Directed connectivity between primary and premotor areas underlying ankle force control in young and older adults

Cerebral hemodynamics underlying ankle force sense modulated by high-definition transcranial direct current stimulation

This study aimed to investigate the effects of high-definition transcranial direct current stimulation on ankle force sense and underlying cerebral hemodynamics. Sixteen healthy adults (8 males and 8 females) were recruited in the study. Each participant received either real or sham high-definition transcranial direct current stimulation interventions in a randomly assigned order on 2 visits. An isokinetic dynamometer was used to assess the force sense of the dominant ankle; while the functional near-infrared spectroscopy was employed to monitor the hemodynamics of the sensorimotor cortex. Two-way analyses of variance with repeated measures and Pearson correlation analyses were performed. The results showed that the absolute error and root mean square error of ankle force sense dropped more after real stimulation than after sham stimulation (dropped by 23.4% vs. 14.9% for absolute error, and 20.0% vs. 10.2% for root mean square error). The supplementary motor area activation significantly increased after real high-definition transcranial direct current stimulation. The decrease in interhemispheric functional connectivity within the Brodmann's areas 6 was significantly correlated with ankle force sense improvement after real high-definition transcranial direct current stimulation. In conclusion, high-definition transcranial direct current stimulation can be used as a potential intervention for improving ankle force sense. Changes in cerebral hemodynamics could be one of the explanations for the energetic effect of high-definition transcranial direct current stimulation.

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity

This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1-51 Hz frequency range. In the preferred 8-30 Hz frequency band, known for motor imagery data classification (including α, μ, and β waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, μ, and β, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8-30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.

Cortico-muscular connectivity is modulated by passive and active Lokomat-assisted Gait

The effects of robotic-assisted gait (RAG) training, besides conventional therapy, on neuroplasticity mechanisms and cortical integration in locomotion are still uncertain. To advance our knowledge on the matter, we determined the involvement of motor cortical areas in the control of muscle activity in healthy subjects, during RAG with Lokomat, both with maximal guidance force (100 GF-passive RAG) and without guidance force (0 GF-active RAG) as customary in rehabilitation treatments. We applied a novel cortico-muscular connectivity estimation procedure, based on Partial Directed Coherence, to jointly study source localized EEG and EMG activity during rest (standing) and active/passive RAG. We found greater cortico-cortical connectivity, with higher path length and tendency toward segregation during rest than in both RAG conditions, for all frequency bands except for delta. We also found higher cortico-muscular connectivity in distal muscles during swing (0 GF), and stance (100 GF), highlighting the importance of direct supraspinal control to maintain balance, even when gait is supported by a robotic exoskeleton. Source-localized connectivity shows that this control is driven mainly by the parietal and frontal lobes. The involvement of many cortical areas also in passive RAG (100 GF) justifies the use of the 100 GF RAG training for neurorehabilitation, with the aim of enhancing cortical-muscle connections and driving neural plasticity in neurological patients.

Effects of tDCS on Foot Biomechanics: A Narrative Review and Clinical Applications

In recent years, neuro-biomechanical enhancement techniques, such as transcranial direct current stimulation (tDCS), have been widely used to improve human physical performance, including foot biomechanical characteristics. This review aims to summarize research on the effects of tDCS on foot biomechanics and its clinical applications, and further analyze the underlying ergogenic mechanisms of tDCS. This review was performed for relevant papers until July 2023 in the following databases: Web of Science, PubMed, and EBSCO. The findings demonstrated that tDCS can improve foot biomechanical characteristics in healthy adults, including proprioception, muscle strength, reaction time, and joint range of motion. Additionally, tDCS can be effectively applied in the field of foot sports medicine; in particular, it can be combined with functional training to effectively improve foot biomechanical performance in individuals with chronic ankle instability (CAI). The possible mechanism is that tDCS may excite specific task-related neurons and regulate multiple neurons within the system, ultimately affecting foot biomechanical characteristics. However, the efficacy of tDCS applied to rehabilitate common musculoskeletal injuries (e.g., CAI and plantar fasciitis) still needs to be confirmed using a larger sample size. Future research should use multimodal neuroimaging technology to explore the intrinsic ergogenic mechanism of tDCS.

