Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution

The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach.Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI).Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected.Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.     

A method for functional network connectivity among spatially independent resting-state components in schizophrenia

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.     

MRI观察精神分裂症静息态下小脑功能连接和解剖连接

目的 应用静息态功能磁共振成像(fMRI)和扩散张量成像(DTI)观察精神分裂症患者与正常人小脑功能连接与解剖连接的差异。方法 分别对10例精神分裂症患者(精神分裂组)及14名健康对照者(正常对照组)行静息态脑fMRI和DTI。对数据进行后处理后得到功能连接的相关系数r值及小脑中脚的平均FA值,应用双样本t检验比较组间差异,并对两种数据进行相关性检验。结果 ①精神分裂组与左侧小脑功能连接改变显著的区域为双侧舌回、右侧额中回、双侧缘上回和左侧小脑(P<0.001,未校正);与右侧小脑功能连接改变显著的区域为双侧舌回、左侧中央前回和左侧缘上回(P<0.001,未校正);②精神分裂组左侧小脑中脚的FA值显著降低(P<0.05);③精神分裂组右侧小脑-右侧舌回的连接强度与右侧小脑中脚的FA值呈显著正相关(r=0.84,P<0.05)。结论 联合运用多种成像方式可能为理解小脑在精神分裂症病理生理学中的作用提供新的方向;精神分裂症患者静息态下小脑与大脑皮层某些区域的连接降低和小脑中脚局部白质纤维完整性受损的同时出现以及二者之间的相关性提示功能连接与解剖解剖之间存在密切关系。     

Anticorrelated resting-state functional connectivity in awake rat brain

Resting-state functional connectivity (RSFC) measured by functional magnetic resonance imaging has played an essential role in understanding neural circuitry and brain diseases. The vast majority of RSFC studies have been focused on positive RSFC, whereas our understanding about its conceptual counterpart - negative RSFC (i.e. anticorrelation) - remains elusive. To date, anticorrelated RSFC has yet been observed without the commonly used preprocessing step of global signal correction. However, this step can induce artifactual anticorrelation (Murphy et al., 2009), making it difficult to determine whether the observed anticorrelation in humans is a processing artifact (Fox et al., 2005). In this report we demonstrated robust anticorrelated RSFC in a well characterized frontolimbic circuit between the infralimbic cortex (IL) and amygdala in the awake rat. This anticorrelation was anatomically specific, highly reproducible and independent of preprocessing methods. Interestingly, this anticorrelated relationship was absent in anesthetized rats even with global signal correction, further supporting its functional significance. Establishing negative RSFC independent of data preprocessing methods will significantly enhance the applicability of RSFC in better understanding neural circuitries and brain networks. In addition, combining the neurobiological data of the IL-amygdala circuit in rodents, the finding of the present study will enable further investigation of the neurobiological basis underlying anticorrelation.     

