Resting-State Functional Connectivity Differences Following Experimental Manipulation of the Orbitofrontal Cortex in Two Directions via Theta-Burst Stimulation

Abstract

Compulsive behaviors (CBs) have been linked to orbitofrontal cortex (OFC) function in animal and human studies. However, brain regions function not in isolation but as components of widely distributed brain networks—such as those indexed via resting-state functional connectivity (RSFC). Sixty-nine individuals with CB disorders were randomized to receive a single session of neuromodulation targeting the left OFC—intermittent theta-burst stimulation (iTBS) or continuous TBS (cTBS)—followed immediately by computer-based behavioral “habit override” training. OFC seeds were used to quantify RSFC following iTBS and following cTBS. Relative to cTBS, iTBS showed increased RSFC between right OFC (Brodmann’s area 47) and other areas, including dorsomedial prefrontal cortex (dmPFC), occipital cortex, and a priori dorsal and ventral striatal regions. RSFC connectivity effects were correlated with OFC/frontopolar target engagement and with subjective difficulty during habit-override training. Findings help reveal neural network-level impacts of neuromodulation paired with a specific behavioral context, informing mechanistic intervention development.

Keywords

theta-burst stimulation transcranial magnetic stimulation compulsive behaviors obsessive-compulsive disorder orbitofrontal cortex

Noninvasive brain stimulation is a promising approach to develop novel interventions that target neural circuits relevant in psychiatric conditions. One widely available form of neuromodulation, repetitive transcranial magnetic stimulation (rTMS), can be used to modulate a relatively focal brain area, and it has most typically been applied to focally target a singular brain region thought to display aberrant function within a particular condition (Lisanby et al., 2002). Yet psychiatric disorders are increasingly understood to reflect circuit-level dysfunctions in the coordinated activation of spatially distributed networks (Price & Duman, 2020; Sporns et al., 2004; Williams, 2016). Convergently, the field of brain stimulation has begun to move from a primary focus on target-region modulation toward a more nuanced understanding of the circuit-level impacts of focal neuromodulation (Huerta & Volpe, 2009; Lisanby et al., 2002), and, in the context of intervention development, an increasing emphasis on how best to modulate desired circuit-level targets to maximize clinical benefit (Micoulaud-Franchi et al., 2013).

Compulsive behaviors (CBs), which typify the obsessive-compulsive and related disorders specified in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), are associated with marked impairment and staggering societal burden (Eaton et al., 2008). CBs can be broadly conceptualized as a failure of flexible goal-directed behavior to override habitual behaviors (Gillan et al., 2016). These habitual CBs may serve a function initially (e.g., short-term reduction of distress, perception of greater certainty) but typically become highly aversive and are recognized as contrary to life goals; yet, in the absence of sufficient capacity to override habitual responses, they persist. On the basis of strong experimental findings in animal models (Ahmari, 2016; Ahmari et al., 2013) as well as consistently altered orbitofrontal cortex (OFC) activation patterns in human neuroimaging studies (Ahmari, 2016), the OFC has recently become a prominent target in rTMS studies for CB disorders. The OFC is an integral component of multiple cortico-striato-thalamo-cortical (CTSC) circuits (Haber, 2016) that together underpin the balance between habitual and goal-directed behavior and are widely implicated as circuit-level substrates of CBs (Ahmari, 2016; Pauls et al., 2014; Saxena & Rauch, 2000).

We recently reported that, in a sample of 69 patients, there were greater improvements in short-term markers of CBs after deactivation of the OFC via continuous theta-burst stimulation (cTBS)—a form of rTMS that can temporarily depotentiate regional brain activation (Chung et al., 2016)—relative to intermittent TBS (iTBS), used to potentiate the OFC (Price et al., 2021). Notably, there is substantial functional heterogeneity within the OFC and neighboring frontopolar cortex (FPC) regions (Kahnt et al., 2012; Ongur & Price, 2000), an issue compounded by the somewhat limited spatial and circuit-level precision of rTMS (e.g., spatial diffusion of the electrical field to both hemispheres). To overcome this and ensure we were augmenting the functional target of interest (i.e., capacity to override habits), we coupled stimulation with a habit-override task. That is, a computer-based automated training was used to provide practice in overriding an avoidance habit, effectively “exercising” clinically relevant habit-override abilities directly following stimulation. When paired with cTBS, this approach yielded improvements in a clinically relevant laboratory probe of CBs. These findings bolstered previously observed correlational patterns in human patients (reviewed by Ahmari, 2016), which adds a strong experimental manipulation design to directly translate experimental (e.g., optogenetic) findings in animal models into human patients.

However, the OFC is only one region situated within much broader CSTC circuits posited to be key substrates of CBs (Pauls et al., 2014; Saxena & Rauch, 2000). Our prior primary report focused strictly on (a) indices of target engagement in the OFC/FPC target region and (b) behavioral outcomes in response to an idiographic laboratory probe of CBs. The present article builds on these previously published findings (Price et al., 2021) by comparing resting-state functional connectivity (RSFC) findings for iTBS and cTBS and exploring RSFC’s associations with other measures collected proximal in time to the TBS manipulation (i.e., within 50 min): (a) TBS target engagement and (b) behavioral data collected immediately after TBS. Given the relevance of circuit- and network-level changes in the context of CBs and neuromodulation more broadly, the current secondary analyses allow us to test whether our combined brain-behavior intervention (cTBS/iTBS plus habit-override training) also yielded an acute impact on features of network-level functional organization, as indexed by RSFC patterns. RSFC offers a glimpse at coordinated network-level activity in the absence of acute task demands. Such circuit-level effects may propagate well beyond focal target-region activation and were therefore expected to affect a much broader circuit.

We predicted that RSFC would be affected for both the ipsilateral and contralateral hemisphere’s target-proximal areas (OFC/ventromedial PFC [vmPFC]), and we predicted that effects would extend across hemispheres via both (a) spatial diffusion of TMS electrical fields and (b) cross-hemisphere propagation of neural-stimulation effects (Shibata et al., 2020; Webler et al., 2020). We hypothesized that increasing (via iTBS) versus decreasing (via cTBS) regional activation (Chung et al., 2016) within bilateral OFC/vmPFC regions (as we reported previously; Price et al., 2021) would produce differential patterns of connectivity between the OFC/vmPFC and the rest of the brain. Thus, we expected to see group differences in the degree to which temporal fluctuations (at rest), in both ipsilateral and contralateral OFC/vmPFC areas, are tightly and intrinsically integrated with spatially distributed brain regions (Raichle, 2010), both within and beyond core CTSC circuits. We further expected to observe greater connectivity differences among those participants with the greatest degree of OFC modulation (greater TMS “target engagement”). Because a behavioral context was provided in our design in order to focus the effects of the OFC stimulation, we reasoned that any regions exhibiting such altered OFC connectivity patterns would reveal particularly relevant circuit-level substrates involved in the acquisition of a highly clinically relevant skill (overriding avoidance habits) in human patients.

