FMRI connectivity analysis of acupuncture effects on the whole brain network in mild cognitive impairment patients

The increased risk for the elderly with mild cognitive impairment (MCI) to progress to Alzheimer's disease makes it an appropriate condition for investigation. While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. We sought to investigate the acupuncture effects on the functional connectivity throughout the entire brain in MCI patients compared to healthy controls (HC). The functional magnetic resonance imaging experiment was performed with two different paradigms, namely, deep acupuncture (DA) and superficial acupuncture (SA), at acupoint KI3. We first identified regions showing abnormal functional connectivity in the MCI group compared to HC during the resting state and subsequently tested whether these regions could be modulated by acupuncture. Then, we made the comparison of MCI vs. HC to test whether there were any specific modulatory patterns in the poststimulus resting brain between the two groups. Finally, we made the comparisons of DA vs. SA in each group to test the effect of acupuncture with different needling depths. We found the temporal regions (hippocampus, thalamus, fusiform gyrus) showing abnormal functional connectivity during the resting state. These regions are implicated in memory encoding and retrieving. Furthermore, we found significant changes in functional connectivity related with the abnormal regions in MCI patients following acupuncture. Compared to HC, the correlations related with the temporal regions were enhanced in the poststimulus resting brain in MCI patients. Compared to SA, significantly increased correlations related with the temporal regions were found for the DA condition. The enhanced correlations in the memory-related brain regions following acupuncture may be related to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. The heterogeneous modulatory patterns between DA and SA may suggest that deep muscle insertion of acupuncture is necessary to achieve the appreciable clinical effect.     

A serial functional connectivity MRI study in healthy individuals assessing the variability of connectivity measures: reduced interhemispheric connectivity in the motor network during continuous performance

To date, little data is available on the reproducibility of functional connectivity MRI (fcMRI) studies. Here, we tested the variability and reproducibility of both the functional connectivity itself and different statistical methods to analyze this phenomenon. In the main part of our study, we repeatedly examined two healthy subjects in 10 sessions over 6 months with fcMRI. Cortical areas involved in motor function were examined under two different cognitive states: during continuous performance (CP) of a flexion/extension task of the fingers of the right hand and while subjects were at rest. Connectivity to left primary motor cortex (lSM1) was calculated by correlation analysis. The resulting correlation coefficients were transformed to z-scores of the standard normal distribution. For each subject, multisession statistical analyses were carried out with the z-score maps of the resting state (RS) and the CP experiments. First, voxel based t tests between the two groups of fcMRI experiments were performed. Second, ROI analyses were carried out for contralateral right SM1 and for supplementary motor area (SMA). For both ROI, mean and maximum z-score were calculated for each experiment. Also, the fraction of significantly (P<.05) correlated voxels (FCV) in each ROI was calculated. To evaluate the differences between the RS and the CP condition, paired t tests were performed for the mean and maximum z-scores, and Wilcoxon signed ranks tests for matched pairs were carried out for the FCV. All statistical methods and connectivity measures under investigation yielded a distinct loss in left–right SM1 connectivity under the CP condition. For SMA, interindividual differences were apparent. We therefore repeated the fcMRI experiments and the ROI analyses in a group of seven healthy subjects (including the two subjects of the main study). In this substudy, we were able to verify the reduction of left–right SM1 connectivity during unilateral performance. Still, the direction of SMA to lSM1 connectivity change during the CP condition remained undefined as four subjects showed a connectivity increase and three showed a decrease. In summary, we were able to demonstrate a distinct reduction in left–right SM1 synchrony in the CP condition compared to the RS both in the longitudinal and in the multisubject study. This effect was reproducible with all statistical methods and all measures of connectivity under investigation. We conclude that despite intra- and interindividual variability, serial and cross-sectional assessment of functional connectivity reveals stable and reliable results.     

Assessing functional connectivity in the human brain by fMRI

Functional magnetic resonance imaging (fMRI) is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree to which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several multivariate statistical methods have been adapted and applied to analyze variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e., without any specific stimulus or task) or in a condition of continuous activation. Interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. Correlations at baseline are changed during the performance of a continuous task. In this review, various methods available for assessing connectivity are described and evaluated.     

