BRAIN CONNECTIVITY Volume 4, Number 3, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/brain.2013.0196

ORIGINAL ARTICLES

Modulation of Resting State Functional Connectivity of the Motor Network by Transcranial Pulsed Current Stimulation Chandler Sours,1–3 Gad Alon,4 Steve Roys,1,2 and Rao P. Gullapalli1–3

Abstract

The effects of transcranial pulsed current stimulation (tPCS) on resting state functional connectivity (rs-FC) within the motor network were investigated. Eleven healthy participants received one magnetic resonance imaging (MRI) session with three resting state functional MRI (rs-fMRI) scans, one before stimulation (PRE-STIM) to collect baseline measures, one during stimulation (STIM), and one after 13 min of stimulation (POST-STIM). RsFC measures during the STIM and POST-STIM conditions were compared to the PRE-STIM baseline. Regions of interest for the rs-FC analysis were extracted from the significantly activated clusters obtained during a finger tapping motor paradigm and included the right primary motor cortex (R M1), left primary motor cortex (L M1), supplemental motor area (SMA), and cerebellum (Cer). The main findings were reduced rs-FC between the left M1 and surrounding motor cortex, and increased rs-FC between the left M1 and left thalamus during stimulation, but increased rs-FC between the Cer and right insula after stimulations. Bivariate measures of connectivity demonstrate reduced strength of connectivity for the whole network average ( p = 0.044) and reduced diversity of connectivity for the network average during stimulation ( p = 0.024). During the POST-STIM condition, the trend of reduced diversity for the network average was statistically weaker ( p = 0.071). In conclusion, while many of the findings are comparable to previous reports using simultaneous transcranial direct current stimulation (tDCS) and fMRI acquisition, we also demonstrate additional changes in connectivity patterns that are induced by tPCS. Key words: electrical stimulation of the brain; functional connectivity; motor network; resting state fMRI; transcranial pulsed current stimulation

Introduction

R

ecent research using noninvasive electrical stimulation of the brain (ESB), such as transcranial direct current stimulation (tDCS) or transcranial pulsed current stimulation (tPCS), provides evidence that these electrical stimulations options are able to alter cortical excitability. tDCS and tPCS are safe to apply in a variety of medical conditions and favorable therapeutic outcomes have been reported in diverse patient groups including stroke (for a review of current literature see Gomez Palacio Schjetnan et al., 2013), Parkinson’s disease (Boggio et al., 2006), patients with chronic pain (Dasilva et al., 2012), clinical depression (Brunoni et al., 2012), and children with cerebral palsy (Alon et al., 1998). Evidence suggests that tDCS over the motor cortex is able to improve somatic impairments related to motor deficits and enhance acquisition and retention of

learned motor behaviors (Galea and Celnik, 2009; Lindenberg et al., 2013). In addition, tDCS of the dorsolateral prefrontal cortex (DLPFC) has shown promise in improving cognitive performance in healthy population ( Jeon and Han, 2012; Kuo and Nitsche, 2012) and in patients with Parkinson’s disease (Fregni et al., 2006). While the therapeutic potential of noninvasive ESB is being actively explored by many groups (e.g., da Silva et al., 2013; Lindenberg et al., 2012; Pirulli et al., 2013; Tseng et al., 2012; Zimerman et al., 2012), there is still an ongoing exploration and considerable uncertainty regarding the physiological basis of these electrical modulators. Further, active work continues to explore and determine the most effective stimulation parameters for a desired outcome (Benninger et al., 2010). The precise mechanism of action is still largely undetermined but it is generally accepted that non-invasive ESB exerts its therapeutic effects through altering cortical and

1

Magnetic Resonance Research Center, University of Maryland School of Medicine, Baltimore, Maryland. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland. Program in Neuroscience, University of Maryland School of Medicine, Baltimore, Maryland. 4 Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, Maryland. 2 3

157

158

subcortical neuronal network excitability. Further, there is robust evidence that anodal stimulation (positive electrode) increases neuronal excitability while cathodal stimulation (negative electrode) reduced neuronal excitability (Sehm et al., 2013; Stagg and Nitsche, 2011). An additional impediment to better understand the specific mechanism of action is the reality that the output properties of the commercially available stimulators in the USA and Europe are quite varied. Two broad classes of stimulators use either a direct current (referred to as tDCS) or a pulsed current (referred to as tPCS). tDCS supplies a direct nonmodulated current that is applied at a predetermined continuous current amplitude (typically 1 or 2 mA) throughout the entire stimulation session. In contrast, tPCS supplies a modulated current consisting of either unidirectional or bidirectional pulses each lasting a few microseconds of different frequencies. tPCS is often used for peripheral nerve stimulation (Zaghi et al., 2010), but has also been shown safe for transcranial, cortical stimulation (Alon et al., 1998, 2012). tPCS may even have an advantage over tDCS due to the inverse relationship between frequency and impedance. In a recently concluded study we reported that the group mean voltage/current ratio during tDCS was 6789 and 4229 O during tPCS (Alon et al., 2011) implying that tPCS may offer less opposition to current flow than tDCS. Moreover, because of the very short duration pulses in tPCS, the pulse charge (calculated as product of pulse duration and current amplitude) needed to cause neural excitation is considerably lower than that of tDCS (Mogyoros et al., 1996). To further investigate the neuromodulation effects of ESB, a number of research teams have taken advantage of recent developments within the field of magnetic resonance imaging (MRI) to characterize changes in neuronal excitability induced by these noninvasive approaches. MRI has been used to detect changes in cortical excitability during stimulation within the MRI scanner and to characterize more long-lasting changes that occur following stimulation (Alon et al., 2011; Antal et al., 2011; Lindenberg et al., 2013; Polania et al., 2012; Sehm et al., 2012, 2013). Two common techniques that have been used include the arterial spin labeling (ASL) technique to measure regional cerebral blood flow (CBF) (Detre et al., 1994) and resting state functional MRI (rs-fMRI) to measure functional connectivity (van den Heuvel and Hulshoff Pol, 2010) during and after stimulation. Using the ASL technique (Detre et al., 1994), an increase in regional CBF during anodal stimulation of the right primary motor cortex (R M1) was demonstrated (Zheng et al., 2011). This increase in CBF was also noted following cessation of the stimulation implying persistent, post-stimulation alterations to cortical perfusion following stimulation. On the other hand, groups have investigated alterations in resting state functional connectivity (rs-FC) following ESB. Functional connectivity can be defined as ‘‘the temporal correlation of a neurophysiological index measured in different brain areas’’ (Friston et al., 1993). While this method does not directly address structural connections within the brain it does have great promise in addressing the questions regarding connectivity between spatially disparate regions and their interactions. Early work in this field by Biswal and colleagues (1995) demonstrated that the resting state temporal fluctuations across the motor network had similar spatial organization to networks found in fMRI activation