Method

Design

Methodological details have been described previously (Price et al., 2021). In brief, the study design included parallel-arm randomization (see Figure S1 in the Supplemental Material available online). All participants were allocated to iTBS or cTBS, using a between-groups design to eliminate crossover and practice effects. As detailed previously (Price et al., 2021) and in Figure S1, the active TBS visit included the following sequence of events: pre-TBS habit acquisition, TBS, habit-override training, and functional MRI (fMRI) assessment of both target engagement and RSFC. Additional clinical and behavioral assessment procedures are included in the primary report.

Participants

Seventy-eight adults (ages 18–55) were randomized and completed all baseline assessment procedures including the single-blind sham TBS session; of these, 69 returned to attempt active TBS (see Consolidated Standards of Reporting Trials [CONSORT] diagram in Figure S2 in the Supplemental Material). Two clinical inclusion criteria (full details of inclusion/exclusion criteria can be found in the Supplemental Material) were used to identify clinically meaningful forms of CB (i.e., both DSM-5 obsessive-compulsive disorder (OCD) and “diagnostic orphans” with similar problematic CBs). First, a score more than 1 SD above the mean of healthy controls on at least one self-report scale of CBs was required, which included the following seven scales administered at screening:

Four relevant CB subscales (washing, checking, ordering, mental neutralizing) taken from the Obsessive Compulsive Inventory–Revised (OCI-R; Foa et al., 2002), a well-validated self-report inventory with excellent subscale factor structure, subscale stability, and discriminant validity¹;

The Massachusetts General Hospital Hairpulling Scale (Keuthen et al., 1995, 2007), a well-validated and widely used self-report scale of the severity of compulsive hairpulling/trichotillomania symptoms;

The Skin Picking Scale (Keuthen et al., 2001), a valid and reliable self-report scale for the assessment of severity in medical and psychiatric patients who endorse compulsive skin picking; and/or

The Threat-Related Reassurance Seeking Scale (Cougle et al., 2012), a validated measure that correlates with symptoms of OCD, social anxiety, and generalized anxiety disorder.

Second, clinically significant CBs were confirmed per clinician rating on the Yale-Brown Obsessive Compulsive Scale (Y-BOCS-II; Storch et al., 2010); a minimum of two out of the five compulsion subscale items had to be scored at moderate severity or higher. According to baseline diagnostic interviews, these transdiagnostic inclusion criteria yielded a sample with an average of 2.22 DSM-5 OCD diagnoses. The sample was predominantly (90.3%) non-Latinx White, lived in the western Pennsylvania geographic region, was 74.2% female, had a median self-reported household income in the range of $15,000 to $29,999, and a had median of 14 years (Associate’s degree level) of education. For further details on sample characteristics, see Table S1 in the Supplemental Material. A power analysis indicated that a sample size of 70 participants would give adequate (80%) power to detect moderate-to-large effects (Cohen’s d = 0.68) in between-groups contrasts and moderate effects (r ≥ .38) for correlational relationships at an α level of .05.

TBS

For targeting the OFC/vmPFC with TMS, either a left or right target area is routinely used to reduce the scalp-to-cortex distance and avoid electrical-field dissipation at medial targets as a result of cerebrospinal fluid accumulation between the two hemispheres. For consistency with previous TBS studies that successfully reached the OFC/vmPFC area with a standard TMS coil (Hanlon et al., 2015, 2017), as well as a prior clinical TMS study in OCD that also targeted left OFC and exhibited a clinical benefit (Ruffini et al., 2009), we selected a left OFC target (left Brodmann’s area [BA] 10, at its orbital junction with BA11; Fig. 1a). After determining the resting motor threshold (RMT) through standard procedures (Rossini et al., 2015), the TMS coil (MCF-B65 figure 8; MagVenture, Farum, Denmark) was positioned stereotactically over a navigational system-identified OFC anatomical target. A neuronavigation system with TMS Navigator software (Localite; Bonn, Germany) was used to allow for stereotaxic registration of the participant’s brain (using the magnetization prepared rapid gradient echo [MPRAGE] structural scan acquired at baseline) with the TMS coil. Registration was begun at the approximate “Fp1” position (according to the international 10-20 electrode system; Jasper, 1958), but it was then further verified through the neuronavigation system that the focal stimulation point fell within the target OFC area (left BA10, ventral area within close proximity to BA11). Further details of neuronavigation methods are provided in the Supplemental Material.

Fig. 1.

The images in (a) show the anatomical stimulation target area and anatomical subregions of interest used as orbitofrontal cortex (OFC) seed regions for resting-state functional connectivity (RSFC) analyses. For participant-level neuronavigation on the active stimulation day, the target location reflecting a focal point of stimulation within the left Brodmann’s area (BA) 10 was verified on the basis of each participant’s individual structural magnetization-prepared rapid acquisition with gradient echo (MPRAGE) data acquired at the baseline scan (details are provided in the Supplemental Material). BA10 is shown in red, BA11 in yellow, and BA47 in orange. The images in (b) show the results of a voxel-wise search across the whole brain, which identified two clusters in which theta-burst stimulation (TBS) condition (intermittent TBS [iTBS] > continuous TBS [cTBS]) was related to RSFC functional connectivity when using a right lateral OFC (BA47) seed (voxel-wise p < .005; map-wise corrected p < .05). The example scatterplot in (c) depicts the linear association between the pseudocontinuous arterial spin labeling (pcASL) target-engagement index as defined in our prior report (Price et al., 2021)—cerebral blood flow (CBF) in ventromedial prefrontal cortex (vmPFC)—and RSFC between the right lateral OFC and dmPFC.

We adapted published protocols (Hanlon et al., 2017) to increase tolerability and comfort and achieved high compliance with TBS procedures, as has been found repeatedly in other clinical populations (Hanlon et al., 2019). This included a stimulation ramp-up block (beginning at 0%), followed by the active block, which was delivered at the target amplitude of 110% RMT (as in prior OFC cTBS studies; Hanlon et al., 2015) or at the maximum amplitude tolerated by the participant during ramp-up. TBS clinical studies targeting PFC (Li et al., 2014; Philip et al., 2019) found that a minimum tolerated amplitude of 85% RMT was considered adequate for efficacious stimulation, and thus participants who completed the active TBS block at ≥ 85% RMT (n = 59 at 110%; n = 4 at 86%–104%) were included in all analyses, which used a per-protocol principle to measure the impact of receiving the intended intervention (in contrast to intent-to-treat analyses, which measure the impact of being randomized to a given intervention; Tripepi et al., 2020).

In all conditions, during each of the ramp-up blocks (600 pulses) and the active/full amplitude blocks (600 pulses), pulses were applied in a theta-burst pattern (bursts of three stimuli at 50 Hz repeated at 5 Hz frequency) using a stimulator (MagPro X100; MagVenture). iTBS consisted of 20 trains, each lasting 2 s with intertrain intervals of 8 s, for a total of 192 s. cTBS consisted of one continuous train of 40 s.