Detecting functional connectivity in the resting brain: a comparison between ICA and CCA

Independent component analysis (ICA) and cross-correlation analysis (CCA) are general tools for detecting resting-state functional connectivity. In this study, we jointly evaluated these two approaches based on simulated data and in vivo functional magnetic resonance imaging data acquired from 10 resting healthy subjects. The influence of the number of independent components (maps) on the results of ICA was investigated. The influence of the selection of the seeds on the results of CCA was also examined. Our results reveal that significant differences between these two approaches exist. The performance of ICA is superior as compared with that of CCA; in addition, the performance of ICA is not significantly affected by structured noise over a relatively large range. The results of ICA could be affected by the number of independent components if this number is too small, however. Converting the spatially independent maps of ICA into z maps for thresholding tends to overestimate the false-positive rate. However, the overestimation is not very severe and may be acceptable in most cases. The results of CCA are dependent on seeds location. Seeds selected based on different criteria will significantly affect connectivity maps.     

Functional MRI using intravascular contrast agents: detrending of the relative cerebrovascular (rCBV) time course

In pharmacological fMRI experiments in animal models, blood pool contrast agents may be used to map cerebral blood volume change as a surrogate for neural activation. When the background signal drift due to contrast agent washout is non-negligible over the duration of the signal changes of interest, time-course detrending is essential for accurate interpretation of the experiment. Detrending approaches based on estimation of the background signal from a baseline period of the time course prior to pharmacological (or functional) challenge were evaluated with the aim of identifying a robust method of estimating the contrast agent washout contribution to the background signal drift. For fMRI studies in the rat, it was found that a constrained fit of a mono-exponential washout model was more accurate than a constant background approximation and unconstrained fits for experiments investigating the functional response to rapid pharmacological challenges such as cocaine and amphetamine. Moreover, the constrained fitting approach allows shorter baseline periods than unconstrained extrapolation, reducing the required duration of the experiment.     

Effects of cocaine on blood flow and oxygen metabolism in the rat brain: implications for phMRI

The effects of cocaine on cerebral blood flow and tissue oxygen levels in the rat brain were investigated with concurrent laser Doppler flowmetry and fluorescence quenching spectroscopy. Responses elicited by mild hypercapnia were used as calibration to assess the effects of cocaine on oxidative metabolism. Intravenous cocaine challenge of 0.5 mg/kg induced significant increases in tissular oxygenation and perfusion in all regions investigated (primary motor cortex, medial prefrontal cortex and dorsal striatum). Mild hypercapnia, a challenge that affects haemodynamics but not metabolism, elicited comparable changes in blood flow but substantially larger changes in tissue oxygen levels. These differences in tissue oxygen build-up suggest that increased oxidative metabolism is a significant component of the cerebral metabolic response to acute cocaine challenge. The implications for the interpretation of pharmacological MRI data are discussed.     

Communities and dynamical processes in a complex software network

Complex technological networks represent a growing challenge to support and maintain as their number of elements become higher and their interdependencies more involved. On the other hand, for networks that grow in a decentralized manner, it is possible to observe certain patterns in their overall structure that may be taken into account for a more tractable analysis. An example of such a pattern is the spontaneous formation of communities or modules. An important question regarding the detection of communities is if these are really representative of any internal network feature. In this work, we explore the community structure of a real complex software network, and correlate this modularity information with the internal dynamical processes that the network is designed to support. Our results show that the dependence between community structure and internal dynamical processes is remarkable, supporting the fact that a community division of this complex network is helpful in the assessment of the underlying dynamical structure, and thus is a useful tool to achieve a simpler representation of the complexity of the network.     

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of São Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model.     

Analyzing topological characteristics of neuronal functional networks in the rat brain

In this study, we recorded spike trains from brain cortical neurons of several behavioral rats in vivo by using multi-electrode recordings. An NFN was constructed in each trial, obtaining a total of 150 NFNs in this study. The topological characteristics of NFNs were analyzed by using the two most important characteristics of complex networks, namely, small-world structure and community structure. We found that the small-world properties exist in different NFNs constructed in this study. Modular function Q was used to determine the existence of community structure in NFNs, through which we found that community-structure characteristics, which are related to recorded spike train data sets, are more evident in the Y-maze task than in the DM-GM task. Our results can also be used to analyze further the relationship between small-world characteristics and the cognitive behavioral responses of rats.     

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.     

Community structure and modularity in networks of correlated brain activity

Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into 'sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function.     