SOURS ET AL.

studies (Biswal et al., 1995). This work has paved the way for numerous studies using rs-fMRI to address questions regarding efficiency and strength of numerous large-scale neuronal networks in the absence of a task and how these networks are altered by various pathology. For example, groups using rs-fMRI to measure functional connectivity (van den Heuvel and Hulshoff Pol, 2010) as an outcome measure have noted that anodal tDCS of the DLPFC altered the rsFC within the default mode network and task positive network (Feusner et al., 2012; Keeser et al., 2011). Further, researchers noted that anodal tDCS of the right DLPFC increases rs-FC with the contralateral DLPFC but concurrently reduces short-range rs-FC in regions close to the anode following stimulation (Park et al., 2013). In addition, anodal tDCS of the primary motor cortex alters connectivity within motor network and inter-hemispheric functional connectivity (IHFC) of the DLPFC directly following stimulation (Polania et al., 2012). We along with others have reported altered rs-FC within the motor network during anodal tDCS (Alon et al., 2011; Lindenberg et al., 2013; Sehm et al., 2012, 2013). One consistent finding across these studies includes reduced IHFC during tDCS stimulation. The main focus of this line of research has been to investigate the between hemisphere neuronal changes within the two primary motor (M1) cortices induced by tDCS. However, with the exception of Alon and colleagues (2011), there has been limited research in regard to the cortical excitability changes within the motor system induced by tPCS. In this study, we aim to further expand upon our previous results by exploring the effects of unilateral anodal tPCS applied over the right primary motor cortex on rs-FC within the motor network including bilateral M1, supplemental motor area (SMA), and cerebellum (Cer) both during and immediately after stimulation to assess whether the effects of the tPCS stimulation lasts after the stimulation has been removed, as has been reported for tDCS. Materials and Methods Subjects

Eleven healthy volunteers participated in the stimulation study (age: 29.8 – 9.1 years; 6 male: 5 female). An additional control population included 11 healthy volunteers (age: 37.5 – 14.6 years; 5 male: 6 female) who participated in two rs-fMRI scans separated by 6 months. All participants were over the age of 18 and were screened to rule out any neurological, musculoskeletal, or psychiatric conditions, and contraindications to MRI. The study was approved by the University of Maryland, Baltimore, Institutional Review Board and written informed consent was obtained from all participants. tPCS protocol

All data analyzed were collected in a single MRI session for each participant. During the MRI, a thin thermoplastic molded cap that included two 7 · 4.5 cm carbon-silicon flexible electrodes was placed over participant’s head and held in place with a strap under the chin. The area of each electrode was 31.5 cm2. The two electrodes were saturated with water to ensure maximum conductivity and were positioned on the

