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Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Resting-state functional connectivity magnetic resonance imaging (fcMRI) studies constitute a growing proportion of functional brain imaging publications. This approach detects temporal correlations in spontaneous blood oxygen level-dependent (BOLD) signal oscillations while subjects rest quietly in the scanner. Although distinct resting-state networks related to vision, language, executive processing, and other sensory and cognitive domains have been identified, considerable skepticism remains as to whether resting-state functional connectivity maps reflect neural connectivity or simply track BOLD signal correlations driven by nonneural artifact. Here we combine diffusion tensor imaging (DTI) tractography with resting-state fcMRI to test the hypothesis that resting-state functional connectivity reflects structural connectivity. These 2 modalities were used to investigate connectivity within the default mode network, a set of brain regions--including medial prefrontal cortex (MPFC), medial temporal lobes (MTLs), and posterior cingulate cortex (PCC)/retropslenial cortex (RSC)--implicated in episodic memory processing. Using seed regions from the functional connectivity maps, the DTI analysis revealed robust structural connections between the MTLs and the retrosplenial cortex whereas tracts from the MPFC contacted the PCC (just rostral to the RSC). The results demonstrate that resting-state functional connectivity reflects structural connectivity and that combining modalities can enrich our understanding of these canonical brain networks.

Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

Localized, task-induced decreases in cerebral blood flow are a frequent finding in functional brain imaging research but remain poorly understood. One account of these phenomena postulates processes ongoing during conscious, resting states that are interrupted or inhibited by task performance. Psychological evidence suggests that conscious humans are engaged almost continuously in adaptive processes involving semantic knowledge retrieval, representation in awareness, and directed manipulation of represented knowledge for organization, problem-solving, and planning. If interruption of such 'conceptual' processes accounts for task-induced deactivation, tasks that also engage these conceptual processes should not cause deactivation. Furthermore, comparisons between conceptual and nonconceptual tasks should show activation during conceptual tasks of the same brain areas that are 'deactivated' relative to rest. To test this model, functional magnetic resonance imaging data were acquired during a resting state, a perceptual task, and a semantic retrieval task. A network of left-hemisphere polymodal cortical regions showed higher signal values during the resting state than during the perceptual task but equal values during the resting and semantic conditions. This result is consistent with the proposal that perceptual tasks interrupt processes ongoing during rest that involve many of the same brain areas engaged during semantic retrieval. As further evidence for this model, the same network of brain areas was activated in two direct comparisons between semantic and perceptual processing tasks. This same 'conceptual processing' network was also identified in several previous studies that contrasted semantic and perceptual tasks or resting and active states. The model proposed here offers a unified account of these findings and may help to explain several unanticipated results from prior studies of semantic processing.

Abstract After a century of struggle, a dozen non‐Western countries with pre‐Second World War manufacturing experience succeeded in entering the orbit of modem industry. The rise of ‘the rest’ was historically unprecedented. For the first time, countries without the competitive asset of proprietary, pioneering technology became economic powers. How industrialization among these prime latecomers succeeded, why it followed a novel path, and what some countries did to advance farther than others are the questions this book addresses. The same seed was contained in the rise of all the rest, a seed that had first germinated in Japan and then grew as might plants in clay pots of differing sizes and shapes, spanning Latin America (Argentina, Brazil, Chile, and Mexico), the Middle East (Turkey) and Asia (India, China, South Korea, Taiwan, Malaysia, Indonesia, and Thailand). To industrialize by borrowing already commercialized technology, devoid of the radically new products and processes that had enriched the North Atlantic, the rise of the rest involved intense learning, an extensive role for the government, and the formation of specific types of business enterprise. Indeed, the rest's unique reciprocal control mechanism differed from Adam Smith's invisible hand and served to reduce government failure and firm mismanagement. By the 1990s, two distinct varieties had taken root: all the economies of the rest had become more globalized, but the ‘integrationists’ (epitomized by Mexico's affiliation to the North Atlantic Free Trade Agreement (NAFTA) and Argentina's adoption of a dollar‐based currency board) were characterized by heavy reliance on foreign direct investment and minimal local expenditures on skills (as measured by research and development), while the ‘independents’, led by China, India, South Korea, and Taiwan, were notable for their nationally controlled firms and surging investments in technological capabilities. Which subspecies of the rest would succeed, and which would serve as the model for later industrializers (‘the remainder’), are questions that challenge the twenty‐first century and are analysed in this book, which is arranged in three main parts: sinking behind, 1850–c. 1950; sneaking ahead, c. 1950–; and squaring off, c. 1980–.

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Recent years have witnessed an upsurge in the usage of resting-state functional magnetic resonance imaging (fMRI) to examine functional connectivity (fcMRI), both in normal and pathological populations. Despite this increasing popularity, concerns about the psychologically unconstrained nature of the "resting-state" remain. Across studies, the patterns of functional connectivity detected are remarkably consistent. However, the test-retest reliability for measures of resting state fcMRI measures has not been determined. Here, we quantify the test-retest reliability, using resting scans from 26 participants at 3 different time points. Specifically, we assessed intersession (>5 months apart), intrasession (<1 h apart), and multiscan (across all 3 scans) reliability and consistency for both region-of-interest and voxel-wise analyses. For both approaches, we observed modest to high reliability across connections, dependent upon 3 predictive factors: 1) correlation significance (significantly nonzero > nonsignificant), 2) correlation valence (positive > negative), and 3) network membership (default mode > task positive network). Short- and long-term measures of the consistency of global connectivity patterns were highly robust. Finally, hierarchical clustering solutions were highly reproducible, both across participants and sessions. Our findings provide a solid foundation for continued examination of resting state fcMRI in typical and atypical populations.