失眠患者杏仁核功能连接的静息态功能磁共振成像

目的分析静息状态下失眠患者杏仁核与其他脑功能连接的改变。方法失眠患者39例为失眠组,体检健康者23例为对照组,2组均进行临床匹兹堡睡眠质量指数量表(Pittsburgh Sleep Quality Index, PSQI)、汉密尔顿抑郁量表(Hamilton Depression Scale, HAMD)、汉密尔顿焦虑量表(Hamilton Anxiety Scale, HAMA)、世界卫生组织-加利福尼亚听觉词语学习测验(World Health Organization University of California Los Angeles Auditory Verbal Learning Test, WHO-UCLA AVLT)评估睡眠、认知、情绪情况,2组均行磁共振常规扫描和静息态功能MRI扫描(resting-state functional MRI, rs-fMRI),以双侧杏仁核为种子点,比较2组脑功能连接的差异;采用Pearson相关分析失眠组长时记忆评分与左侧杏仁核-左侧海马旁回、左侧杏仁核-右侧海马旁回的功能连接强度的相关性。结果失眠组PSQI[(13.46±2.74)分]、HAMA[(10.10±3.97)分]、HAMD[(9.56±3.48)分]评分及认知障碍评分[(1.23±0.67)分]明显高于对照组[(2.52±1.75)、(1.57±1.90)、(0.78±1.24)、(0.08±0.07)分](P<0.05),WHO-UCLA学习记忆量表中长时记忆[(10.67±2.35)分]和再认记忆[(12.36±2.40)分]评分低于对照组[(11.96±1.85)、(13.39±1.31)分](P<0.05);与对照组比较,失眠组左侧杏仁核与后扣带回、双侧海马旁回、双侧海马、颞叶、舌回和枕叶的功能连接增强(P<0.05),右侧杏仁核与边缘叶、右侧海马旁回、舌回、枕叶、右侧距状皮层、后扣带回、前额叶功能连接增强(P<0.05);失眠组长时记忆评分与左侧杏仁核-左侧海马旁回、左侧杏仁核-右侧海马旁回的功能连接强度呈正相关(r=0.556,P<0.001;r=0.150,P=0.032)。结论失眠患者双侧杏仁核与多个脑区的功能连接出现异常,可能是失眠患者出现情绪调节障碍、认知障碍等的机制之一。     

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.     

Role of local network oscillations in resting-state functional connectivity

Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes is found to be correlated, instantiating the empirically observed RSNs. Between clusters, patterns of positive and negative correlations are observed, as described in experimental studies. These results are found to be robust with respect to a biologically plausible range of model parameters. In conclusion, our model suggests how resting-state neural activity can originate from the interplay between the local neural dynamics and the large-scale structure of the brain.     

静息态磁共振功能连接方法的多模态应用

目的 探讨度中心度(DC)、镜像组织功能连接(VMHC)和基于感兴趣区的功能连接(ROI-FC)3种多模态静息态磁共振功能连接类数据处理方法在反映脑功能信息中的价值.方法 采集10名正常志愿者和10例亚急性期脑桥梗死患者的静息态fMRI数据.首先采用DC和VMHC对数据进行预分析,获得梗死患者脑功能显著改变的脑区作为ROI,然后采用ROI-FC分析梗死患者脑功能网络的改变.结果 采用DC未得到有意义的结果,VMHC发现患者左右顶下小叶/角回功能连接显著降低,以此为ROI,ROI-FC发现患者右楔前叶、左顶下小叶、左右颞中回、左右额中回功能连接降低,左额中回/中央前回、左额上回和右扣带回后部/楔前叶的功能连接增强.结论 综合应用DC、VMHC和ROI-FC 3种静息态fMRI功能连接类数据处理方法,可发现脑桥梗死患者相关的脑功能网络紊乱,为脑梗死的临床研究提供有价值的信息.     

Modeling the outcome of structural disconnection on resting-state functional connectivity

A growing body of experimental evidence suggests that functional connectivity at rest is shaped by the underlying anatomical structure. Furthermore, the organizational properties of resting-state functional networks are thought to serve as the basis for an optimal cognitive integration. A disconnection at the structural level, as occurring in some brain diseases, would then lead to functional and presumably cognitive impairments. In this work, we propose a computational model to investigate the role of a structural disconnection (encompassing putative local/global and axonal/synaptic mechanisms) on the organizational properties of emergent functional networks. The brain's spontaneous neural activity and the corresponding hemodynamic response were simulated using a large-scale network model, consisting of local neural populations coupled through white matter fibers. For a certain coupling strength, simulations reproduced healthy resting-state functional connectivity with graph properties in the range of the ones reported experimentally. When the structural connectivity is decreased, either globally or locally, the resultant simulated functional connectivity exhibited a network reorganization characterized by an increase in hierarchy, efficiency and robustness, a decrease in small-worldness and clustering and a narrower degree distribution, in the same way as recently reported for schizophrenia patients. Theoretical results indicate that most disconnection-related neuropathologies should induce the same qualitative changes in resting-state brain activity.     