MODULATION OF RESTING MOTOR CONNECTIVITY USING TPCS

scalp with the positive electrode (anode) over the R M1 and the negative electrode (cathode) over the left supraorbital area (Dasilva et al., 2012). Custom-made connectors were prepared using shielded conductive leads that were passed on to the scanner room through a penetration panel using appropriate radio frequency filters and connected to the tPCS stimulator/controller at the MRI control room. The tPCS stimulator (Fisher Wallace model FW 100Ctm, New York, NY) delivered a monophasic waveform with a pulse duration of 33.3 ls and an interpulse interval of 33.3 ls with a carrier frequency of 15 kHz. The stimulus train had 1 msec inter-burst intervals, decreasing the number of pulses per second to 7500 pulses which were ON for 50 msec resulting in 375 pulses/50 msec. Delivering 15 bursts of pulses per second resulted in effective frequency of 5625 pulses per second (15 · 375). Magnetic resonance imaging MRI protocol. All imaging was performed on a Siemens Tim-Trio 3T MRI scanner using a 12-channel receive only head coil. A high resolution T1-weighted-MPRAGE (TE = 3.44 msec, TR = 2250 msec, TI = 900 msec, flip angle = 9, resolution = 256 · 256 · 96, FOV = 22 cm, sl. thick. = 1.5 mm) was acquired for anatomic reference. Participants then performed a bilateral finger tapping task in the MRI prior to tPCS stimulation. The motor task was presented using EPRIME 2.0 software (Psychology Software Tools, Pittsburgh, PA) and projected onto a screen displayed in the scanner. During the motor paradigm, participants were instructed to perform self-paced bilateral finger tapping during a block design that consisted of 24 sec finger tapping and 24 sec rest for a total of eight cycles. In addition, three functional rs-fMRI scans were obtained on each participant. During all resting state scans extraneous auditory and visual stimuli were removed, and the participants were instructed to rest peacefully with eyes closed. One resting state scan was collected prior to tPCS stimulation (PRE-STIM), one was collected during tPCS stimulation (STIM), and the third was collected following tPCS stimulation (POST-STIM). For both resting state and the motor paradigm fMRI scans, T2*-weighted images were acquired using a single-shot EPI sequence (TE = 30 msec, TR = 3000 msec, FOV = 230 mm, resolution = 64 · 64) with 36 axial slices (sl. thick. = 4 mm) for a total acquisition time of 6 min and 24 sec. Stimulation was applied throughout the *6 min and 30 sec resting state scan and for an additional 5 min ( – 1 min). Including transition time between individual scans, this resulted in a total tPCS stimulation of a mean of 13 min ( – 1.5 min) and was applied entirely within the MRI scanner. The final POST-STIM resting state scan was acquired within 6 min following stimulation. For the test-retest control population, each participant received an identical high resolution T1-weighted-MPRAGE (TE = 3.44 msec, TR = 2250 msec, TI = 900 msec, flip angle = 9, resolution = 256 · 256 · 96, FOV = 22 cm, sl. thick. = 1.5 mm) for anatomic reference. Rs-fMRI was obtained at two time points 6 months apart using a T2*-weighted images using a single-shot EPI sequence (TE = 30 msec, TR = 2000 msec, FOV = 220 mm, resolution = 64 · 64) with 36 axial slices (sl. thick. = 4 mm) over 5 min 42 sec.

159

Data analysis Motor fMRI. The motor fMRI was analyzed using FMRIB Software Library (FSL) Edition 4.1. Specifically, fMRI Expert Analysis Tool (FEAT) version 5 was used for analysis of activation patterns during the bilateral finger tapping paradigm. Preprocessing of the fMRI data included removal of the first five volumes of the time series to remove any steady state effects, motion corrections using MCFLIRT, slice timing correction, and intensity normalization. The data were spatially smoothed using a 5-mm FWHM Gaussian kernel. FMRIB’s Linear Registration Tool (FLIRT) was used to register the fMRI data to standard Montreal Neurological Institute (MNI) space using the individual subjects T1MPRAGE. For each participant, the motion parameters and the registration were visually inspected. Participants with inadequate registration or excessive motion ( > 3 mm translation, or > 3 rotation) were excluded from further analysis. A general linear model approach was used to tease out the activations associated with the bilateral finger tapping task. Group effects analysis were performed using FMRIB’s local analysis of mixed effects (FLAME). The contrast was thresholded at Z > 2.3 and cluster significance threshold of p < 0.001. A high threshold for activations was used as these activation regions were further used as seed points for the analysis of resting state data. Resting state fMRI. Processing of the rs-fMRI data was identical for the stimulation population and the test-retest control population. Preprocessing of the rs-fMRI data was performed using SPM 8 (www.fil.ion.ucl.ac.uk/spm) where the preprocessing steps included motion correction of the time series, slice timing correction, band pass filtering (0.009 Hz < f < 0.08 Hz), and registration of all the 171 volumes to the first volume of the time series. The resting state series were then registered to the individual’s T1MPRAGE images and spatially normalized to standard space using the MNI template available within SPM 8. Spatial blurring was then applied to the resting state data using a 5-mm Gaussian kernel. Individual T1-MPRAGE images in MNI space were segmented into white matter (WM), gray matter, and cerebral spinal fluid (CSF) using SPM8 default settings. The masks of the CSF and WM were used to extract the time series of these two components from the entire brain and used in later analysis to account for time series variance related to non-neuronal contributions. The CONN-fMRI Functional Connectivity toolbox v13.h (www.nitric.org/projects/conn) was used to process the rsfMRI data using a seed-based method of analysis. Seed regions were selected based on the group activation patterns from the motor paradigm and included regions of interest (ROIs) in the R M1, left primary motor cortex (L M1), SMA, and Cer. For each ROI, the mean BOLD time series was extracted and correlated with the time series of each voxel of the entire brain. The mean BOLD time series from the WM mask, CSF mask, and the six motion correction parameters were included in the model as regressors to remove the variance related to non-neuronal contributions and motion. Within group rs-FC maps for the four motor ROIs (R M1, L M1, SMA, and Cer) were created using the CONN fMRI toolbox in SPM8. For rs-FC maps, correlations were converted to standardized z-score prior to further

