From pulse width modulated TENS to cortical modulation: based on EEG functional connectivity analysis

Abstract

Modulation in the temporal pattern of transcutaneous electrical nerve stimulation (TENS), such as Pulse width modulated (PWM), has been considered a new dimension in pain and neurorehabilitation therapy. Recently, the potentials of PWM TENS have been studied on sensory profiles and corticospinal activity. However, the underlying mechanism of PWM TENS on cortical network which might lead to pain alleviation is not yet investigated. Therefore, we recorded cortical activity using electroencephalography (EEG) from 12 healthy subjects and assessed the alternation of the functional connectivity at the cortex level up to an hour following the PWM TENS and compared that with the effect of conventional TENS. The connectivity between eight brain regions involved in sensory and pain processing was calculated based on phase lag index and spearman correlation. The alteration in segregation and integration of information in the network were investigated using graph theory. The proposed analysis discovered several statistically significant network changes between PWM TENS and conventional TENS, such as increased local strength and efficiency of the network in high gamma-band in primary and secondary somatosensory sources one hour following stimulation. Our findings regarding the long-lasting desired effects of PWM TENS support its potential as a therapeutic intervention in clinical research.

Keywords: functional brain connectivity; graph theory; modulated TENS; neurorehabilitation; pain.

Figure 1

Experimental and data processing outline. (A) The experimental procedure consisted of three EEG recording phases at baseline (Pre), immediately and 60 min after TENS (Post and Post60, respectively). (B) Data processing steps. For each Subject, pre-processed EEG signal was divided into 2 s epochs, and LORETA was implemented to localize the sources of activity. Eight pain-related regions were selected, and functional connectivity between all pairs of selected ROIs was quantified based on phase and amplitude synchronization in seven frequency bands. Eventually, the cortical network consisted of ROIs as nodes and the mean of PLV or Cor values across epochs defined edges.

Figure 2

The normalized PLV-based cortical networks in high gamma within three time phases (Pre, Post, and Post60) for HF TENS pattern (first row) and PWM TENS (second row). The size of each node indicates the nodal strength, and the connection between two nodes represents the mean PLV value across subjects normalized to the maximum connection weight. The edges with a value below 0.3 of the overall maximum value were excluded.

Figure 3

Local characteristics of functional brain network extracted from PLVFC (top row) and CorFC (bottom row) in high gamma-band. Each radar plot represents changes in local indexes by interventions (PWM: orange area and HF: blue area) compared to Pre value (Post – Pre and Post60 – Pre) for each ROIs. Brain regions with significantly different induced changes by two interventions are depicted by *.

Figure 4

Global characteristics of functional brain network extracted from PLVFC (first row) and CorFC (Second row). Each radar plot illustrates changes in global indexes by interventions (PWM: orange area and HF: blue area) compared to Pre value (Post – Pre and Post60 – Pre) within all frequency bands. Alteration in high gamma global metrics was detected significantly different by two interventions and depicted by *.