Effective Connectivity Network Research Articles

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.

Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations.

Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms) are thought be involved in the “rapid” transcortical reaction to the perturbation while the late cortical responses (>100 ms) are related to the “slow” transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a “ramp-and-hold” mechanical perturbation, in both the early (<100 ms) and late (>100 ms) periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG). We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.

Quantify neuromagnetic network changes from pre-ictal to ictal activities in absence seizures

ObjectiveThe cortico–thalamo–cortical network plays a key role in childhood absence epilepsy (CAE). However, the exact interaction between the cortex and the thalamus remains incompletely understood. This study aimed to investigate the dynamic changes of frequency-dependent neural networks during the initialization of absence seizures. MethodsMagnetoencephalography data from 14 patients with CAE were recorded during and between seizures at a sampling rate of 6000Hz and analyzed in seven frequency bands. Neuromagnetic sources were volumetrically scanned with accumulated source imaging. Effective connectivity networks of the entire brain, including the cortico–thalamo–cortical network, were evaluated at the source level through Granger causality analysis. ResultsThe low-frequency (1–80Hz) activities showed significant frontal cortical and parieto–occipito–temporal junction source localization around seizures. The high-frequency (80–250Hz) oscillations showed predominant activities consistently localized in deep brain areas and medial frontal cortex. The increased cortico–thalamic effective connectivity was observed around seizures in both low- and high-frequency ranges. The direction was predominantly from the cortex to the thalamus at the early time, although the cortex that drove connectivity varied among subjects. ConclusionsThe cerebral cortex plays a key role in driving the cortico–thalamic connections at the early portion of the initialization of absence seizures. The oscillatory activities in the thalamus could be triggered by networks from various regions in the cortex. SignificanceThe dynamic changes of neural network provide evidences that absence seizures are probably resulted from cortical initialized cortico–thalamic network.

188. Effect of Stress on Prefrontal Network Effective Connectivity during Working Memory Computation

188. Effect of Stress on Prefrontal Network Effective Connectivity during Working Memory Computation

Different patterns of age-related central olfactory decline in men and women as quantified by olfactory fMRI.

Age-related olfactory decline, or presbyosmia, is a prevalent condition with potentially devastating consequences on both quality of life and safety. Despite clear evidence for this decline, it is unknown whether presbyosmia is sex-dependent and also whether it is due to central or peripheral olfactory system deterioration. Therefore, the goals of this study were to investigate the neurofunctional substrate of olfactory decline and examine its relationship to age and sex in thirty-seven (18 women, 19 men) healthy older participants using olfactory functional MRI (fMRI). The olfactory fMRI paradigm utilized unique odor+visual and visual-only conditions to contrast peripheral-to-central and central-to-central olfactory processing, respectively. Age was negatively correlated with fMRI activation in olfactory-related regions. Significant aging effects were identifiable in male participants in all target regions. Female participants, however, showed a different pattern of functional decline. Extended unified structural equation modeling (euSEM) analysis revealed that the effective functional connectivity profile was drastically different between male and female participants, with females manifesting a top-down mechanism to offset age-related olfactory activation decline. Our results support the hypotheses that the central olfactory system is involved in age-related olfactory decline, and that resilience to age-related olfactory decline in women may be due to their profuse olfactory network effective connectivity.

Altered Effective Connectivity Network in Childhood Absence Epilepsy: A Multi-frequency MEG Study.

Using multi-frequency magnetoencephalography (MEG) data, we investigated whether the effective connectivity (EC) network of patients with childhood absence epilepsy (CAE) is altered during the inter-ictal period in comparison with healthy controls. MEG data from 13 untreated CAE patients and 10 healthy controls were recorded. Correlation analysis and Granger causality analysis were used to construct an EC network at the source level in eight frequency bands. Alterations in the spatial pattern and topology of the network in CAE were investigated by comparing the patients with the controls. The network pattern was altered mainly in 1-4Hz, showing strong connections within the frontal cortex and weak connections in the anterior-posterior pathways. The EC involving the precuneus/posterior cingulate cortex (PC/PCC) significantly decreased in low-frequency bands. In addition, the parameters of graph theory were significantly altered in several low- and high-frequency bands. CAE patients display frequency-specific abnormalities in the network pattern even during the inter-ictal period, and the frontal cortex and PC/PCC might play crucial roles in the pathophysiology of CAE. The EC network of CAE patients was over-connective and random during the inter-ictal period. This study is the first to reveal the frequency-specific alteration in the EC network during the inter-ictal period in CAE patients. Multiple-frequency MEG data are useful in investigating the pathophysiology of CAE, which can serve as new biomarkers of this disorder.

Changes in Effective Connectivity Network Patterns in Drug Abusers, Treated With Different Methods.

Introduction:Various treatment methods for drug abusers will result in different success rates. This is partly due to different neural assumptions and partly due to various rate of relapse in abusers because of different circumstances. Investigating the brain activation networks of treated subjects can reveal the hidden mechanisms of the therapeutic methods.Methods:We studied three groups of subjects: heroin abusers treated with abstinent based therapy (ABT) method, heroin abusers treated with Methadone Maintenance Therapy (MMT) method, and a control group. They were all scanned with functional magnetic resonance imaging (fMRI), using a 6-block task, where each block consisted of the rest-craving-rest-neutral sequence. Using the dynamic causal modeling (DCM) algorithm, brain effective connectivity network (caused by the drug craving stimulation) was quantified for all groups. In this regard, 4 brain areas were selected for this analysis based on previous findings: ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC), amygdala, and ventral striatum.Results:Our results indicated that the control subjects did not show significant brain activations after craving stimulations, but the two other groups showed significant brain activations in all 4 regions. In addition, VMPFC showed higher activations in the ABT group compared to the MMT group. The effective connectivity network suggested that the control subjects did not have any direct input from drug-related cue indices, while the other two groups showed reactions to these cues. Also, VMPFC displayed an important role in ABT group. In encountering the craving pictures, MMT subjects manifest a very simple mechanism compared to other groups.Conclusion:This study revealed an activation network similar to the emotional and inhibitory control networks observed in drug abusers in previous works. The results of DCM analysis also support the regulatory role of frontal regions on bottom regions. Furthermore, this study demonstrates the different effective connectivity patterns after drug abuse treatment and in this way helps the experts in the field.

Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification.

Functional connectivity network derived from resting-state fMRI data has been found as effective biomarkers for identifying patients with mild cognitive impairment from healthy elderly. However, the ordinary functional connectivity network is essentially a low-order network with the assumption that the brain is static during the entire scanning period, ignoring the temporal variations among correlations derived from brain region pairs. To overcome this weakness, we proposed a new type of high-order network to more accurately describe the relationship of temporal variations among brain regions. Specifically, instead of the commonly used undirected pairwise Pearson's correlation coefficient, we first estimated the low-order effective connectivity network based on a novel sparse regression algorithm. By using the similar approach, we then constructed the high-order effective connectivity network from low-order connectivity to incorporate signal flow information among the brain regions. We finally combined the low-order and the high-order effective connectivity networks using two decision trees for MCI classification and experimental results obtained demonstrate the superiority of the proposed method over the conventional undirected low-order and high-order functional connectivity networks, as well as the low-order and high-order effective connectivity networks when they were used separately.

Novel Effective Connectivity Network Inference for MCI Identification.

Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What's more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.

Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification.

Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

Altered effective connectivity network of the thalamus in post-traumatic stress disorder: a resting-state FMRI study with granger causality method

Post-traumatic stress disorder (PTSD) is an anxiety disorder that can develop following a traumatic event. Previous studies have found abnormal functional connectivity between the thalamus and other brain regions. However, the traditional functional connectivity method cannot investigate the directional flow of the influence in PTSD. In the present study, we used an effective connectivity method based on Granger causality to explore altered direction of causal information flow within a network associated with the thalamus in PTSD. Employing this method, we found that PTSD patients exhibited increased influence from thalamus to middle/inferior frontal gyrus and insula, and increased bidirectional influences between thalamus and medial prefrontal cortex compared to healthy controls. This is the first study to reveal a network of abnormal effective connectivity in PTSD. In addition, using the machine learning approach, we found that the altered functional measurements could differentiate patients from healthy controls. Our findings may have important implications for the pathophysiological basis underlying PTSD.

Graphlet characteristics in directed networks.

Graphlet analysis is part of network theory that does not depend on the choice of the network null model and can provide comprehensive description of the local network structure. Here, we propose a novel method for graphlet-based analysis of directed networks by computing first the signature vector for every vertex in the network and then the graphlet correlation matrix of the network. This analysis has been applied to brain effective connectivity networks by considering both direction and sign (inhibitory or excitatory) of the underlying directed (effective) connectivity. In particular, the signature vectors for brain regions and the graphlet correlation matrices of the brain effective network are computed for 40 healthy subjects and common dependencies are revealed. We found that the signature vectors (node, wedge, and triangle degrees) are dominant for the excitatory effective brain networks. Moreover, by considering only those correlations (or anti correlations) in the correlation matrix that are significant (>0.7 or <−0.7) and are presented in more than 60% of the subjects, we found that excitatory effective brain networks show stronger causal (measured with Granger causality) patterns (G-causes and G-effects) than inhibitory effective brain networks.

Localization of epileptogenic zone based on graph analysis of stereo-EEG

Localization of the epileptogenic zone (EZ) is essential for the successful surgical treatment of medically intractable epilepsy. In the present study, stereo-EEG (SEEG) recordings were obtained from seven patients underwent presurgical evaluation for treatment of intractable epilepsy. Partial directed coherence (PDC) analysis was applied to construct peri-ictal effective connectivity networks. The graphic measures, in-degree, out-degree and betweenness centrality, were evaluated to localize the EZ. A receiver operating characteristic (ROC) analysis was used to quantify the localization accuracy. We found that the in-degree coincided well with the EZ identified by epileptologists’ visual inspection in all seven patients who had a significant improvement in seizure outcomes, however, the other two measures were effective only in some cases. Furthermore, in all seven patients the electrode contact with the highest in-degree was always located within the EZ identified by epileptologists’ visual inspection. These results indicate that the graph theory is an effective method to localize the EZ when suitable graphic measures were chosen. Furthermore, the in-degree was the most effective measure among the three graphic measures in localizing the EZ when the PDC method was used.

Aberrant pulvinar effective connectivity in generalized social anxiety disorder.

Recent neuroimaging findings in general social anxiety disorder (gSAD) have extended our understanding of the neural mechanisms of gSAD beyond an amygdala-centric fear-based hyperactivity model to include other brain regions and networks relevant to salient cues. In particular, higher order areas compromising visual networks that process emotional and social information have been implicated. The pulvinar anchors this network and is a key regulatory node that mediates complex sensory inputs and the integration between limbic and frontal brain systems. However, the role of the pulvinar and specifically alteration of its effective connectivity with the rest of the brain has not been examined in the pathophysiology of gSAD, a disorder characterized by aberrant socio-emotional processing. The main aim of this study was to examine the pulvinar network effective connectivity in gSAD. In this study, we recruited 21 individuals with gSAD and 19 demographically matched healthy controls (HC), who performed an emotional face processing task while brain activity was recorded using functional magnetic resonance imaging (fMRI). To examine pulvinar-based network dynamics, Granger causality (GC) based effective connectivity (EC) analysis was applied on fMRI data to compare gSAD and HC. The EC analysis revealed heightened casual influential dynamics between pulvinar in higher order visual and frontal regions in gSAD. In conclusion, these preliminary data suggest a novel network-based cortico-pulvino-cortical neural mechanism in the pathophysiology of gSAD.