160

analysis. Positive rs-FC maps were thresholded at voxel-wise p-value of 0.001 (uncorrected) and cluster extent threshold of p-value of 0.05 using a family wise error correction for multiple comparisons. Between group rs-FC maps for each ROI were made for the contrast of PRE-STIM versus STIM and PRE-STIM versus POST-STIM. Between group rs-FC were thresholded at voxel-wise p-value of 0.005 (uncorrected) and cluster extent threshold of p-value of 0.05 using a family wise error correction for multiple comparisons. In addition, we opted to assess the functional interactions between the four motor ROIs using a bivariate approach. First, we created a 4 · 4 connectivity matrix. For each within group rs-FC the map (R M1, L M1, SMA, and Cer), the average z-score of all voxels within the other three motor ROIs was extracted using the Rex toolbox within SPM. Our first line of investigation was to compare the specific pairwise interactions between the nodes within our motor network across our three conditions. Therefore, we opted to perform univariate analysis using paired t-tests to compare changes in pairwise connections between the STIM or POST-STIM condition and the baseline PRE-STIM condition. Since we were particularly interested in how each node in the motor network was communicating with the rest of the network, not just how pairs of nodes were interacting, we calculated two bivariate measures of functional connectivity; strength and diversity (Lynall et al., 2010). The strength of node i is defined as the mean value of ith column of the connectivity matrix. The diversity of node i is defined as the variance of the ith column of the connectivity matrix (Lynall et al., 2010). The strength is a measure of the average connectivity of an ROI with the rest of the ROIs in the network while the diversity of a region is a measure of the variability in the strength of the correlation between a single region compared to the other regions in the network. In addition, a laterality index (LI) was calculated for the strength and diversity of the R M1 and L M1. For each of the four regions and a network average, the strength and diversity was compared between the PRE-STIM and STIM conditions and between the PRE-STIM and POST-STIM conditions using paired t-tests. Since the four ROIs we selected are both highly coactivated (as evidenced by our motor task) and show high correlation during resting conditions, in a final exploratory analysis, we repeated our initial ROI analysis using semipartial correlations (Liao et al., 2013). The goal of this analysis was to measure the unique contribution between each pair of nodes controlling the contributions of the other two nodes in the selected network. Once again for each of the four regions and a network average, the strength and diversity was compared between the PRE-STIM and STIM conditions and between the PRE-STIM and POST-STIM conditions using paired t-tests.

SOURS ET AL.

Table 1. Location of Activated Clusters from Motor Paradigm Region

Number voxels

R M1 L M1 SMA Cerebellum

885 1664 947 1702

X

Y

Z

34 32 34 22

12 16 12 50

74 58 74 22

R M1, right primary motor cortex; L M1, left primary motor cortex; SMA, supplemental motor area.

Resting state fMRI

Rs-FC maps for the four ROIs for each of the three conditions (PRE-STIM, STIM, and POST-STIM) are shown in Figure 2. Across the three conditions, rs-FC maps for the R M1 shows connectivity with the bilateral M1, bilateral premotor area, SMA, bilateral insula, bilateral visual association areas, and bilateral anterior prefrontal cortex (aPFC) bilateral (Fig. 2A). Similarly, rs-FC maps for the L M1 demonstrate connectivity with the bilateral M1, bilateral premotor area, SMA, and bilateral visual association areas (Fig. 2B). RsFC maps for the SMA seed demonstrate a similar pattern of connectivity consisting of the bilateral primary M1, bilateral premotor, SMA, bilateral associative visual cortex, and bilateral aPFC (Fig. 2C). Finally, rs-FC maps for the Cer ROI show that in addition to connectivity within the Cer there is connectivity with the right premotor area during the PRE-STIM; connectivity with the bilateral associative visual cortex and left premotor area during the STIM condition; and connectivity with the left M1, left premotor area, left insula/superior temporal gyrus, right primary auditory cortex, and left associative visual cortex during the POSTSTIM condition (Fig. 2D). Network connectivity changes during each of the conditions can be visualized in Figure 3 along with the average pairwise connectivity matrices for PRE-STIM, STIM, and POST-STIM conditions. The results of pairwise univariate analysis demonstrate reduced rs-FC

Results Motor fMRI

Results from the volitional, active motor paradigm analysis demonstrate the expected activation pattern for a bilateral finger tapping task. Activations were observed in the bilateral primary motor cortex (R M1 and L M1), and in the SMA, and the Cer (Table 1 and Fig. 1). Clusters of activation as seen in Figure 1 were used as ROIs in subsequent rs-fMRI analysis.

FIG. 1. Motor Paradigm regions of interest (ROIs) and electrode placement. Anode shown in blue and cathode shown in pink. Results from motor functional magnetic resonance imaging (fMRI) paradigm from n = 11 healthy subjects thresholded at Z > 2.3 and cluster significance threshold of p < 0.001. Cerebellum (Cer) shown in red, right primary motor cortex (R M1) shown in yellow, left primary motor cortex (L M1) shown in orange, and supplemental motor area (SMA) shown in green.

