fMRI在经颅直流电刺激研究中的应用进展

经颅直流电刺激（tDCS）是一种新颖的神经系统疾病治疗手段,可非侵入性调控中枢神经活动.它具有安全、成本低、疗效显著等特点,因此在神经科学等领域被大量研究和应用.功能磁共振成像（fMRI）具备高时空分辨率的优势,近年来被广泛用于tDCS治疗效果的评估及其作用机制的研究.本文从临床研究与动物模型实验两方面总结fMRI技术应用于tDCS研究中的最新进展,并讨论其应用前景.     

EEG-correlated fMRI of P3b component in P300 waves

Electroencephalography-correlated functional magnetic resonance imaging （EEG/fMRI） can be used to identify blood oxygen level-dependent （BOLD） signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to P3b component of P300, and 64 channels of EEG were recorded in II subjects during Landot Ring task inside a 1.5 T functional magnet resonance （MR） scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 2 s, leaving gaps of 2 s without scanning. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data. Additionally, a P300 wave matched filter was constructed to inspect P300 wave occurrence following every target stimulus, target stimuli inspected to induce P300 were detected and their MRI scan number were then used as input for an event-related fMRI analysis. Finally MRI statistical parametric maps were constructed and corrected for multiple comparisons. By random effect group analysis, activations were detected in the right superior parietal lobule and bilaterally in inferior parietal lobule（p〈0.001, uncorrected）. The results demonstrated the upper regions were sources of P3b component and involved in target detection in memory comparison task.     

Functional magnetic resonance imaging: Comparison between activation maps and computation pipelines in a clinical context

In this study new evaluation strategies for comparing different Statistical Parametric Maps computed from fMRI time-series analysis software tools are proposed. The aim of our work is to assess and quantitatively evaluate the statistical agreement of activation maps. Some pre-processing steps are necessary to compare SPMs (Statistical Parametric Maps), including segmentation and co-registration. The study of the statistical agreement is carried out following two ways. The first way considers SPMs as the result of two classification processes and extracts confusion matrix and Cohen's kappa index to assess agreement. Some considerations will be made on the statistical dependence of classes and a new formulation of kappa index will be used for overcoming this problem. The second way considers SPMs as two 3D images, and computes the similarity of SPMs images with a fuzzy formulation of the Jaccard Index. Several experiments were conducted both to assess the performance of the proposed evaluation tools and to compare activation maps computation pipelines from two widely used software tools in a clinical context.     

Deconvolution filtering: Temporal smoothing revisited

Inferences made from analysis of BOLD data regarding neural processes are potentially confounded by multiple competing sources: cardiac and respiratory signals, thermal effects, scanner drift, and motion-induced signal intensity changes. To address this problem, we propose deconvolution filtering, a process of systematically deconvolving and reconvolving the BOLD signal via the hemodynamic response function such that the resultant signal is composed of maximally likely neural and neurovascular signals. To test the validity of this approach, we compared the accuracy of BOLD signal variants (i.e., unfiltered, deconvolution filtered, band-pass filtered, and optimized band-pass filtered BOLD signals) in identifying useful properties of highly confounded, simulated BOLD data: (1) reconstructing the true, unconfounded BOLD signal, (2) correlation with the true, unconfounded BOLD signal, and (3) reconstructing the true functional connectivity of a three-node neural system. We also tested this approach by detecting task activation in BOLD data recorded from healthy adolescent girls (control) during an emotion processing task.     

Quantification of the power changes in BOLD signals using Welch spectrum method during different single-hand motor imageries

Motor imagery is an experimental paradigm implemented in cognitive neuroscience and cognitive psychology. To investigate the asymmetry of the strength of cortical functional activity due to different single-hand motor imageries, functional magnetic resonance imaging (fMRI) data from right handed normal subjects were recorded and analyzed during both left-hand and right-hand motor imagery processes. Then the average power of blood oxygenation level-dependent (BOLD) signals in temporal domain was calculated using the developed tool that combines Welch power spectrum and the integral of power spectrum approach of BOLD signal changes during motor imagery. Power change analysis results indicated that cortical activity exhibited a stronger power in the precentral gyrus and medial frontal gyrus with left-hand motor imagery tasks compared with that from right-hand motor imagery tasks. These observations suggest that right handed normal subjects mobilize more cortical nerve cells for left-hand motor imagery. Our findings also suggest that the approach based on power differences of BOLD signals is a suitable quantitative analysis tool for quantification of asymmetry of brain activity intensity during motor imagery tasks.     

