Feedback of real-time fMRI signals: From concepts and principles to therapeutic interventions

The feedback of real-time functional magnetic resonance imaging (rtfMRI) signals, dubbed “neurofeedback”, has found applications in the treatment of clinical disorders and enhancement of brain performance. However, knowledge of the basic underlying mechanism on which neurofeedback is based is rather limited. This article introduces the concepts, principles and characteristics of feedback control systems and its applications to electroencephalography (EEG) and rtfMRI signals. Insight into the underlying mechanisms of feedback systems may lead to the development of novel feedback protocols and subsystems for rtfMRI and enhance therapeutic solutions for clinical interventions.     

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.     

High-resolution, multiple gradient-echo functional MRI at 1.5 T

A multiple gradient echo, high resolution imaging method is proposed to better visualize different sources of activation in functional magnetic resonance imaging (fMRI) experiments. Eight echoes are collected from 30 ms to 205 ms with an echo spacing of 25 ms. All echoes show significant activation, but each echo reveals its own pattern of activation. From this variability, it appears that large vessel contributions can be separated from small vessel contributions using a fuzzy cluster analysis across echo times. The results demonstrate the importance of a multiple gradient echo data acquisition approach in localizing various vascular contributions to brain activation in fMRI.     

Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets

A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions.     

Functional magnetic resonance imaging of awake monkeys: some approaches for improving imaging quality

Functional magnetic resonance imaging (fMRI) at high magnetic field strength can suffer from serious degradation of image quality because of motion and physiological noise, as well as spatial distortions and signal losses due to susceptibility effects. Overcoming such limitations is essential for sensitive detection and reliable interpretation of fMRI data. These issues are particularly problematic in studies of awake animals. As part of our initial efforts to study functional brain activations in awake, behaving monkeys using fMRI at 4.7 T, we have developed acquisition and analysis procedures to improve image quality with encouraging results.We evaluated the influence of two main variables on image quality. First, we show how important the level of behavioral training is for obtaining good data stability and high temporal signal-to-noise ratios. In initial sessions, our typical scan session lasted 1.5 h, partitioned into short (<10 min) runs. During reward periods and breaks between runs, the monkey exhibited movements resulting in considerable image misregistrations. After a few months of extensive behavioral training, we were able to increase the length of individual runs and the total length of each session. The monkey learned to wait until the end of a block for fluid reward, resulting in longer periods of continuous acquisition. Each additional 60 training sessions extended the duration of each session by 60 min, culminating, after about 140 training sessions, in sessions that last about 4 h. As a result, the average translational movement decreased from over 500 μm to less than 80 μm, a displacement close to that observed in anesthetized monkeys scanned in a 7-T horizontal scanner.Another major source of distortion at high fields arises from susceptibility variations. To reduce such artifacts, we used segmented gradient-echo echo-planar imaging (EPI) sequences. Increasing the number of segments significantly decreased susceptibility artifacts and image distortion. Comparisons of images from functional runs using four segments with those using a single-shot EPI sequence revealed a roughly twofold improvement in functional signal-to-noise-ratio and 50% decrease in distortion. These methods enabled reliable detection of neural activation and permitted blood-oxygenation-level-dependent-based mapping of early visual areas in monkeys using a volume coil.In summary, both extensive behavioral training of monkeys and application of segmented gradient-echo EPI sequence improved signal-to-noise ratio and image quality. Understanding the effects these factors have is important for the application of high field imaging methods to the detection of submillimeter functional structures in the awake monkey brain.     

Functional exploration of the human spinal cord during voluntary movement and somatosensory stimulation

Demonstrations of the possibility of obtaining functional information from the spinal cord in humans using functional magnetic resonance imaging (fMRI) have been growing in number and sophistication, but the technique and the results that it provides are still perceived by the scientific community with a greater degree of scepticism than fMRI investigations of brain function. Here we review the literature on spinal fMRI in humans during voluntary movements and somatosensory stimulation. Particular attention is given to study design, acquisition and statistical analysis of the images, and to the agreement between the obtained results and existing knowledge regarding spinal cord anatomy and physiology.     

Effects of spatial smoothing on fMRI group inferences

The analysis of functional magnetic resonance imaging (fMRI) data involves multiple stages of data pre-processing before the activation can be statistically detected. Spatial smoothing is a very common pre-processing step in the analysis of functional brain imaging data. This study presents a broad perspective on the influence of spatial smoothing on fMRI group activation results. The data obtained from 20 volunteers during a visual oddball task were used for this study. Spatial smoothing using an isotropic gaussian filter kernel with full width at half maximum (FWHM) sizes 2 to 30 mm with a step of 2 mm was applied in two levels — smoothing of fMRI data and/or smoothing of single-subject contrast files prior to general linear model random-effects group analysis generating statistical parametric maps. Five regions of interest were defined, and several parameters (coordinates of nearest local maxima, t value, corrected threshold, effect size, residual values, etc.) were evaluated to examine the effects of spatial smoothing. The optimal filter size for group analysis is discussed according to various criteria. For our experiment, the optimal FWHM is about 8 mm. We can conclude that for robust experiments and an adequate number of subjects in the study, the optimal FWHM for single-subject inference is similar to that for group inference (about 8 mm, according to spatial resolution). For less robust experiments and fewer subjects in the study, a higher FWHM would be optimal for group inference than for single-subject inferences.     

Anatomical correlates of the functional organization in the human occipitotemporal cortex

The connectivity between functionally distinct areas in the human brain is unknown because of the limitations posed by current postmortem anatomical labeling techniques. Diffusion tensor imaging (DTI) has previously been used to define large white matter tracts based on well-known anatomical landmarks in the living human brain. In the present study, we used DTI coupled with functional magnetic resonance imaging (fMRI) to assess neuronal connections between human striate and functionally defined extrastriate ventral cortical areas. Functional areas were identified with conventional fMRI mapping procedures and then used as seeding points in a DTI analysis to ascertain connectivity patterns between cortical areas, thus yielding the pattern of connections between human occipitoventral visual areas in vivo.     

Comparison of functional MR-venography and EPI-BOLD fMRI at 1.5 t

Functional magnetic resonance imaging (fMRI) of the brain using blood oxygenation level dependent (BOLD) contrast relies on the changes of paramagnetic deoxyhemoglobin concentration, which affects brain parenchyma and draining venous vessels. These changes in deoxyhemoglobin concentration in venous vessels can also be monitored using a high-resolution susceptibility-based MR-venography technique. Four volunteers participated in the study in which functional MR-venograms were compared with conventional echo-planar imaging (EPI)-BOLD-fMRI. In all cases, small venous vessels could be identified close to the areas of activation detected by conventional fMRI. In the venograms, task performance (finger tapping) resulted in a loss of venous vessel contrast compared to the resting state, which is consistent with a local decrease of deoxyhemoglobin concentration. MR-venography allows a direct visualization of the BOLD-effect at high spatial resolution. In combination with conventional fMRI, this technique may help to separate the contribution of brain parenchyma and venous vessels in fMRI studies.     

A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.     

静息状态下脑功能连接的磁共振成像研究

静息状态下脑功能连接的磁共振成像研究近年来取得了迅猛发展. 通过对fMRI信号低频涨落成分的同步性分析,可以得到大脑静息态任意脑区的功能连接和多套网络系统,其中“默认网络”的发现可能为人脑固有网络的研究提供新的思路. 而静息态网络与解剖连接之间可能存在的对应,以及在神经精神疾病患者脑中性质和连接的异常改变,使其具有重要的研究和临床应用价值. 该文总结了静息状态功能磁共振成像的主要研究成果,对静息状态脑功能网络的发现和发展、研究方法、各网络及其特点以及在临床方面的应用进行简单的介绍和分析.     

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.     

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.     

Nonlinear responses of cerebral blood volume, blood flow and blood oxygenation signals during visual stimulation

Hemodynamic-based functional magnetic resonance imaging (fMRI) techniques provide a great utility for noninvasive functional brain mapping. However, because the hemodynamic signals reflect underlying neural activity indirectly, characterization of these signals following brain activation is essential for experimental design and data interpretation. In this report, the linear (or nonlinear) responses to neuronal activation of three hemodynamic parameters based primarily on changes of cerebral blood volume, blood flow and blood oxygenation were investigated by testing these hemodynamic responses' additivity property. Using a recently developed fMRI technique that acquires vascular space occupancy (VASO), arterial spin labeling (ASL) perfusion and blood oxygenation level-dependent (BOLD) signals simultaneously, the additivity property of the three hemodynamic responses in human visual cortex was assessed using various visual stimulus durations. Experiments on healthy volunteers showed that all three hemodynamic-weighted signals responded nonlinearly to stimulus durations less than 4 s, with the degree of nonlinearity becoming more severe as the stimulus duration decreased. Vascular space occupancy and ASL perfusion signals showed similar nonlinearity properties, whereas the BOLD signal was the most nonlinear. These data suggest that caution should be taken in the interpretation of hemodynamic-based signals in fMRI.     

Improving the resolution of functional brain imaging: analyzing functional data in anatomical space

The accurate mapping of functional magnetic resonance imaging (fMRI) activations to anatomical structures is critical for fMRI studies of brain organization. In the commonly used functional space analysis method, functional images are realigned to a functional reference image and processed in low-resolution functional space. The average functional activations are then projected into high-resolution anatomical space for visualization. Here, we describe a new technique, anatomical space analysis (ASA), whereby low-resolution functional images are first coregistered and resampled directly into high-resolution anatomical space with all subsequent data processing performed in high-resolution space. A major advantage of ASA is that minor scanner sampling instabilities and small head movements can increase spatial resolution by providing multiple samples of the relationship between functional and anatomical space. Both simulations and analyses of real fMRI data show that ASA improves the precision, objectivity and reproducibility of functional brain mapping.     

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.     

Component structure of event-related fMRI responses in the different neurovascular compartments

In most functional magnetic resonance imaging (fMRI) studies, brain activity is localized by observing changes in the blood oxygenation level-dependent (BOLD) signal that are believed to arise from capillaries, venules and veins in and around the active neuronal population. However, the contribution from veins can be relatively far downstream from active neurons, thereby limiting the ability of BOLD imaging methods to precisely pinpoint neural generators. Hemodynamic measures based on apparent diffusion coefficients (ADCs) have recently been used to identify more upstream functional blood flow changes in the capillaries, arterioles and arteries. In particular, we recently showed that, due to the complementary vascular sensitivities of ADC and BOLD signals, the voxels conjointly activated by both measures may identify the capillary networks of the active neuronal areas. In this study, we first used simultaneously acquired ADC and BOLD functional imaging signals to identify brain voxels activated by ADC only, by both ADC and BOLD and by BOLD only, thereby delineating voxels relatively dominated by the arterial, capillary, and draining venous neurovascular compartments, respectively. We then examined the event-related fMRI BOLD responses in each of these delineated neurovascular compartments, hypothesizing that their event-related responses would show different temporal componentries. In the regions activated by both the BOLD and ADC contrasts, but not in the BOLD-only areas, we observed an initial transient signal reduction (an initial dip), consistent with the local production of deoxyhemoglobin by the active neuronal population. In addition, the BOLD-ADC overlap areas and the BOLD-only areas showed a clear poststimulus undershoot, whereas the compartment activated by only ADC did not show this component. These results indicate that using ADC contrast in conjunction with BOLD imaging can help delineate the various neurovascular compartments, improve the localization of active neural populations, and provide insight into the physiological mechanisms underlying the hemodynamic signals.     

Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI

Analysis of resting-state functional magnetic resonance imaging (fMRI) data is based on detecting low-frequency signal fluctuations in functionally connected brain areas. These synchronous fluctuations in resting-state networks have been observed in several studies with healthy subjects. In this study, we explored if independent component analysis (ICA) can be used to localize the sensorimotor area from resting-state fMRI data in patients with brain tumors. Finger-tapping activation task and resting-state blood-oxygenation-level-dependent fMRI data were acquired from 8 patients with brain tumors and 10 healthy volunteers. Sensorimotor task independent components (IC_task) were used to verify resting-state independent components (IC_rest) individually. In addition, sensorimotor IC_rests were compared between the groups and no significant differences were detected in volume, spatial correlation or temporal correlation. These results show that it is possible to localize a sensorimotor area from resting-state data using ICA in patients with brain tumors. This offers a complementary method for assessing the sensorimotor area in subjects with brain tumors who have difficulties in performing motor paradigms.     

Percept-related activity in the human somatosensory system: functional magnetic resonance imaging studies

In this paper, we review blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies addressing the neural correlates of touch, thermosensation, pain and the mechanisms of their cognitive modulation in healthy human subjects. There is evidence that fMRI signal changes can be elicited in the parietal cortex by stimulation of single mechanoceptive afferent fibers at suprathreshold intensities for conscious perception. Positive linear relationships between the amplitude or the spatial extents of BOLD fMRI signal changes, stimulus intensity and the perceived touch or pain intensity have been described in different brain areas. Some recent fMRI studies addressed the role of cortical areas in somatosensory perception by comparing the time course of cortical activity evoked by different kinds of stimuli with the temporal features of touch, heat or pain perception. Moreover, parametric single-trial functional MRI designs have been adopted in order to disentangle subprocesses within the nociceptive system.

Available evidence suggest that studies that combine fMRI with psychophysical methods may provide a valuable approach for understanding complex perceptual mechanisms and top-down modulation of the somatosensory system by cognitive factors specifically related to selective attention and to anticipation. The brain networks underlying somatosensory perception are complex and highly distributed. A deeper understanding of perceptual-related brain mechanisms therefore requires new approaches suited to investigate the spatial and temporal dynamics of activation in different brain regions and their functional interaction.     

Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect

There has been vast interest in determining the feasibility of functional magnetic resonance imaging (fMRI) as an accurate method of imaging brain function for patient evaluations. The assessment of fMRI as an accurate tool for activation localization largely depends on the software used to process the time series data. The performance evaluation of different analysis tools is not reliable unless truths in motion and activation are known. Lack of valid truths has been the limiting factor for comparisons of different algorithms. Until now, currently available phantom data do not include comprehensive accounts of head motion. While most fMRI studies assume no interslice motion during the time series acquisition in fMRI data acquired using a multislice and single-shot echo-planar imaging sequence, each slice is subject to a different set of motion parameters. In this study, in addition to known three-dimensional motion parameters applied to each slice, included in the time series computation are geometric distortion from field inhomogeneity and spin saturation effect as a result of out-of-plane head motion. We investigated the effect of these head motion-related artifacts and present a validation of the mapping slice-to-volume (MSV) algorithm for motion correction and activation detection against the known truths. MSV was evaluated, and showed better performance in comparison with other widely used fMRI data processing software, which corrects for head motion with a volume-to-volume realignment method. Furthermore, improvement in signal detection was observed with the implementation of the geometric distortion correction and spin saturation effect compensation features in MSV.