Habit-override training

During the expected window of TBS modulation (for a schematic of the overall study design, see Fig. S1 in the Supplemental Material), a habit override training task was administered, modeled after previous OCD research (Gillan et al., 2015), as described in detail previously (Price et al., 2021) and in the Habit Override Task section of the Supplemental Material (see also Fig. S5). In brief, in habit acquisition (delivered before TBS), participants were instructed that their goal was to avoid receiving shocks to the left and right foot by pressing appropriate buttons whenever a conditioned cue appeared. Across 480 trials (3 s/trial, 24 min total), participants overlearned these avoidance behaviors. In the subsequent habit-override task (240 trials, 3 s/trial, 12 min total), which was administered immediately (15–30 min) after TBS, the same pairs of cues were presented, but one of the two overlearned habits was “devalued.” Written and verbal instructions informed participants which one of the two electrodes had been disconnected and stated that they should attempt to resist the relevant devalued avoidance response (i.e., override that habit) while continuing the remaining “valued” habit. Because of a strongly bimodal distribution of response patterns (see the Supplemental Material), behavioral performance in overriding devalued cues was dichotomized using a > 50% threshold for button presses made in response to devalued cues. At the close of the entire task, participants rated (on a Likert-like scale from 1 to 5) their anxiety during the task, the strength of their urges to respond to devalued cues, and the effort needed to suppress such urges. As detailed previously (Price et al., 2021), performance patterns conformed to expectations and suggested overriding the shock-avoidance habit was challenging for participants, eliciting anxiety and urges to press for the devalued side, as intended—thus providing a clinically relevant state in which to practice this neurocognitive skill.

fMRI RSFC assessment

Resting-state data were acquired during a 7-min block on the active TMS day using a 3-T MRI scanner (MAGNETOM Prisma; Siemens Medical Solutions USA, Malvern, PA) beginning approximately 30 min after TBS, immediately following completion of the habit-override task and an additional 4-min measure of target engagement (described below). Thus, all fMRI collection occurred within the anticipated window of acute TBS modulation. Blood-oxygen-level-dependent (BOLD) imaging data were acquired with a multiband/multiecho (MB-ME) sequence optimized to capture BOLD signal in ventral/OFC regions (repetition time [TR] = 1 s; echo time [TE] = 13.2, 38.76, and 64.32 ms; multiband factor = 5; flip angle = 49°; matrix = 68 × 68; resolution = 3 mm isotropic voxels; 40 oblique slices; 480 volumes), which has been found to substantially reduce susceptibility artifact and increases BOLD signal-to-noise ratio by up to 80% in OFC and CSTC regions (Kim et al., 2016; Kundu et al., 2013; Poser et al., 2006). Because of budgetary and scanner time constraints, the MB-ME sequence was acquired only at the second MRI visit, following active iTBS or cTBS. At the first (baseline) session, a high-resolution structural scan (MPRAGE) was obtained instead, for input to the TMS neuronavigation system.

Standard preprocessing steps in Analysis of Functional NeuroImages (AFNI) software (detailed in the Supplemental Material) included the following recommended steps to reduce artifacts: nuisance regression with six de-meaned motion regressors and their first derivatives, white-matter artifact correction via AFNI’s fast_anaticor algorithm, motion scrubbing (removal from analysis of TRs that had motion of more than 0.5 mm or 0.5°, along with the preceding TR), and high-pass filtering (≤ 0.01 Hz). Residual time-series data were then averaged across all voxels within the following six OFC/vmPFC seed regions, on the basis of our a priori interest in OFC/vmPFC circuits: left and right BAs 47, 11, and 10. Both ipsilateral and contralateral OFC seed regions were included on the basis of our expectation that spatial diffusion of the TMS electrical fields as well as cross-hemisphere propagation of neural stimulation effects (Shibata et al., 2020; Webler et al., 2020) would be likely to occur (as further suggested by the fact that the bilateral vmPFC region exhibited TMS modulation effects in our primary report; Price et al., 2021). For each participant, each seed region time series was correlated across all voxels in the brain, and voxel-wise correlation coefficients were Fisher’s z-transformed. Fisher’s z scores were then compared across the iTBS and cTBS groups at each voxel via unpaired-samples t tests and corrected for multiple comparisons via AFNI’s 3dClustSim (voxel-wise p < .005; map-wise p < .05).

To more specifically probe the OFC-striatal circuitry strongly implicated in previous animal models of CBs (Ahmari, 2016; Ahmari et al., 2013), we also extracted mean connectivity values for each seed region in relation to a priori anatomically defined left and right ventral (nucleus accumbens) and dorsal (caudate, putamen) striatal regions defined per AFNI’s TT_Daemon atlas² and compared these connectivity values across the iTBS and cTBS groups via unpaired t-tests. False-discovery-rate correction was applied to adjust for multiple comparisons across these six striatal target areas.

fMRI target-engagement indices

pcASL

During a 4-min resting-state block, collected both at a baseline scan and at the active TBS visit (just before the resting-state BOLD sequence), a 2D pcASL was acquired (TR = 4.9 s; TE = 16 ms; 25 4-mm slices; interslice gap = 1 mm; 3.28 × 3.28-mm voxels; labeling duration = 1,800 ms; postlabeling delay = 1,800 ms; 25 pairs of labeled/control acquisitions) and preprocessed as recommended using the ASL Perfusion MRI Signal Processing Toolbox (ASLtbx) for the Statistical Parametric Mapping (SPM) software (https://www.fil.ion.ucl.ac.uk/spm/software; for details, see the Supplemental Material). The primary pcASL analyses described previously revealed a bilateral vmPFC functional region where TBS condition modulated cerebral blood flow in the expected directions (decreased following cTBS; increased following iTBS). Because it was collected at both baseline and post-TBS, this was considered our primary index of target engagement for the study, and allowed us to define a functional region-of-interest (ROI) to be used in the present secondary analyses, where target engagement in both intended directions (increased following iTBS; decreased following cTBS) was known to have been achieved. We related change in cerebral blood flow (CBF) in this functional ROI to RSFC patterns collected after TBS only. For these analyses, we calculated difference scores for each participant by subtracting baseline CBF values from post-TBS values, averaging across all voxels in this functional vmPFC ROI. The resulting difference scores were correlated via Pearson’s r with individuals’ RSFC scores, extracted as connectivity averages within both functional and a priori anatomical (striatal) regions.

fALFF

In addition, the MB-ME resting-state BOLD sequence used for RSFC analyses also provided a secondary index of target engagement via a distinct (and independent) analytic approach to quantify fractional amplitude of low-frequency (0.01–0.1Hz) fluctuations (fALFF)—a putative index of the absolute level of resting activation (Zou et al., 2008). These data were used as a secondary measure of target engagement, in addition to CBF data within the functional vmPFC ROI described above, because the customized MB-ME sequence provided superior signal coverage of orbital areas relative to the pcASL sequence. Following standard preprocessing in AFNI (for details, see the Supplemental Material), AFNI’s 3dRSFC tool was applied to quantify the intensity of spontaneous brain activity during the resting state (Zou et al., 2008). As described in our prior report, mean fALFF within left BA47 (defined within AFNI’s TT_Daemon atlas) was successfully modulated by TBS condition. To test for cross-hemisphere generalization of present findings, we also extracted fALFF values from the contralateral right OFC (BA47). This contralateral region exhibited a nearly significant difference in fALFF between the two TBS groups.

