Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large-scale network connectivity.

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Available online at www.sciencedirect.com

www.elsevier.com/locate/brainres

Research Report

Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large-scale network connectivity Michael A. Huntera,b,c,d, Brian A. Coffmana,b,c, Charles Gasparovicc, Vince D. Calhounc,d,e,f, Michael C. Trumboa,b,c, Vincent P. Clarka,b,c,e,n a

Psychology Clinical Neuroscience Center, The University of New Mexico, Albuquerque, NM, USA Department of Psychology, The University of New Mexico, NM, USA c The Mind Research Network, Albuquerque, NM, USA d Department of Psychiatry, The University of New Mexico School of Medicine, Albuquerque, NM, USA e Department of Neurosciences, The University of New Mexico, Albuquerque, NM, USA f Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USA b

art i cle i nfo

ab st rac t

Article history:

Transcranial direct current stimulation (tDCS) modulates glutamatergic neurotransmission

Received 20 May 2014

and can be utilized as a novel treatment intervention for a multitude of populations.

Accepted 28 September 2014

However, the exact mechanism by which tDCS modulates the brain's neural architecture, from the micro to macro scales, have yet to be illuminated. Using a within-subjects design,

Keywords:

resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic reso-

Transcranial direct current

nance spectroscopy (1H MRS) were performed immediately before and after the adminis-

stimulation (tDCS)

tration of anodal tDCS over right parietal cortex. Group independent component analysis

Resting-state functional magnetic

(ICA) was used to decompose fMRI scans into 75 brain networks, from which 12 resting-state

resonance imaging (rs-fMRI)

networks were identified that had significant voxel-wise functional connectivity to anato-

Magnetic resonance spectroscopy

mical regions of interest. 1H MRS was used to obtain estimates of combined glutamate and

(MRS)

glutamine (Glx) concentrations from bilateral intraparietal sulcus. Paired sample t-tests

Independent component analysis

showed significantly increased Glx under the anodal electrode, but not in homologous

(ICA)

regions of the contralateral hemisphere. Increases of within-network connectivity were

Functional network connectivity

observed within the superior parietal, inferior parietal, left frontal–parietal, salience and

(FNC)

cerebellar intrinsic networks, and decreases in connectivity were observed in the anterior

Glutamine–glutamate (Glx)

cingulate and the basal ganglia (po0.05, FDR-corrected). Individual differences in Glx

Parietal lobe

concentrations predicted network connectivity in most of these networks. The observed relationships between glutamatergic neurotransmission and network connectivity may be used to guide future tDCS protocols that aim to target and alter neuroplastic mechanisms in healthy individuals as well as those with psychiatric and neurologic disorders. & 2014 Elsevier B.V. All rights reserved.

n

Corresponding author at: Department of Psychology, 1 University of New Mexico, MSC03 2220, Albuquerque, NM 87131, USA. E-mail address: [email protected] (V.P. Clark).

http://dx.doi.org/10.1016/j.brainres.2014.09.066 0006-8993/& 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Hunter, M.A., et al., Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large-scale network connectivity. Brain Research (2014), http://dx.doi.org/10.1016/j. brainres.2014.09.066

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brain research ] (]]]]) ]]]–]]]

1.

Introduction

Neuroplasticity is essential for brain development and adaptation as it enables the nervous system to reorganize neural pathways based on new experiences. Research is underway to examine ways to harness neuroplasticity in order to promote healing and recovery (see Peled, 2004; 2005; Spedding et al., 2003; and Kays et al., 2012 for review). Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that modulates the Q3 excitability of functional brain networks (Polanía et al., 2011; Keeser et al., 2011; Sehm et al., 2012; Peña-Gómez et al., 2012). It is thought to increase the spontaneous firing of cortical neurons near the anodal electrode (with positive polarity) while decreasing it near the cathode (with negative polarity) (Nitsche and Paulus, 2000; Dieckhöfer et al., 2006). By modulating the excitability of glutamatergic pyramidal neurons in the underlying cortex (Radman et al., 2009), tDCS influences neurophysiological mechanisms responsible for neuroplasticity. These mechanisms involve the potentiation of synaptic glutamatergic receptors (Liebetanz et al., 2002; Nitsche et al., Q6 2005), and decreased neurotransmission of γ-aminobutyric acid interneurons (GABA) (Nitsche et al., 2004; Stagg et al., 2009; Stagg and Nitsche, 2011; also see Medeiros et al., 2012 for review). In particular, NMDA and AMPA receptors are essential to synaptic plasticity by influencing long-term potentiation and depression (LTP and LTD) across structurally-connected brain regions (Bliss and Collingridge, 1993). These synaptic and neuronal pathways consolidate into stable and long-lasting functional brain networks (Fricke et al., 2011; Venkatakrishnan et al., 2011; Venkatakrishnan and Sandrini, 2012). However, the effects of tDCS on glutamate levels and its relation to largescale network connectivity have yet to be fully elucidated; that is, there must be a better understanding of how tDCS interacts across different scales within the brain's neural architecture by combining different, yet complementary, imaging modalities (see Hunter et al., 2013 for a review), which was the primary objective of the present study.

1.1.

tDCS-induced effects on neurometabolites

Proton magnetic resonance spectroscopy (1H MRS) enables quantification of certain neurometabolites within a localized Q7 region of the brain (Gruetter et al., 2001; Steen et al., 2005). This method has been used to examine the effects of tDCS on specific neurometabolites. To date, anodal tDCS has been associated with increases in combined glutamine and glutamate (Glx) (Stagg et al., 2009; Clark et al., 2011) and myoinositol concentrations (Rango et al., 2008). Concordantly, reductions have been found in GABA concentration with anodal tDCS (Stagg et al., 2009). Consistent with these findings, the activation of metabotropic glutamate receptors, in conjunction with stable long-range intrinsic membrane oscillations, has been shown to entrain local and distributed GABAergic interneurons (Whittington et al., 1995). Together, the observed changes in glutamatergic and GABAergic activity may translate to subsequent alterations in both local and

distributed processing—influenced by both excitatory and inhibitory signaling pathways—in functional brain networks.

1.2.