MODULATION OF RESTING MOTOR CONNECTIVITY USING TPCS

161

FIG. 2. Resting state networks extracted from resting state fMRI scans before stimulation (PRE-STIM), during stimulation (STIM), and after stimulation (POSTSTIM) using ROI seeds from the four selected motor ROIs. (A) right primary motor cortex (R M1) (B) left primary motor cortex (L M1) (C) SMA (D) Cer. Results are overlaid on MNI template. Maps are thresholded at voxel-wise p-value of 0.001 (uncorrected) and cluster extent threshold of p-value of 0.05 using a family wise error correction for multiple comparisons.

between the L M1 and R M1 during the STIM condition compared with the PRE-STIM condition ( p = 0.019) (Fig. 3), but not during the POST-STIM condition ( p = 0.354). There was no statistical evidence for altered rs-FC between any other pairs of nodes during the STIM or POST-STIM condition (all p-values > 0.05). Contrast STIM versus PRE-STIM. During stimulation, no significant voxel-wise differences in rs-FC for the SMA, R M1, or Cer compared to the PRE-STIM baseline condition were observed. However, the rs-FC with the L M1 was significantly reduced during the STIM compared with PRE-STIM conditions in the regions surrounding the left primary motor cortex (Fig. 4A). Concurrently, there was significant increase of rs-FC with the L M1 in the left thalamus during the STIM condition compared with the PRE-STIM condition (Fig. 4B). Bivariate connectivity results based on the ROI analysis indicate that the average network overall strength was re-

duced during STIM compared with PRE-STIM ( p = 0.044). Likewise, both the right and left M1 show reduced strength during STIM compared with the PRE-STIM condition ( p = 0.048, p = 0.042) respectively (Fig. 5A). In addition, as illustrated in Figure 5B, the average network diversity is significantly reduced during the stimulation ( p = 0.024). Further, a trend for reduced diversity during stimulation, in both the right and left M1 compared with the PRE-STIM condition ( p = 0.10, p = 0.068 respectively) was observed (Fig. 5B). There were no significant differences in the LI for the strength or diversity during the STIM condition (all p > 0.05). There were no significant differences in strength or diversity for the semi-partial correlation analysis during the STIM condition (all p > 0.05). Contrast POST-STIM versus PRE-STIM. After stimulation, no significant voxel wise differences in rs-FC with the R M1, L M1, or SMA resting state networks was observed.

FIG. 3. Connectivity matrices of univariate connectivity measures. Connectivity matrices presented for PRE-STIM, STIM, and POST-STIM condition. Color represents the z-score of the connectivity between pairs of ROIs. * Significance assessed using paired t-tests to compare changes in pairwise connectivity between the STIM or POST-STIM condition and the baseline PRE-STIM condition. *p < 0.05.

162

SOURS ET AL.

FIG. 4. Between group differences in functional connectivity. (A, B) With left primary motor cortex (L M1). (C) With Cer. (A) Regions showing reduced resting state functional connectivity (rs-FC) during stimulation. (B) Regions showing increased rsFC during stimulation. (C) regions showing increased rs-FC after stimulation compared to before stimulation. Results are overlaid on MNI template. Maps were thresholded at voxel-wise p-value of 0.005 (uncorrected) and cluster extent threshold of p-value of 0.05 using a family wise error correction for multiple comparisons.

In contrast, the Cer was the only region to show altered rs-FC during the POST-STIM condition, with areas of increased rsFC in the right posterior insula (Fig. 4C) compared with the baseline PRE-STIM condition suggestive of specific increase connectivity between the Cer and cortex following stimulation. Bivariate connectivity results based on the ROI analysis fail to indicate significant differences in strength of connectivity for any region during the POST-STIM condition (Fig. 5A). However, the average network diversity trended toward a reduction ( p = 0.071) during the POST-STIM condition. A similar trend toward reduced diversity during the POST-STIM condition compared with the PRE-STIM condition was also noted in the R M1 ( p = 0.090) and L M1 ( p = 0.058) (Fig. 5B). There were no significant differences in the LI for the strength or diversity during the STIM condition (all p > 0.05). There were no significant differences in strength or diversity for the semi-partial correlation analysis during the POST-STIM condition (all p > 0.05). Contrast control population visit 1 versus visit 2. No significant voxel-wise differences in rs-FC with the R M1, L M1, SMA, or Cer resting state networks were observed between the two visits separated by 6 months. In addition, bivariate connectivity results based on the ROI analysis fail to indicate significant differences in strength or diversity

of connectivity for any of the four regions or the network average between visit 1 and visit 2 (Supplementary Fig. S1; Supplementary Data are available online at www.liebertpub .com/brain). Discussion

This study provides the most detailed fMRI-derived findings to date regarding the neuromodulatory effects of tPCS. Expanding upon our previously reported findings, the current results demonstrate that similar to tDCS, tPCS is able to alter neuronal connectivity within the motor network not only in the regions directly under the site of the anode or cathode, but also across the entire motor network. This finding suggests that tPCS modulates critical communication pathways connecting both ipsilateral and contralateral (interhemispheric) cortical and subcortical regions during stimulation (Figs. 2 and 3). Further, the cerebellar networks remain altered after the stimulation is stopped (Fig. 4C). Following the analytical model of Sehm and colleagues (2012, 2013), we contrasted the effects on the rs-FC during and after tPCS. Specifically, during stimulation we observed that tPCS reduces the strength of rs-FC within the motor network, but facilitates rs-FC between the left M1 (contralateral to the anode) and subcortical structures. In addition, while increased cerebro–cerebellar interactions following unilateral