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity.     

Bedside optical imaging of occipital resting-state functional connectivity in neonates

Resting-state networks derived from temporal correlations of spontaneous hemodynamic fluctuations have been extensively used to elucidate the functional organization of the brain in adults and infants. We have previously developed functional connectivity diffuse optical tomography methods in adults, and we now apply these techniques to study functional connectivity in newborn infants at the bedside. We present functional connectivity maps in the occipital cortices obtained from healthy term-born infants and premature infants, including one infant with an occipital stroke. Our results suggest that functional connectivity diffuse optical tomography has potential as a valuable clinical tool for the early detection of functional deficits and for providing prognostic information on future development.     

Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers

Studies have revealed abnormalities in resting-state functional connectivity in those with major depressive disorder specifically in areas such as the dorsal anterior cingulate, thalamus, amygdala, the pallidostriatum and subgenual cingulate. However, the effect of antidepressant medications on human brain function is less clear and the effect of these drugs on resting-state functional connectivity is unknown. Forty volunteers matched for age and gender with no previous psychiatric history received either citalopram (SSRI; selective serotonergic reuptake inhibitor), reboxetine (SNRI; selective noradrenergic reuptake inhibitor) or placebo for 7 days in a double-blind design. Using resting-state functional magnetic resonance imaging and seed based connectivity analysis we selected the right nucleus accumbens, the right amygdala, the subgenual cingulate and the dorsal medial prefrontal cortex as seed regions. Mood and subjective experience were also measured before and after drug administration using self-report scales. Despite no differences in mood across the three groups, we found reduced connectivity between the amygdala and the ventral medial prefrontal cortex in the citalopram group and the amygdala and the orbitofrontal cortex for the reboxetine group. We also found reduced striatal-orbitofrontal cortex connectivity in the reboxetine group. These data suggest that antidepressant medications can decrease resting-state functional connectivity independent of mood change and in areas known to mediate reward and emotional processing in the brain. We conclude that hypothesis-driven seed based analysis of resting-state fMRI supports the proposition that antidepressant medications might work by normalising the elevated resting-state functional connectivity seen in depressed patients.     

基于静息态功能连接模式的正常人脑扣带皮层亚区划分

基于静息态功能连接(rsFC)模式对正常人脑扣带皮层进行亚区划分,并分析不同亚区的功能连接模式。方法 收集47名右利手健康志愿者。采集结构磁共振图像及静息态功能磁共振图像。在蒙特利尔脑研究所标准空间勾画扣带皮层ROI。基于个体计算ROI中每个体素与全脑其他体素时间序列的Pearson线性相关系数,得到互相关矩阵,采用K-均值聚类算法自动聚类。采用交互验证的方法选择合适分割数。最终计算最大概率图谱。通过单样本t检验确定与每个亚区具有正功能连接的脑区。结果 扣带皮层被分为6个亚区:前扣带皮层、背侧中前扣带皮层、腹侧中前扣带皮层、中后扣带皮层、背侧后扣带皮层及腹侧后扣带皮层亚区。每个亚区有特异的静息态功能连接模式。结论 人脑扣带皮层依据不同的静息态功能连接模式分为6个亚区,每个亚区分属不同的脑功能网络,参与不同的功能。     

A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality

Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on correlational analysis or structural equations analysis, assuming a time-invariant connectivity structure. In this paper, a novel method of continuous time-varying connectivity analysis is proposed, based on the wavelet expansion of functions and vector autoregressive model (wavelet dynamic vector autoregressive-DVAR). The model also allows identification of the direction of information flow between brain areas, extending the Granger causality concept to locally stationary processes. Simulation results show a good performance of this approach even using short time intervals. The application of this new approach is illustrated with fMRI data from a simple AB motor task experiment.     

Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain

The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.     