Results

As reported previously (Price et al., 2021), the two TBS conditions were equally well tolerated—94% (33 of 35) tolerated cTBS stimulation at ≥ 85% RMT; 88% (30 of 34) tolerated iTBS stimulation (Fisher’s exact p = .43). The most commonly reported side effects included discomfort/focal pain at the stimulation site, eye watering, nose tingling, and involuntary twitching of the eyelid muscle.

Whole-brain analysis

Significant differences in RSFC between iTBS and cTBS were observed for the connectivity between one specific seed region—the right BA47—and two cortical clusters: the dmPFC and the left occipital cortex (Table 1; Fig. 1b); there was greater connectivity in both clusters for the iTBS group than for the cTBS group. No other seed regions exhibited robust connectivity differences across the two groups.

Table 1.

Results of the iTBS > cTBS Contrast for Regional Resting State Functional Connectivity to the Right Lateral Orbitofrontal Cortex (Right BA47 Seed Region)

Region	Center-of-mass coordinates (MNI)
Region	x	y	z	Cluster extent (voxels)	t(57)	Cohen’s d	p
Whole-brain analysis
dmPFC (BA8, BA9)	4	36	47	104	4.0	1.06^a	< .001
Left occipital cortex (area V3)	−23	−90	8	76	4.3	1.14^a	< .001
A priori anatomical striatal ROIs
Left nucleus accumbens	−12	11	−8	5	0.11	0.029^b	.911, .78^c
Right nucleus accumbens	12	9	−7	7	2.74	0.726^b	.008, .048*^c
Left putamen	−24	0	4	219	2.47	0.654^b	.021, .042*^c
Right putamen	23	1	4	217	2.00	0.530^b	.051, .077^c
Left caudate	−11	8	9	159	1.09	0.289^b	.281, .37^c
Right caudate	11	8	9	143	2.42	0.641^b	.016, .048*^c

Note: Coordinates indicate peak voxels for the whole-brain analyses and centroid voxels for the a priori anatomical striatal ROIs. The location of the peak voxel for the dorsomedial prefrontal cortex (dmPFC) was the right superior middle gyrus, and the location of the peak voxel for the left occipital cortex was the left middle occipital gyrus. The unrestricted whole-brain analysis compared iTBS and cTBS groups via unpaired-samples t tests, with a voxel-wise error rate of p < .005, and a map-wise error rate of p < .05. iTBS = intermittent theta-burst stimulation; cTBS = continuous theta-burst stimulation; MNI = Montreal Neurological Institute; BA = Brodmann’s area; ROI = region of interest; FDR = false-discovery rate.

These values are post hoc Cohen’s ds. ^bThese values are unbiased Cohen’s ds. ^cThese p values are uncorrected ps followed by FDR-corrected ps.

p < .05

The connectivity values in both of these clusters were positively correlated with target-engagement indices, quantified (as in our primary report) using both CBF changes in the ventromedial PFC (pcASL sequence) and fALFF in bilateral BA47 (MB-ME resting-state sequence; Table 2 and Fig. 1c).

Table 2.

Association of Right BA7 RSFC Across Functional and Anatomical Striatal ROIs With Indices of Target Engagement and Habit-Override Difficulty

Region	Target engagement			Habit-override self-reportdifficulty
Region	vmPFC CBF	Left BA47 fALFF	Right BA47 fALFF	Anxiety	Urge to press	Effort to suppress urge
Functional ROIs (whole-brain search)
dmPFC	.392**	.591***	.552***	−.170	−.314*	−.295*
Left occipital cortex	.280*	.464**	.379**	−.060	−.250^†	−.246^†
Anatomical striatal ROIs
Left nucleus accumbens	−.129	.166	.142	.015	.023	−.162
Right nucleus accumbens	.013	−.021	−.066	.158	−.037	−.019
Left putamen	.181	.593***	.540***	−.297*	−.353**	−.271*
Right putamen	.246^†	.576***	.503***	−.339**	−.427***	−.293*
Left caudate	−.105	−.027	.006	.076	−.049	−.120
Right caudate	−.166	−.234	−.190	.192	.118	−.047

Note: Values are Pearson’s correlation coefficients. CBF = cerebral blood flow; RSFC = resting-state functional connectivity; ROI = region of interest; vmPFC = ventromedial prefrontal cortex; BA = Brodmann’s area; fALFF = fractional amplitude of low-frequency fluctuations.

†

p < .10. *p < .05. **p < .01.***p < .001.

Striatal regions

As in the whole-brain analysis, unpaired-samples t tests revealed significant differences in RSFC between iTBS and cTBS (greater positive connectivity strength in the iTBS group than in the cTBS group) for the connectivity between one specific seed region—the right BA47—and three a priori striatal areas spanning both the ventral and dorsal striatum (adjusted p < .05; Table 1). No other OFC seed regions exhibited RSFC group differences for striatal areas. Right BA47-striatal RSFC was correlated with target-engagement indices only for the right and left putamen and only when using the fALFF metric (Table 2).

Exploratory analysis: behavioral data

In exploratory analyses, we probed whether the connectivity patterns that differentiated the two groups were related to either behavioral performance or subjective ratings collected during the habit-override task. Participants who did and did not succeed in consistently overriding avoidance habits (i.e., button presses to the “devalued”/disconnected cue) did not differ on RSFC between the right BA47 seed and either functional (dmPFC, occipital cortex) or anatomically defined striatal regions (ps > .12). However, higher subjective ratings of anxiety, urges to press for the devalued (disconnected) side, and effort needed to suppress these urges during the habit-override task were related to lower RSFC between BA47 and a number of functional (dmPFC) and anatomically defined (striatal) areas (Table 2).

Discussion

In the current secondary analysis of data from a study published previously (Price et al., 2021), we found group differences, according to TBS condition (iTBS vs. cTBS), in the strength of RSFC between one specific OFC region (right BA47) and other brain areas. This included areas both within (dorsal and ventral striatum) and beyond (dmPFC, left occipital cortex) the CTSC circuitry that has been most widely implicated in the pathophysiology of CBs (Ahmari, 2016; Pauls et al., 2014; Saxena & Rauch, 2000). This adds to our previously reported primary findings from this study, in which a single session of iTBS or cTBS, delivered to transdiagnostic patients with CB disorders, was successful in (a) modulating the target OFC/FPC region (according to both a CBF measure and a BOLD measure of spontaneous brain activity [fALFF]) and (b) modulating clinically relevant CBs measured in the laboratory. Specifically, in this prior report, the cTBS group, but not the iTBS group, exhibited significant improvements from baseline that endured at a 1-week follow-up (Price et al., 2021). Within the novel RSFC data presented here, as expected, the impact of TBS condition on connectivity was strongest in those with the highest degree of OFC/FPC target modulation, as quantified by independent indices of target engagement. Similar correlational patterns were also evident, although statistical significance was more variable (given smaller sample sizes) when examining RSFC-target engagement correlations separately within each TBS group (see Table S1 in the Supplemental Material). This suggests that even within individuals receiving the same form of neuromodulation, more successful manipulation of the target region may have generalized to a correspondingly stronger shift in RSFC. Because our single session of iTBS or cTBS was consistently paired with the context of behavioral training in habit override—completed before RSFC assessment—these experimental findings provide novel insight into the circuit-level substrates of learning this critical, clinically relevant skill.

