Internal representation of hierarchical sequences involves the default network

Background  

The default network is a set of brain regions that exhibit a reduction in BOLD response during attention-demanding cognitive tasks, and distinctive patterns of functional connectivity that typically include anti-correlations with a fronto-parietal network involved in attention, working memory, and executive control. The function of the default network regions has been attributed to introspection, self-awareness, and theory of mind judgments, and some of its regions are involved in episodic memory processes.     

脑功能激活光诱发机理小世界网络分析研究

研究光诱发和静息两种状态下的脑功能网络的信息传输枢纽、网络聚合能力和信息传输的最小路径的差异性。采用小世界网络理论对脑功能网络进行建模,通过对脑功能网络连接度、簇系数和最小路径进行分析,得出光诱发状态下的信息传输重要枢纽为岛叶、后扣带回功能区;丘脑、海马两处功能网络有较大聚合能力。光诱发过程从额上回经颞中回传输到枕中回。静息状态下的信息传输重要枢纽为楔叶、舌回;中央旁小叶、颞上回脑功能网络有较大聚合能力。静息状态下的左半区最佳信息传输路径为左额上回、左颞中回、右楔叶最后到左枕中回;右脑半区的为右额上回、右前扣带回、左枕下回最后到右枕中回。光诱发状态与静息状态的最佳传输路径有明显的区别。     

Investigation of the large-scale functional brain networks modulated by acupuncture

Previous neuroimaging studies have primarily focused on the neural activities involving the acute effects of acupuncture. Considering that acupuncture can induce long-lasting effects, several researchers have begun to pay attention to the sustained effects of acupuncture on the resting brain. Most of these researchers adopted functional connectivity analysis based on one or a few preselected brain regions and demonstrated various function-guided brain networks underlying the specific effect of acupuncture. Few have investigated how these brain networks interacted at the whole-brain level. In this study, we sought to investigate the functional correlations throughout the entire brain following acupuncture at acupoint ST36 (ACUP) in comparison with acupuncture at nearby nonacupoint (SHAM). We divided the whole brain into 90 regions and constructed functional brain network for each condition. Then we examined the network hubs and identified statistically significant differences in functional correlations between the two conditions. Following ACUP, but not SHAM, the limbic/paralimbic regions such as the amygdala, hippocampus and anterior cingulate gyrus emerged as network hubs. For direct comparisons, increased correlations for ACUP compared to SHAM were primarily related with the limbic/paralimbic and subcortical regions such as the insula, amygdala, anterior cingulate gyrus, and thalamus, whereas decreased correlations were mainly related with the sensory and frontal cortex. The heterogeneous modulation patterns between the two conditions may relate to the functional specific modulatory effects of acupuncture. The preliminary findings may help us to better understand the long-lasting effects of acupuncture on the entire resting brain, as well as the neurophysiological mechanisms underlying acupuncture.     

Can MRI Contribute to the Understanding and Therapy of Mental Diseases?

The nowadays most accepted hypothesis of the origin of mental diseases is an impaired connectivity between various brain areas. Magnetic resonance tractography reveals structural connectivities through neuronal fibers and resting state functional imaging allows one to visualize functional connectivities. The detection of the small signal changes needs the highest sensitivity and the magnetic resonance imaging scans must be repeated as fast as possible. Statistical evaluation and cross correlation of the signals in all voxels show synchrony of signal-level fluctuations even in remote brain areas. This makes it possible to establish brain networks. The most important of them are the default mode network, the salience network, and the central executive network. Applications to schizophrenia and depression will be discussed. In deep brain stimulation, the excitation, currently used for therapy of Parkinson’s disease, is now introduced for therapy of major depressive disorder and can be taylored to the necessary brain location.     

Brain networks responsive to aversive visual stimuli in humans

The neural mechanisms subserving recognition of noxious stimuli and empathy for pain appear to involve at least in part the cortical regions associated with the processing of pain affect. An important issue concerns the specificity of brain networks associated with observing and representing painful conditions, in comparison with other unpleasant stimuli. Recently, we found both similarities and differences between the brain patterns of activity related to the observation of noxious or disgusting stimuli delivered to one hand or foot. Overlap regions included the perigenual anterior cingulate (pACC), whose activity was related to the perceived unpleasantness. We aimed here at revealing how pACC functional connectivity changes in relationship to the different experimental conditions, using a psychophysiological interaction model. Activity in pACC during the observation of painful stimuli was specifically and positively related to regions in the right hemisphere, including portions of the prefrontal, midcingulate and insular cortex. On the other hand, positive changes in pACC connectivity during the vision of disgusting stimuli were present in the right basal ganglia. These data suggest that pACC activity is part of different networks involved in the recognition of painful or disgusting stimuli.     

