Effective Connectivity Of Brain Networks Research Articles

Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders.

Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Case Report: Modulation of Effective Connectivity in Brain Networks after Prosthodontic Tooth Loss Repair

INTRODUCTION. Recent neuroimaging studies suggest that dental loss replacements induce changes in neuroplasticity as well as in correlated connectivity between brain networks. However, as the typical temporal delay in detecting brain activity by neuroimaging cannot account for the influence one neural system exerts over another in a context of real activation (“effective” connectivity), it seems of interest to approach this dynamic aspect of brain networking in the time frame of milliseconds by exploiting electroencephalographic (EEG) data. MATERIAL AND METHODS. The present study describes one subject who received a new prosthodontic provisional implant in substitution for previous dental repairs. Two EEG sessions led with a portable device were recorded before and after positioning the new dental implant. By following MATLAB-EEGLAB processing supported by the plugins FIELDTRIP and SIFT, the independent component analysis (ICA) derived from EEG raw signals was rendered as current density fields and interpolated with the dipoles generated by each electrode for a dynamic study of the effective connectivity. One more recording session was undertaken six months after the placement of the final implant. RESULTS. Compared to the baseline, the new prosthodontic implant induced a novel modulation of the neuroplasticity in sensory-motor areas which was maintained following the definitive implant after six months, as revealed by changes in the effective connectivity from the basal strong enslavement of a single brain area over the others, to an equilibrate inter-related connectivity evenly distributed along the frontotemporal regions of both hemispheres. CONCLUSIONS. The rapid shift of the effective connectivity after positioning the new prosthodontic implant and its substantial stability after six months suggest the possibility that synaptic modifications, induced by novel sensory motor conditions, modulate the neuroplasticity and reshape the final dynamic frame of the interarea connectivity. Moreover, given the viability of the EEG practice, this approach could be of some interest in assessing the association between oral pathophysiology and neuronal networking.

Effective connectivity of brain networks controlling human thermoregulation.

Homeostatic centers in the mammalian brainstem are critical in responding to thermal challenges. These centers play a prominent role in human thermoregulation, but humans also respond to thermal challenges through behavior modification. Behavioral modifications are presumably sub served by interactions between the brainstem and interoceptive, cognitive and affective elements in human brain networks. Prior evidence suggests that interoceptive regions such as the insula, and cognitive/affective regions such as the orbitofrontal cortex and anterior cingulate cortex are crucial. Here we used dynamic causal modeling (DCM) to discover likely generative network architectures and estimate changes in the effective connectivity between nodes in a hierarchically organized thermoregulatory network (homeostatic-interoceptive-cognitive/affective). fMRI data were acquired while participants (N = 20) were subjected to a controlled whole body thermal challenge that alternatingly evoked sympathetic and parasympathetic responses. Using a competitive modeling framework (ten competing modeling architectures), we demonstrated that sympathetic responses (evoked by whole-body cooling) resulted in more complex network interactions along two ascending pathways: (i) homeostatic interoceptive and (ii) homeostatic cognitive/affective. Analyses of estimated connectivity coefficients demonstrated that sympathetic responses evoked greater network connectivity in key pathways compared to parasympathetic responses. These results reveal putative mechanisms by which human thermoregulatory networks evince a high degree of contextual sensitivity to thermoregulatory challenges. The patterns of the discovered interactions also reveal how information propagation from homeostatic regions to both interoceptive and cognitive/affective regions sub serves the behavioral repertoire that is an important aspect of thermoregulatory defense in humans.

Graph theoretical analysis of evoked potentials shows network influence of epileptogenic mesial temporal region.

It is now widely accepted that seizures arise from the coordinated activity of epileptic networks, and as a result, traditional methods of analyzing seizures have been augmented by techniques like single‐pulse electrical stimulation (SPES) that estimate effective connectivity in brain networks. We used SPES and graph analytics in 18 patients undergoing intracranial EEG monitoring to investigate effective connectivity between recording sites within and outside mesial temporal structures. We compared evoked potential amplitude, network density, and centrality measures inside and outside the mesial temporal region (MTR) across three patient groups: focal epileptogenic MTR, multifocal epileptogenic MTR, and non‐epileptogenic MTR. Effective connectivity within the MTR had significantly greater magnitude (evoked potential amplitude) and network density, regardless of epileptogenicity. However, effective connectivity between MTR and surrounding non‐epileptogenic regions was of greater magnitude and density in patients with focal epileptogenic MTR compared to patients with multifocal epileptogenic MTR and those with non‐epileptogenic MTR. Moreover, electrodes within focal epileptogenic MTR had significantly greater outward network centrality compared to electrodes outside non‐epileptogenic regions and to multifocal and non‐epileptogenic MTR. Our results indicate that the MTR is a robustly connected subnetwork that can exert an overall elevated propagative influence over other brain regions when it is epileptogenic. Understanding the underlying effective connectivity and roles of epileptogenic regions within the larger network may provide insights that eventually lead to improved surgical outcomes.

