposed to the magnitude data, suggesting that at a higher spatial resolution the phase signal may contain more information on physiological processes than the magnitude signal.     

Spatial statistical analysis of the effects of urban form indicators on road-traffic noise exposure of a city in South Korea

The purpose of this study is to present a statistical model which can predict the noise level of road-traffic in urban area. A spatial statistical model which can take into account spatial dependency on geographically neighboring areas is constructed from a noise map of a city in South Korea. A system of 250 m × 250 m grid cells is placed on the city of Cheongju, South Korea, and the noise level and urban form indicators are averaged over each cell. The population-weighted mean of the noise level is subsequently regressed on the average urban form by adopting the spatial autoregressive model (SAR) and the spatial error model (SEM), as well as an ordinary least squares (OLS) model. Direct and indirect impacts are analyzed for a valid interpretation of the spatial statistical models. Factors such as GSI, FSI, traffic volume, traffic speed, road area density, and the fraction of industrial area turn out to have significant impacts on the noise level.     

Investigation of the large-scale functional brain networks modulated by acupuncture

Previous neuroimaging studies have primarily focused on the neural activities involving the acute effects of acupuncture. Considering that acupuncture can induce long-lasting effects, several researchers have begun to pay attention to the sustained effects of acupuncture on the resting brain. Most of these researchers adopted functional connectivity analysis based on one or a few preselected brain regions and demonstrated various function-guided brain networks underlying the specific effect of acupuncture. Few have investigated how these brain networks interacted at the whole-brain level. In this study, we sought to investigate the functional correlations throughout the entire brain following acupuncture at acupoint ST36 (ACUP) in comparison with acupuncture at nearby nonacupoint (SHAM). We divided the whole brain into 90 regions and constructed functional brain network for each condition. Then we examined the network hubs and identified statistically significant differences in functional correlations between the two conditions. Following ACUP, but not SHAM, the limbic/paralimbic regions such as the amygdala, hippocampus and anterior cingulate gyrus emerged as network hubs. For direct comparisons, increased correlations for ACUP compared to SHAM were primarily related with the limbic/paralimbic and subcortical regions such as the insula, amygdala, anterior cingulate gyrus, and thalamus, whereas decreased correlations were mainly related with the sensory and frontal cortex. The heterogeneous modulation patterns between the two conditions may relate to the functional specific modulatory effects of acupuncture. The preliminary findings may help us to better understand the long-lasting effects of acupuncture on the entire resting brain, as well as the neurophysiological mechanisms underlying acupuncture.     

Image feature based automatic correction of low-frequency spatial intensity variations in MR images

An automatic method of compensating for low-frequency variations in magnetic resonance images is presented. Small variations within a tissue type are modelled and a correction function is generated. The methods is based completely on image features and does not need a phantom or user interaction to generate the compensation function. This image correction simplifies digital image analysis and may enhance clinical evaluation. As a result, the correction technique reduces inhomogeneity and improves contrast. Our results show that the radiofrequency response variation of coils can be reduced. The segmentation process, even with a simple threshold method, produces more reliable results when corrected images are used. The presented method is most useful for images acquired in the sagital and coronal planes with circular local coils, or using surface coils, e.g., spine coils.     

Scenario analysis and noise action planning: Modelling the impact of mitigation measures on population exposure

This paper reports on strategic noise mapping research conducted in Dublin, Ireland. Noise maps are constructed for the day–evening–night-time and night-time periods and levels of population exposure are estimated for the same periods. In methodological terms, the research uses the UK’s calculation of road traffic noise (CRTN) method for calculating noise levels in the study area. This method has been adopted as the interim calculation method by the Irish authorities responsible for meeting the obligations set out in the EU Environmental Noise Directive (END). The research also investigates the usefulness of three noise mitigation measures for ‘acoustical planning’ purposes: traffic reductions, speed reductions and erection of acoustical barriers. The results indicate that levels of population exposure during night-time are extremely high relative to guideline limits set down by the World Health Organisation. In addition, the results highlight the significant role that certain noise mitigation measures can play in good ‘acoustical planning’.