CB disorders such as OCD are posited to be driven by excess tone within a “direct,” excitatory—relative to an “indirect,” inhibitory—CSTC circuit (Pauls et al., 2014; Saxena & Rauch, 2000). When patients with OCD are presented with devalued cues, they exhibit a bias in favor of habitual over goal-directed responses (Gillan et al., 2015; Gillan et al., 2014) accompanied by altered function in the OFC and caudate (Gillan et al., 2015). However, whereas prior studies comparing patients with control subjects were correlational, the current study leverages neuromodulation to create an experimental design. This design is thus a close parallel to the design of experimental optogenetic studies in mouse models, in which an OFC → ventral striatum pathway has been specifically highlighted as playing a causal role in repetitive compulsive-like behaviors (Ahmari, 2016; Ahmari et al., 2013). In addition, a prior report in patients was partially convergent with ours in suggesting that patients with OCD who responded to rTMS targeting the dmPFC experienced decreased connectivity between dmPFC and striatal areas (Dunlop et al., 2015).

Unexpectedly, however, we noted that connectivity differences following iTBS versus cTBS were specific to the right lateral OFC (BA47), whereas TBS explicitly targeted the left OFC, at a more dorsomedial point (junction of BAs 10 and 11). As expected given spatial diffusion of the TMS signal and as shown in our prior report (Price et al., 2021), modulation reached a larger OFC/PFC area spreading across both hemispheres; however, in the behavioral context of habit-override training, the network-level connectivity differences that emerged were linked to right BA47 specifically. This behavioral task, delivered immediately after TBS, may thus have focused and confined TBS’s acute modulatory effects on RSFC in a manner potentially similar to “state-dependent” neuromodulation (Micoulaud-Franchi et al., 2013; Silvanto et al., 2008; Silvanto & Pascual-Leone, 2008), yielding a set of circuit- or network-level differences that are fine-tuned by the neural activity that the habit-override task state itself provoked. Given the pattern of right-lateralized findings we observed, we speculate that repeated practice in overriding an overlearned avoidance habit may crucially involve the right lateral OFC’s functional connections to both ventral and dorsal striatal regions—connections that are a well-known and strongly implicated component of relevant CTSC circuits—as well as to regions beyond these circuits: the dmPFC and occipital cortex, which may work in tandem with the CTSC to overcome habits and optimize goal-directed behavior in response to visual cues. However, although we designed the study specifically around this mechanistic framework, we did not causally manipulate the habit-override training itself, and so future research will be needed to test for task-dependent neuromodulation conclusively. Furthermore, animal and/or human studies directly contrasting left versus right-lateralized stimulation targets in an experimental design would help to clarify whether neural circuits centered on the right PFC are differentially involved in the acquisition of the habit-override skill.

Connectivity patterns between BA47 and several other regions were inversely linked to subjective ratings of difficulty with the habit-override task, such that those with the lowest connectivity levels following either iTBS or cTBS reported the highest subjective level of difficulty during the habit-override task (specifically, stronger subjective anxiety, urges to press in response to devalued cues, and effort needed to resist such urges; Table 2). These inverse patterns were also evident—albeit to a more variable degree in terms of statistical significance (likely because of smaller sample sizes; n = ~30 per group)—when examining correlations separately within each TBS group (see Table S1 in the Supplemental Material). Direction and magnitude of effect sizes across the two TBS conditions as well as the full sample were fairly consistent (note that one particular inverse relationship—between urges to press and right BA47-left putamen connectivity—was largely driven by the iTBS group alone). The direction of these effects is notable given that the cTBS group overall—i.e., the group that exhibited overall connectivity decreases (relative to the iTBS condition)—also exhibited a clinical benefit on a subsequent laboratory probe of CBs, as described in our primary report (Price et al., 2021).

Together, these patterns suggest that acutely decreased connectivity between right BA47 and other regions may have facilitated a more challenging subjective experience during the automated habit-override training, which may, in turn, have enhanced the clinical efficacy of this neurocognitive “exercise” during a post-TBS window of opportunity. By pulling the OFC and its functional connections within and beyond CSTC circuitry down from a chronically hyperactive “ceiling” state (Ahmari, 2016; Saxena & Rauch, 2000), cTBS may have enabled the override task to more effectively exercise its neurocognitive target, ultimately leading to downstream behavioral improvements. It is notable that, within the context of “gold standard” behavioral treatments (e.g., exposure with response prevention), similar opposing patterns for acute (increased urges/anxiety) versus long-term (improved CBs, anxiety relief) outcomes are fully expected, and, indeed, an enhanced acute degree of distress-inducing challenge or “exercise” for resisting compulsive urges is considered critical for enduring therapeutic outcomes. The present RSFC findings suggest that acutely down-regulating right lateral OFC-based circuitry may represent a key neural mechanism supporting the adaptive transition from habit-driven (i.e., driven by short-term anxiety/urge relief) to goal-driven adaptive function, consistent with circuit-based animal stimulation studies in which lateral OFC-striatal circuits have been implicated in expression of compulsive-like behaviors (Pascoli et al., 2015, 2018; Renteria et al., 2021).

Our findings may have implications for future development of paired or synergistic treatments combining acute neuromodulation with a behavioral learning opportunity. The specific RSFC findings reported here are notable for the ways in which they differ from the principle target-engagement and behavioral patterns we reported previously (e.g., right-lateralized RSFC findings for a left-lateralized TMS target; higher subjective training-task difficulty in the group that had the more favorable short-term clinical outcomes). One specific implication is that the right OFC—which is a neurostimulation target in one published study (Nauczyciel et al., 2014) and multiple ongoing treatment trials in OCD (e.g., NCT04286126, NCT03918837, NCT03207633)—may be more relevant to the critical neurocognitive substrate of habit override, particularly in the context of its circuit-level connections to other brain regions (Table 1; Fig. 1b). More broadly, findings suggest that each of the components of the intervention may have exerted an influence on the other—that is, just as acute neuromodulatory impacts (e.g., RSFC patterns) may have helped to alter the subjective difficulty of the behavioral training task, the behavioral context may likewise have fine-tuned the circuit-level patterns that were engaged by the TMS procedures (Micoulaud-Franchi et al., 2013; Silvanto et al., 2008; Silvanto & Pascual-Leone, 2008). Likewise, studies in OCD (Carmi et al., 2019), substance use disorders (Kearney-Ramos et al., 2019), depression (Donse et al., 2018), and healthy aging (Beynel et al., 2020) have sought to leverage a specific behavioral context in order to cue a desired clinically relevant or therapeutic mental state just before, during, and/or after acute neuromodulation, though these studies rarely incorporate a TMS-only control condition, and thus cannot directly evaluate the moderating impact of the behavioral context. In an effort to clarify these outstanding questions, our ongoing study extending the present findings (R01-MH124707; NCT04580043) has an increased focus on the behavioral context around neuromodulation, leveraging a fully crossed factorial design in which both the behavioral context and the neuromodulation condition are experimentally manipulated independently of one another.

