Frequency-Specific Reorganization of Brain Networks during Visuomotor Tasks

Research Background

Executing movements is a complex cognitive function that relies on the coordinated activation of spatially proximal and distal brain regions. Visuomotor integration tasks require processing and interpreting visual inputs to plan motor execution and adjust human movements for interacting with the environment. Functional magnetic resonance imaging (fMRI) studies have shown that frontal and parietal regions play a crucial role in visuomotor integration processes. Additionally, the sensorimotor cortex is also involved. However, existing research has primarily explored these processes using fMRI techniques, with relatively fewer studies employing electroencephalography (EEG) signals.

In numerous studies, functional connectivity analyses have elucidated statistical dependencies between different brain regions and investigated how they interact and communicate under different conditions. Some studies have explored brain connectivity in the gamma band using magnetoencephalography (MEG) and intracranial EEG, revealing dynamic brain involvement during visuomotor processes. Moreover, EEG-based research has confirmed the role of frontoparietal areas in visuomotor processes and that their activity and interactions are frequency-dependent.

Furthermore, handedness differences have been observed in right-handed individuals, where brain activation patterns during simple motor tasks like finger tapping, finger-thumb opposition, and finger extension differ between right-handed and non-right-handed individuals. However, these studies primarily involved simple motor tasks without processing visual inputs. In EEG research, the focus has been on investigating brain signal differences between left and right-hand activities. Therefore, the brain activity associated with left and right-hand visuomotor integration tasks requires further exploration. This study aims to fill this gap by employing the Nine-Hole Peg Test (NHPT), a complex visuomotor integration task.

Research Source

This research paper was written by Alessandra Calcagno, Stefania Coelli, Martina Corda, Federico Temporiti, Roberto Gatti, Manuela Galli, and Anna Maria Bianchi, affiliated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano, the Physiotherapy Unit at IRCCS Humanitas Research Hospital, and the Department of Biomedical Sciences at Humanitas University. The paper was published in 2023 in the IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Research Methodology

Experimental Setup

Participants

The study recruited 44 healthy right-handed volunteers (23 males and 21 females, aged between 18 and 30 years) without any musculoskeletal or neurological functional impairments. The study was approved by the Ethics Committee of the Humanitas Clinical and Research Center (n. CLF20/08, July 2020) and conducted at the Motion and Posture Analysis Laboratory and the B3Lab at Politecnico di Milano. All volunteers provided informed consent before the experiment, and their privacy was ensured through data anonymization.

Data Acquisition Protocol

The experimental protocol included the following stages: First, EEG data were acquired during one minute of eyes-closed resting and one minute of eyes-open resting. Subsequently, the participants performed the NHPT task twice with their right hand (RH) and twice with their left hand (LH). Data collection focused on the eyes-open resting stage (baseline, BL) and the EEG signals during RH and LH NHPT execution. EEG signals were recorded using a 64-electrode system (SD LTM 64 Express System) from Micromed at a sampling rate of 1024 Hz, with channel placement following the international 10-20 system, resulting in 55 usable channels.

EEG Preprocessing

EEG preprocessing was performed using the EEGLAB MATLAB toolbox. First, the raw EEG signals were bandpass filtered between 1-45 Hz and downsampled to 256 Hz. Next, noisy channels were manually identified and removed, and artifactual sources were identified and removed through Independent Component Analysis (ICA) with the support of the ICLabel plugin. The final preprocessing steps included channel re-interpolation and re-referencing to the common average reference.

Spectral Analysis

The power spectral density (PSD) of the EEG signals was estimated using Welch’s method, applying a 1-second window with 50% overlap and tapering with a window function. Within each window, the signals were normalized to zero mean and unit variance through a normalization process. Then, the power changes were computed with respect to the 20-second eyes-open baseline (BL) condition:

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The study focused on the ϑ (4-8 Hz), µ (7-13 Hz), and ß (14-24 Hz) frequency bands. Specific ranges were selected for each band to avoid overlap with the µ band.

Connectivity Analysis

Functional connectivity between EEG signal pairs was investigated using the Phase Slope Index (PSI). PSI is calculated based on the phase difference between two time series. The resulting PSI connectivity matrix is antisymmetric, providing directional information.

Graph Theory Analysis

Graph theory analysis was employed to describe the topological structure of the networks, including Global Efficiency (GE), Modularity (Q), and Degree (D). Global Efficiency and Degree are measures of network integration, while Modularity is a measure of network segregation. Graph theory analysis was applied to three known Functional Networks (FNs): the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Attention Network (AN).

Results and Discussion

Spectral Analysis Results

In the ϑ band, the increased frontal-parietal ϑ power and the desynchronization in the µ and ß bands were consistent during right and left-hand movements. These spectral features are in line with findings relating ϑ rhythm increases to motor control, especially motor planning, spatial memory processing, and motor learning. However, these spectral features alone were insufficient to distinctly differentiate the brain mechanisms associated with left and right-hand movements.

Connectivity Analysis Results

The empirical analysis of Simplified Networks (SIMNs) revealed a significant increase in the number of connections in the ϑ band during right-hand movement, while both the µ and ß band networks significantly decreased during movement execution. These findings are consistent with the motor-related desynchronization phenomena in the µ and ß bands. Additionally, quantitative graph theory analyses of functional connectivity revealed changes in network topology across different frequency bands.

Graph Theory Analysis Results

In the µ band, the network during movement execution exhibited fewer connections, lower integration (decreased D and GE), and higher segregation (increased Q). These changes were particularly pronounced during right-hand movement, while in the Attention Network, µ modulation was similar during task execution with both hands. These results support the desynchronization phenomenon of the µ band network during movement execution.

In the ß band, a positive correlation was found between the integrative properties of the global ß rhythm network (increased GE) and better task performance (shorter execution times) during both right and left-hand task execution. Similar findings were observed in the ϑ band, suggesting that effective communication among distributed brain regions supports visuomotor function.

Limitations and Future Work

The study acknowledges methodological considerations, such as potential volume conduction (VC) issues and the robustness of brain connectivity estimation. Additionally, similar studies in left-handed individuals and patients with neurological disorders are necessary to provide a broader physiological and methodological understanding. This will further validate the effectiveness of the research methods and identify potential biomarkers for assessing the behavioral effects of neurorehabilitation techniques on neurological and functional impairments.

Conclusion

This study revealed frequency-specific modulations of brain network reorganization associated with visuomotor integration processes, exhibiting different network organization patterns across different frequency bands, functional network types, and laterality conditions. Network measures in the global network, as well as in the µ and ß bands, correlated with task performance, providing insights into brain network changes in right-handed volunteers during visuomotor tasks. Furthermore, these brain network descriptors could provide new insights for motor rehabilitation and training strategies in the future.