Measuring directed functional connectivity using non-parametric directionality analysis: Validation and comparison with non-parametric Granger Causality

Connectivity of neural signals to the primary motor area during preparatory periods for movement following external and internal cues

PURPOSE

We investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement.

MATERIALS AND METHODS

Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant's own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks.

RESULTS

For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods.

CONSLUSIONS

In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.

Brain Synchronization and Multivariate Autoregressive (MVAR) Modeling in Cognitive Neurodynamics

This paper is a review of cognitive neurodynamics research as it pertains to recent advances in Multivariate Autoregressive (MVAR) modeling. Long-range synchronization between the frontoparietal network (FPN) and forebrain subcortical systems occurs when multiple neuronal actions are coordinated across time (Chafee and Goldman-Rakic, 1998), resulting in large-scale measurable activity in the EEG. This paper reviews the power and advantages of the MVAR method to analyze long-range synchronization between brain regions (Kaminski et al., 2016; Kaminski and Blinowska, 2017). It explores the synchronization expressed in neurocognitive networks that is observable in the local field potential (LFP), an EEG-like signal, and in fMRI time series. In recent years, the surge in MVAR modeling in cognitive neurodynamics experiments has highlighted the effectiveness of the method, particularly in analyzing continuous neural signals such as EEG and fMRI (Pereda et al., 2005). MVAR modeling has been particularly useful in identifying causality, a multichannel time-series measure that can only be consistently computed with multivariate processes. Due to the multivariate nature of neuronal communication, multiple non-linear multivariate-analysis models are successful, presenting results with much greater accuracy and speed than non-linear univariate-analysis methods. Granger’s framework provides causal information about neuronal flow using neural time and frequency analysis, comprising the basis of the MVAR model. Recent advancements in MVAR modeling have included Directed Transfer Function (DTF) and Partial Directed Coherence (PDC), multivariate methods based on MVAR modeling that are capable of determining causal influences and directed propagation of EEG activity. The related Granger causality is an increasingly popular tool for measuring directed functional interactions from time series data.

Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit

Synchronization of neural oscillations is thought to facilitate communication in the brain. Neurodegenerative pathologies such as Parkinson’s disease (PD) can result in synaptic reorganization of the motor circuit, leading to altered neuronal dynamics and impaired neural communication. Treatments for PD aim to restore network function via pharmacological means such as dopamine replacement, or by suppressing pathological oscillations with deep brain stimulation. We tested the hypothesis that brain stimulation can operate beyond a simple “reversible lesion” effect to augment network communication. Specifically, we examined the modulation of beta band (14–30 Hz) activity, a known biomarker of motor deficits and potential control signal for stimulation in Parkinson’s. To do this we setup a neural mass model of population activity within the cortico-basal ganglia-thalamic (CBGT) circuit with parameters that were constrained to yield spectral features comparable to those in experimental Parkinsonism. We modulated the connectivity of two major pathways known to be disrupted in PD and constructed statistical summaries of the spectra and functional connectivity of the resulting spontaneous activity. These were then used to assess the network-wide outcomes of closed-loop stimulation delivered to motor cortex and phase locked to subthalamic beta activity. Our results demonstrate that the spatial pattern of beta synchrony is dependent upon the strength of inputs to the STN. Precisely timed stimulation has the capacity to recover network states, with stimulation phase inducing activity with distinct spectral and spatial properties. These results provide a theoretical basis for the design of the next-generation brain stimulators that aim to restore neural communication in disease.

Diseases of the brain lead to a wide range of disabling symptoms for patients, by affecting their ability to move or think properly. These symptoms arise from disruption to both the organization of networks in the brain, but also the timing of neural activity that propagates around it. Treatments for disease with drugs can restore the organization of these networks to some extent, yet it is very difficult to deliver drugs with good spatial or temporal selectivity. Brain stimulation provides one way in which to improve the spatial specificity of treatment, yet understanding how to stimulate at the right time to achieve the best outcome for patients, remains an outstanding question. In this work we use simulations of an important circuit involved in Parkinson’s disease that has parameters chosen to reflect recordings made in animal models of the disease. Using this computer model, we show how brain rhythms can act as signatures of underlying changes in networks. Further, we simulate intervention with temporally precise stimulation to show how future approaches to brain stimulation can act to restore or even augment neural networks following their degeneration in disease.