Our study has several limitations. Because of budgetary and timing constraints, RSFC was assessed only after active TBS and not at baseline; thus, group comparisons for iTBS and cTBS have reduced statistical power and cannot disambiguate increases in the iTBS group from decreases in the cTBS group or from the absence of change in one of the two groups relative to baseline. This design also precludes definitive conclusions regarding whether group differences in RSFC reflect the impact of TBS per se. As discussed above, both TBS conditions were paired with behavioral habit-override training, precluding conclusions regarding the specificity of these findings to the behavioral training context that was provided. The sample consisted predominantly of non-Latinx White patients, which reflects the geographic county where the study was performed but not the general population in the United States or the world. Care must be taken in future research to recruit diverse samples enabling researchers to better characterize TMS effects among underserved populations that have historically been underrepresented in psychiatric research. Because of modest sample sizes, the study was powered only to detect medium effect sizes or larger. Finally, a single session of neuromodulation was used here as an experimental manipulation; however, to inform future development of potentially synergistic biobehavioral treatments (Wilkinson et al., 2019), multisession protocols are likely needed to affect enduring, clinically meaningful change.

Conclusions

When experimentally manipulating the left OFC/FPC in patients with CBs, a single session of iTBS was associated (relative to cTBS) with increased RSFC between the contralateral right OFC and multiple brain regions, both within and beyond CSTC circuits well known for their role in CB pathophysiology (Ahmari, 2016; Pauls et al., 2014; Saxena & Rauch, 2000). Consistent with prior work (e.g., Dunlop et al., 2015; Hanlon et al., 2015; Rastogi et al., 2017), findings support TBS-induced neural patterns both proximal and distal to the neuromodulatory target. Stronger positive connectivity was associated with greater focal OFC/FPC target engagement and, in exploratory analyses, with lower subjective difficulty of habit-override training. We posit that these specific connectivity patterns may reflect the fine-tuning of neuromodulation at the neural network level via the paired behavioral context of habit-override training. Thus, they provide preliminary experimental data to suggest which specific circuit-level patterns are most relevant to the acquisition of this highly clinically relevant skill. Future work to target and remediate key neurocognitive substrates of CBs may benefit from an integrative approach that considers and fully explores the potential bidirectional impact of both neural and behavioral manipulations on relevant, brain-wide circuitry.

Supplemental Material

sj-pdf-1-cpx-10.1177_21677026221103136 – Supplemental material for Resting-State Functional Connectivity Differences Following Experimental Manipulation of the Orbitofrontal Cortex in Two Directions via Theta-Burst Stimulation

Supplemental material, sj-pdf-1-cpx-10.1177_21677026221103136 for Resting-State Functional Connectivity Differences Following Experimental Manipulation of the Orbitofrontal Cortex in Two Directions via Theta-Burst Stimulation by Rebecca B. Price, Fabio Ferrarelli, Colleen Hanlon, Claire M. Gillan, Tae Kim, Greg J. Siegle, Meredith L. Wallace, Marlee Renard, Rachel Kaskie, Michelle Degutis, Anna Wears, Vanessa Brown, Manivel Rengasamy and Susanne E. Ahmari in Clinical Psychological Science

Footnotes

Acknowledgements

We gratefully acknowledge Daniel M. Blumberger, Sabine Wilhelm, and the study participants for their contributions to this work.

Transparency

Action Editor: Vina Goghari

Editor: Jennifer L. Tackett

Author Contributions

R. B. Price, F. Ferrarelli, C. Hanlon, C. M. Gillan, T. Kim, G. J. Siegle, M. L. Wallace, and S. E. Ahmari developed the study concept and contributed to the study design. Testing and data collection were performed by R. B. Price, M. Renard, R. Kaskie, M. Degutis, and A. Wears. R. B. Price, M. L. Wallace, M. Rengasamy, and V. Brown performed the data analysis and interpretation. R. B. Price drafted the manuscript. All of the authors provided revisions and approved the final version of the manuscript for submission.

ORCID iD

Rebecca B. Price

Notes

References

Ahmari

S. E.

(2016). Using mice to model obsessive compulsive disorder: From genes to circuits. Neuroscience, 321, 121–137. https://doi.org/10.1016/j.neuroscience.2015.11.009

Ahmari

S. E.

Spellman

Douglass

N. L.

Kheirbek

M. A.

Simpson

H. Blair

Deisseroth

Gordon

J. A.

Hen

(2013). Repeated cortico-striatal stimulation generates persistent OCD-like behavior. Science, 340(6137), 1234–1239. https://doi.org/10.1126/science.1234733

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596

Beynel

Davis

S. W.

Crowell

C. A.

Dannhauer

Lim

Palmer

Hilbig

S. A.

Brito

Hile

Luber

Lisanby

S. H.

Peterchev

A. V.

Cabeza

Appelbaum

L. G.

(2020). Site-specific effects of online rTMS during a working memory task in healthy older adults. Brain Science, 10(5), Article 0255. https://doi.org/10.3390/brainsci10050255

Carmi

Tendler

Bystritsky

Hollander

Blumberger

D. M.

Daskalakis

Ward

Lapidus

Goodman

Casuto

Feifel

Barnea-Ygael

Roth

Zangen

Zohar

(2019). Efficacy and safety of deep transcranial magnetic stimulation for obsessive-compulsive disorder: A prospective multicenter randomized double-blind placebo-controlled trial. American Journal of Psychiatry, 176(11), 931–938. https://doi.org/10.1176/appi.ajp.2019.18101180

Chung

S. W.

Hill

A. T.

Rogasch

N. C.

Hoy

K. E.

Fitzgerald

P. B.

(2016). Use of theta-burst stimulation in changing excitability of motor cortex: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 63, 43–64. https://doi.org/10.1016/j.neubiorev.2016.01.008

Cougle

J. R.

Fitch

K. E.

Fincham

F. D.

Riccardi

C. J.

Keough

M. E.

Timpano

K. R.

(2012). Excessive reassurance seeking and anxiety pathology: Tests of incremental associations and directionality. Journal of Anxiety Disorders, 26(1), 117–125. https://doi.org/10.1016/j.janxdis.2011.10.001

Donse

Padberg

Sack

A. T.

Rush

A. J.

Arns

(2018). Simultaneous rTMS and psychotherapy in major depressive disorder: Clinical outcomes and predictors from a large naturalistic study. Brain Stimulation, 11(2), 337–345. https://doi.org/10.1016/j.brs.2017.11.004

Dunlop

Woodside

Olmsted

Colton

Giacobbe

Downar

(2015). Reductions in cortico-striatal hyperconnectivity accompany successful treatment of obsessive-compulsive disorder with dorsomedial prefrontal rTMS. Neuropsychopharmacology, 41(5), 1395–1403. https://doi.org/10.1038/npp.2015.292

10.

Eaton

W. W.

Martins

S. S.

Nestadt

Bienvenu

O. J.

Clarke

Alexandre

(2008). The burden of mental disorders. Epidemiological Review, 30, 1–14. https://doi.org/10.1093/epirev/mxn011

11.

Foa

E. B.

Huppert

J. D.

Leiberg

Langner

Kichic

Hajcak

Salkovskis

P. M.

(2002). The Obsessive-Compulsive Inventory: Development and validation of a short version. Psychological Assessment, 14(4), 485–495. https://doi.org/10.1037/1040-3590.14.4.485

12.