MODULATION OF RESTING MOTOR CONNECTIVITY USING TPCS

FIG. 5. Bivariate network functional connectivity measures. Bar graph of bivariate connectivity measures (A) strength and (B) diversity for right primary motor cortex (R M1), left primary motor cortex (L M1), and a network average. *p < 0.05 and #p < 0.10 compared with baseline PRE-STIM condition. M1 have been noted after 23 min of tDCS (Sehm et al., 2012), the present study provides new evidence that 12– 14 min of tPCS (a 40% shorter stimulation period) is able to increase rs-FC between the Cer and cortical regions for at least *10 min following stimulation (Fig. 4). We designed our study protocol to permit indirect comparison of tPCS stimulation with published data using tDCS by replicating the common, single channel uni-hemispheric anodal stimulation electrode over the primary motor area (M1) as reported by leading research groups who applied tDCS during simultaneous fMRI acquisition (Antal et al., 2011; Lindenberg et al., 2013; Sehm et al., 2012, 2013). The univariate results presented in this study provide support for a reduction in IHFC between the right and left M1 during stimulation (Fig. 3). Further, the bivariate measure of strength does point to reduced rs-FC of the right and left M1 with the motor network as a whole, paralleling the findings of reduced IHFC during tDCS stimulation (Antal et al., 2011; Sehm et al., 2013) (Fig. 5A). Further, contrary to the application of tDCS (Sehm et al., 2013), we did not observe an increase in IHFC after termination of the stimulation. Conceivably, this difference may be attributed to the duration of stimulation and post-stimulation data collection periods. Our tPCS stimulation and post-stimulation protocol

163

lasted only 12–14 and 6.5 min respectively while Sehm and colleagues (2013) applied the tDCS almost twice as long (*23 min) and collected post-stimulation data *15 min after the end of stimulation. While tPCS weakened the rs-FC between the main nodes of the motor network during stimulation, we did observe that the tPCS increased rs-FC between the left M1 and the left thalamus, which has not been reported by previous investigators. A computer-based high-resolution modeling of the brain had predicted the possible existence of such a connection (Datta et al., 2013), which is confirmed for the first time by our in vivo study. While the thalamus was not activated using our simple finger tapping motor paradigm, the thalamus does play a large role in coordinating ascending sensory and motor inputs with descending cognitive control systems. Accordingly, we postulate that the tPCS-dependent increase of thalamo-cortical connectivity noted during stimulation, may contribute to improvements in motor performance. However, further research is needed that combine both simultaneous tPCS and fMRI with more advanced measures of behavioral performance to test this hypothesis. The fMRI data analysis reveals that tPCS can significantly reduce diversity, or variability of functional connectivity between nodes of the motor network during stimulation, especially with the right and left M1 (Fig. 5B). Further, a trend of a continued reduction in diversity was observed after the stimulation ended. It stands to reason that prolonging the stimulation from 12–14 min to 23–25 min may lead to longer-lasting effects on functional network properties of the motor system. However, since diversity is a measure of the degree of variation in connectivity among nodes of the network, this reduction in diversity may suggest that the motor network is behaving as a more cohesive unit during and for some period after tPCS stimulation. Whether or not this strengthened between node connectivity coherence contributes to the behavioral improvements in motor functioning following transcranial stimulation as discussed by other investigators (Galea and Celnik, 2009; Lindenberg et al., 2013) should be a subject of future research. We and others have reported that the typical perception by the subject during tDCS was limited to tingling and itching sensation (Alon et al., 2011; Polania et al., 2012). On the other hand, the perception during the tPCS was distinctly different, with all subjects reporting visualization of repeated flashing light. The itching and tingling sensations noted with tDCS are rarely reported during tPCS. This perceived visualization of flashing light was reported by all participants in the current study and in addition was reported by patients with Parkinson’s disease (Alon et al., 2012). Although this visual stimulation has been rendered harmless and its clinical consequences undetermined, it raises the question whether such visual stimulation plays a role in modulating the motor network and potentially its interaction with other sensory networks. Conceivably the optic nerve or the visual cortex or both are excited directly during tPCS. Clinical experience with the specific tPCS used in our study confirms that the flashing lights appear only when the stimulation amplitude approaches maximum intensity indicating a threshold of excitation. The flashing lights may be a unique response to the specific pulse properties of the device as described in the method section. The modeling of the responses to pulsed current stimulators of

164

different pulse properties imply that simple monophasic, symmetric, or asymmetric biphasic pulses may not cause a flashing lights phenomenon (Datta et al., 2013). Indeed unpublished data and clinical experience of our team confirm the dependence of the flashing light phenomenon on tPCS pulse properties and does not occur during tDCS (Alon et al., 1998, 2011, 2012). Further probing of the phenomenon and more importantly the potential added value to neuromodulation will require additional investigation. While our findings provide further evidence for the neuromodulator effects of tPCS, the conclusions that can be drawn from this work must be taken in the context of the limitations of this study. First of all, the sample size of this study was limited (n = 11); however, this sample size is in the same range as other studies investigating noninvasive cortical stimulation. An additional limitation is the lack of a sham condition in our experimental design. We initially considered a sham condition in the experimental design; however, all of our participants reported a tactile stimulation present throughout the entire duration of the stimulation. The standard practice for sham electrical stimulation is to turn the stimulation on for 30 sec, then turn the stimulation off for the remainder of the sham stimulation session. This only results in a tactile stimulation for the first 30 sec unlike the active stimulation condition, which produces a tactile stimulation for the entire session. This results in an imperfect sham condition, and we believe that further research is needed to create a better sham stimulation condition. Nevertheless, to address the test-retest reliability of our specific method of analysis provided in this article, we have included an additional set of analysis of a second healthy volunteer population. We have performed identical analysis (voxel-wise and bivariate connectivity analysis) on a set of 11 healthy volunteers who each received two separate rsfMRI scans 6 months apart. Our voxel-wise analysis yielded no regions of significant differences between the two visits. In addition, our bivariate connectivity analysis showed no significant differences in strength of diversity for any of the four regions or a network average (Supplementary Fig. S1). Our group has previously published on the reliability of task-based fMRI within the somatosensory system (Gullapalli et al., 2005; Maitra et al., 2002) and other groups have provided evidence supporting strong test-retest reliability of resting state networks (Franco et al., 2013; Mannfolk et al., 2011). Therefore, since we do not see changes in voxelwise analysis, strength or diversity in our test-retest control group, we are confident that the changes we see in our STIM and POST-STIM sessions are due to the effect of the stimulation. Further, our sample population consisted of healthy participants with the majority of volunteers under the age of 30. To extend this work to patient populations with marked motor deficits such as stroke or Parkinson’s disease further work is needed to ascertain the efficacy of tPCS on modulating network connectivity in these and other aging populations. In addition, we constrained our investigation to the effect of tPCS on rs-FC with the motor network. Since our findings suggest altered rs-FC between the motor network and subcortical and cerebellar networks, future work investigating the alterations in the communication within and between other resting state networks involved in sensory or cognitive processing is warranted. However, despite the limitations