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.     

Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance

Functional brain imaging studies have identified a set of brain areas typically activated during cognitive tasks (task-positive brain areas) and another set of brain areas typically deactivated during cognitive tasks (task-negative brain areas). Negative correlations, or anticorrelations, between task-positive and task-negative brain areas have been reported at rest. Furthermore, the strength of these anticorrelations appears to be related to cognitive function. However, studies examining anticorrelations have typically employed global regression or similar analysis steps that force anticorrelated relationships to exist between brain areas. Therefore the validity of these findings has been questioned. Here we examine anticorrelations between a task-negative region in the medial frontal gyrus/anterior cingulate cortex and dorsolateral prefrontal cortex, a classic task-positive area, using an analysis that does not include global regression. Instead, we control for whole-brain correlations in the group-level analysis. Using this approach, we demonstrate that the strength of the functional connection between the medial frontal cortex and the dorsolateral prefrontal cortex is related to cognitive function and that this relationship is not an artifact of global regression.     

Testosterone regulation of sex steroid-related mRNAs and dopamine-related mRNAs in adolescent male rat substantia nigra

ABSTRACT: BACKGROUND: Increased risk of schizophrenia in adolescent males indicates that a link between the development of dopamine-related psychopathology and testosterone-driven brain changes may exist. However, contradictions as to whether testosterone increases or decreases dopamine neurotransmission are found and most studies address this question in adult animals. Testosterone-dependent actions in neurons are direct via activation of androgen receptors (AR) or indirect by conversion to 17beta-estradiol and activation of estrogen receptors (ER). How midbrain dopamine neurons respond to sex steroids depends on the presence of sex steroid receptor(s) and the level of steroid conversion enzymes (aromatase and 5alpha-reductase). We investigated whether gonadectomy and sex steroid replacement could influence dopamine levels by changing tyrosine hydroxylase (TH) protein and mRNA and/or dopamine breakdown enzyme mRNA levels [catechol-O-methyl transferase (COMT) and monoamine oxygenase (MAO) A and B] in the adolescent male rat substantia nigra. We hypothesized that adolescent testosterone would regulate sex steroid signaling through regulation of ER and AR mRNAs and through modulation of aromatase and 5alpha-reductase mRNA levels. RESULTS: We find ERalpha and AR in midbrain dopamine neurons in adolescent male rats, indicating that dopamine neurons are poised to respond to circulating sex steroids. We report that androgens increase TH protein and increase COMT, MAOA and MAOB mRNAs in the adolescent male rat substantia nigra. We report that all three sex steroids increase AR mRNA. Differential action on ER pathways, with ERalpha mRNA down-regulation and ERbeta mRNA up-regulation by testosterone was found. 5alpha reductase-1 mRNA was increased by AR activation, and aromatase mRNA was decreased by gonadectomy. CONCLUSIONS: We conclude that increased testosterone at adolescence can shift the balance of sex steroid signaling to favor androgenic responses through promoting conversion of T to DHT and increasing AR mRNA. Further, testosterone may increase local dopamine synthesis and metabolism, thereby changing dopamine regulation within the substantia nigra. We show that testosterone action through both AR and ERs modulates synthesis of sex steroid receptor by altering AR and ER mRNA levels in normal adolescent male substantia nigra. Increased sex steroids in the brain at adolescence may alter substantia nigra dopamine pathways, increasing vulnerability for the development of psychopathology.     

Development of the time course for processing conflict: an event-related potentials study with 4 year olds and adults

Background  

Tasks involving conflict are widely used to study executive attention. In the flanker task, a target stimulus is surrounded by distracting information that can be congruent or incongruent with the correct response. Developmental differences in the time course of brain activations involved in conflict processing were examined for 22 four year old children and 18 adults. Subjects performed a child-friendly flanker task while their brain activity was registered using a high-density electroencephalography system.     