Gillan

C. M.

Apergis-Schoute

A. M.

Morein-Zamir

Urcelay

G. P.

Sule

Fineberg

N. A.

Sahakian

B. J.

Robbins

T. W.

(2015). Functional neuroimaging of avoidance habits in obsessive-compulsive disorder. American Journal of Psychiatry, 172(3), 284–293. https://doi.org/10.1176/appi.ajp.2014.14040525

13.

Gillan

C. M.

Morein-Zamir

Urcelay

G. P.

Sule

Voon

Apergis-Schoute

A. M.

Fineberg

N. A.

Sahakian

B. J.

Robbins

T. W.

(2014). Enhanced avoidance habits in obsessive-compulsive disorder. Biological Psychiatry, 75(8), 631–638. https://doi.org/10.1016/j.biopsych.2013.02.002

14.

Gillan

C. M.

Robbins

T. W.

Sahakian

B. J.

van den Heuvel

O. A.

van Wingen

(2016). The role of habit in compulsivity. European Neuropsychopharmacology, 26(5), 828–840. https://doi.org/10.1016/j.euroneuro.2015.12.033

15.

Haber

S. N.

(2016). Corticostriatal circuitry. Dialogues in Clinical Neuroscience, 18(1), 7–21. https://doi.org/10.31887/DCNS.2016.18.1/shaber

16.

Hanlon

C. A.

Dowdle

L. T.

Austelle

C. W.

DeVries

Mithoefer

Badran

B. W.

George

M. S.

(2015). What goes up, can come down: Novel brain stimulation paradigms may attenuate craving and craving-related neural circuitry in substance dependent individuals. Brain Research, 1628, 199–209. https://doi.org/10.1016/j.brainres.2015.02.053

17.

Hanlon

C. A.

Dowdle

L. T.

Correia

Mithoefer

Kearney-Ramos

Lench

Griffin

Anton

R. F.

George

M. S.

(2017). Left frontal pole theta burst stimulation decreases orbitofrontal and insula activity in cocaine users and alcohol users. Drug and Alcohol Dependence, 178, 310–317. https://doi.org/10.1016/j.drugalcdep.2017.03.039

18.

Hanlon

C. A.

Philip

N. S.

Price

R. B.

Bickel

W. K.

Downar

(2019). A case for the frontal pole as an empirically derived neuromodulation treatment target. Biological Psychiatry, 85(3), e13–e14. https://doi.org/10.1016/j.biopsych.2018.07.002

19.

Huerta

P. T.

Volpe

B. T.

(2009). Transcranial magnetic stimulation, synaptic plasticity and network oscillations. Journal of Neuroengineering and Rehabilitation, 6, 7. https://doi.org/10.1186/1743-0003-6-7

20.

Jasper

H. H.

(1958). Report of the committee on methods of clinical examination in electroencephalography. Electroencephalography and Clinical Neurophysiology, 10, 370–375.

21.

Kahnt

Chang

L. J.

Park

S. Q.

Heinzle

Haynes

J. D.

(2012). Connectivity-based parcellation of the human orbitofrontal cortex. Journal of Neuroscience, 32(18), 6240–6250. https://doi.org/10.1523/jneurosci.0257-12.2012

22.

Kearney-Ramos

T. E.

Dowdle

L. T.

Mithoefer

O. J.

Devries

George

M. S.

Hanlon

C. A.

(2019). State-dependent effects of ventromedial prefrontal cortex continuous thetaburst stimulation on cocaine cue reactivity in chronic cocaine users. Frontiers in Psychiatry, 10, Article 317. https://doi.org/10.3389/fpsyt.2019.00317

23.

Keuthen

N. J.

Flessner

C. A.

Woods

D. W.

Franklin

M. E.

Stein

D. J.

, & Trichotillomania Learning Center Scientific Advisory Board. (2007). Factor analysis of the Massachusetts General Hospital Hairpulling Scale. Journal of Psychosomatic Research, 62(6), 707–709. https://doi.org/10.1016/j.jpsychores.2006.12.003

24.

Keuthen

N. J.

O’Sullivan

Ricciardi

Shera

Savage

Borgmann

Jenike

M. A.

Baaer

(1995). The Massachusetts General Hospital (MGH) Hairpulling Scale: 1. Development and factor analyses. Psychotherapy and Psychosomatics, 64(3-4), 141–145. https://doi.org/10.1159/000289003

25.

Keuthen

N. J.

Wilhelm

Deckersbach

Engelhard

I. M.

Forker

A. E.

Baer

Jenike

M. A.

(2001). The Skin Picking Scale: Scale construction and psychometric analyses. Journal of Psychosomatic Research, 50(6), 337–341.

26.

Kim

Zhao

Bae

K. T.

(2016). Enhancement of functional MRI signal at high-susceptibility regions of brain using simultaneous multiecho multithin-slice summation imaging technique. Journal of Magnetic Resonance Imaging, 44(2), 478–485. https://doi.org/10.1002/jmri.25170

27.

Kundu

Brenowitz

N. D.

Voon

Worbe

Vertes

P. E.

Inati

S. J.

Saad

Z. S.

Bandettini

P. A.

Bullmore

E. T.

(2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proceedings of the National Academy of Sciences, USA, 110(40), 16187–16192. https://doi.org/10.1073/pnas.1301725110

28.

C.-T.

Chen

M.-H.

Juan

C.-H.

Huang

H.-H.

Chen

L.-F.

Hsieh

J.-C.

P.-C.

Bai

Y.-M.

Tsai

S.-J.

Lee

Y.-C.

T.-P.

(2014). Efficacy of prefrontal theta-burst stimulation in refractory depression: A randomized sham-controlled study. Brain, 137(7), 2088–2098. https://doi.org/10.1093/brain/awu109

29.

Lisanby

S. H.

Kinnunen

L. H.

Crupain

M. J.

(2002). Applications of TMS to therapy in psychiatry. Journal of Clinical Neurophysiology, 19(4), 344–360. https://doi.org/10.1097/00004691-200208000-00007

30.

Micoulaud-Franchi

J. A.

Fond

Dumas

(2013). Cyborg psychiatry to ensure agency and autonomy in mental disorders. A proposal for neuromodulation therapeutics. Frontiers in Human Neuroscience, 7, Article 463. https://doi.org/10.3389/fnhum.2013.00463

31.

Nauczyciel

Le Jeune

Naudet

Douabin

Esquevin

Verin

Dondaine

Robert

Drapier

Millet

(2014). Repetitive transcranial magnetic stimulation over the orbitofrontal cortex for obsessive-compulsive disorder: A double-blind, crossover study. Translational Psychiatry, 4, Article e436. https://doi.org/10.1038/tp.2014.62

32.

Ongur

Price

J. L.

(2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10(3), 206–219. https://doi.org/10.1093/cercor/10.3.206

33.

Pascoli

Hiver

Van Zessen

Loureiro

Achargui

Harada

Flakowski

Lüscher

(2018). Stochastic synaptic plasticity underlying compulsion in a model of addiction. Nature, 564(7736), 366–371. https://doi.org/10.1038/s41586-018-0789-4

34.

