Our findings provide the first experimental evidence that modulating activity in the DLPFC reduces vigilance to threatening stimuli. This significant reduction in fear vigilance is similar to that seen with anxiolytic treatments in the same cognitive paradigm. The finding that DLPFC tDCS acutely alters the processing of threatening information suggests a potential cognitive mechanism that could underwrite treatment effects in clinical populations.

IMPORTANCE Transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC) is under clinical investigation as a treatment for major depressive disorder. However, the mechanisms of action are unclear, and there is a lack of neuroimaging evidence, particularly among individuals with affective dysfunction. Furthermore, there is no direct causal evidence among humans that the prefrontal-amygdala circuit functions as described in animal models (ie, that increasing activity in prefrontal cortical control regions inhibits amygdala response to threat). OBJECTIVE To determine whether stimulation of the prefrontal cortex reduces amygdala threat reactivity in individuals with trait anxiety. DESIGN, SETTING, AND PARTICIPANTS This community-based randomized clinical trial used a double-blind, within-participants design (2 imaging sessions per participant). Eighteen women with high trait anxiety (age range, 18-42 years) who scored greater than 45 on the trait measure of State-Trait Anxiety Inventory were randomized to receive active or sham tDCS of the DLPFC during the first session and the other intervention during the next session. Each intervention was followed immediately by a functional imaging scan during which participants performed an attentional task requiring them to ignore threatening face distractors. Data were collected from May 7 to October 6, 2015. MAIN OUTCOMES AND MEASURES Amygdala threat response, measured with functional magnetic resonance imaging. RESULTS Data from 16 female participants (mean age, 23 years; range, 18-42 years), with 8 in each group, were analyzed. Compared with sham stimulation, active DLPFC stimulation significantly reduced bilateral amygdala threat reactivity (z = 3.30, P = .04) and simultaneously increased activity in cortical regions associated with attentional control (z = 3.28, P < .001). In confirmatory behavioral analyses, there was a mean improvement in task accuracy of 12.2% (95% CI, 0.30%-24.0%; mean [SD] difference in number of correct answers, 2.2 [4.5]; t 15 = 1.94, P = .04) after active DLPFC stimulation. CONCLUSIONS AND RELEVANCE These results reveal a causal role for prefrontal regulation of amygdala function in attentional capture by threat in individuals with high trait anxiety. The finding that prefrontal stimulation acutely increases attentional control signals and reduces amygdala threat reactivity may indicate a neurocognitive mechanism that could contribute to tDCS treatment effects in affective disorders.

Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state‐level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS‐fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS‐fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS‐fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

Background: Maladaptive approach-avoidance behavior has been implicated in the pathophysiology of major depressive disorder (MDD), but the neural basis of these abnormalities in decision-making remains unclear. Capitalizing on recent preclinical findings, we adapted an approach-avoidance conflict task from non-human primate research for use in human functional MRI.Methods: Forty-two female participants, including 18 unmedicated individuals with current MDD (mean age 25.2 ± 5.1) and 24 psychiatrically healthy controls (mean age 26.3 ± 7.6) completed the adapted approach-avoidance task during functional MRI. To probe potential mechanistic factors underlying the observed behavioral and fMRI findings and inform interpretation of putative group differences, we examined electrophysiological data from two female Macaca mulatta monkeys performing the approach-avoidance conflict task mimicked in the fMRI study.

Acute and chronic stress have dissociable effects on reward sensitivity, and a better understanding of these effects promises to elucidate the pathophysiology of stress-related disorders, particularly depression. Recent preclinical and human findings suggest that stress particularly affects reward anticipation; chronic stress perturbates dopamine signaling in the medial prefrontal cortex and ventral striatum; and such effects are further moderated by early adversities. Additionally, a systems-level approach is uncovering the interplay among striatal, limbic and control networks giving rise to stress-related, blunted reward sensitivity. Together, this cross-species confluence has not only enriched our understanding of stress-reward links but also highlighted the role of neuropeptides and opioid receptors in such effects, and thereby identified novel targets for stress-related neuropsychiatric disorders.

Purpose Gamma‐aminobutyric acid (GABA) abnormalities have been implicated in a range of neuropsychiatric disorders. Despite substantial interest in probing GABA in vivo, human imaging studies relying on magnetic resonance spectroscopy (MRS) have generally been hindered by technical challenges, including GABA’s relatively low concentration and spectral overlap with other metabolites. Although past studies have shown moderate‐to‐strong test‐retest repeatability and reliability of GABA within certain brain regions, many of these studies have been limited by small sample sizes. Methods GABA+ (macromolecular‐contaminated) test‐retest reliability and repeatability were assessed via a Meshcher‐Garwood point resolved spectroscopy (MEGA‐PRESS) MRS sequence in the rostral anterior cingulate cortex (rACC; n = 21) and dorsolateral prefrontal cortex (dlPFC; n = 20) in healthy young adults. Data were collected on a 3T scanner (Siemens Prisma, Siemens Healthcare, Erlangen, Germany) and GABA+ results were reported in reference to both total creatine (GABA+/tCr) and water (GABA+/water). Results Results showed strong test‐retest repeatability (mean GABA+/tCr coefficient of variation [CV] = 4.6%; mean GABA+/water CV = 4.0%) and reliability (GABA+/tCr intraclass correlation coefficient [ICC] = 0.77; GABA+/water ICC = 0.87) in the dlPFC. The rACC showed acceptable (but comparatively lower) repeatability (mean GABA+/tCr CV = 8.0%; mean GABA+/water CV = 7.5%), yet low‐moderate reliability (GABA+/tCr ICC = 0.40; GABA+/water ICC = 0.44). Conclusion The present study found excellent GABA+ MRS repeatability and reliability in the dlPFC. The rACC showed inferior results, possibly because of a combination of shimming impedance and measurement error. These data suggest that MEGA‐PRESS can be utilized to reliably distinguish participants based on dlPFC GABA+ levels, whereas the mixed results in the rACC merit further investigation.

Integration of tDCS with fMRI holds promise for investigation the underlying mechanism of stimulation effect. There are 118 published tDCS studies (up to 1st Oct 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review we explore methodological parameter space of tDCS integration with fMRI. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a case study with both task and resting state fMRI before and after tDCS in a cross-over design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability in responsiveness. Through the case study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g. target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders.