Investigating directed influences between activated brain areas in a motor-response task using fMRI

Localization of cognitive processes is a strength of functional neuroimaging. However, information about functional interactions between brain areas is crucial for a deeper understanding of brain function. We applied vector autoregressive modeling in the context of Granger causality as a method to analyze directed connectivity in a standard event-related fMRI study using a simple auditory-motor paradigm. The basic idea is to use temporal information in stochastic time series of a brain region in order to predict signal time courses in other brain regions. Thus, we predicted that the method should demonstrate causal influence of the auditory cortex and the supplementary motor area (SMA) on primary motor cortex. Eleven right-handed healthy female subjects were instructed to press a ball with either their left or their right hand when hearing the command "left" or "right" in the scanner. Influence to the left motor cortex was found from bilateral auditory cortex as well as from the SMA in 9 of 11 subjects. Granger causality to the right motor cortex existed from bilateral auditory cortex in 5 and from SMA in 6 subjects. Granger causality to the SMA existed from right auditory cortex in 7 subjects and from left auditory cortex in 8 subjects. Our findings in a simple task show that even under suboptimal circumstances (a relatively long TR of 2440 ms), Granger causality can be a useful tool to explore effective connectivity. Temporally optimized scanning should increase that potential.     

Functional MRI and multivariate autoregressive models

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.     

Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping

We present a framework aimed to reveal directed interactions of activated brain areas using time-resolved fMRI and vector autoregressive (VAR) modeling in the context of Granger causality. After describing the underlying mathematical concepts, we present simulations helping to characterize the conditions under which VAR modeling and Granger causality can reveal directed interactions from fluctuations in BOLD-like signal time courses. We apply the proposed approach to a dynamic sensorimotor mapping paradigm. In an event-related fMRI experiment, subjects performed a visuomotor mapping task for which the mapping of two stimuli (“faces” vs “houses”) to two responses (“left” or “right”) alternated periodically between the two possible mappings. Besides expected activity in sensory and motor areas, a fronto-parietal network was found to be active during presentation of a cue indicating a change in the stimulus-response (S-R) mapping. The observed network includes the superior parietal lobule and premotor areas. These areas might be involved in setting up and maintaining stimulus-response associations. The Granger causality analysis revealed a directed influence exerted by the left lateral prefrontal cortex and premotor areas on the left posterior parietal cortex.     

Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG

Different brain imaging devices are presently available to provide images of the human functional cortical activity, based on hemodynamic, metabolic or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. While the functional connectivity is defined as the temporal coherence among the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally among cortical sites. The structural equation modeling (SEM) is the most used method to estimate effective connectivity in neuroscience, and its typical application is on data related to brain hemodynamic behavior tested by functional magnetic resonance imaging (fMRI), whereas the directed transfer function (DTF) method is a frequency-domain approach based on both a multivariate autoregressive (MVAR) modeling of time series and on the concept of Granger causality.

This study presents advanced methods for the estimation of cortical connectivity by applying SEM and DTF on the cortical signals estimated from high-resolution electroencephalography (EEG) recordings, since these signals exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. To estimate correctly the cortical signals, we used a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual MRI, a distributed source model and a regularized linear inverse source estimates of cortical current density. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high-resolution EEG data, we performed a simulation study, in which different main factors (signal-to-noise ratio, SNR, and simulated cortical activity duration, LENGTH) were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity patterns under reasonable operative conditions, that is, when data exhibit an SNR of at least 3 and a LENGTH of at least 75 s of nonconsecutive EEG recordings at 64 Hz of sampling rate.

Hence, effective and functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high-resolution EEG techniques and linear inverse estimation with SEM or DTF methods. We conclude that the estimation of cortical connectivity can be performed not only with hemodynamic measurements, but also with EEG signals treated with advanced computational techniques.     

Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI

Amnestic mild cognitive impairment (aMCI) is a syndrome associated with faster memory decline than normal aging and frequently represents the prodromal phase of Alzheimer's disease. When a person is not actively engaged in a goal-directed task, spontaneous functional magnetic resonance imaging (fMRI) signals can reveal functionally connected brain networks, including the so-called default mode network (DMN). To date, only a few studies have investigated DMN functions in aMCI populations. In this study, group-independent component analysis was conducted for resting-state fMRI data, with slices acquired perpendicular to the long axis of the hippocampus, from eight subjects with aMCI and eight normal control subjects. Subjects with aMCI showed an increased DMN activity in middle cingulate cortex, medial prefrontal cortex and left inferior parietal cortex compared to the normal control group. Decreased DMN activity for the aMCI group compared to the normal control group was noted in lateral prefrontal cortex, left medial temporal lobe (MTL), left medial temporal gyrus, posterior cingulate cortex/retrosplenial cortex/precuneus and right angular gyrus. Although MTL volume difference between the two groups was not statistically significant, a decreased activity in left MTL was observed for the aMCI group. Positive correlations between the DMN activity and memory scores were noted for left lateral prefrontal cortex, left medial temporal gyrus and right angular gyrus. These findings support the premise that alterations of the DMN occur in aMCI and may indicate deficiencies in functional, intrinsic brain architecture that correlate with memory function, even before significant MTL atrophy is detectable by structural MRI.     

Hierarchical clustering to measure connectivity in fMRI resting-state data

Low frequency oscillations, which are temporally correlated in functionally related brain regions, characterize the mammalian brain, even when no explicit cognitive tasks are performed. Functional connectivity MR imaging is used to map regions of the resting brain showing synchronous, regional and slow fluctuations in cerebral blood flow and oxygenation. In this study, we use a hierarchical clustering method to detect similarities of low-frequency fluctuations. We describe one measure of correlations in the low frequency range for classification of resting-state fMRI data. Furthermore, we investigate the contribution of motion and hardware instabilities to resting-state correlations and provide a method to reduce artifacts. For all cortical regions studied and clusters obtained, we quantify the degree of contamination of functional connectivity maps by the respiratory and cardiac cycle. Results indicate that patterns of functional connectivity can be obtained with hierarchical clustering that resemble known neuronal connections. The corresponding voxel time series do not show significant correlations in the respiratory or cardiac frequency band.     

Aberrant resting-state cerebral blood flow and its connectivity in primary dysmenorrhea on arterial spin labeling MRI

PurposeThis study aimed to clarify the resting-state cerebral blood flow alteration patterns induced by primary dysmenorrhea, investigate the relationships between cerebral blood flow alterations and clinical parameters of patients with primary dysmenorrhea, and explore whether brain regions with abnormal cerebral blood flow also feature functional connectivity changes.MethodsArterial spin labeling imaging and clinical parameters were acquired in 42 patients with primary dysmenorrhea and 41 healthy controls during their menstrual phases. Differences in cerebral blood flow were compared between the two groups, and the clusters with significant group differences were selected as the regions of interest for further statistical analyses.ResultsCompared to healthy controls, patients with primary dysmenorrhea exhibited increased cerebral blood flow in the bilateral precuneus, left posterior cingulate cortex, and right rolandic operculum. Among patients with primary dysmenorrhea, we identified a negative correlation between the cerebral blood flow in the right rolandic operculum and the visual analogue score for anxiety, and greater correlation between the functional connectivity in the precuneus/posterior cingulate cortex and the right middle cingulate cortex, and between the right rolandic operculum and the left inferior parietal lobule and the bilateral postcentral gyrus.DiscussionCerebral blood flow abnormalities associated with primary dysmenorrhea were mainly concentrated in the areas comprising the default mode network in primary dysmenorrhea patients, which could be involved in the central mechanism of primary dysmenorrhea. Cerebral blood flow alteration in the rolandic operculum may underlie an anxiety-induced compulsive tendency in patients with primary dysmenorrhea. Investigating the enhanced connectivity among various pain-related brain regions could improve understanding of the onset and development of primary dysmenorrhea.     

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.     

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.     

Toward a physical basis of attention and self-regulation

The concept of self-regulation is central to the understanding of human development. Self-regulation allows effective socialization and predicts both psychological pathologies and levels of achievement in schools. What has been missing are neural mechanisms to provide understanding of the cellular and molecular basis for self-regulation. We show that self-regulation can be measured during childhood by parental reports and by self-reports of adolescents and adults. These reports are summarized by a higher order factor called effortful control, which reflects perceptions about the ability of a given person to regulate their behavior in accord with cultural norms. Throughout childhood effortful control is related to children's performance in computerized conflict related tasks. Conflict tasks have been shown in neuroimaging studies to activate specific brain networks of executive attention. Several brain areas work together at rest and during cognitive tasks to regulate competing brain activity and thus control resulting behavior. The cellular structure of the anterior cingulate and insula contain cells, unique to humans and higher primates that provide strong links to remote brain areas. During conflict tasks, anterior cingulate activity is correlated with activity in remote sensory and emotional systems, depending upon the information selected for the task. During adolescence the structure and activity of the anterior cingulate has been found to be correlated with self-reports of effortful control.Studies have provided a perspective on how genes and environment act to shape the executive attention network, providing a physical basis for self-regulation. The anterior cingulate is regulated by dopamine. Genes that influence dopamine levels in the CNS have been shown to influence the efficiency of self-regulation. For example, alleles of the COMT gene that influence the efficiency of dopamine transmission are related to the ability to resolve conflict. Humans with disorders involving deletion of this gene exhibit large deficits in self-regulation. Alleles of other genes influencing dopamine and serotonin transmission have also been found to influence ability to resolve conflict in cognitive tasks. However, as is the case for many genes, the effectiveness of COMT alleles in shaping self-regulation depends upon cultural influences such as parenting. Studies find that aspects of parenting quality and parent training can influence child behavior and the efficiency of self-regulation.During development, the network that relates to self-regulation undergoes important changes in connectivity. Infants can use parts of the self-regulatory network to detect errors in sensory information, but the network does not yet have sufficient connectivity to organize brain activity in a coherent way. During middle childhood, along with increased projection cells involved in remote connections of dorsal anterior cingulate and prefrontal and parietal cortex, executive network connectivity increases and shifts from predominantly short to longer range connections. During this period specific exercises can influence network development and improve self-regulation. Understanding the physical basis of self-regulation has already cast light on individual differences in normal and pathological states and gives promise of allowing the design of methods to improve aspects of human development.     

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.     

Nonlinear statistical properties of fMRI signals in human visual cortex during resting-state

The purpose of the current work is to study nonlinear dynamics in neuronal activity within human brain visual cortex based on blood-oxygen-level dependent (BOLD) contrast imaging. In particular, based on functional magnetic resonance imaging (fMRI) signals, measures of fractality, complexity, and state disorder are estimated from central and peripheral eccentricity bands across three visual areas. Statistical results from analysis of 48750 resting-state fMRI signals show evidence that nonlinear dynamics of neuronal activity in resting-state in central and peripheral eccentricity bands of human visual cortex are persistent. However, they exhibit heterogeneous variability across eccentricity bands and visual areas. Also, information content in first visual area is more ordered than in the second one, whilst information content in the third visual area is the least ordered. These interesting nonlinear statistical properties are a further step toward understanding neuronal activity and nonlinear dynamics in human brain visual cortex.     

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.     

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.     

Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas

Functional neuroimaging first allowed researchers to describe the functional segregation of regionally activated areas during a variety of experimental tasks. More recently, functional integration studies have described how these functionally specialized areas, interact within a highly distributed neural network. When applied to the field of neurosciences, structural equation modeling (SEM) uses theoretical and/or empirical hypotheses to estimate the effects of an experimental task within a putative network. SEM represents a linear technique for multivariate analysis of neuroimaging data and has been developed to simultaneously examine ratios of multiple causality in an experimental design; the method attempts to explain a covariance structure within an anatomical constrained model. This method, when combined with the concept of effective connectivity, can provide information on the strength and direction of the functional interactions that take place between identified brain regions of a putative network.     

The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.     

Disrupted functional connectivity in social anxiety disorder: a resting-state fMRI study

Dysfunction of the corticolimbic circuitry has been highlighted in social anxiety disorder (SAD) during social stimuli. However, few studies have investigated functional connectivity in SAD during the resting state, which may improve our understanding of SAD pathophysiology. The aim of this study was to investigate whether whole-brain functional connectivity might be aberrant in SAD patients, and if so, whether these changes are related to the measured clinical severity. Seventeen SAD patients and 19 healthy controls participated in resting-state functional magnetic resonance imaging. The brain was first divided into 90 paired brain regions and functional connectivity was then estimated by temporal correlation between each of these regions. Furthermore, connections that were significantly disrupted in SAD patients were correlated with clinical severity measured using the Liebowitz Social Anxiety Scale. Compared with healthy controls, SAD patients showed decreased positive connections within the frontal lobe and decreased negative connections between the frontal and occipital lobes. In particular, the weaker negative connections between the frontal lobe, which mainly involved the right median prefrontal cortex, and the occipital lobe had a significant positive correlation with the severity of SAD symptoms. The results support the hypothesis that some abnormalities of functional connectivity exist in SAD patients, which relate to the frontal cortex and occipital cortex. In addition, decreased functional connectivity between the frontal and occipital lobes and within the frontal lobe might be related to abnormal information processing and reflect disturbed neural organization resulting in defective social cognition, which could represent an early imaging biomarker for SAD.     

Functional Connectivity Methods and Their Applications in fMRI Data

The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organization of functional communication in the brain network. Based on the principle that distinct brain regions are functionally connected and continuously share information with each other, various approaches to finding these functional networks have been proposed in the literature. In this paper, we present an overview of the most common methods to estimate and characterize functional connectivity in fMRI data. We illustrate these methodologies with resting-state functional MRI data from the Human Connectome Project, providing details of their implementation and insights on the interpretations of the results. We aim to guide researchers that are new to the field of neuroimaging by providing the necessary tools to estimate and characterize brain circuitry.     

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.     

Graphical display of fMRI data: visualizing multidimensional space

Visualization of multidimensional data is an integral part of computational statistics and exploratory data analysis (EDA). We show how visualization of fMRI time-courses may be used to reveal the fMRI data structure. We consider fMRI time-courses (TCs) as points in multidimensional space. In simulated and in vivo data, we show that minimum spanning tree (MST)-based sequencing of multivariate time-courses, in combination with a homogeneity map visualization, allows for effective and useful graphical display of the groups of coactivated time-courses obtained by temporal clustering. This display may serve as a tool for investigation of brain connectivity. We also suggest a simple overall display of the entire fMRI data set.