tDCS-induced effects on network-based connectivity

Functional magnetic resonance imaging (fMRI) is a noninvasive technique for acquiring dynamic changes in blood oxygenation, measured as the blood-oxygenation-level dependent (BOLD) signal. While at rest, spontaneous fluctuations in the BOLD signal (0.10–0.15 Hz) show high correlations across structurally connected and functionally related brain regions (Biswal et al., 1995; Skudlarski et al., 2010; see Fox and Raichle, 2007 for review). These fluctuations reflect a stable, intrinsic organization of the brain that maintains and reinforces established synaptic connections that support cognitive and behavioral functions (see Fox and Raichle, 2007 and Van Den Heuvel and Hulshoff Pol, 2010 for reviews). The most commonly observed intrinsic network is the default-mode network (DMN), which links precuneus and posterior cingulate cortex (PCC) with bilateral inferior parietal and medial frontal cortices, with the highest activations observed in the posterior regions (Raichle et al., 2001; Greicius et al., 2003). Independent component analysis (ICA) can be used to decompose resting-state fMRI (rs-fMRI) signals into functionally related “groups” of voxels that comprise functionally connected brain networks (Erhardt et al., 2011). The strength of ICA is its ability to resolve data into maximally independent sources, thereby revealing the dynamics of intrinsic networks (McKeown et al., 1997; Calhoun et al., 2001; Beckmann et al., 2005; Calhoun et al., 2011; Calhoun and Adalı, 2012). A recent study of anodal (2.0 mA) tDCS over the dorsolateral prefrontal cortex (DLPFC), with the cathode placed over contralateral supraorbital area, resulted in increased intrinsic functional connectivity within the frontal node of the DMN and the left frontal–parietal network (Keeser et al., 2011). Similarly, Peña-Gómez et al. (2012) found that this same tDCS montage produced a redistribution of ICA-generated functional network connectivity (FNC), a measure of the temporal relationship among ICA components. Increase FNC between networks that overlapped with the site of stimulation and with superior parietal networks (comprising task-related circuits) was observed; whereas a decrease in FNC was found between networks that comprise the DMN (Peña-Gómez et al., 2012). These results suggest that anodal tDCS over DLPFC may enhance the flexible balance between brain networks by enhancing network connectivity for cognitive demands while reducing its anti-correlated DMN activity. Furthermore, a recent rs-fMRI study placed the anode over the right angular gyrus, with the cathode over contralateral supraorbital region (Clemens et al., 2014). Increases in ICAgenerated functional connectivity in the cerebellum, medial occipital, sensorimotor, right frontal parietal, and superior frontal gyrus were observed, while decreases were found in the right putamen and lateral occipital areas. Furthermore, active tDCS has also contributed to both inter-hemispheric (Sehm et al., 2012; 2013; Park et al., 2013) and corticostriatal functional connectivity (Polanía et al., 2012; Clemens et al., 2014). Overall, studies that combine fMRI and tDCS show widespread changes in functional connectivity at both local


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and distant brain regions relative to the area of stimulation (Polanía et al., 2011; Keeser et al., 2011; Amadi et al., 2014).

1.3.

Established relationships between imaging modalities

In order to examine the relationship between neurotransmission (either excitatory or inhibitory) and the variability of functional brain responses, there are a number of studies that combined 1H MRS with fMRI hemodynamic responses (Donahue et al., 2010; Muthukumaraswamy et al., 2012; Falkenberg et al., 2012), task-related negative BOLD responses (Walter et al., 2009; Northoff et al., 2007), resting-state functional connectivity (Horn et al., 2010; Kapogiannis et al., 2012) and effective connectivity (Duncan et al., 2011). The results from these multi-modal imaging studies demonstrate that increases in glutamate concentrations predict increases in BOLD activity, whereas increases in GABA predict decreases in BOLD activity. For instance, increases in glutamatergic concentrations in the precuneus predicted increases in ICA-generated intrinsic network connectivity within the precuneus node of the DMN, while decreases in GABA predicted overall increases in DMN activity (Kapogiannis et al., 2012).

1.4.

Study objectives & rationale

In summary, tDCS may alter changes in the BOLD signal by modulating neuronal and glial metabolic demands, perhaps related to the glutamate/glutamine cycle (Bonvento et al., 2002). Furthermore, given that Glx was increased by tDCS in our previous study (Clark et al., 2011), and that there is a strong relationship between glutamate and functional connectivity in other studies that have not used tDCS (Horn et al., 2010; Kapogiannis et al., 2012), the objective of the current study was to examine whether tDCS-evoked changes in Glx may predict variation in functional connectivity within and between both local and distributed intrinsic brain networks. To preferentially modulate excitatory signaling in largescale functional connectivity, the present study positioned the anode over the right superior parietal cortex, with an extracephalic cathode tDCS montage. By removing the direct influence of the cathode on cortical activity, this montage allows for a more precise evaluation of anodal tDCS on cortical excitability. The parietal cortex is an essential network hub that supports many different attentional processes, including endogenous attentional control and spatial orienting of attention (Corbetta et al., 2008; Vandenberghe and Gillebert, 2009). Moreover, recent tDCS studies have shown that right parietal stimulation produces significant increases in visual-spatial processing (Sparing et al., 2009), covert visual orienting (Bolognini et al., 2010), learning in a complex visual detection task (Clark et al., 2012), attentional selection (Moos et al., 2012) and perceived position (Wright and Krekelberg, 2013).

1.5.

Hypotheses

Based on the literature summarized above, we hypothesized that tDCS would increase functional connectivity within networks located near the right parietal cortex (the targeted area of anodal stimulation), while decreasing functional connectivity within networks in the medial regions of the DMN

3

(Keeser et al., 2011; Peña-Gómez et al., 2012), which could be tested using ICA to assess the network connectivity before and after tDCS. We also hypothesized that tDCS would increase FNC between network pairs that contain circuits supporting attention-related functions, as tDCS to this region has been found to enhance attentive cognitive functions. Given that previous studies found that anodal tDCS enhanced the balance between brain networks, partly by decreasing FNC between DMN and subcortical functional circuits (PeñaGómez et al. 2012; Polanía et al., 2012; Clemens et al., 2014), we also hypothesized that FNC between the DMN and subcortical networks would be decreased after tDCS. Lastly, given that tDCS enhances voxel-wise functional connectivity via glutamatergic signaling pathways, we expected that tDCSrelated increases in Glx post-tDCS would relate to increases within and between networks.

2.

Results

2.1.

Group independent component analysis

Following Allen et al. (2011), a high-dimensional model-order ICA was performed using Group ICA fMRI Toolbox (GIFTv3.0a; http://mialab.mrn.org/software/gift/). Prior to the first and second PCA data reductions (1st at c ¼100; final at c¼ 75), which were computed on each subjects' dataset before doing the group PCA, the time-series at each voxel were scaled to percent signal change using the intensity normalization function in GIFT. This option was selected based on a recent study which showed that this pre-processing method maximizes the detectability of individual differences in functional connectivity measures at the group-level (Khullar et al., 2011; Q5 Erhardt et al., 2011). The infomax ICA algorithm, which incorporates nonlinearities in the transfer function to capture higher-order moments in the BOLD resting-state fluctuations (Bell and Sejnowski, 1995), was then used to detect the independent source components in the data. ICA was repeated 15 times using ICASSO toolbox in order to estimate the algorithmic and statistical reliability of the ICA decomposition (Himberg, Hyvärinen, and Esposito, 2004). Backreconstruction was then run using group ICA (GICA3), with component spatial maps of functional connectivity scaled to represent the original data units in percent signal change.

2.2.