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed information transfer between EEG signals within δ, θ, α, β and γ-bands. The developed methodology is then used to study the properties of directed BNs in children with attention-deficit hyperactivity disorder (ADHD) and compare them with that of the healthy controls using both statistical and receiver operating characteristic (ROC) analyses. The results indicate that directed information transfer between scalp EEG electrodes in the ADHD subjects differs significantly compared to the healthy ones. The results of the statistical and ROC analyses of frequency-specific graph measures demonstrate their highly discriminative ability between the two groups. Specifically, the graph measures extracted from the estimated directed BNs in the β-band show the highest discrimination between the ADHD and control groups. These findings are in line with the fact that β-band reflects active concentration, motor activity, and anxious mental states. The reported results show that the developed methodology has the capacity to be used for investigating patterns of directed BNs in neuropsychiatric disorders.

MRI Compatible, Customizable, and 3D-Printable Microdrive for Neuroscience Research.

The effective connectivity of brain networks can be assessed using functional magnetic resonance imaging (fMRI) to quantify the effects of local electrical microstimulation (EM) on distributed neuronal activity. The delivery of EM to specific brain regions, particularly with layer specificity, requires MRI compatible equipment that provides fine control of a stimulating electrode’s position within the brain while minimizing imaging artifacts. To this end, we developed a microdrive made entirely of MRI compatible materials. The microdrive uses an integrated penetration grid to guide electrodes and relies on a microdrilling technique to eliminate the need for large craniotomies, further reducing implant maintenance and image distortions. The penetration grid additionally serves as a built-in MRI marker, providing a visible fiducial reference for estimating probe trajectories. Following the initial implant procedure, these features allow for multiple electrodes to be inserted, removed, and repositioned with minimal effort, using a screw-type actuator. To validate the design of the microdrive, we conducted an EM-coupled fMRI study with a male macaque monkey. The results verified that the microdrive can be used to deliver EM during MRI procedures with minimal imaging artifacts, even within a 7 Tesla (7T) environment. Future applications of the microdrive include neuronal recordings and targeted drug delivery. We provide computer aided design (CAD) templates and a parts list for modifying and fabricating the microdrive for specific research needs. These designs provide a convenient, cost-effective approach to fabricating MRI compatible microdrives for neuroscience research.

A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect

Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, are affected. The Granger-causality analysis is often used to infer effective connectivity. First, we investigate if a new method, Bayesian topology identification, can be used as an alternative. Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data lengths. In the second part, the Bayesian method is extended to enable the inference of changes in effective connectivity between groups of subjects. Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after the exposure to Mozart's sonata K448 at least 2 hours a day for 7 days. Here, we investigate if the effective connectivity of the subjects significantly changed after listening to Mozart music. The Bayesian method detected changes in effective connectivity between networks related to cognitive processing and control in the connection from the central executive to the superior sensori-motor network, in the connection from the posterior default mode to the fronto-parietal right network, and in the connection from the anterior default mode to the dorsal attention network. This last connection was only detected in a subgroup of subjects with a longer listening duration. Only in this last connection, an effect was found by the Granger-causality analysis.

Estimating Effective Connectivity from fMRI Data Using Factor-based Subspace Autoregressive Models

We consider the problem of identifying large-scale effective connectivity of brain networks from fMRI data. Standard vector autoregressive (VAR) models fail to estimate reliably networks with large number of nodes. We propose a new method based on factor modeling for reliable and efficient high-dimensional VAR analysis of large networks. We develop a subspace VAR (SVAR) model from a factor model (FM), where observations are driven by a lower-dimensional subspace of common latent factors with an AR dynamics. We consider two variants of principal components (PC) methods that provide consistent estimates for the FM hence the implied SVAR model, even of large dimensions. Information criterion is used to select the optimal subspace dimension. We established asymptotic normality and convergence rates for the estimated SVAR coefficients matrix. Evaluation on simulated resting-state fMRI shows that the SVAR models are more robust and produce better connectivity estimates than the classical model for a moderately-large network analysis. Results on real data by varying the subspace dimensions identify strong connections in the default mode network and reveal hierarchical connectivity of resting-state networks with distinct functional relevance.

