There is a topographic organization in human cortico-pontine connectivity.

Of the three largest outputs of the cerebral cortex, two have been extensively studied and mapped. Topographic maps of cortico-thalamic and cortico-striatal functional connectivity in humans are well established. However, for the third largest cerebral output, to the pontine nuclei, which connect the cerebrum to the cerebellum, the existence of such an organized connectivity pattern in humans is unknown. Here, using high-resolution functional MRI and a large sample size, we found a topographically organized pattern of functional connectivity between the human cerebral cortex and pons. Our results indicate a rostral-caudal topography; rostral (frontal) cerebral cortex shows connectivity to the rostral pons, and the more caudal cortical areas (i.e. the sensorimotor cortices) show functional connectivity more caudally in the pons, with the occipital lobe connectivity being most caudal. While prefrontal, sensorimotor and occipital cortices have a connectivity to the medial pontine nuclei, posterior parietal cortex and temporal lobe correlate with lateral pontine nuclei. Topography is sufficiently detailed to identify distinct connectivity for leg, trunk, hand and face areas of the motor cortex. These findings reveal the existence of a topographic organization in human cortico-pontine connectivity and provide the topographic map for future studies of cortico-ponto-cerebellum pathway in a variety of disorders.

Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-Frequency Phase Coupling.

BACKGROUND: Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as n : m phase locking value (where two coupling frequencies are linked as: mf 1 = nf 2) and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. f, 2f/3) and across three frequencies (e.g. f 1, f 2, f 1 + f 2), respectively. However, the existing phase coupling metrics have their limitations and limited applications. They cannot be used to detect or quantify phase coupling across multiple frequencies (e.g. f 1, f 2, f 3, f 4, f 1 + f 2 + f 3 - f 4), or coupling that involves non-integer multiples of the frequencies (e.g. f 1, f 2, 2f 1/3 + f 2/3). NEW METHODS: To address the gap, this paper proposes a generalized approach, named multi-phase locking value (M-PLV), for the quantification of various types of instantaneous multi-frequency phase coupling. Different from most instantaneous phase coupling metrics that measure the simultaneous phase coupling, the proposed M-PLV method also allows the detection of delayed phase coupling and the associated time lag between coupled oscillators. RESULTS: The M-PLV has been tested on cases where synthetic coupled signals are generated using white Gaussian signals, and a system comprised of multiple coupled Rössler oscillators, as well as a human subject dataset. Results indicate that the M-PLV can provide a reliable estimation of the time window and frequency combination where the phase coupling is significant, as well as a precise determination of time lag in the case of delayed coupling. This method has the potential to become a powerful new tool for exploring phase coupling in complex nonlinear dynamic systems.

Assessing the Usage of Indirect Motor Pathways Following a Hemiparetic Stroke.

A hallmark impairment in a hemiparetic stroke is a loss of independent joint control resulting in abnormal co-activation of shoulder abductor and elbow flexor muscles in their paretic arm, clinically known as the flexion synergy. The flexion synergy appears while generating shoulder abduction (SABD) torques as lifting the paretic arm. This likely be caused by an increased reliance on contralesional indirect motor pathways following damage to direct corticospinal projections. The assessment of functional connectivity between brain and muscle signals, i.e., brain-muscle connectivity (BMC), may provide insight into such changes to the usage of motor pathways. Our previous model simulation shows that multi-synaptic connections along the indirect motor pathway can generate nonlinear connectivity. We hypothesize that increased usage of indirect motor pathways (as increasing SABD load) will lead to an increase of nonlinear BMC. To test this hypothesis, we measured brain activity, muscle activity from shoulder abductors when stroke participants generate 20% and 40% of maximum SABD torque with their paretic arm. We computed both linear and nonlinear BMC between EEG and EMG. We found dominant nonlinear BMC at contralesional/ipsilateral hemisphere for stroke, whose magnitude increased with the SABD load. These results supported our hypothesis and indicated that nonlinear BMC could provide a quantitative indicator for determining the usage of indirect motor pathways following a hemiparetic stroke.

Assessing Neural Connectivity and Associated Time Delays of Muscle Responses to Continuous Position Perturbations.