Identified networks of interest

All 12 networks are displayed in Fig. 1. Three networks were identified with aggregate spatial maps that overlapped with parietal regions near the area of stimulation and 1H MRS voxels. These parietal networks included a bilateral superior parietal lobule (SPL) network, a bilateral inferior parietal network and a bilateral superior parietal sulcus (SPS) network. When taken together, 3 networks captured the full extent of the DMN along the anterior-posterior and inferiorsuperior axes (Raichle et al., 2001; Allen et al., 2011): a precuneus network, a PCC and bilateral angular gyrus network, and an ACC network. Three additional networks were identified with distributed spatial maps that overlapped with well-known attention-related networks, which included


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Group-level ICA-generated Resting-State Networks RSNs that comprise the Default-Mode Network

Parietal RSNs that overlap with MRS Voxel Placement Bilateral SPL & SM

Bilateral Inferior Parietal

Bilateral SPS

Distributed RSNs RH FrontalParietal

LH FrontalParietal

Precuneus

PCC & Angular Gyrus

Frontal RSNs “Salience”

Bilateral Bilateral Superior Anterior PFC Frontal Gyri

ACC

Sub-cortical RSNs Basal Ganglia

LH Cerebellum

Fig. 1 – ICA-generated spatial maps of the 13 networks of interest: spatial maps are plotted as Z-scores, Z43.0 and are displayed at the slice showing highest network connectivity. The (x,y,z) MNI coordinates are labeled underneath each component as well as the low-frequency to high-frequency power ratio (LF/HF) for each network. Higher values indicate more power in lower frequency. Color bars correspond to the range of connectivity values derived from a one-sample t-test. Abbreviations: RH, right hemisphere; LH, left hemisphere; PCC, posterior cingulate cortex; ACC, anterior cingulate cortex; SPL, superior parietal lobule; SM, sensorimotor; SPS, superior parietal sulcus.

a left-lateralized frontal–parietal network, which showed the highest activation in the parietal regions and a smaller cluster in the right posterior parietal (k¼ 241) and PCC (k¼299) cortices. There was also a right-lateralized frontal–parietal network, which showed the highest voxel-wise functional connectivity in the right lateral frontal regions. A network that displayed the highest functional connectivity in bilateral insulae and dorsal ACC, comprising the well-known “salience” Q17 network was also included (Seeley et al., 2007; Menon and Uddin, 2010; Bonnelle et al., 2012; Ham et al., 2013; Uddin et al., 2013). There was one frontal component, which comprised bilateral anterior prefrontal cortex (PFC), with additional voxels observed in the thalamus (k¼ 160). Lastly, two sub-cortical networks were found: one represented by the putamen and palladium—labeled the basal ganglia network (BG), and the other consisted of the left cerebellum.

2.3.

Changes in neurometabolite concentrations

As reported in the larger sample in Clark et al., 2011, pairedsample t-tests showed that there was a statistically significant

increase in the Glx obtained in the right parietal cortex after anodal stimulation, t(8) ¼ 2.61, p ¼0.03, but not the left, t(8) ¼ 1.41, p ¼ 0.20.

2.4. Paired t-tests of voxel-wise functional connectivity before and after tDCS Table 1 displays summary statistics for all the statistically significant cluster- and peak-level differences. For the bilateral SPL network, the right-hemisphere premotor cortex exhibited one of the largest significant increases in functional connectivity (see Table 1 and Fig. 2). For the bilateral inferior parietal network, the largest increases were primarily observed in the left hemisphere with only a trend-level increase in the right IPS (p¼ 0.06, FDR-corrected; see Table 1). The ACC network also exhibited robust decreases in functional connectivity after tDCS in ACC proper and medial frontal gyrus (see Table 1 and Fig. 3). No significant differences were observed in the other 3 networks/nodes that comprise the DMN.


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Table 1 – Summary statistics for paired-sample t-tests on component spatial maps, comparing before and after tDCS. Component

Direction of effect

n

Cluster-level

T-scoren

X Y Z (MNI)

# of voxels

FDR p-value

Post4Pre

16.74

36 30 72

11

0.04

Post4Pre Post4Pre Post4Pre

20.28 17.93 3.66 (u.c.)

39 75 36 27 78 45 24 84 45

14 15 16

0.04 0.04 0.06

PostoPre PostoPre

16.5 10.74

12 39 6 12 57 18

47 19

o0.001 0.005

Post4Pre Post4Pre

10.24 19.9

42 66 45 63 45 36

28 20

0.002 0.006

Post4Pre

12.98

48 27 0

23

0.09

Post4Pre Post4Pre

10.41 13.05

27 48 36 36 42 18

54 14

o0.001 0.01

PostoPre PostoPre

21.62 18.17

27 9 12 21 0 15

242 323

o0.001 o0.001

Post4Pre Post4Pre

9.61 9.59

39 81 24 21 90 27

19 14

0.003 0.005

Component sub-regions Parietal (local tDCS) RSNs Bilateral SPL Network RH SPL and precentral-motor Bilateral inferior parietal LH IPS LH lateral IPS RH IPS Node within DMN ACC Network RH ACC RH medial frontal gyrus Distributed RSNs LH Frontal–parietal LH superior division of LOC LH inf. parietal “Salience” Network Right IFG& Ins Frontal RSN Bilateral Anterior PFC Network RH aPFC & SFG LH MFG Sub-cortical RSNs BG Network RH Putamen (dorsal) LH Putamen (rostral) LH Cerebellum Network LH Crus I LH Crus II

Peak-level

All peak-level differences were thresholded using po0.05 (FWE-corrected), unless noted otherwise.

Superior Parietal Lobule and SM Network: Increased network connectivity and its relation to post-tDCS Glx concentrations. = Post > Pre T = 16.74

36, 30, 72

= RH Glx T = 7.26

= LH Glx T = 8.27

6, -72, 33

-30, -56, 55

Surface-based effects combined

Fig. 2 – Statistical parametric maps for the superior parietal lobule network: differences after tDCS are displayed in red. Voxels exhibiting significant relationships with post-tDCS RH Glx measures are displayed in yellow and LH Glx measures in green (note the Glx relations in precuneus). Of the three distributed intrinsic networks, the left frontal– parietal network exhibited the largest increases in functional connectivity, specifically in the left inferior parietal lobe, extending to the upper division of lateral occipital cortex (see Table 1 and supplemental material Fig. 1). For the

ACC Network: Decreased network connectivity and relation with post-tDCS Glx concentrations. = Post < Pre = RH Glx Surface-based = LH Glx effects combined T = 16.5 T = 9.72 T = 5.57

12, 39, 6

-15, -75, 27

6, -45, 60

Fig. 3 – Statistical parametric maps for the anterior cingulate network: statistically significant differences after tDCS are displayed in red. Voxels exhibiting significant relationships with post-tDCS RH Glx measures are displayed in yellow (note the Glx relations in precuneus). “salience” network, increases in functional connectivity were observed in the right insula, extending to the IFG (see Table 1 and supplemental material Fig. 2). No statistically significant


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differences were observed in the right frontal–parietal network; however, given the hypothesized increases in this network, SPMs for both pre- and post-tDCS voxel-wise functional connectivity are displayed in supplemental material Fig. 3. Results show widespread, albeit sub-threshold differences, in the right IFG (p¼ 0.004, uncorrected) and right lateral posterior parietal cortex (p ¼0.059, uncorrected). For intrinsic networks comprised primarily of frontal regions, only the bilateral anterior PFC network displayed significantly increased functional connectivity after tDCS, specifically within left MFG and right anterior PFC and superior frontal gyrus. Both subcortical intrinsic networks displayed significant differences: the BG displayed the largest decreases, specifically in bilateral putamen. The left cerebellum network exhibited increases within Crus I and II (see Table 1).