Dynamics of cortical and corticomuscular connectivity during planning and execution of visually guided steps in humans

The cortical mechanisms underlying the act of taking a step-including planning, execution, and modification-are not well understood. We hypothesized that oscillatory communication in a parieto-frontal and corticomuscular network is involved in the neural control of visually guided steps. We addressed this hypothesis using source reconstruction and lagged coherence analysis of electroencephalographic and electromyographic recordings during visually guided stepping and 2 control tasks that aimed to investigate processes involved in (i) preparing and taking a step and (ii) adjusting a step based on visual information. Steps were divided into planning, initiation, and execution phases. Taking a step was characterized by an upregulation of beta/gamma coherence within the parieto-frontal network during planning followed by a downregulation of alpha and beta/gamma coherence during initiation and execution. Step modification was characterized by bidirectional modulations of alpha and beta/gamma coherence in the parieto-frontal network during the phases leading up to step execution. Corticomuscular coherence did not exhibit task-related effects. We suggest that these task-related modulations indicate that the brain makes use of communication through coherence in the context of large-scale, whole-body movements, reflecting a process of flexibly fine-tuning inter-regional communication to achieve precision control during human stepping.

Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models

Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent dynamics. On the other hand, emerging evidence suggests that dynamic functional connectivities (DFC) may be responsible for neural activity patterns underlying cognition or behavior. We are interested in studying how DFC are associated with the low-dimensional structure of neural activities. Most existing LVMs are based on a point process and fail to model evolving relationships. In this work, we introduce a dynamic graph as the latent variable and develop a Variational Dynamic Graph Latent Variable Model (VDGLVM), a representation learning model based on the variational information bottleneck framework. VDGLVM utilizes a graph generative model and a graph neural network to capture dynamic communication between nodes that one has no access to from the observed data. The proposed computational model provides guaranteed behavior-decoding performance and improves LVMs by associating the inferred latent dynamics with probable DFC.

Reorganization of functional and directed corticomuscular connectivity during precision grip from childhood to adulthood

How does the neural control of fine movements develop from childhood to adulthood? Here, we investigated developmental differences in functional corticomuscular connectivity using coherence analyses in 111 individuals from four different age groups covering the age range 8–30 y. EEG and EMG were recorded while participants performed a uni-manual force-tracing task requiring fine control of force in a precision grip with both the dominant and non-dominant hand. Using beamforming methods, we located and reconstructed source activity from EEG data displaying peak coherence with the EMG activity of an intrinsic hand muscle during the task. Coherent cortical sources were found anterior and posterior to the central sulcus in the contralateral hemisphere. Undirected and directed corticomuscular coherence was quantified and compared between age groups. Our results revealed that coherence was greater in adults (20–30 yo) than in children (8–10 yo) and that this difference was driven by greater magnitudes of descending (cortex-to-muscle), rather than ascending (muscle-to-cortex), coherence. We speculate that the age-related differences reflect maturation of corticomuscular networks leading to increased functional connectivity with age. We interpret the greater magnitude of descending oscillatory coupling as reflecting a greater degree of feedforward control in adults compared to children. The findings provide a detailed characterization of differences in functional sensorimotor connectivity for individuals at different stages of typical ontogenetic development that may be related to the maturational refinement of dexterous motor control.

Sensitivity of amplitude and phase based MEG measures of interhemispheric connectivity during unilateral finger movements

Interactions between different brain regions can be revealed by dependencies between their neuronal oscillations. We examined the sensitivity of different oscillatory connectivity measures in revealing interhemispheric interactions between primary motor cortices (M1s) during unilateral finger movements. Based on frequency, amplitude, and phase of the oscillations, a number of metrics have been developed to measure connectivity between brain regions, and each metric has its own strengths, weaknesses, and pitfalls. Taking advantage of the well-known movement-related modulations of oscillatory amplitude in M1s, this study compared and contrasted a number of leading connectivity metrics during distinct phases of oscillatory power changes. Between M1s during unilateral movements, we found that phase-based metrics were effective at revealing connectivity during the beta (15-35 Hz) rebound period linked to movement termination, but not during the early period of beta desynchronization occurring during the movement itself. Amplitude correlation metrics revealed robust connectivity during both periods. Techniques for estimating the direction of connectivity had limited success. Granger Causality was not well suited to studying these connections because it was strongly confounded by differences in signal-to-noise ratio linked to modulation of beta amplitude occurring during the task. Phase slope index was suggestive but not conclusive of a unidirectional influence between motor cortices during the beta rebound. Our findings suggest that a combination of amplitude and phase-based metrics is likely required to fully characterize connectivity during task protocols that involve modulation of oscillatory power, and that amplitude-based metrics appear to be more sensitive despite the lack of directional information.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