Ventral premotor cortex encodes task relevant features during eye and head movements

Visual exploration of the environment is achieved through gaze shifts or coordinated movements of the eyes and the head. The kinematics and contributions of each component can be decoupled to fit the context of the required behavior, such as redirecting the visual axis without moving the head or rotating the head without changing the line of sight. A neural controller of these effectors, therefore, must show code relating to multiple muscle groups, and it must also differentiate its code based on context. In this study we tested whether the ventral premotor cortex (PMv) in monkey exhibits a population code relating to various features of eye and head movements. We constructed three different behavioral tasks or contexts, each with four variables to explore whether PMv modulates its activity in accordance with these factors. We found that task related population code in PMv differentiates between all task related features and conclude that PMv carries information about task relevant features during eye and head movements. Furthermore, this code represents both lower-level (effector and movement direction) and higher-level (context) information.

Odor valence modulates cortico-cortical interactions: a preliminary study using DCM for EEG

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity.

Increased prefrontal top-down control in older adults predicts motor performance and age-group association

Bimanual motor control declines during ageing, affecting the ability of older adults to maintain independence. An important underlying factor is cortical atrophy, particularly affecting frontal and parietal areas in older adults. As these regions and their interplay are highly involved in bimanual motor preparation, we investigated age-related connectivity changes between prefrontal and premotor areas of young and older adults during the preparatory phase of complex bimanual movements using high-density electroencephalography. Generative modelling showed that excitatory inter-hemispheric prefrontal to premotor coupling in older adults predicted age-group affiliation and was associated with poor motor-performance. In contrast, excitatory intra-hemispheric prefrontal to premotor coupling enabled older adults to maintain motor-performance at the cost of lower movement speed. Our results disentangle the complex interplay in the prefrontal-premotor network during movement preparation underlying reduced bimanual control and the well-known speed-accuracy trade-off seen in older adults.

Cortical signatures of precision grip force control in children, adolescents, and adults

Human dexterous motor control improves from childhood to adulthood, but little is known about the changes in cortico-cortical communication that support such ontogenetic refinement of motor skills. To investigate age-related differences in connectivity between cortical regions involved in dexterous control, we analyzed electroencephalographic data from 88 individuals (range 8-30 years) performing a visually guided precision grip task using dynamic causal modelling and parametric empirical Bayes. Our results demonstrate that bidirectional coupling in a canonical 'grasping network' is associated with precision grip performance across age groups. We further demonstrate greater backward coupling from higher-order to lower-order sensorimotor regions from late adolescence in addition to differential associations between connectivity strength in a premotor-prefrontal network and motor performance for different age groups. We interpret these findings as reflecting greater use of top-down and executive control processes with development. These results expand our understanding of the cortical mechanisms that support dexterous abilities through development.

Effect of muscle fatigue on brain activity in healthy individuals

Fatigue is affected by both peripheral and central factors. However, the interrelationship between muscle fatigue and brain activity has not yet been clarified. This study aimed to clarify the effect of muscle fatigue due to sustained pinch movement on brain activity in healthy individuals using functional near-infrared spectroscopy (fNIRS). Ten healthy adults participated in the study. Pinch movement of isometric contraction was the task to be performed, and electromyogram of the first dorsal interosseous muscle and brain activity by fNIRS were measured in this period. The median power frequency (MdPF) was calculated as an index of muscle fatigue and the oxygen-Hb value in the bilateral premotor and motor areas was calculated as an index of brain activity. As a result, MdPF showed a significant decrease in the middle and later phases compared with that in the early phase (p < 0.05, p < 0.001, respectively) and a significant decrease in the later phase compared with that in the middle phase (p < 0.05). The oxygen-Hb values in the motor cortex were not significantly different between the analysis sections. The oxygen-Hb values in the premotor cortex was significantly increased in the later phase (p < 0.05) compared with that in the early phase. The premotor cortex was found to be specifically activated during muscle fatigue.