Within the MRS-mask in the salience network, right Glx predicted right SPL functional connectivity. No other significant relationships were observed in the pre-tDCS measures; however, given the previously observed relationships between glutamate and functional connectivity within the precuneus (Kapogiannis et al., 2012), we imposed an even less stringent significance threshold (po0.004, uncorrected) and found relationships between Glx and functional connectivity in the ipsilateral region of the precuneus network (po0.003, uncorrected)

2.5.2.

2.5. Relationships between within-network functional connectivity and Glx 2.5.1.

Post-tDCS measures

The strongest post-tDCS relationships were observed between left Glx and functional connectivity in the right precuneus within the bilateral SPL network (See Fig. 2). Within the precuneus network, only left Glx predicted functional connectivity in the left precuneus (See Table 2). Within the ACC network, right Glx predicted functional connectivity in the right precuneus. Furthermore, when the network-mask was applied, right Glx predicted right medial frontal functional connectivity only (see Table 2 and Fig. 3). Significant relationships were observed between left Glx and inferior–parietal functional connectivity in the left frontal–parietal network (see Table 2). In addition, given the cluster of significant functional connectivity in the right parietal cortex within this network (k ¼ 241)—i.e., the targeted

Pre-tDCS measures

All statistically significant relationships between Glx and voxel-wise functional connectivity are displayed in Table 2. As reported in studies by others, all relationships between Glx and functional connectivity were positive; that is, greater Glx predicted greater within-network functional connectivity.

Table 2 – Summary of relationships between left and right hemisphere Glx concentrations and “within” network functional connectivity. GLX and ICA-spatial map correlations Components of interest

Pre TDCS

Post TDCS

t-score

Type of mask

Glx Hem

Peak voxel

t-score

Type of mask

Glx Hem

Peak voxel

– – –

– – –

– – –

– – –

7.26 20.62nn 8.27

MRS MRS MRS

RH LH LH

6 72 33 9 45 51 30 57 54

–

–

–

–

10.27nn

MRS

RH

12 42 60

4.73 (uc) 3.92 (uc)

MRS MRS

LH RH

24 60 63 33 48 63

8.64 –

MRS –

LH –

3 72 39 –

– – – –

– – – –

– – – –

– – – –

9.72 6.84 5.57 5.75

MRS MRS MRS Network

RH RH LH RH

15 75 27 9 63 33 6 45 60 12 45 24

– –

– –

– –

– –

10.74nn 6.08

MRS Network

LH RH

9 63 30 48 63 39

9.6 –

MRS –

RH –

33 51 69 –

– 8.35nn

– MRS

– RH

– 24 81 27

–

–

–

–

12.16nn

MRS

LH

6 72 42

Component sub-regions Parietal RSNs (local tDCS) Bilateral SPL network RH precuneus RH precuneus LH SPL Bilateral SPS network LH SPS DMN Precuneus network LH precun RH precuneus ACC network LH precuneus RH precuneus RH precuneus RH medial frotnal gyrus Distributed RSNs Left frontal–parietal network LH precuneus RH inferior parietal “Salience” network RH SPL LH precuneus Subcortical RSNs BG network LH precun n

All reported T-scores correspond to a p-value o0.001, uncorrected.nn Corresponds to a t-score po0.05 (voxel-wise FWE-corrected).


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region of stimulation—an additional mask was computed from this cluster. A significant relationship was observed between right Glx and right inferior parietal functional connectivity in the left frontal–parietal network (see Table 2). For the salience network, right Glx predicted left inferior parietal functional connectivity. Lastly, the BG network displayed

the second largest effects, where left Glx predicted left precuneus functional connectivity within this network (Fig. 4).

2.6.

Paired t-tests on FNC

Group means and SDs for the FNC pairs of interest are displayed in Table 3 (the full FNC map is displayed in supplementary material, Fig. 5). For the parietal networks,

Left Frontal-Parietal Network: Increased functional connectivity and relations with post-tDCS Glx. = Post > Pre T = 19.9

-63, -45, 36

= RH Glx T = 6.08

48, -63, 39

= LH Glx T = 10.74

Salience Network: Increase network connectivity and its relations to pre- and post-tDCS Glx.

Surface-based effects combined

-9, 63, 30

= Post>Pre T = 12.98

= RH Pre-Glx T = 9.60

48, 27, 0

33, -51, 69

= RH Post-Glx T = 8.35

Surface-effects combined

-24, -81, 27

Fig. 4

Fig. 5

Table 3 – Summary statistics for paired-sample t-tests on selected FNC pairs and Pearson correlations between Glx and FNC before and after tDCS. FNC pairs Among parietal nodes (local to anodal tDCS) RH frontal–parietal & Lh frontal–Parietal RH frontal–parietal & Precuneus Lh frontal–parietal & Bilateral SPS Lh frontal–parietal & bilateral inf. parietal Bilateral SPL & precuneus Bilateral SPL & bilateral inf. parietal Bilateral SPS & precuneus Bilateral SPS & bilateral inf. parietal Nodes that comprise DMN ACC & PCC ACC & precuneus Nodes that comprise distributed networks Superior frontal gyrus & bilateral SPL Bilateral aPFC & bilateral inf. parietal Salience & ACC Salience & BG Among cerebellum network Bilateral SPL Bilateral inf. parietal Bilateral SPS Precuneus PCC LH frontal–parietal RH frontal–parietal BG

Pre-tDCS

Post-tDCS

T-score

Pre Glx & FNC

Post Glx and FNC

0.52 (0.28) 0.24 (0.16) 0.27 (0.28) 0.10 (0.39) 0.29 (0.45) 0.27 (0.28) 0.40 (0.28) 0.20 (0.34)

0.56 0.18 0.02 0.29 0.19 0.19 0.33 0.42

0.35 0.46 2.15* 2.02* 0.72 0.48 0.47 1.64

0.70* (LH) – – – – – – –

– – – – 0.76* (LH) – – –

0.54 (0.21) 0.05 (0.28)

0.51 (0.26) 0.30 (0.19)

0.34 2.44*

– –

– –

0.27 (0.33) 0.28 (0.30) 0.24 (0.32) 0.23 (0.25)

0.04 (0.43) 0.15 (0.27) 0.07 (0.33) 0.25 (0.25)

2.76* 1.48 2.02* 0.15

– – – 0.84* (LH)

0.65* (LH) 0.77* (LH) 0.69* (RH) 0.74*(RH)

0.53 (0.24) 0.14 (0.30) 0.21 (0.37) 0.53 (0.37) 0.14 (0.25) 0.06 (0.29) 0.23 (0.25) 0.34 (0.18)

0.55 0.30 0.31 0.72 0.30 0.15 0.35 0.02

0.23 1.65 1.25 1.76* 1.3 2.01* 1.52 1.75*

– 0.66*(LH) – 0.71* (LH) – – 0.76* (LH) –

– – – – – – – –

(0.24) (0.40) (0.25) (0.39) (0.43) (0.33) (0.29) (0.17)

(0.19) (0.25) (0.23) (0.16) (0.23) (0.33) (0.22) (0.52)

All FNC pairs are displayed as Fisher Z. LH, left hemisphere; RH, right hemisphere; Inf, inferior. n po0.05 (uc).