From scanners to cell phones: neural and real-world responses to social evaluation in adolescent girls

While expanded use of neuroimaging seemed promising to elucidate typical and atypical elements of social sensitivity, in many ways progress in this space has stalled. This is in part due to a disconnection between neurobiological measurements and behavior outside of the laboratory. The present study uses a developmentally salient fMRI computer task and novel ecological momentary assessment protocol to examine whether early adolescent females (n = 76; ages 11–13) with greater neural reactivity to social rejection actually report greater emotional reactivity following negative interactions with peers in daily life. As hypothesized, associations were found between reactivity to perceived social threat in daily life and neural activity in threat-related brain regions, including the left amygdala and bilateral insula, to peer rejection relative to a control condition. Additionally, daily life reactivity to perceived social threat was associated with functional connectivity between the left amygdala and dorsomedial prefrontal cortex during rejection feedback. Unexpectedly, daily life social threat reactivity was also related to heightened amygdala and insula activation to peer acceptance relative to a control condition. These findings may inform key brain–behavior associations supporting sensitivity to social evaluation in adolescence.

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

Stroke impairments often present as cognitive and motor deficits, leading to a decline in quality of life. Recovery strategy and mechanisms, such as neuroplasticity, are important factors, as these can help improve the effectiveness of rehabilitation. The present study investigated chiropractic spinal manipulation (SM) and its effects on resting-state functional connectivity in 24 subacute to chronic stroke patients monitored by electroencephalography (EEG). Functional connectivity of both linear and non-linear coupling was estimated by coherence and phase lag index (PLI), respectively. Non-parametric cluster-based permutation tests were used to assess the statistical significance of the changes in functional connectivity following SM. Results showed a significant increase in functional connectivity from the PLI metric in the alpha band within the default mode network (DMN). The functional connectivity between the posterior cingulate cortex and parahippocampal regions increased following SM, t (23) = 10.45, p = 0.005. No significant changes occurred following the sham control procedure. These findings suggest that SM may alter functional connectivity in the brain of stroke patients and highlights the potential of EEG for monitoring neuroplastic changes following SM. Furthermore, the altered connectivity was observed between areas which may be affected by factors such as decreased pain perception, episodic memory, navigation, and space representation in the brain. However, these factors were not directly monitored in this study. Therefore, further research is needed to elucidate the underlying mechanisms and clinical significance of the observed changes.

Defective corticomuscular connectivity during walking in patients with Parkinson's disease

Gait disturbances are common in individuals with Parkinson's disease (PD). Although the basic patterns of walking are thought to be controlled by the brainstem and spinal networks, recent studies have found significant corticomuscular coherence in healthy individuals during walking. However, it still remains unknown how PD affects the cortical control of muscles during walking. As PD typically develops in older adults, it is important to investigate the effects of both aging and PD when examining disorders in patients with PD. Here, we assessed the effects of PD and aging on corticomuscular communication during walking by investigating corticomuscular coherence. We recorded electroencephalographic and electromyographic signals in 10 individuals with PD, 9 healthy older individuals, and 15 healthy young individuals. We assessed the corticomuscular coherence between the motor cortex and two lower leg muscles, tibialis anterior (TA) and medial gastrocnemius, during walking. Older and young groups showed sharp peaks in muscle activation patterns at specific gait phases, whereas the PD group showed prolonged patterns. Smaller corticomuscular coherence was found in the PD group compared with the healthy older group in the α band (8-12 Hz) for both muscles, and in the β band (16-32 Hz) for TA. Older and young groups did not differ in the magnitude of corticomuscular coherence. Our results indicated that PD decreased the corticomuscular coherence during walking, whereas it was not affected by aging. This lower corticomuscular coherence in PD may indicate lower-than-normal corticomuscular communication, although direct or indirect communication is unknown, and may cause impaired muscle control during walking.NEW & NOTEWORTHY Mechanisms behind how Parkinson's disease (PD) affects cortical control of muscles during walking remain unclear. As PD typically develops in the elderly, investigation of aging effects is important to examine deficits regarding PD. Here, we demonstrated that PD causes weak corticomuscular synchronization during walking, but aging does not. This lower-than-normal corticomuscular communication may cause impaired muscle control during walking.