Identification of activated regions during a language task

Functional magnetic resonance imaging (fMRI) techniques are based on the assumption that changes in neural activity are accompanied by modulation in the blood-oxygenation-level-dependent (BOLD) signal. In addition to conventional increases in BOLD signals, sustained negative BOLD signal changes are occasionally observed in many fMRI experiments, which show regions of cortex that seem to respond in antiphase with primary stimulus. The existence of this so-called negative BOLD response (NBR) has been observed and investigated in many functional studies. Several theoretical mechanisms have been proposed to account for it, but its origin has never been fully explained. In this study, the variability of fMRI activation, including the sources of the negative BOLD signal, during phonological and semantic language tasks, was investigated in six right-handed healthy subjects. We found significant activations in the brain regions, mainly in the left hemisphere, involved in the language stimuli [prominent in the inferior frontal gyrus, approximately Brodmann Areas (BA)7, BA44, BA45 and BA47, and in the precuneus]. Moreover, we observed activations in motor regions [precentral gyrus and supplementary motor area (SMA)], a result that suggests a specific role of these areas (particularly the SMA) in language processing. Functional analysis have also shown that certain brain regions, including the posterior cingulate cortex and the anterior cingulate cortex, have consistently greater activity during resting states compared to states of performing cognitive tasks. In our study, we observed diffuse NBR at the cortical level and a stronger negative response in correspondence to the main sinuses. These phenomena seem to be unrelated to a specific neural activity, appearing to be expressions of a mechanical variation in hemodynamics. We discussed about the importance of these responses that are anticorrelated with the stimulus. Our data suggest that particular care must be considered in the interpretation of fMRI findings, especially in the case of presurgical studies.     

Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

Background  

Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length.     

Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI

Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique used to investigate the spontaneous correlations of blood-oxygen-level-dependent signals across different regions of the brain. Using functional connectivity tools, it is possible to investigate a specific RS-fMRI network, referred to as "default-mode" (DM) network, that involves cortical regions deactivated in fMRI experiments with cognitive tasks. Previous works have reported a significant effect of aging on DM regions activity. Independent component analysis (ICA) is often used for generating spatially distributed DM functional connectivity patterns from RS-fMRI data without the need for a reference region. This aspect and the relatively easy setup of an RS-fMRI experiment even in clinical trials have boosted the combined use of RS-fMRI and ICA-based DM analysis for noninvasive research of brain disorders. In this work, we considered different strategies for combining ICA results from individual-level and population-level analyses and used them to evaluate and predict the effect of aging on the DM component. Using RS-fMRI data from 20 normal subjects and a previously developed group-level ICA methodology, we generated group DM maps and showed that the overall ICA-DM connectivity is negatively correlated with age. A negative correlation of the ICA voxel weights with age existed in all DM regions at a variable degree. As an alternative approach, we generated a distributed DM spatial template and evaluated the correlation of each individual DM component fit to this template with age. Using a "leave-one-out" procedure, we discuss the importance of removing the bias from the DM template-generation process.     

Network dilution and asymmetry in an efficient brain

ABSTRACT

The ultimate goal of neuroscience is to ultimately understand how the brain functions. The advancement of brain imaging shows us how the brain continuously alternates complex activity patterns and experimentally reveals how these patterns are responsible for memory, association, reasoning, and countless other tasks. Two fundamental parameters, dilution (the number of connections per node), and symmetry (the number of bidirectional connections of the same weight) characterise two fundamental features underlying the networks that connect the single neurons in the brain and generate these patterns. Mammalian brains show large variations of dilution, and mostly asymmetric connectivity, unfortunately the advantages which drove evolution to these state of network dilution and asymmetry are still unknown. Here, we studied the effects of symmetry and dilution on a discrete-time recurrent neural network with McCulloch–Pitts neurons. We use an exhaustive approach, in which we probe all possible inputs for several randomly connected neuron networks with different degrees of dilution and symmetry. We find an optimum value for the synaptic dilution and symmetry, which turns out to be in striking quantitative agreement with what previous researchers have found in the brain cortex, neocortex and hippocampus. The diluted asymmetric brain shows high memory capacity and pattern recognition speed, but most of all it is the less energy-consumptive with respect to fully connected and symmetric network topologies.     

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.     

A Resiliency-Connectivity metric in wireless sensor networks with key predistribution schemes and node compromise attacks

Applications for wireless sensor networks require widespread, highly reliable communications even in the face of adversarial influences. Maintaining connectivity and secure communications between entities are vital networking properties towards ensuring the successful and accurate completion of desired sensing tasks. We examine the required communication range for nodes in a wireless sensor network with respect to several parameters. Network properties such as key predistribution schemes and node compromise attacks are modelled with several network parameters and studied in terms of how they influence global network connectivity. These networks are physically vulnerable to malicious behavior by way of node compromise attacks that may affect global connectivity. We introduce a metric that determines the resilience of a network employing a key predistribution scheme with respect to node compromise attacks. In this work,we provide the first study of global network connectivity and its relationship to node compromise attacks. Existing work considers the relationship between the probability of node compromise and the probability of link compromise and the relationship of the probability of secure link establishment and overall network connectivity for the Erdős network model. Here, we present novel work which combines these two relationships to study the relationship between node compromise attacks and global network connectivity. Our analysis is performed with regard to large-scale networks; however, we provide simulation results for both large-scale and small-scale networks. First, we derive a single expression to determine the required communication radius for wireless sensor networks to include the effects of key predistribution schemes. From this, we derive an expression for determining required communication range after an adversary has compromised a fraction of the nodes in the network. The required communication range represents the resource usage of nodes in a network to cope with key distribution schemes and node compromise attacks. We introduce the Resiliency-Connectivity metric, which measures the resilience of a network in expending its resources to provide global connectivity in adverse situations.     

The anxiolytic effects of midazolam during anticipation to pain revealed using fMRI

BACKGROUND AND PURPOSE: Functional neuroimaging can distinguish components of the pain experience associated with anticipation to pain from those associated with the experience of pain itself. Anticipation to pain is thought to increase the suffering of chronic pain patients. Inappropriate anxiety, of which anticipation is a component, is also a cause of disability. We present a pharmacological functional magnetic resonance imaging (fMRI) study in which we investigate the selective modulation by midazolam of brain activity associated with anticipation to pain compared to pain itself. METHODS: Eight right-handed male volunteers underwent fMRI combined with a thermal pain conditioning paradigm and midazolam (30 mug/kg) or saline administration on different occasions, with order randomized across volunteers. Volunteers learned to associate a colored light with either painful, hot stimulation or nonpainful, warm stimulation to the back of the left hand. RESULTS: Comparison of the period during thermal stimulation (pain-warm) revealed a network of brain activity commonly associated with noxious stimulation, including activities in the anterior cingulate cortex (ACC), the bilateral insular cortices (anterior and posterior), the thalamus, S1, the motor cortex, the brainstem, the prefrontal cortex and the cerebellum. Comparison of the periods preceding thermal stimulation (anticipation to pain-anticipation to warm) revealed activity principally in the ACC, the contralateral anterior insular cortex and the ipsilateral S2/posterior insula. Detected by a region-of-interest analysis, midazolam reduced the activity associated specifically with anticipation to pain while controlling for anticipation to warm. This was most significant in the contralateral anterior insula (P<.05). There were no significant drug effects on the activity associated with pain itself. CONCLUSION: In identifying a pharmacological effect on activity preceding but not during pain, we have successfully demonstrated an fMRI assay that can be used to study the action of anxiolytic agents in a relatively small cohort of humans.     

Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

Recent findings of neurological functioning in autism spectrum disorder (ASD) point to altered brain connectivity as a key feature of its pathophysiology. The cortical underconnectivity theory of ASD (Just et al., 2004) provides an integrated framework for addressing these new findings. This theory suggests that weaker functional connections among brain areas in those with ASD hamper their ability to accomplish complex cognitive and social tasks successfully. We will discuss this theory, but will modify the term underconnectivity to ‘disrupted cortical connectivity’ to capture patterns of both under- and over-connectivity in the brain. In this paper, we will review the existing literature on ASD to marshal supporting evidence for hypotheses formulated on the disrupted cortical connectivity theory. These hypotheses are: 1) underconnectivity in ASD is manifested mainly in long-distance cortical as well as subcortical connections rather than in short-distance cortical connections; 2) underconnectivity in ASD is manifested only in complex cognitive and social functions and not in low-level sensory and perceptual tasks; 3) functional underconnectivity in ASD may be the result of underlying anatomical abnormalities, such as problems in the integrity of white matter; 4) the ASD brain adapts to underconnectivity through compensatory strategies such as overconnectivity mainly in frontal and in posterior brain areas. This may be manifested as deficits in tasks that require frontal–parietal integration. While overconnectivity can be tested by examining the cortical minicolumn organization, long-distance underconnectivity can be tested by cognitively demanding tasks; and 5) functional underconnectivity in brain areas in ASD will be seen not only during complex tasks but also during task-free resting states. We will also discuss some empirical predictions that can be tested in future studies, such as: 1) how disrupted connectivity relates to cognitive impairments in skills such as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD.     

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.     

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.     

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.