Changes in network connectivity during motor imagery and execution

Abstract

Background: Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.

Method: Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.

Results: Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.

Conclusions: These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.

Fig 1. Motor task block components.

The starting finger was indicated with a red dot. The experimental paradigm was divided into two sessions of motor execution and imagery. Each session was performed individually. A white dot was presented in the middle of the monitor every 1300 ms as a signal to progress to the next finger, both for motor execution and imagery. Each block was composed of at least three trials. Subjects pressed the appropriate button at the end of the task block as directed.

Fig 2. Regions of interest (ROIs) and connectivity models constructed by anatomical and structural imaging and computational modeling.

(A) The regions of interest (ROIs) were PMC, SMA, DLPFC, and M1. (B) Connectivity models were constructed from anatomical and structural imaging and computational modeling. An extrinsic input through the posterior parietal cortex (PPC) entered the PMC or SMA, which was connected to M1, SMA, and DLPFC. PMC (+), Premotor Cortex; SMA (X), Supplementary Motor Area; M1 (O), Primary Motor Cortex; DLPFC (◈), Dorsolateral Prefrontal Cortex.

Fig 3. Event-related spectral perturbation over the C3 and C4 electrodes and topography for the mu and beta bands in each trial.

(A) Event-related spectral perturbation over the C3 and C4 electrodes during motor execution (ME) and motor imagery (MI), (B) Topography at the mu band in each trial, (C) Topography at beta bands in each trial.

Fig 4. Family-level analysis and Bayesian model selection (BMS).

(A) Correct responses during motor execution (ME), (B) Incorrect response during ME, (C) Correct responses during motor imagery (MI), and (D) Incorrect responses during MI.

Fig 5. DCM coupling strength based on modulatory connectivity (DCM-B matrix).

(A) Correct responses during motor execution (ME), (B) Incorrect response during ME, (C) Correct responses during motor imagery (MI), (D) Incorrect responses during MI, (E) Higher coupling strength during ME compared with correct MI, (F) Higher coupling strength for MI compared with correct ME, (G) Connectivity characteristic of correct MI responses compared with incorrect MI, and (H) Connectivity characteristics of incorrect MI responses compared with correct MI.