Pascoli

Terrier

Hiver

Luscher

(2015). Sufficiency of mesolimbic dopamine neuron stimulation for the progression to addiction. Neuron, 88(5), 1054–1066. https://doi.org/10.1016/j.neuron.2015.10.017

35.

Pauls

D. L.

Abramovitch

Rauch

S. L.

Geller

D. A.

(2014). Obsessive–compulsive disorder: An integrative genetic and neurobiological perspective. Nature Reviews Neuroscience, 15(6), 410–424. https://doi.org/10.1038/nrn3746

36.

Philip

N. S.

Barredo

Aiken

Larson

Jones

R. N.

Shea

M. Tracie

Greenberg

B. D.

van ‘t Wout-Frank

(2019). Theta-burst transcranial magnetic stimulation for posttraumatic stress disorder. American Journal of Psychiatry, 176(11), 939–948. https://doi.org/10.1176/appi.ajp.2019.18101160

37.

Poser

B. A.

Versluis

M. J.

Hoogduin

J. M.

Norris

D. G.

(2006). BOLD contrast sensitivity enhancement and artifact reduction with multiecho EPI: Parallel-acquired inhomogeneity-desensitized fMRI. Magnetic Resonance in Medicine, 55(6), 1227–1235. https://doi.org/10.1002/mrm.20900

38.

Price

R. B.

Duman

(2020). Neuroplasticity in cognitive and psychological mechanisms of depression: An integrative model. Molecular Psychiatry, 25(3), 530–543. https://doi.org/10.1038/s41380-019-0615-x

39.

Price

R. B.

Gillan

C. M.

Hanlon

Ferrarelli

Kim

Karim

H. T.

Renard

Kaskie

Degutis

Wears

Vienneau

E. P.

Peterchev

A. V.

Brown

Siegle

G. J.

Wallace

M. L.

Ahmari

S. E.

(2021). Experimental manipulation of the orbitofrontal cortex impacts short-term markers of human compulsive behavior: A theta burst stimulation study. American Journal of Psychiatry, 178(5), 459–468. https://doi.org/10.1176/appi.ajp.2020.20060821

40.

Raichle

M. E.

(2010). Two views of brain function. Trends in Cognitive Science, 14(4), 180–190. https://doi.org/10.1016/j.tics.2010.01.008

41.

Rastogi

Cash

Dunlop

Vesia

Kucyi

Ghahremani

Downar

Chen

(2017). Modulation of cognitive cerebello-cerebral functional connectivity by lateral cerebellar continuous theta burst stimulation. NeuroImage, 158, 48–57. https://doi.org/10.1016/j.neuroimage.2017.06.048

42.

Renteria

Cazares

Baltz

E. T.

Schreiner

D. C.

Yalcinbas

E. A.

Steinkellner

Hnasko

T. S.

Gremel

C. M.

(2021). Mechanism for differential recruitment of orbitostriatal transmission during actions and outcomes following chronic alcohol exposure. eLife, 10, Article e67065. https://doi.org/10.7554/eLife.67065

43.

Rossini

P. M.

Burke

Chen

Cohen

L. G.

Daskalakis

Iorio

R. Di

Lazzaro

V. Di

Ferreri

Fitzgerald

P. B.

George

M. S.

Hallett

Lefaucheur

J. P.

Langguth

Matsumoto

Miniussi

Nitsche

M. A.

Leone

A. Pascual

Paulus

Rossi

Ziemann

(2015). Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee. Clinical Neurophysiology, 126(6), 1071–1107. https://doi.org/10.1016/j.clinph.2015.02.001

44.

Ruffini

Locatelli

Lucca

Benedetti

Insacco

Smeraldi

(2009). Augmentation effect of repetitive transcranial magnetic stimulation over the orbitofrontal cortex in drug-resistant obsessive-compulsive disorder patients. The Primary Care Companion to The Journal of Clinical Psychiatry, 11(5), 226–230. https://doi.org/10.4088/PCC.08m00663

45.

Saxena

(2008). Recent advances in compulsive hoarding. Current Psychiatry Reports, 10(4), 297–303. https://doi.org/10.1007/s11920-008-0048-8

46.

Saxena

Rauch

S. L.

(2000). Functional neuroimaging and the neuroanatomy of obsessive-compulsive disorder. Psychiatry Clinics of North America, 23(3), 563–586. https://doi.org/10.1016/s0193-953x(05)70181-7

47.

Shibata

Watanabe

Yukawa

Minakuchi

Shimomura

Mima

(2020). Effect of transcranial static magnetic stimulation on intracortical excitability in the contralateral primary motor cortex. Neuroscience Letters, 723, 134871. https://doi.org/10.1016/j.neulet.2020.134871

48.

Silvanto

Muggleton

Walsh

(2008). State-dependency in brain stimulation studies of perception and cognition. Trends in Cognitive Sciences, 12(12), 447–454. https://doi.org/10.1016/j.tics.2008.09.004

49.

Silvanto

Pascual-Leone

(2008). State-dependency of transcranial magnetic stimulation. Brain Topography, 21(1), Article 1. https://doi.org/10.1007/s10548-008-0067-0

50.

Sporns

Chialvo

D. R.

Kaiser

Hilgetag

C. C.

(2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8(9), 418–425. https://doi.org/10.1016/j.tics.2004.07.008

51.

Storch

E. A.

Rasmussen

S. A.

Price

L. H.

Larson

M. J.

Murphy

T. K.

Goodman

W. K.

(2010). Development and psychometric evaluation of the Yale-Brown Obsessive-Compulsive Scale—Second edition. Psychological Assessment, 22(2), 223–232. https://doi.org/10.1037/a0018492

52.

Tripepi

Chesnaye

N. C.

Dekker

F. W.

Zoccali

Jager

K. J.

(2020). Intention to treat and per protocol analysis in clinical trials. Nephrology, 25(7), 513–517. https://doi.org/10.1111/nep.13709

53.

Webler

R. D.

Hamady

Molnar

Johnson

Bonilha

Anderson

B. S.

Bruin

Bohning

D. E.

George

M. S.

Nahas

(2020). Decreased interhemispheric connectivity and increased cortical excitability in unmedicated schizophrenia: A prefrontal interleaved TMS fMRI study. Brain Stimulation, 13(5), 1467–1475. https://doi.org/10.1016/j.brs.2020.06.017

54.

Wilkinson

Holtzheimer

P. E.

Gao

Kirwin

Price

(2019). Leveraging neuroplasticity to enhance adaptive learning: The potential for synergistic somatic-behavioral treatment combinations to improve clinical outcomes in depression. Biological Psychiatry, 85, 454–465. https://doi.org/10.1016/j.biopsych.2018.09.004

55.

Williams

L. M.

(2016). Precision psychiatry: A neural circuit taxonomy for depression and anxiety. The Lancet Psychiatry, 3(5), 472–480. https://doi.org/10.1016/s2215-0366(15)00579-9

56.

Zou

Q.-H.

Zhu

C.-Z.

Yang

Zuo

X.-N.

Long

X.-Y.

Cao

Q.-J.

Wang

Y.-F.

Zang

Y.-F.

(2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.38 MB