SOURS ET AL.

noted, this work provides some of the first evidence supporting the neuromodulatory effect of tPCS. Conclusion

This study for the first time demonstrates that tPCS is able to modulate the functional characteristics of the motor network during stimulation and induce changes in network connectivity even after the stimulation is stopped. While these data confirm previous findings by other investigators using tDCS, we also demonstrated additional changes in connectivity patterns that are specific to tPCS including thalamocortical involvement. Expanding upon previous work with tPCS, our data strengthen the evidence for the neuromodulatory effects of tPCS; however, future work is needed to directly link these alterations in neuronal excitability to changes in behavior with the anticipated goal of transitioning tPCS into a clinical setting. Acknowledgments

We are grateful for the support from the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, for providing the time necessary for obtaining the necessary imaging data for this pilot study. Author Disclosure Statement

None of the authors have any relevant disclosures. References

Alon G, Roys SR, Gullapalli RP, Greenspan JD. 2011. Noninvasive electrical stimulation of the brain (ESB) modifies the resting-state network connectivity of the primary motor cortex: a proof of concept fMRI study. Brain Res 1403:37–44. Alon G, Syron SC, Smith GV. 1998. Is transcranial electrical stimulation (TCES) a safe intervention for children with cerebral palsy? J Neuroeng Rehabil 12:65–72. Alon G, Yungher DA, Shulman LM, Rogers MW. 2012. Safety and immediate effect of noninvasive transcranial pulsed current stimulation on gait and balance in Parkinson disease. Neurorehabil Neural Repair 26:1089–1095. Antal A, Polania R, Schmidt-Samoa C, Dechent P, Paulus W. 2011. Transcranial direct current stimulation over the primary motor cortex during fMRI. Neuroimage 55:590–596. Benninger DH, Lomarev M, Lopez G, Wassermann EM, Li X, Considine E, Hallett M. 2010. Transcranial direct current stimulation for the treatment of Parkinson’s disease. J Neurol Neurosurg Psychiatry 81:1105–1111. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541. Boggio PS, Ferrucci R, Rigonatti SP, Covre P, Nitsche M, PascualLeone A, Fregni F. 2006. Effects of transcranial direct current stimulation on working memory in patients with Parkinson’s disease. J Neurol Sci 249:31–38. Brunoni AR, Ferrucci R, Fregni F, Boggio PS, Priori A. 2012. Transcranial direct current stimulation for the treatment of major depressive disorder: a summary of preclinical, clinical and translational findings. Prog Neuropsychopharmacol Biol Psychiatry 39:9–16. da Silva MC, Conti CL, Klauss J, Alves LG, do Nascimento Cavalcante HM, Fregni F, Nitsche MA, Nakamura-Palacios