Both linear and nonlinear electromyographic (EMG) connectivity has been reported during the expression of stretch reflexes, though it is not clear whether they are generated by the same neural pathways. To answer this question, we aim to distinguish linear and nonlinear connectivity, as well as their delays in muscle responses, resulting from continuous elbow joint perturbations. We recorded EMG from Biceps Brachii muscle when eight able-bodied participants were performing a steady elbow flexion torque while simultaneously receiving a continuous position perturbation. Using a recently developed phase coupling metric, we estimated linear and nonlinear connectivity as well as their associated delays between Biceps EMG responses and perturbations. We found that the time delay for linear connectivity (24.5 ± 5.4 ms) is in the range of short-latency stretch reflex period (< 35 ms), while that for nonlinear connectivity (53.8 ± 3.2 ms) is in the range of long-latency stretch reflex period (40-70 ms). These results suggest that the estimated linear connectivity between EMG and perturbations is very likely generated by the mono-synaptic spinal stretch reflex loop, while the nonlinear connectivity may be associated with multi-synaptic supraspinal stretch reflex loops. As such, this study provides new evidence of the nature of neural connectivity related to the stretch reflex.

Effects of Electrode Configurations and Injected Current Intensity on the Electrical Field of Transcranial Direct Current Stimulation: A Simulation Study.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technology that modulates the excitability of the brain by delivering weak electric currents to the brain via scalp electrodes. Electrode configuration and injected current intensity are two important parameters in the tDCS design. This simulation study examined three commercially available electrode configurations, i.e. conventional low definition rectangular pad, high-definition Disc, and high-definition 4 x 1 with different electrode distances and different injected current intensity. Simulation results show that increasing the injected current intensity of HD-tDCS mainly increases the electrical field strength for all configurations. Both Disc and 4 x 1 high definition tDCS (HD-tDCS) have better focality than the conventional low-definition rectangular pad. Increasing the inter-electrode distance in HD-tDCS enlarges the electrical field strength and the depth of stimulation but reduces the focality. In motor rehabilitation, a trade-off needs to be made in the tDCS design to allow the electrical field reaching the white matter to facilitate the usage of the cortico-spinal tract without influencing other undesirable regions in the brain.

Quantifying Altered Neural Connectivity of the Stretch Reflex in Chronic Hemiparetic Stroke.

Post-stroke flexion synergy limits arm/hand function and is also linked to hyperactive stretch reflexes or spasticity. It is implicated in the increased role of indirect motor pathways following damage to direct corticospinal projections. We hypothesized that this maladaptive neuroplasticity also affects stretch reflexes. Specifically, multi-synaptic interactions in indirect motor pathways may increase nonlinear neural connectivity and time lag between stretch and reflex muscle response. Continuous position perturbations were applied to the elbow joint when eleven participants with stroke generated two levels of shoulder abduction (SABD) torques with their paretic arm to induce synergy-related spasticity. Likewise, the perturbations were applied to eleven control subjects while performing SABD and elbow flexion levels matching the synergy torques in stroke. We quantified linear and non-linear connectivity and the corresponding time lags between perturbations and muscle activity. Enhanced nonlinear connectivity with a prolonged time lag was found in stroke as compared to controls. Non-linear connectivity and time lag also increased with the expression of the flexion synergy, as induced by greater SABD load levels, in stroke. This study provides new evidence of changes in neural connectivity and long-latency time lag in the stretch reflex response post-stroke. The results suggest the contribution of indirect motor pathways to synergy-related spasticity.

A Novel Approach for Modeling Neural Responses to Joint Perturbations Using the NARMAX Method and a Hierarchical Neural Network.

The human nervous system is an ensemble of connected neuronal networks. Modeling and system identification of the human nervous system helps us understand how the brain processes sensory input and controls responses at the systems level. This study aims to propose an advanced approach based on a hierarchical neural network and non-linear system identification method to model neural activity in the nervous system in response to an external somatosensory input. The proposed approach incorporates basic concepts of Non-linear AutoRegressive Moving Average Model with eXogenous input (NARMAX) and neural network to acknowledge non-linear closed-loop neural interactions. Different from the commonly used polynomial NARMAX method, the proposed approach replaced the polynomial non-linear terms with a hierarchical neural network. The hierarchical neural network is built based on known neuroanatomical connections and corresponding transmission delays in neural pathways. The proposed method is applied to an experimental dataset, where cortical activities from ten young able-bodied individuals are extracted from electroencephalographic signals while applying mechanical perturbations to their wrist joint. The results yielded by the proposed method were compared with those obtained by the polynomial NARMAX and Volterra methods, evaluated by the variance accounted for (VAF). Both the proposed and polynomial NARMAX methods yielded much better modeling results than the Volterra model. Furthermore, the proposed method modeled cortical responded with a mean VAF of 69.35% for a three-step ahead prediction, which is significantly better than the VAF from a polynomial NARMAX model (mean VAF 47.09%). This study provides a novel approach for precise modeling of cortical responses to sensory input. The results indicate that the incorporation of knowledge of neuroanatomical connections in building a realistic model greatly improves the performance of system identification of the human nervous system.

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study.

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.