Community structure of complex software systems: Analysis and applications

Due to notable discoveries in the fast evolving field of complex networks, recent research in software engineering has also focused on representing software systems with networks. Previous work has observed that these networks follow scale-free degree distributions and reveal small-world phenomena, while we here explore another property commonly found in different complex networks, i.e. community structure. We adopt class dependency networks, where nodes represent software classes and edges represent dependencies among them, and show that these networks reveal a significant community structure, characterized by similar properties as observed in other complex networks. However, although intuitive and anticipated by different phenomena, identified communities do not exactly correspond to software packages. We empirically confirm our observations on several networks constructed from Java and various third party libraries, and propose different applications of community detection to software engineering.     

Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the 'resting' brain

Functional magnetic resonance imaging techniques using the blood oxygenation level-dependent (BOLD) contrast are widely used to map human brain function by relating local hemodynamic responses to neuronal stimuli compared to control conditions. There is increasing interest in spontaneous cerebral BOLD fluctuations that are prominent in the low-frequency range (<0.1 Hz) and show intriguing spatio-temporal correlations in functional networks. The nature of these signal fluctuations remains unclear, but there is accumulating evidence for a neural basis opening exciting new avenues to study human brain function and its connectivity at rest. Moreover, an increasing number of patient studies report disease-dependent variation in the amplitude and spatial coherence of low-frequency BOLD fluctuations (LFBF) that may afford greater diagnostic sensitivity and easier clinical applicability than standard fMRI. The main disadvantage of this emerging tool relates to physiological (respiratory, cardiac and vasomotion) and motion confounds that are challenging to disentangle requiring thorough preprocessing. Technical aspects of functional connectivity fMRI analysis and the neuroscientific potential of spontaneous LFBF in the default mode and other resting-state networks have been recently reviewed. This review will give an update on the current knowledge of the nature of LFBF, their relation to physiological confounds and potential for clinical diagnostic and pharmacological studies.     

Detecting community structure in complex networks via node similarity

The detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. Based on node similarity, a fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with maximum similarity to this node to form a new community. The presented method has low computational complexity because of requiring only the local information of the network, and it does not need any prior knowledge about the communities and its detection results are robust on the selection of the initial node. Some real-world and computer-generated networks are used to evaluate the performance of the presented method. The simulation results demonstrate that this method is efficient to detect community structure in complex networks, and the ZLZ metrics used in the proposed method is the most suitable one among local indices in community detection.     

Detecting communities by the core-vertex and intimate degree in complex networks

In this paper, we present a new approach to extract communities in the complex networks with considerable accuracy. We introduce the core-vertex and the intimate degree between the community and its neighboring vertices. First, we find the core-vertices as the initial community. These core-vertices are then expanded using intimate degree function during extracting community structure from the given network. In addition, our algorithm successfully finds common nodes between communities. Experimental results using some real-world networks data shows that the performance of our algorithm is satisfactory.     

Self-similar scaling of density in complex real-world networks

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations–network coarse-graining–and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks [P.J. Laurienti, K.E. Joyce, Q.K. Telesford, J.H. Burdette, S. Hayasaka, Universal fractal scaling of self-organized networks, Physica A 390 (20) (2011) 3608–3613] and imply an existence of a scale-free density also within–among different self-similar scales of–complex real-world networks. The latter further improves the comprehension of self-similar structure in large real-world networks with several possible applications.     

A fast and efficient heuristic algorithm for detecting community structures in complex networks

Community structure is an important property of complex networks. How to detect the communities is significant for understanding the network structure and to analyze the network properties. Many algorithms, such as K-L and GN, have been proposed to detect community structures in complex networks. According to daily experience, a community should have many nodes and connections. Based on these principles and existing researches, a fast and efficient algorithm for detecting community structures in complex networks is proposed in this paper. The key strategy of the algorithm is to mine a node with the closest relations with the community and assign it to this community. Four real-world networks are used to test the performance of the algorithm. Experimental results demonstrate that the algorithm proposed is rather efficient for detecting community structures in complex networks.     

Semi-supervised clustering algorithm for community structure detection in complex networks

Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks. In this paper, we discuss an equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density. Based on this equivalence, we develop a new algorithm, named the so-called SNMF-SS, by combining SNMF and a semi-supervised clustering approach. Previous NMF-based algorithms often suffer from the restriction of measuring network topology from only one perspective, but our algorithm uses a semi-supervised mechanism to get rid of the restriction. The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks. Experimental results show the significance of the proposed approach, particularly, in the cases when community structure is obscure.