MODULATION OF RESTING MOTOR CONNECTIVITY USING TPCS

EM. 2013. Behavioral effects of transcranial Direct Current Stimulation (tDCS) induced dorsolateral prefrontal cortex plasticity in alcohol dependence. J Physiol Paris 107:493–502. Dasilva AF, Mendonca ME, Zaghi S, Lopes M, Dossantos MF, Spierings EL, Bajwa Z, Datta A, Bikson M, Fregni F. 2012. tDCS-induced analgesia and electrical fields in pain-related neural networks in chronic migraine. Headache 52:1283–1295. Datta A, Dmochowski JP, Guleyupoglu B, Bikson M, Fregni F. 2013. Cranial electrotherapy stimulation and transcranial pulsed current stimulation: a computer based high-resolution modeling study. Neuroimage 65:280–287. Detre JA, Zhang W, Roberts DA, Silva AC, Williams DS, Grandis DJ, Koretsky AP, Leigh JS. 1994. Tissue specific perfusion imaging using arterial spin labeling. NMR Biomed 7:75–82. Feusner JD, Madsen S, Moody TD, Bohon C, Hembacher E, Bookheimer SY, Bystritsky A. 2012. Effects of cranial electrotherapy stimulation on resting state brain activity. Brain Behav 2:211–220. Franco AR, Mannell MV, Calhoun VD, Mayer AR. 2013. Impact of analysis methods on the reproducibility and reliability of resting-state networks. Brain Connect 3:363–374. Fregni F, Boggio PS, Santos MC, Lima M, Vieira AL, Rigonatti SP, Silva MT, Barbosa ER, Nitsche MA, Pascual-Leone A. 2006. Noninvasive cortical stimulation with transcranial direct current stimulation in Parkinson’s disease. Mov Disord 21:1693–1702. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. 1993. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13:5–14. Galea JM, Celnik P. 2009. Brain polarization enhances the formation and retention of motor memories. J Neurophysiol 102:294–301. Gomez Palacio Schjetnan A, Faraji J, Metz GA, Tatsuno M, Luczak A. 2013. Transcranial direct current stimulation in stroke rehabilitation: a review of recent advancements. Stroke Res Treat 2013:170256. Gullapalli RP, Maitra R, Roys S, Smith G, Alon G, Greenspan J. 2005. Reliability estimation of grouped functional imaging data using penalized maximum likelihood. Magn Reson Med 53:1126–1134. Jeon SY, Han SJ. 2012. Improvement of the working memory and naming by transcranial direct current stimulation. Ann Rehabil Med 36:585–595. Keeser D, Meindl T, Bor J, Palm U, Pogarell O, Mulert C, Brunelin J, Moller HJ, Reiser M, Padberg F. 2011. Prefrontal transcranial direct current stimulation changes connectivity of resting-state networks during fMRI. J Neurosci 31: 15284–15293. Kuo MF, Nitsche MA. 2012. Effects of transcranial electrical stimulation on cognition. Clin EEG Neurosci 43:192–199. Liao W, Zhang S, Mantini D, Xu Q, Wang Z, Chen G, Jiao Q, Zang Y-F, Lu G. 2013. Relationship between large-scale functional and structural covariance networks in idiopathic generalized epilepsy. Brain Connect 3:240–254. Lindenberg R, Nachtigall L, Meinzer M, Sieg MM, Floel A. 2013. Differential effects of dual and unihemispheric motor cortex stimulation in older adults. J Neurosci 33:9176–9183. Lindenberg R, Zhu LL, Schlaug G. 2012. Combined central and peripheral stimulation to facilitate motor recovery after stroke: the effect of number of sessions on outcome. Neurorehabil Neural Repair 26:479–483.

165

Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, Bullmore E. 2010. Functional connectivity and brain networks in schizophrenia. J Neurosci 30:9477–9487. Maitra R, Roys SR, Gullapalli RP. 2002. Test-retest reliability estimation of functional MRI data. Magn Reson Med 48:62–70. Mannfolk P, Nilsson M, Hansson H, Stahlberg F, Fransson P, Weibull A, Svensson J, Wirestam R, Olsrud J. 2011. Can resting-state functional MRI serve as a complement to taskbased mapping of sensorimotor function? A test-retest reliability study in healthy volunteers. J Magn Reson Imaging 34:511–517. Mogyoros I, Kiernan MC, Burke D. 1996. Strength-duration properties of human peripheral nerve. Brain 119(Pt 2):439–447. Park CH, Chang WH, Park JY, Shin YI, Kim ST, Kim YH. 2013. Transcranial direct current stimulation increases resting state interhemispheric connectivity. Neurosci Lett 539:7–10. Pirulli C, Fertonani A, Miniussi C. 2013. The role of timing in the induction of neuromodulation in perceptual learning by transcranial electric stimulation. Brain Stimul 6:683–689. Polania R, Paulus W, Nitsche MA. 2012. Reorganizing the intrinsic functional architecture of the human primary motor cortex during rest with non-invasive cortical stimulation. PLoS One 7:e30971. Sehm B, Kipping J, Schafer A, Villringer A, Ragert P. 2013. A comparison between uni- and bilateral tDCS effects on functional connectivity of the human motor cortex. Front Hum Neurosci 7:183. Sehm B, Schafer A, Kipping J, Margulies D, Conde V, Taubert M, Villringer A, Ragert P. 2012. Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation. J Neurophysiol 108:3253–3263. Stagg CJ, Nitsche MA. 2011. Physiological basis of transcranial direct current stimulation. Neuroscientist 17:37–53. Tseng P, Hsu TY, Chang CF, Tzeng OJ, Hung DL, Muggleton NG, Walsh V, Liang WK, Cheng SK, Juan CH. 2012. Unleashing potential: transcranial direct current stimulation over the right posterior parietal cortex improves change detection in lowperforming individuals. J Neurosci 32:10554–10561. van den Heuvel MP, Hulshoff Pol HE. 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534. Zaghi S, Acar M, Hultgren B, Boggio PS, Fregni F. 2010. Noninvasive brain stimulation with low-intensity electrical currents: putative mechanisms of action for direct and alternating current stimulation. Neuroscientist 16:285–307. Zheng X, Alsop DC, Schlaug G. 2011. Effects of transcranial direct current stimulation (tDCS) on human regional cerebral blood flow. Neuroimage 58:26–33. Zimerman M, Heise KF, Hoppe J, Cohen LG, Gerloff C, Hummel FC. 2012. Modulation of training by single-session transcranial direct current stimulation to the intact motor cortex enhances motor skill acquisition of the paretic hand. Stroke 43:2185–2191.

Address correspondence to: Rao P. Gullapalli Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine 22 South Greene Street Baltimore, MD 21201 E-mail: [email protected]