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

In the absence of any overt task performance, it has been shown that spontaneous, intrinsic brain activity is expressed as systemwide, resting-state networks in the adult brain. However, the route to adult patterns of resting-state activity through neuronal development in the human brain is currently unknown. Therefore, we used functional MRI to map patterns of resting-state activity in infants during sleep. We found five unique resting-states networks in the infant brain that encompassed the primary visual cortex, bilateral sensorimotor areas, bilateral auditory cortex, a network including the precuneus area, lateral parietal cortex, and the cerebellum as well as an anterior network that incorporated the medial and dorsolateral prefrontal cortex. These results suggest that resting-state networks driven by spontaneous signal fluctuations are present already in the infant brain. The potential link between the emergence of behavior and patterns of resting-state activity in the infant brain is discussed.

Real-time reverse transcription followed by polymerase chain reaction (RT-PCR) is the most suitable method for the detection and quantification of mRNA. It offers high sensitivity, good reproducibility and a wide quantification range. Today, relative expression is increasingly used, where the expression of a target gene is standardised by a non-regulated reference gene. Several mathematical algorithms have been developed to compute an expression ratio, based on real-time PCR efficiency and the crossing point deviation of an unknown sample versus a control. But all published equations and available models for the calculation of relative expression ratio allow only for the determination of a single transcription difference between one control and one sample. Therefore a new software tool was established, named REST (relative expression software tool), which compares two groups, with up to 16 data points in a sample and 16 in a control group, for reference and up to four target genes. The mathematical model used is based on the PCR efficiencies and the mean crossing point deviation between the sample and control group. Subsequently, the expression ratio results of the four investigated transcripts are tested for significance by a randomisation test. Herein, development and application of REST is explained and the usefulness of relative expression in real-time PCR using REST is discussed. The latest software version of REST and examples for the correct use can be downloaded at http://www.wzw.tum.de/gene-quantification/.

PURPOSE OF REVIEW: This review considers recent advances in the application of resting-state functional magnetic resonance imaging to the study of neuropsychiatric disorders. RECENT FINDINGS: Resting-state functional magnetic resonance imaging is a relatively novel technique that has several potential advantages over task-activation functional magnetic resonance imaging in terms of its clinical applicability. A number of research groups have begun to investigate the use of resting-state functional magnetic resonance imaging in a variety of neuropsychiatric disorders including Alzheimer's disease, depression, and schizophrenia. Although preliminary results have been fairly consistent in some disorders (for example, Alzheimer's disease) they have been less reproducible in others (schizophrenia). Resting-state connectivity has been shown to correlate with behavioral performance and emotional measures. It's potential as a biomarker of disease and an early objective marker of treatment response is genuine but still to be realized. SUMMARY: Resting-state functional magnetic resonance imaging has made some strides in the clinical realm but significant advances are required before it can be used in a meaningful way at the single-patient level.

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm.

Resting state studies of spontaneous fluctuations in the functional MRI (fMRI) blood oxygen level dependent (BOLD) signal have shown great promise in mapping the brain's intrinsic, large-scale functional architecture. An important data preprocessing step used to enhance the quality of these observations has been removal of spontaneous BOLD fluctuations common to the whole brain (the so-called global signal). One reproducible consequence of global signal removal has been the finding that spontaneous BOLD fluctuations in the default mode network and an extended dorsal attention system are consistently anticorrelated, a relationship that these two systems exhibit during task performance. The dependence of these resting-state anticorrelations on global signal removal has raised important questions regarding the nature of the global signal, the validity of global signal removal, and the appropriate interpretation of observed anticorrelated brain networks. In this study, we investigate several properties of the global signal and find that it is, indeed, global, not residing preferentially in systems exhibiting anticorrelations. We detail the influence of global signal removal on resting state correlation maps both mathematically and empirically, showing an enhancement in detection of system-specific correlations and improvement in the correspondence between resting-state correlations and anatomy. Finally, we show that several characteristics of anticorrelated networks including their spatial distribution, cross-subject consistency, presence with modified whole brain masks, and existence before global regression are not attributable to global signal removal and therefore suggest a biological basis.

Recent studies have identified the important contribution of glial cells to the plasticity of neuronal circuits. Resting microglia, the primary immune effector cells in the brain, dynamically extend and retract their processes as if actively surveying the microenvironment. However, just what is being sampled by these resting microglial processes has not been demonstrated in vivo, and the nature and function of any interactions between microglia and neuronal circuits is incompletely understood. Using in vivo two-photon imaging of fluorescent-labeled neurons and microglia, we demonstrate that the resting microglial processes make brief (approximately 5 min) and direct contacts with neuronal synapses at a frequency of about once per hour. These contacts are activity-dependent, being reduced in frequency by reductions in neuronal activity. After transient cerebral ischemia, the duration of these microglia-synapse contacts are markedly prolonged (approximately 1 h) and are frequently followed by the disappearance of the presynaptic bouton. Our results demonstrate that at least part of the dynamic motility of resting microglial processes in vivo is directed toward synapses and propose that microglia vigilantly monitor and respond to the functional status of synapses. Furthermore, the striking finding that some synapses in the ischemic areas disappear after prolonged microglial contact suggests microglia contribute to the subsequent increased turnover of synaptic connections. Further understanding of the mechanisms involved in the microglial detection of the functional state of synapses, and of their role in remodeling neuronal circuits disrupted by ischemia, may lead to novel therapies for treating brain injury that target microglia.

SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.