Detecting functional connectivity in the resting brain: a comparison between ICA and CCA

Independent component analysis (ICA) and cross-correlation analysis (CCA) are general tools for detecting resting-state functional connectivity. In this study, we jointly evaluated these two approaches based on simulated data and in vivo functional magnetic resonance imaging data acquired from 10 resting healthy subjects. The influence of the number of independent components (maps) on the results of ICA was investigated. The influence of the selection of the seeds on the results of CCA was also examined. Our results reveal that significant differences between these two approaches exist. The performance of ICA is superior as compared with that of CCA; in addition, the performance of ICA is not significantly affected by structured noise over a relatively large range. The results of ICA could be affected by the number of independent components if this number is too small, however. Converting the spatially independent maps of ICA into z maps for thresholding tends to overestimate the false-positive rate. However, the overestimation is not very severe and may be acceptable in most cases. The results of CCA are dependent on seeds location. Seeds selected based on different criteria will significantly affect connectivity maps.     

The anxiolytic effects of midazolam during anticipation to pain revealed using fMRI

BACKGROUND AND PURPOSE: Functional neuroimaging can distinguish components of the pain experience associated with anticipation to pain from those associated with the experience of pain itself. Anticipation to pain is thought to increase the suffering of chronic pain patients. Inappropriate anxiety, of which anticipation is a component, is also a cause of disability. We present a pharmacological functional magnetic resonance imaging (fMRI) study in which we investigate the selective modulation by midazolam of brain activity associated with anticipation to pain compared to pain itself. METHODS: Eight right-handed male volunteers underwent fMRI combined with a thermal pain conditioning paradigm and midazolam (30 mug/kg) or saline administration on different occasions, with order randomized across volunteers. Volunteers learned to associate a colored light with either painful, hot stimulation or nonpainful, warm stimulation to the back of the left hand. RESULTS: Comparison of the period during thermal stimulation (pain-warm) revealed a network of brain activity commonly associated with noxious stimulation, including activities in the anterior cingulate cortex (ACC), the bilateral insular cortices (anterior and posterior), the thalamus, S1, the motor cortex, the brainstem, the prefrontal cortex and the cerebellum. Comparison of the periods preceding thermal stimulation (anticipation to pain-anticipation to warm) revealed activity principally in the ACC, the contralateral anterior insular cortex and the ipsilateral S2/posterior insula. Detected by a region-of-interest analysis, midazolam reduced the activity associated specifically with anticipation to pain while controlling for anticipation to warm. This was most significant in the contralateral anterior insula (P<.05). There were no significant drug effects on the activity associated with pain itself. CONCLUSION: In identifying a pharmacological effect on activity preceding but not during pain, we have successfully demonstrated an fMRI assay that can be used to study the action of anxiolytic agents in a relatively small cohort of humans.     

Registering and analyzing rat fMRI data in the stereotaxic framework by exploiting intrinsic anatomical features

The value of analyzing neuroimaging data on a group level has been well established in human studies. However, there is no standard procedure for registering and analyzing functional magnetic resonance imaging (fMRI) data into common space in rodent fMRI studies. An approach for performing rat imaging data analysis in the stereotaxic framework is presented. This method is rooted in the biological observation that the skull shape and size of rat brain are essentially the same as long as their weights are within certain range. Registration is performed using rigid-body transformations without scaling or shearing, preserving the unique properties of the stable shape and size inherent in rat brain structure. Also, it does not require brain tissue masking and is not biased towards surface coil sensitivity profile. A standard rat brain atlas is used to facilitate the identification of activated areas in common space, allowing accurate region of interest analysis. This technique is evaluated from a group of rats (n=11) undergoing routine MRI scans; the registration accuracy is estimated to be within 400 μm. The analysis of fMRI data acquired with an electrical forepaw stimulation model demonstrates the utility of this technique. The method is implemented within the Analysis of Functional NeuroImages (AFNI) framework and can be readily extended to other studies.     

Massively Parallel Nonparametric Regression,With an Application to Developmental Brain Mapping

A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online.     

Sexual dimorphism in the human brain: evidence from neuroimaging

In recent years, more and more emphasis has been placed on the investigation of sex differences in the human brain. Noninvasive neuroimaging techniques represent an essential tool in the effort to better understand the effects of sex on both brain structure and function. In this review, we provide a comprehensive summary of the findings that were collected in human neuroimaging studies in vivo thus far: we explore sexual dimorphism in the human brain at the level of (1) brain structure, in both gray and white matter, observed by voxel-based morphometry (VBM) and diffusion tensor imaging (DTI), respectively; (2) baseline neural activity, studied using resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET); (3) neurochemistry, visualized by means of neuroreceptor ligand PET; and (4) task-related neural activation, investigated using fMRI. Functional MRI findings from the literature are complemented by our own meta-analysis of fMRI studies on sex-specific differences in human emotional processing. Specifically, we used activation likelihood estimation (ALE) to provide a quantitative approach to mapping the consistency of neural networks involved in emotional processing across studies. The presented evidence for sex-specific differences in neural structure and function highlights the importance of modeling sex as a contributing factor in the analysis of brain-related data.     