Effect of Chinese tuina massage therapy on resting state brain functional network of patients with chronic neck pain

ObjectiveCervical disease, a type of chronic pain, can greatly impact quality of life. Traditional Chinese tuina, a form of therapeutic massage and manipulation, has been shown to be effective in relieving pain and other symptoms in patients with chronic neck pain. This study applied functional magnetic resonance imaging (fMRI) to explore the features of the resting state network of patients with chronic neck pain caused by cervical radiculopathy, and how tuina affects the causality between intrinsic brain networks. MethodsUsing Granger causality analysis, effective connectivity of brain networks of 10 patients with chronic neck pain was compared with 10 healthy control subjects. Resting state fMRI data were using magnetic resonance scanning. Cervical spondylosis symptom scores were evaluated before and after 4 weeks of tuina therapy. Independent component analysis was applied to extract the specific networks related to sensation, execution, and cognition, including sensorimotor network (SMN), visual network (VN), auditory network (AN), anterior and posterior default mode network (aDMN, pDMN), left frontoparietal network and right frontoparietal network. ResultsCompared with the control group, data from the treatment group revealed two major findings: before tuina therapy, SMN had a profound influence on aDMN and AN greatly affected pDMN; however, after 4 weeks of tuina therapy, aDMN and SMN showed reversed causality. ConclusionChronic neck pain caused by cervical radiculopathy may influence the DMN, which plays an important role in emotion, cognition, and memory, by stimulating the sensory afferent network. Tuina not only significantly relieves pain and discomfort, but also reverses the causality between aDMN and SMN.

Multivariate granger causality analysis of acupuncture effects in mild cognitive impairment patients: an FMRI study.