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there was a significant increase in FNC between the left frontal–parietal and bilateral inferior parietal networks, as expected. For the networks that comprise the DMN, the FNC between the ACC and precuneus networks displayed the largest increase in FNC. For FNC between nodes that comprise distributed networks, there was a decrease in FNC between the salience and ACC networks, from being significantly anti-correlated to zero-correlation after tDCS (see Table 3). For the FNC among subcortical networks and all other structurally connected brain regions, there was a significant increase in FNC between the cerebellum and the precuneus network, and between the cerebellum and the left frontal–parietal network. There was also a decrease in FNC between the cerebellum and the BG network (see Table 3).

2.7.

Relationships between Glx and FNC

2.7.1.

Post-tDCS Measures

All statistically significant correlations between Glx estimates and FNC are displayed in Table 3. The strongest post-tDCS Glx relationship was observed between left Glx and FNC between the bilateral SPL and precuneus networks. Left Glx was also correlated with FNC between bilateral inferior parietal and bilateral anterior PFC networks. Interestingly, right Glx was positively correlated with FNC between the salience and ACC networks, while it was negatively correlated with FNC between the salience and BG networks.

3.

Discussion

By combining 1H MRSand rs-fMRI, the present study investigated how tDCS interacts with different levels of the brain’s neural architecture. The observed after-effects of tDCS included increases in Glx in right parietal cortex, underneath the site of anodal stimulation, which predicted increases in network connectivity in the precuneus for several different networks. This may indicate that the precuneus acts as an intermediate node that modulates glutamatergic signaling in other pathways, including bilateral SPL, ACC, salience, left frontal–parietal, and BG networks. As discussed below, crosshemispheric connectivity, specifically within the bilateral inferior parietal network, may be more readily influenced by inhibitory signaling pathways. While the majority of the results support our hypotheses, we also encountered additional, unexpected findings, discussed below in terms of their potential systems-level contributions to behavior, which may inform future hypotheses for the most optimal cortical targets for enhancing cognitive functions in healthy and clinical populations.

3.1. Hypothesized increases in parietal network connectivity As expected, tDCS increased functional connectivity within the right hemisphere of the bilateral SPL network; however, the premotor location of this effect was somewhat unexpected. Anatomical studies have confirmed that most of the parietal afferent connections to the dorsal premotor cortex

966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 3.1.1. Increased inter-hemispheric network connectivity in the 998 parietal lobe 999 The present study found that anodal stimulation produced a 1000 redistribution of parietal inter-hemispheric functional con1001 nectivity. This is an important finding because tDCS-induced 1002 increases in inter-hemispheric functional connectivity are 1003 thought to be largely dependent on the contributions of the 1004 cathode electrode (Sehm et al., 2012; 2013; Park et al., 2013). In 1005 the present study, network connectivity was increased pre1006 dominantly in the left lateral inferior parietal regions of the 1007 bilateral inferior parietal and the left frontal–parietal net1008 works. The enhancement of functional connectivity in the 1009 left hemisphere by right hemisphere excitatory stimulation is 1010 consistent with the role of transcallosal excitation (Yazgan 1011 et al., 1995; see Bloom and Hynd, 2005 for review). 1012 However, given that Glx did not predict functional con1013 nectivity within the bilateral inferior parietal network, the 1014 observed tDCS-induced increases in cross-hemispheric net1015 work connectivity may be more readily influenced by other 1016 neurotransmitter pathways, such as GABA. Inferior parietal 1017 structures are thought to have an inhibitory influence on one 1018 another through transcallosal fiber connections (Kinsbourne, 1019 1993; Sparing et al., 2009). Thus, the current results for the 1020 bilateral inferior parietal network should be considered in 1021 line with the recent TMS and tDCS studies that targeted the inferior parietal lobe (e.g., Dambeck, 2006; Bologini et al., 2010; Q18 1022 1023 Moos et al., 2012), which were all in support of the inhibitory 1024 hemispheric rivalry hypothesis (Kinsbourne, 1993). In this 1025 view, the two hemispheres have a mutual, reciprocal originate from SPL (see Wise et al., 1997 for review). It is therefore possible that parietal stimulation specifically increased the synchronization of these afferent signaling pathways from superior parietal to premotor cortices. This increase may translate to an enchantment of attention to visual processing and the execution of movements (Rushworth et al., 2001; Molenberghs et al., 2007; Moos et al., 2012). With regard to the bilateral SPL network's relation to glutamatergic activity, right and left Glx concentrations predicted increased functional connectivity in the right precuneus only. This relationship was the strongest in comparison to the other networks, which indicates that tDCS-induced alterations in glutamatergic signaling are localized within the area of stimulation, but also extends to nearby, structurally connected brain regions. The precuneus in particular is thought to be a central core in the cortical anatomical network (Powers et al., 2013), which exhibits heterogeneous functional connectivity with the default-mode, motor, visual, and attentional systems (White et al., 2010; Allen et al., 2011). Given that left Glx also predicted functional connectivity in the right precuneus and the left SPL, which exceeded the FWE-corrected threshold, it is possible that this brain structure acts as an intermediate node that modulates interhemispheric communication via micro-scale alterations in glutamatergic neurotransmission. Indeed, we also observed that only after tDCS, left Glx was positively correlated with the FNC between the bilateral SPL and precuneus network, further demonstrating that tDCS may influence the degree to which two networks communicate between each other via glutamatergic processing.



inhibitory influence on one another. It predicts that stimula1026 tion over the parietal cortex should result in trans1027 hemispheric release of inhibition to the contralateral hemi1028 1029 sphere that will in turn result in hyperactivity in the ipsilateral hemisphere (see Fecteau et al., 2006 for review). Thus, 1030 during stimulation, the right hemisphere would exert more of 1031 an inhibitory influence on the left via GABAergic signaling. 1032 Indeed, it has been found that inhibitory network oscillations 1033 occur in response to the activation of metabotropic glutamate 1034 receptors, which may have implications on large-scale func1035 tional connectivity and ultimately cognition (Whittington 1036 1037 et al., 1995). For the present study, however, it remains 1038 unclear which micro-scale signaling pathways drove the 1039 enhancement of within- and between-network connectivity 1040 between the bilateral inferior parietal and left frontal–parietal 1041 networks. Given that V1 BOLD signal variations have been 1042 Q19 linked to baseline GABA levels (Donahue et al., 2010), further 1043 research is needed address any involvement of GABAergic 1044 signaling pathways that may influence network connectivity 1045 between inferior parietal networks. 1046 3.2. Expected decreases within nodes of the DMN 1047 1048 As expected, tDCS produced a significant decrease in func1049 1050 tional connectivity within the ACC network, which was 1051 specifically observed in the right medial frontal gyrus. This finding is consistent with the general hypothesis that tDCS 1052 may enhance the flexible balance between putative func1053 tional brain networks by suppressing functional connectivity 1054 within the DMN. In addition, bilateral Glx concentrations 1055 1056 predicted bilateral precuneus and right medial frontal gyrus 1057 functional connectivity within the ACC network, which high1058 lights the potential role of glutamatergic signaling in decreas1059 ing functional connectivity in more distant brain regions 1060 (Enzi et al., 2012). To this end, we also observed that the 1061 FNC between the ACC and precuneus was significantly 1062 increased after tDCS. This finding is of interest given the 1063 concurrent decrease in ACC functional connectivity, which 1064 suggests that parietal stimulation may reduce attention 1065 directed to the ACC (e.g., internal visceral signals), while 1066 enhancing more attentive (tonic monitoring) states with the 1067 environment via enhanced communication with precuneus 1068 network (Lin et al., 2011; Vogt and Derbyshiret, 2009). 1069 1070 3.3. Expected Increases in the distributed salience network 1071 1072 Prior to the administration of tDCS, there was a statistically 1073 significant relationship between right Glx and right SPL 1074 functional connectivity within the salience network. This 1075 association is consistent with the hypothesis that tonic 1076 excitatory neurotransmission is necessary in order to readily 1077 Q20 engage other networks when attending to stimuli (Eichler and 1078 Meier, 2008). This is indeed consistent with salience net1079 work’s active role in switching between the default-mode and 1080 task-related states of brain connectivity (Seeley et al., 2007; ; 1081 Bonnelle et al., 2012; Ham et al., 2013). 1082 After anodal stimulation, increased functional connectiv1083 ity in the right insula extending contiguously to IFG were also 1084 observed. Together, the increased functional connectivity in 1085 the right insula and IFG may establish an enhancement of the