Elastic-Net Copula Granger Causality for Inference of Biological Networks.

AimIn bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively. However, with recent advances in data procurement technology, such as DNA microarray analysis and fMRI that can simultaneously process a large amount of data, it yields high-dimensional data sets. These high dimensional dataset analyses possess challenges for the analyst.BackgroundTraditional methods of Granger causality inference use ordinary least-squares methods for structure estimation, which confront dimensionality issues when applied to high-dimensional data. Apart from dimensionality issues, most existing methods were designed to capture only the linear inferences from time series data.Method and ConclusionIn this paper, we address the issues involved in assessing Granger causality for both linear and nonlinear high-dimensional data by proposing an elegant form of the existing LASSO-based method that we call “Elastic-Net Copula Granger causality”. This method provides a more stable way to infer biological networks which has been verified using rigorous experimentation. We have compared the proposed method with the existing method and demonstrated that this new strategy outperforms the existing method on all measures: precision, false detection rate, recall, and F1 score. We have also applied both methods to real HeLa cell data and StarPlus fMRI datasets and presented a comparison of the effectiveness of both methods.

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades.

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm.

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith’s simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity.

Effect of handedness on brain activity patterns and effective connectivity network during the semantic task of Chinese characters.

Increasing efforts have been denoted to elucidating the effective connectivity (EC) among brain regions recruited by certain language task; however, it remains unclear the impact of handedness on the EC network underlying language processing. In particularly, this has not been investigated in Chinese language, which shows several differences from alphabetic language. This study thereby explored the functional activity patterns and the EC network during a Chinese semantic task based on functional MRI data of healthy left handers (LH) and right handers (RH). We found that RH presented a left lateralized activity pattern in cerebral cortex and a right lateralized pattern in cerebellum; while LH were less lateralized than RH in both cerebral and cerebellar areas. The conditional Granger causality method in deconvolved BOLD level further demonstrated more interhemispheric directional connections in LH than RH group, suggesting better bihemispheric coordination and increased interhemispheric communication in LH. Furthermore, we found significantly increased EC from right middle occipital gyrus to bilateral insula (INS) while decreased EC from left INS to left precentral gyrus in LH group comparing to RH group, implying that handedness may differentiate the causal relationship of information processing in integration of visual-spatial analysis and semantic word retrieval of Chinese characters.

Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation.

Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood. To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task. We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity. These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability.

Modulation of effective connectivity in the default mode network at rest and during a memory task.

It is known that the default mode network (DMN) may be modulated by a cognitive task and by performance level. Changes in the DMN have been examined by investigating resting-state activation levels, but there have been very few studies examining the modulation of effective connectivity of the DMN during a task in healthy older subjects. In this study, the authors examined how effective connectivity changed in the DMN between rest and during a memory task. The authors also investigated whether there was any relationship between effective connectivity modulation in the DMN and memory performance, to establish whether variations in cognitive performance are related to neural network effective connectivity, either at rest or during task performance. Twenty-eight healthy older participants underwent a resting-state functional magnetic resonance imaging scan and an emotional face-name encoding task. Effective connectivity analyses were performed on the DMN to examine the effective connectivity modulation in these two different conditions. During the resting state, there was strong self-influence in the regions of the DMN, while the main regions with statistically significant cross-regional effective connectivity were the posterior cingulate cortex (PCC) and the hippocampus (HP). During the memory task, the self-influence effective connectivities remained statistically significant across the DMN, and there were statistically significant effective connectivities from the PCC, HP, amygdala (AM), and parahippocampal region to other DMN regions. The authors found that effective connectivities from PCC, HP, and AM (in both resting state and during task) were linearly correlated to memory performance. The results suggest that superior memory ability in this older cohort was associated with effective connectivity both at rest and during the memory task of three DMN regions, which are also known to be important for memory function.