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

Neurobiological Mechanisms of Acupuncture

Acupuncture, an age-old healing art, has been accepted to effectively treat various diseases, particularly chronic pain and neurodegenerative diseases. Since its public acceptance and good efficacy, increasing attention has been now paid to exploring the physiological and biochemical mechanisms underlying acupuncture, particularly the brain mechanisms. Basic and clinical acupuncture studies on neurobiological mechanisms of acupuncture are crucial for the development of acupuncture. This issue compiles 32 exciting papers, most of which are very novel and excellent investigations in this field. The neural mechanism underlying acupuncture analgesia was addressed in four articles. J. Wang et al. established a postincisional pain model of rats and investigated electroacupuncture effect on the brain oscillations involving postoperative pain. B.-S. Lim et al., using interesting bee venom acupuncture, explored whether it can relieve oxaliplatin-induced cold allodynia and which endogenous analgesic system is implicated. The paper by Yumi Maeda et al. aimed to evaluate this linkage between brain response to acupuncture and subsequent analgesia in chronic pain patients with carpal tunnel syndrome. Since many articles aimed to assess the effect of electroacupuncture induced analgesia, W. Kim et al. systemically conducted a review to assess the efficacy and clarify its mechanism on neuropathic pain. In addition, as a great challenge in acupuncture analgesia and treatment evaluations, D. Zhu et al. outlined the advantages and disadvantages of kinds of acupuncture controls and highlighted how the differences among placebo devices can be used to isolate distinct components of acupuncture treatment. Five papers deal with potential neural mechanism underlying acupuncture treatments on the stroke, hypertension, and mild cognitive impairments. The paper by L. Liu and R.T.F. Cheung aimed to investigate whether the combination of melatonin and electroacupuncture therapies could be beneficial against transient focal cerebral ischemia in a rat model of transient middle cerebral artery occlusion. W. Qin et al. accompanied by L. Liu and R.T.F. Cheung's paper explored the importance of anti-inflammatory acupuncture treatment for the focal cerebral ischemia/reperfusion by inhibiting the NF-κB signaling pathway. Another two papers mainly focused on the acupuncture modulation of neural mechanism for hypertension regulation with long-term as well as short-term treatments. The paper by G.-H. Tian et al. addressed the long-term electroacupuncture on cerebral microvessels and neurons in CA1 region of hippocampus in spontaneously hypertensive rats. H. Chen et al. aimed to explore the hypothalamus-anchored resting brain network underlying primary hypertension patients after short-term acupuncture treatments. Moreover, for mild cognitive impairments, S. Chen et al. have pointed out that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivity of brain networks. The neuroendocrine system involving acupuncture has been addressed by the following three papers. Z. Yu et al. has suggested that TRPV1 receptor is partially involved in the electroacupuncture-mediated modulation of gastric motility. The paper by C.-C. Kuo et al. explored the mechanism of electroacupuncture (EAc) induced antinociception involved opioid receptors and the serotonergic system. Q.-Q. Li et al. conducted a systemic review about the central mechanism of acupuncture in modulating various autonomic responses. Moreover, other papers focused on the relatively acupoint specificity from wide aspects. One of the studies by L. Li et al. suggested that both the size and function of the acupoints comply with the functionality of the internal organs; thus the sensitive degree of acupoints changed according to malfunction of internal organs. C.-Y. Chen et al. implied that somatoparasympathetic neuronal connection (groin-spinal dorsal horn-NTS/DMX-uterus) and a somatosympathetic neuronal connection (groin-spinal dorsal horn-NTS-PVN-uterus) could be the prerequisites to the neuronal basis of the somatovisceral reflex and also the neuronal mechanism of acupuncture. Z. Wang et al. advanced that the modulatory effects of different needling sensations induced by relatively different acupoints contribute to acupuncture modulations of limbic-paralimbic-neocortical network. Although the acupoints were spatially adjacent, there was also relatively functional specificity inflected by brain networks. By gathering these papers, we hope to enrich our readers and researchers with respect to the underlying neurological mechanism of acupuncture, and we look forward to an increasing number of both clinical trials and experimental studies to further promote the development of understanding the neurological mechanism involving acupuncture. Lijun Bai Richard E. Harris Jian Kong Lixing Lao Vitaly Napadow Baixiao Zhao

Detecting effective connectivity in networks of coupled neuronal oscillators

The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

Subcortical contributions to effective connectivity in brain networks supporting imitation

Using functional magnetic resonance imaging (fMRI), we investigated effective connectivity in brain networks supporting imitation. Despite extensive reports of regional functional specialization underlying action perception, action execution and imitation, our understanding of the potential contribution of subcortical sites is limited, as is our knowledge of how regions displaying functional specialization interact with each other on a system level. While in the scanner, participants performed a simple imitation paradigm with three conditions: Observe trials, in which participants passively viewed a human actor executing a sequence of four finger presses on a keypad; Imitate trials, in which participants imitated the actor's finger presses on a keyboard; and Execute trials, in which participants also executed finger presses but did so based on visuospatial cues in the absence of the actor's hand. Relative to the Execute condition, Imitate trials evoked significantly more activity in superior and inferior parietal lobules (SPL, IPL), posterior superior temporal sulcus (pSTS), and in a ventral aspect of dorsal premotor cortex (PMd). Psychophysiological interaction (PPI) analysis, a means of assessing effective connectivity, revealed significant interactions with regions of cerebellar lobule VII from seeds both in the right pSTS and right SPL, such that activity in these sites was more highly correlated during imitation. A similar interaction was found between right pSTS and left IPL. These results clarify the role of cortical regions supporting action observation, action execution and imitation, and highlight the role the cerebellum may play in facilitating both motor and nonmotor aspects of imitation.

The applicability of effective connectivity measures to time series of neuronal oscillators