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1086 casual outflow from the right insula and IFG to other nodes 1087 that anchor the salience network for further attentional 1088 engagement. Across stimulus modalities, the right anterior 1089 insula plays a causal role in enabling the recruitment of 1090 contextually relevant brain regions (Sridharan et al. 2008)— 1091 thereby activating task-related networks and deactivating the 1092 DMN. Thus, by enhancing the network activity within right 1093 anterior insula and IFG, parietal stimulation may improve 1094 cognitive performance by enhancing the initial phase of 1095 information processing, specifically as it relates to integrating external stimuli with internal homeostatic context (Seeley Q21 1096 1097 et al., 2007; Singer et al., 2009; Menon and Uddin, 2010; Seth 1098 et al., 2012). 1099 1100 1101 3.3.1. Increased functional connectivity in frontal networks 1102 Functional connectivity was significantly increased after 1103 tDCS within the bilateral anterior PFC network, specifically 1104 in right BA10. This brain region is consistently engaged when 1105 subjects are instructed to learn new behavioral routines 1106 (Koechlin et al., 2002; Strange et al., 2001) and when the 1107 amount of uncertainty (i.e., entropy) is increased in regard to 1108 multiple options that subjects are simultaneously tracking 1109 (Yoshida and Ishii, 2006). After tDCS, the FNC between this 1110 network and the bilateral inferior parietal network was 1111 significantly correlated with left Glx concentrations. Taken 1112 together, tDCS may contribute to learning as it relates to 1113 maintaining and switching back and forth between multiple 1114 behavioral alternatives in search of optimal behavior 1115 (Koechlin et al., 2002; Yoshida and Ishii, 2006; Andersson 1116 et al., 2009). However, this hypothesis has to be investigated 1117 in the context of problem-solving before and after tDCS. 1118 1119 1120 3.4. Expected decreases in the basal ganglia 1121 1122 Anodal tDCS decreased functional connectivity in bilateral 1123 putamen. This finding is consistent with the previously 1124 observed intrinsic connectivity between the putamen and 1125 parietal cortex (Martino et al., 2008; Cao et al., 2009). To this 1126 end, given that the putamen consists primarily of spiny 1127 neurons which receive direct input from glutamatergic sig1128 naling, tDCS may also influence the inhibitory medium spiny 1129 neurons in this brain structure (Garcia et al., 2010; Girault, 1130 2012). However, the putamen and the cortex have no mono1131 synaptic anatomical connections, and so further research is 1132 needed to identify the mediating structures and signaling 1133 pathways by which tDCS operates. 1134 Furthermore, it has been previously shown that anodal 1135 tDCS over M1 (cathode contralateral frontopolar region) 1136 resulted in a reduction in functional connectivity between 1137 the left caudate nucleus and the PCC node of the DMN 1138 (Polanía et al., 2012). The authors suggested that this reduc1139 tion, or “switch” in activation patterns, was due to the motor1140 related loop being activated by tDCS. To this end, given the 1141 Glx relationships with FNC between the BG and salience 1142 networks, further research is needed to test the extent that 1143 these two networks engage and disengage one another and 1144 whether a “switch” in hemispheric reactivity influences 1145 hierarchical attention-processing.


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3.5.


Exploratory analysis of the cerebellum

The cerebellum plays an important role in the planning, initiation, stability, organization, and long-term memory of movements (Schmahmann and Sherman, 1998; Imamizu et al., 2000; Schmahmann and Caplan, 2006; Miall et al., 2007). Parietal stimulation increased network connectivity in left Crus 1 and 2, with no observed relationships with Glx. In a previous rs-fMRI study, the human cerebellum was analyzed using both lobular anatomically-driven and selforganizing map-driven approaches (Bernard et al., 2012). This study found that subdivisions of Crus I and II were correlated with distinct regions of prefrontal, temporal and parietal cortices; furthermore, subdivisions of Crus I contained multiple smaller functional clusters that may reflect the heterogeneity of cognitive processing. Thus, it may be the case that tDCS over the parietal lobe enhanced intra-cerebellar processing, which may further enhance local processing of information related to cognition. Moreover, the FNC between the cerebellum and precuneus and FNC between the cerebellum and left frontal–parietal networks were all significantly increased after stimulation. Given that there are no monosynaptic connections between the cerebrum and cerebellum (Buckner et al., 2011), the current FNC results provide further evidence for the cerebellum’s indirect influence on intrinsically coupled cerebral association areas, such as the parietal cortex (Habas et al. 2009; O’Reilly et al. 2010). Furthermore, given that the cerebellum exerts an overall inhibitory tone over the cortex during rest—specifically with M1 (Galea et al., 2009), the observed increases in FNC might result from a complex relationship between GABAergic and glutamatergic signaling. Stimulation also resulted in a significant decrease in FNC between the BG and cerebellum networks. The BG and cerebellum are thought to form multi-synaptic loops with the cerebral cortex (Haber, 2003; Bostan et al., 2010), with the anterior parietal regions playing an integrative role that mediates activity to and from the BG and to the frontal lobe. Clower et al. (2005) demonstrated that the anterior inferior parietal cortex (in the cebus monkey) is the target of both cerebellar and BG output. Thus, from a computational perspective, the BG and cerebellum have been viewed as segregated modules that implement different learning algorithms, with the BG supporting reinforcement learning and the cerebellum supporting supervised learning (Doya, 1999; Houk et al., 2007).

3.6.