无灶性癫痫静息态功能MRI的功能连接研究

目的应用静息态功能MRI(rs-f MRI)功能连接方法(FC)研究无灶性癫痫(NLE)的脑功能改变,探论FC对NLE的应用价值。加深对NLE病理生理机制的理解,为其诊断及治疗提供可靠的理论依据。材料与方法对43例NLE患者和46例性别、年龄、教育程度及利手相匹配的健康志愿者(对照组),采用3.0 T超导MR扫描仪进行静息态功能序列扫描,然后将病例组与对照组分别进行FC分析,再对ALFF分析结果进行两样本t检验分析,并分析FC统计脑图与病程的相关性。结果与正常对照组相比,病例组右侧海马FC增加的脑区位于右颞上回、左颞下回、双颞中回、双前额内侧回,右额中回;左侧海马FC增加的脑区位于双颞上回、左海马旁回、左额叶、右前额内侧回、左顶下小叶、右中央后回。右侧海马FC降低的脑区位于左内囊膝、左顶下小叶、右扣带回、左额上回、左额叶;左侧海马降低的脑区位于右小脑后叶、左小脑前叶、右颞上回、右丘脑。病例组右侧海马FC病程正相关的脑区位于:左额下回、左颞下回、左颞上回、右额叶、右楔前叶、右顶叶、右顶下小叶、右额中回;左侧海马FC与病程正相关的脑区位于左舌回、双额中回、左顶下小叶、右中央后回。病例组右侧海马FC与病程呈负相关的脑区位于左小脑前叶、左内囊膝;病例组左侧海马FC与病程负相关的脑区位于左小脑扁桃体、左额上回、右颞上回、左楔前叶。结论 ALFF和FC方法可作为一种无创的脑功能研究方法,能检测出NLE静息态脑功能变化,了解与临床变量(病程)的相关性,为癫痫的病理生理机制研究提供可靠的理论依据。     

基于静息态功能磁共振成像的动态功能连接分析及临床应用研究进展

基于静息态功能磁共振成像的脑功能连接分析方法已经在临床医学研究中被普遍用于疾病的神经机制和诊疗研究。近年来,研究者发现仅考虑静态的功能连接不足以解释大脑时变动态的信息交互,而应该研究功能连接的动态性以揭示大脑网络复杂多变的特性及其机制。已有许多临床研究成果表明动态功能连接分析能为临床疾病的病理探究和辅助诊断提供更好的依据,但同时也存在着问题与局限。笔者通过归纳总结常用估算方法、特征提取方法和可靠性检验及统计分析方法,综合论述了基于静息态功能磁共振成像的动态功能连接分析研究进展,并介绍了基于静息态数据的动态功能连接分析在常见临床疾病中的应用及前景。     

Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage

A number of recent studies have begun to show the promise of magnetoencephalography (MEG) as a means to non-invasively measure functional connectivity within distributed networks in the human brain. However, a number of problems with the methodology still remain - the biggest of these being how to deal with the non-independence of voxels in source space, often termed signal leakage. In this paper we demonstrate a method by which non-zero lag cortico-cortical interactions between the power envelopes of neural oscillatory processes can be reliably identified within a multivariate statistical framework. The method is spatially unbiased, moderately conservative in false positive rate and removes linear signal leakage between seed and target voxels. We demonstrate this methodology in simulation and in real MEG data. The multivariate method offers a powerful means to capture the high dimensionality and rich information content of MEG signals in a single imaging statistic. Given a significant interaction between two areas, we go on to show how classical statistical tests can be used to quantify the importance of the data features driving the interaction.     

Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies

Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. Furthermore, we aimed at clarifying possible causes of anticorrelations and test the hypothesis that anticorrelations are introduced via certain preprocessing approaches, with particular focus on the effects of regression against the global signal. Our results show that preprocessing in general greatly increased connection specificity, in particular correction for global signal fluctuations almost doubled connection specificity. However, widespread anticorrelated networks were only found when regression against the global signal was applied. Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.