A study on neural mechanism of face processing based on fMRI

Recently, there were debates about the specificity of lateral middle fusiform in face processing. The debates focused on whether these areas were specialized in face processing or involved in processing of visual expertise and categorization at individual level. The present study aims to investigate the neural mechanism of face processing, using Chinese characters as comparison stimuli. Chinese characters are greatly similar to faces on a variety of dimensions, among which the most significant one is that both faces and Chinese characters not only are extremely familiar to literate Chinese adults but also are processed at individual level. In the present study, faces and Chinese characters activated bilateral middle fusiform with great correlation. Greater activities were observed in the right fusiform face area （FFA） for faces than for Chinese characters. These results demonstrate that FFA is specialized in face processing per se rather than the processing of visual expertise and categorization at individual level.     

Genetically controlled MRI contrast mechanisms and their prospects in systems neuroscience research

Application of MRI contrast agents to neural systems research is complicated by the need to deliver agents past the blood-brain barrier or into cells, and the difficulty of targeting agents to specific brain structures or cell types. In the future, these barriers may be wholly or partially overcome using genetic methods for producing and directing MRI contrast. Here we review MRI contrast mechanisms that have used gene expression to manipulate MRI signal in cultured cells or in living animals. We discuss both fully genetic systems involving endogenous biosynthesis of contrast agents, and semi-genetic systems in which expressed proteins influence the localization or activity of exogenous contrast agents. We close by considering which contrast-generating mechanisms might be most suitable for applications in neuroscience, and we ask how genetic control machinery could be productively combined with existing molecular agents to enable next-generation neuroimaging experiments.     

Using partial correlation to enhance structural equation modeling of functional MRI data

In functional magnetic resonance imaging (fMRI) data analysis, effective connectivity investigates the influence that brain regions exert on one another. Structural equation modeling (SEM) has been the main approach to examine effective connectivity. In this paper, we propose a method that, given a set of regions, performs partial correlation analysis. This method provides an approach to effective connectivity that is data driven, in the sense that it does not require any prior information regarding the anatomical or functional connections. To demonstrate the practical relevance of partial correlation analysis for effective connectivity investigation, we reanalyzed data previously published [Bullmore, Horwitz, Honey, Brammer, Williams, Sharma, 2000. How good is good enough in path analysis of fMRI data? NeuroImage 11, 289–301]. Specifically, we show that partial correlation analysis can serve several purposes. In a pre-processing step, it can hint at which effective connections are structuring the interactions and which have little influence on the pattern of connectivity. As a post-processing step, it can be used both as a simple and visual way to check the validity of SEM optimization algorithms and to show which assumptions made by the model are valid, and which ones should be further modified to better fit the data.     

Dense mode clustering in brain maps

A mode-based clustering method is developed for identifying spatially dense clusters in brain maps. This type of clustering focuses on identifying clusters in brain maps independent of their shape or overall variance. This can be useful for both localization in terms of interpretation and for subsequent graphical analysis that might require more coherent or dense regions of interest as starting points. The method automatically does signal/noise sharpening through density mode seeking. We also discuss the problem of parameter selection with this method and propose a new method involving 2-parameter control surface, in which we show that the same cluster solution results from tradeoff of these 2 parameters (the local density k and the radius r of the spherical kernel). We benchmark the new dense mode clustering by using several artificially created data sets and brain imaging data sets from an event perception task by perturbing the data set with noise and measuring three kinds of deviation from the original cluster solution. We present benchmark results that demonstrate that the mode clustering method consistently outperforms the commonly used single-linkage clustering, k means method (centroid method) and Ward's method (variance method).