Clinical applications and further considerations

Understanding the mechanisms by which micro and macro changes occur after tDCS could help to increase its effectiveness in altering neuroplastic mechanisms in order promote healing and recovery (Peled, 2004; 2005; Spedding et al., 2003; Kays et al., 2012; Kuo et al., 2013). For instance, increasing both the glutamatergic neurotransmission and functional connectivity that is otherwise impaired in schizophrenia (Friston, 1998; Stephan et al., 2006; Williamson and Allman, 2012; Szulc et al., 2013), could lead to effective therapies that combine the synergistic effects of cognitive training, psychopharmacology and stimulation protocols. However,

medication and other factors should always be taken into consideration when tailoring stimulation protocols for clinical populations, as the present results may differ as a nonlinear function of dose. Accordingly, there is a similar issue of nonlinearity with duration of stimulation and homeostatic plasticity. That is, the effects of tDCS on neuroplasticity depends upon the recent history of tDCS-induced excitation, such that repeated stimulation (with short intervals) has resulted in a reversal of excitability (Fricke et al., 2011), similarly for increasing intensity from 1 mA to 2 mA (Batsikadze et al., 2013). More specifically, Batsikadze et al. (2013) found that compared to 1 mA anodal stimulation over motor cortex for 20 min, 2 mA induced after-effects with a delay, which may have occurred from several reasons, including a transient homeostatic counter-regulation, alterations of intracellular calcium, and/ or that deeper cortical layers were affected by 2 mA but not 1 mA current. Thus, the observed results with increased functional connectivity in the opposite hemisphere of stimulation may have been a result of this time-dependent homeostatic balance governing the response to tDCS, which should be considered if a particular clinical population is already exhibiting preexisting (baseline) perturbations in the underlying cortex.

3.7.

Limitations

As mentioned above, a limiting factor of the current study is its inability to track the real time influence of tDCS on glutamatergic signaling on large-scale functional connectivity. Future studies are needed to assess the extent that the intrinsic inter-hemispheric balance is maintained during cortical polarization. Identifying any differences in this timing may be necessary to reveal the optimal strengthening of the synaptic efficacy resulting in observable changes in both micro-level signaling and network-level interactions. Another limitation is that the 1H MRS sequence did not measure GABA concentrations. Future studies are needed to test whether there are a differential relationships between Glx and GABA across different partitions of the inferior parietal, BG, ACC and cerebellum networks. Another potential limitation is the small sample size, which may have reduced statistical power of finding other differences or relationships in the current sample. Although there was no sham tDCS group to compare the results with, the within-subjects design of our study effectively categorized the “comparison” group as the same participant's baseline scan, which can be a more sensitive and powerful design in capturing and accounting for important individual differences in network fluctuations. Moreover, previous rs-fMRI studies have shown that sham tDCS does not have a significant effect on large-scale intrinsic network dynamics (Peña-Gómez et al., 2012; Park et al., 2013). In addition, future studies should include several types of cognitive tasks to probe the functional relevance of the present findings. Lastly, given the many different parameters that impact the effects of tDCS (e.g., electrode position, size, duration, intensity, and others), future studies are needed to compare the present results with other stimulation protocols.


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3.8. Conclusions 1266 1267 The present study provides evidence that the “excitatory” 1268 1269 after-effects of anodal tDCS on glutamatergic signaling and network connectivity contribute to local, cross-hemispheric 1270 and subcortical alterations. The observed after-effects of tDCS 1271 1272 include increases in glutamatergic signaling that originate 1273 from the parietal site of stimulation, with the precuneus 1274 possibly acting as an intermediate node that modulates 1275 glutamatergic signaling in other pathways that include bilat1276 eral SPL, ACC, salience, left frontal–parietal, and BG networks. 1277 Cross-hemispheric connectivity, specifically for the bilateral 1278 inferior parietal network, may be more readily influenced by 1279 inhibitory signaling pathways. Overall, the importance of 1280 investigating potential interactions across these levels of 1281 analyses could inform future hypotheses for the most opti1282 mal cortical targets for healthy and clinical populations. 1283 1284 4. Experimental procedure 1285 1286 4.1. Participants 1287 1288 Data from the 11 right-handed volunteers reported in Clark 1289 et al. (2011) were included in the present study. However, two 1290 participants were excluded due to missing post-tDCS rs-fMRI 1291 data. Data from the remaining 9 subjects (mean age¼ 22, 1292 SD¼ 3 years old; 6 female) were analyzed. All participants 1293 reported English as their primary language, with no history of 1294 head injuries or concussions, current or previous history of 1295 mental, neurological, alcohol or drug abuse disorders, current 1296 prescription medication affecting CNS function, or hearing/ 1297 visual impairments. Subjects were also excluded for metal 1298 implants, claustrophobia and pregnancy, as these are contra1299 indications for MRI. 1300 1301 1302 4.2. Procedure and tDCS administration 1303 1304 After screening and consent, pre-tDCS 1H MRS and rs-fMRI 1305 scans were obtained, which lasted a total of 2.1 h on average. 1306 Immediately after, participants were removed from the scan1307 ner and tDCS was applied for 30 min while participants sat in 1308 a sound-attenuated room. An Iomed Phoresor PM850 was 1309 connected to square-shaped, 11 cm2, saline-soaked sponge 1310 electrodes. The anode was placed over 10–20 electrode site P4, 1311 with a current of 2.0 mA delivered for 30 min. The cathode 1312 was placed over the contralateral upper arm. To better 1313 Q25 control for activation state of the brain (Silvanto and 1314 Pascual-Leone, 2008), participants were asked to rest with 1315 their eyes open through stimulation. This ensured the detec1316 tion of tDCS-induced neuroplastic changes by removing any 1317 possible “spill over” effects related to performing a cognitive 1318 task (Clemens et al., 2014). In order to reduce the length of our 1319 experimental procedure, no sham condition was used; more1320 over, other rs-fMRI studies have shown that sham tDCS does 1321 not have a significant effect on large-scale intrinsic network 1322 dynamics (Peña-Gómez et al., 2012; Park et al., 2013). 1323 Within 5–10 min after tDCS, post-tDCS 1H MRS and rs-fMRI 1324 scans were obtained, with the 1H MRS collected at the same voxel locations (left and right) as the pre-tDCS voxel 1325

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placements. The same 1H MRS and rs-fMRI parameters were used for both pre- and post-tDCS sequences.