The use of time series analysis techniques such as Granger causality (GC), partial directed coherence (PDC) and phase dynamics modeling (PDM) to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining the effective connectivity structure of brain networks [1,2], the results they produce can be incorrect when the recording channels have a high degree of coherence [3] or a large bias in their degree of noise [4]. Nevertheless, it is standard practice to apply these techniques to multichannel datasets with little or no regard for whether the channels fall into one of these problematic categories. In this work, we address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method based on decision tree classifiers to assess the ability of GC, PDC, generalized PDC, and PDM to accurately determine effective connectivity in a neuronal network. We begin by generating an ensemble of time series datasets by systematically varying the parameters of a simple network of two coupled Morris-Lecar (ML) neurons. The varied parameters include the coupling type (linear diffusive, nonlinear fast threshold, nonlinear synaptic), coupling strength, intrinsic firing rates and noise levels. Next, we characterize each dataset of the ensemble using the following time series features: i) phase coherence, ii) difference in the coefficient of variation of interspike intervals, iii) mean coefficient of variation of interspike intervals, and iv) difference in firing frequency. We then apply four effective connectivity measures to the datasets in the ensemble to obtain estimates for the direction of interactions in the network. These results are used to label the datasets as “correct” (+1) or “incorrect” (0) depending on whether the measure correctly or incorrectly predicts the known connectivity structure of the underlying ML network. Finally, we use the calculated time series features and the labeled ML network datasets to develop a decision tree classifier. We apply the decision tree classifiers to larger networks of three neuronal oscillators by considering time series features for all pairwise interactions in the network and determining the conditions under which the effective connectivity measures can successfully detect directionality of coupling in these systems. We find that if a majority of pairwise interactions in the network are classified as 0 for a given measure, then that measure should not be applied since it will most likely (probability >50%) result in failure. Extending our observations to multichannel datasets with greater than three nodes, we conclude that if all pairwise interactions in a network are classified as 0 by our decision tree classifier, then a given measure should not be applied to the data since effective connectivity estimates are likely to be incorrect.

FIAR: AnRPackage for Analyzing Functional Integration in the Brain

Functional integration in the brain refers to distributed interactions among functionally segregated regions. Investigation of effective connectivity in brain networks, i.e, the directed causal influence that one brain region exerts over another region, is being increasingly recognized as an important tool for understanding brain function in neuroimaging studies. Methods for identifying intrinsic relationships among elements in a network are increasingly in demand. Over the last few decades several techniques such as Bayesian networks, Granger causality, and dynamic causal models have been developed to identify causal relations in dynamic systems. At the same time, established techniques such as structural equation modeling (SEM) are being modified and extended in order to reveal underlying interactions in imaging data. In the R package FIAR, which stands for Functional Integration Analysis in R, we have implemented many of the latest techniques for analyzing brain networks based on functional magnetic resonance imaging (fMRI) data. The package can be used to analyze experimental data, but also to simulate data under certain models.

Effective connectivity of brain networks during self-initiated movement in Parkinson's disease

Patients with Parkinson's disease (PD) have difficulty in performing self-initiated movements. The neural mechanism of this deficiency remains unclear. In the current study, we used functional MRI (fMRI) and psychophysiological interaction (PPI) methods to investigate the changes in effective connectivity of the brain networks during performance of self-initiated movement in PD patients. Effective connectivity is defined as the influence one neuronal system exerts over another. fMRIs were acquired in 18 PD patients and in 18 age- and sex-matched healthy controls, when performing a self-initiated right hand tapping task. We chose the left primary motor cortex (M1), rostral supplementary motor area (pre-SMA), left premotor cortex (PMC), left putamen, and right cerebellum as index areas for PPI analysis. During the performance of self-initiated movement, connectivity between the putamen and M1, PMC, SMA, and cerebellum was decreased in PD patients compared to controls. In contrast, connections between the M1, pre-SMA, PMC, parietal cortex, and cerebellum were increased in PD patients compared to controls. In addition, the M1, pre-SMA, PMC, and cerebellum also had less connectivity with the dorsal lateral prefrontal cortex in PD. In PD patients, the effective connectivity between the putamen and M1, PMC, SMA, and cerebellum negatively correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) motor scores; whereas the connectivity between the M1, pre-SMA, PMC, and cerebellum positively correlated with the UPDRS motor scores. Our findings demonstrate that the pattern of interactions of brain networks is disrupted in PD during performance of self-initiated movements. The striatum-cortical and striatum-cerebellar connections are weakened. In contrast, the connections between cortico-cerebellar motor regions are strengthened and may compensate for basal ganglia dysfunction. These altered interregional connections are more deviant when the disorder is more severe, and, therefore, our results give further insight into the explanation for the difficulty in performing self-initiated movements in PD.

Assessing and Compensating for Zero-Lag Correlation Effects in Time-Lagged Granger Causality Analysis of fMRI

Effective connectivity in brain networks can be studied using Granger causality analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does "leak" into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.