Analysis of brain electrical topography by spatio-temporal wavelet decomposition

Currently, the Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and Electroencephalography (EEG) recordings are the major techniques of neuroimaging. The EEG with its highest temporal resolution is still a crucial measurement for localization of activities arising from the electrical behaviour of the brain. A scalp topographic map for an EEG may be a superposition of several simpler subtopographic maps, each resulting from an individual electrical source located at a certain depth. Furthermore, this source may have a temporal characteristic as an oscillation or a rhythm that extends in a certain time window which has been a basis of assumption for the time-frequency analysis methods. A method for the spatio-temporal wavelet decomposition of multichannel EEG data is proposed which facilitates the localization of electrical sources separate and/or overlapping on a continuum of time, frequency and space domains. The subtopographic maps asociated with each of these individual components are then used in the MUSIC source localization algorithm. The validations are performed on simulated EEG data. Spatio-temporal wavelet decomposition as a preprocessing method improves the source localization by simplifying the topographic data formed by the superposition of EEG generators, having possible combinations of temporal, frequency and/or spatial overlappings. Spatio-temporal analysis of EEG will help enhance the accuracy of dipole source reconstruction in neuroimaging.     

Reduced-Rank Regularized Multivariate Model for High-Dimensional Data

This article proposes a modeling framework for high-dimensional experimental data, such as brain images or microarrays, that discovers statistically significant structures most relevant to the experimental covariates. To deal with the curse of dimensionality, three regularization schemes are used: a reduced-rank model, penalization of the covariance matrix, and regularization of the basis-expanded predictor set. The latter allows us to flexibly model associations while controlling for overfitting. The modeling framework is derived from a reduced-rank multiresponse linear model, which offers a familiar interface for researchers. The novel regularizations of both sides of the model make it applicable in high-dimensional settings, without a need for prior dimension reduction, and can model nonlinear relationships. An efficient, dual-space algorithm is proposed to estimate its components in low-dimensional space. It permits the use of the bootstrap, to provide pointwise standard error bands on association graphs, and other resampling techniques to optimize hyperparameters. We evaluate the model on a small neuroimaging dataset, and in a simulation study using simple images corrupted by additive Gaussian iid and random field noise components with signal-to-noise ratios below 0.1. Our model compares well with a general linear model (GLM) even when the nonlinear associations are specified explicitly in GLM.     

Understanding changes in DTI metrics in patients with different stages of neurocysticercosis

Diffusion tensor imaging (DTI) was performed on 25 patients with neurocysticercosis (NCC). The aim of this study was to investigate the changes in DTI measures during the evolutionary course of NCC lesions from vesicular to calcified stage in the brain. DTI measures were quantified from the NCC lesions of all patients. On the basis of conventional imaging findings, NCC lesions were classified into vesicular, vesicular colloidal, granular nodular and calcified stages. Significant inverse correlation was observed between the evolutionary stage of NCC lesion and mean diffusivity (MD; r=−0.748, P<0.001) and spherical anisotropy (CS; r=−0.585, P<.001) values. Significant direct correlations were observed between evolutionary stages of NCC lesion and mean fractional anisotropy (FA; r=0.575, P<0.001), linear anisotropy (CL; r=0.478, p<0.001) and planar anisotropy (CP; r=0.561, p<0.001) values. Successive decrease in MD values calculated from NCC lesions was observed, moving from vesicular to granular nodular stage. On FA, CL and CP maps, a significant increase in signal intensity value was observed in calcified as compared to other stages. We conclude that DTI measures may indicate the evolutionary changes in NCC from vesicular to calcified stage.     

EEMD-ICA在功能性近红外光谱特征信号提取中的应用

近年来,功能性近红外光谱技术(fNIRS)广泛应用于神经影像学领域。为解决fNIRS特征信号提取中的信噪频谱混叠问题,依据近红外光谱脑功能成像信号非线性与非平稳特点,提出一种结合集合经验模态分解法和独立成分分析的多分辨率联合信号提取方法EEMD-ICA。在脑功能成像仪器平台上采集多通道多波长脑功能成像近红外光密度信号,先对该信号进行集合经验模态分解将其按频率成分分解为多层本征模态函数,之后将独立成分分析应用于目标频率分量函数进行自适应去噪,最后将处理后的分量累加、重构获得近红外光谱脑功能成像的特征信号。将Valsalva氏实验测试数据作为研究对象进行滤噪处理,与经验模态分解法和集合经验模态分解法对fNIRS特征信号的提取效果对比。对实测数据的处理结果进行信噪比和误差参数分析,结果表明,该方法能够有效解决去噪过程中丢失原始信号有用信息及由于信噪频谱混叠不能完整去除噪声的问题,信号处理效果理想,对比另外两种信号提取方法更为优化。