1326 1327 1328 1329 1 4.3. H MRS data Collection & preprocessing 1330 1331 1 The same H MRS data and analysis previously reported 1332 in Clark et al., 2011 were used here for a subset of subjects 1333 that also received rs-fMRI testing. All fMRI and MRS scans 1334 were obtained on a 3T Siemens TIM Trio MRI system with a 1335 12-channel radiofrequency head coil. All parameter details 1336 1 for the H MRS sequence can found in Clark et al., 2011. In 1337 summary, A PRESS (point-resolved spectroscopy) sequence 1338 was collected (TR/TE¼ 1.5 s/40 ms, voxel size¼ 20 mm 1339 20 mm 20 mm, averages¼ 192), with an unsuppressed water 1340 signal used as a concentration reference and eddy current 1341 correction in post-processing. 1H MRS spectra were analyzed using LCModel (Provencher, 2001). Metabolite concentrations Q26 1342 1343 were estimated as previously reported (Gasparovic et al., 1344 2009). Only Glx estimates with a Cramer–Rao lower bound. 1345 Glx estimates from two participants that did not meet this 1346 criterion. However, in order to include these subjects in 1347 further analyses, these 2 data points were interpolated using 1348 SPSS 21 (IBM SPSS Statistics for Windows, Armonk, NY: IBM 1349 Corp.), where missing values were replaced with the mean 1350 of their designated group (i.e., from right pre- or left post1351 measures). 1352 1353 4.4. fMRI data collection & processing 1354 1355 High-resolution T2n-weighted functional images were 1356 acquired using a gradient-echo EPI sequence with TE ¼29 ms, 1357 TR¼2 s, flip angle¼ 751, slice thickness¼ 3.5 mm. Resting1358 state scans were 5 min in duration, and participants were 1359 instructed to keep their eyes open, relax and attend to a cross 1360 hair on the center of the screen. 1361 Preprocessing was completed using SPM 5 (Wellcome 1362 Department of Imaging Neuroscience, London, UK. http// 1363 www.fil.ion.ucl.ac.uk/spm), within MATLAB (Mathworks Inc., 1364 Sherbon MA). The first three EPI volumes were discarded to 1365 remove T1 equilibration effects. Realignment was then com1366 pleted using INRIalign and slice-timing correction was 1367 applied, with the middle slice used as the reference frame. 1368 Data were then spatially normalized into the standard Mon1369 treal Neurological Institute (MNI) space, resliced to 3 mm 3 1370 mm 3 mm voxels, and smoothed using a Gaussian kernel 1371 with a full-width at half-maximum of 10 mm. No subjects 1372 were excluded due to excessive head motion, defined as more 1373 than 3.125 mm of translation in any plane or more than 5 1374 degrees of rotation in any plane (Gusnard et al., 2001). Pre1375 and post-tDCS measures did not significantly differ in any of 1376 the six head position parameters assessed here (i.e., transla1377 tional displacements along X, Y, and Z axes and rotational 1378 displacements of pitch, yaw, and roll; all p's40.40). 1379 1380 1381 4.4.1. Network identification 1382 Please see supplementary information for details regarding 1383 the exclusion of networks not of interest. In summary, 63/75 1384 components were excluded, and the remaining 12 compo1385 nents were included in all further analysis based off their


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anatomical regions that comprise each network and are 1386 described in detail in the results section. 1387 To ensure that the topological false-discovery rate (FDR) 1388 1389 Q27 procedures based on both spatial extent and peak values were utilized (Chumbley and Friston, 2009; Chumbley et al., 1390 1391 Q28 2010), all spatial comparisons on ICA-derived statistical parametric maps (i.e., output images from GIFT) were con1392 ducted in SPM 8. First, to test the significance of individual 1393 1394 component voxel-wise functional connectivity (difference 1395 from zero) for both pre- and post-tDCS fMRI measurements, 1396 one-sample t-tests were computed at each voxel across all 1397 subjects and sessions. The resulting significant clusters 1398 within each component (FDR-corrected, po0.01) were then 1399 saved as a spatial mask to limit the number of voxel-wise 1400 comparisons in subsequent analyses. 1401 1402 4.5. Comparing within-network functional connectivity 1403 1404 To assess whether the voxel-wise functional connectivity was altered after tDCS, paired-sample t-tests were computed 1405 on each voxel within component spatial maps. Head motion 1406 (Euclidian linear translation) was included as the only cov1407 ariate. Model parameters were estimated based on restricted 1408 maximum likelihoods, which is the classical estimation 1409 method used in SPM. Pair-wise differences are reported only 1410 1411 for effects that exceed FDR-correction (po0.05), with a cluster voxel extent threshold of k410. 1412 1413 4.6. Assessing relationships between voxel-wise 1414 functional connectivity and Glx concentrations 1415 1416 To examine the linear relationships between Glx and the 1417 functional connectivity within components before and after 1418 1419 tDCS, second-level multiple regression analyses were performed 1420 within SPM 8. Each subject’s spatial maps were entered as 1421 dependent variables, separately for pre- and post-tDCS mea1422 sures, and right and left Glx levels were entered as the predictor 1423 variables. As 1H MRS voxels were obtained within the right SPS, 1424 this analysis was initially conducted only on those voxels within 1425 this region, but also included inferior parietal and precuneus 1426 (BA7) cortices—given the close proximity with the 1H MRS voxel, 1427 herein labeled as the MRS-mask (See supplementary material, 1428 Fig. 4). If significant relationships were observed within this MRS1429 mask, a second, extended regression analysis was performed 1430 including all significant voxels within that network, herein 1431 labeled the network-mask. Results are reported only for effects 1432 that exceed peak-voxel threshold of po0.05 (FWE-corrected). 1433 However, given previous glutamate and ICA-generated func1434 tional connectivity relationships were observed with moderate 1435 effects (Kapogiannis et al., 2012: tr3.40, pZ0.004), we also 1436 assessed relationships significant at po0.001 (uncorrected) in 1437 order to capture similar relationships as previous studies that 1438 Q29 may still be informative for future research (Fig. 6). 1439 1440 4.7. Comparing FNC among specific network pairs and 1441 their relation to Glx 1442 1443 To compute the relationship between the network activation 1444 time courses of each of the 14 components, a constrained-lag 1445 correlation was performed within the FNC toolbox v.2.3 (Jafri

1446 1447 1448 1449 RH Parietal PCC RH Frontal 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 56, 46, 0 40, -68, 32 10, -58, 40 1462 1463 Fig. 6 1464 1465 et al., 2008; http://mialab.mrn.org/software/fnctb/). First, 1466 a band-pass filter was applied (0.012 Hz and 0.12 Hz), and 1467 the maximum lag interval was constrained to 74 s (to 1468 remove the impact of lag differences on the correlation 1469 results). Thereafter, a Fisher's z transformation was applied 1470 on all pair-wise FNC correlations. Pair-wise t-tests were then 1471 computed on FNC for component-pairs of interests. Further1472 more, using a one-sample t-test (α¼ 0.05), all further analyses 1473 were conducted only on network pairs that resulted in 1474 significant FNC in either the pre- or post-tDCS measures. 1475 To examine the relationship between Glx and FNC for 1476 component pairs of interest, Pearson correlations were com1477 puted separately for pre- and post-tDCS measures, and for 1478 left- and right-hemisphere Glx. 1479 1480 1481 Financial disclosure 1482 1483 All authors have no conflicts of interest to report. 1484 1485 Uncited references Q2 1486 1487 1488 Corbetta et al. (1998), Choi et al. (2012), Goldberg et al. (2006), 1489 Machado-Vieira et al. (2009), Qin (2011), Raichle and Snyder 1490 (2007), Schall (1995), and Wagner et al. (2006). 1491 1492 1493 Acknowledgments 1494 1495 This work was supported by the Department of Energy 1496 (Government contract DE-FG02-99ER62764) and Office of 1497 Director of National Intelligence (2014-131270006) awarded 1498 to Dr. Vincent Clark, the National Institute of Mental Health, 1499 NIH (R21MH097201) awarded to Dr. Vince Calhoun, and the 1500 National Academies Ford Pre-Doctoral Fellowship and the 1501 National Science Foundation Graduate Research Fellowship 1502 Program (DGE-0903444) awarded to Michael A. Hunter and by 1503 R21MH097201 awarded to Dr. Terran Lane. 1504 The authors would like to thank the participants who 1505 enrolled into this study and to the MRI technicians, Diana

RH Frontal-Parietal Network: Pre- and post-tDCS network connectivity. Pre-tDCS Post-tDCS Overlap



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