Repetitive Transcranial Magnetic Stimulation for Adolescent Major Depressive Disorder: A Focus on Neurodevelopment

Abstract

Adolescent depression is a potentially lethal condition and a leading cause of disability for this age group. There is an urgent need for novel efficacious treatments since half of adolescents with depression fail to respond to current therapies and up to 70% of those who respond will relapse within 5 years. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising treatment for major depressive disorder (MDD) in adults who do not respond to pharmacological or behavioral interventions. In contrast, rTMS has not demonstrated the same degree of efficacy in adolescent MDD. We argue that this is due, in part, to conceptual and methodological shortcomings in the existing literature. In our review, we first provide a neurodevelopmentally focused overview of adolescent depression. We then summarize the rTMS literature in adult and adolescent MDD focusing on both the putative mechanisms of action and neurodevelopmental factors that may influence efficacy in adolescents. We then identify limitations in the existing adolescent MDD rTMS literature and propose specific parameters and approaches that may be used to optimize efficacy in this uniquely vulnerable age group. Specifically, we suggest ways in which future studies reduce clinical and neural heterogeneity, optimize neuronavigation by drawing from functional brain imaging, apply current knowledge of rTMS parameters and neurodevelopment, and employ an experimental therapeutics platform to identify neural targets and biomarkers for response. We conclude that rTMS is worthy of further investigation. Furthermore, we suggest that following these recommendations in future studies will offer a more rigorous test of rTMS as an effective treatment for adolescent depression.

Introduction

Adolescent depression is a leading cause of disability, yet its treatment remains unsatisfactory. Thus, there exists an urgent need for novel, neurodevelopmentally-informed, targeted therapeutics. Repetitive Transcranial magnetic stimulation (rTMS) has emerged as a promising treatment modality for adult major depressive disorder (MDD). rTMS is a non-invasive method to modulate brain network functioning through the application of pulsed magnetic fields (1). Several reviews concluded that rTMS could be a potentially safe and effective treatment for adolescent depression (2–6), however, empirical studies yield mixed results.

Indeed, the sole large-scale (n = 103) randomized controlled trial (RCT), an industry-sponsored effort to extend FDA clearance for rTMS to depression in adolescents, was negative (7), that is, did not show a difference between rTMS and sham control.

In light of this trial, this review focuses on how neurodevelopment creates challenges for development of rTMS protocols in this population and provides specific recommendations to overcome these complexities. We highlight the developmental pathophysiology underlying symptoms of adolescent depression and relate these to the putative mechanisms of action of rTMS. We propose that future studies employ experimental therapeutics approaches to identify predictive biomarkers of response and to develop individualized, neurodevelopmentally informed rTMS targets.

Relevant studies were ascertained via a literature search of PubMed and Google Scholar. The search was limited to English-language peer-reviewed articles. The search terms used were: “adolescent depression” and “TMS” or “rTMS.” Also, to ensure that we accurately represented the full extent of the literature, we examined previously published review articles. We excluded studies that applied single-pulse TMS, paired-pulse TMS, or other non-therapeutic TMS protocols, trials. We also excluded studies that evaluated depressive symptoms in adolescents with other primary clinical conditions [e.g., Tourette syndrome or autism spectrum disorder (8, 9)].

A Neurodevelopmental Overview of Adolescent Depression

The World Health Report suggests that depression is the leading cause of disability worldwide, affecting over 264 million people (10, 11). The prevalence of moderate-to-severe depressive symptoms in youth between the ages of 12 and 17 is estimated to be 5.7% (12) with a cumulative prevalence of around 10% by age 16 (13). Moreover, depressed adolescents are about 30 times more likely to commit suicide compared to their non-depressed counterparts (14). Suicide is one of the leading causes of death in adolescents in the US and adolescence is also the time of peak incidence of suicidal behaviors and suicidal ideation (15). Despite the substantial individual and societal impact associated with depression in youth, treatment options are limited (16, 17). Traditional treatment methods include psychopharmacology (e.g., serotonin reuptake inhibitors) and behavioral therapy [e.g., cognitive behavioral therapy (CBT)]. The Treatment of Adolescent Depression Study (TADS), however, found that only 37% of patients experienced full remission of symptoms after 12 weeks of these first-line treatments (18). Furthermore, even with the combination of two evidence-based treatment modalities, at least a third of youths treated for depression do not respond, 20–37% only have a partial response, and 40–70% experience a relapse or recurrence (19–21). Thus, there remains a significant need for the development of new treatments.

In addition to the morbidity and mortality, there is significant financial burden of adolescent depression including: costs of health care use, productivity lost, and time off of work for caregivers. Estimates of direct costs of adolescent depression amount to ~$2,900 additional dollars per year. This does not consider the indirect costs of reduced/lost productivity, which in adult depression is estimated as high as $12,000 per year (22). Given this financial burden, the cost-efficiency of treatment options is also a consideration. Pharmacotherapy, estimated at $100 per month is the least expensive therapy, followed by psychotherapy, estimated at $100–$150 per session. Both pharmacotherapy and psychotherapy are significantly less expensive than rTMS for a given depressive episode. However, one industry-sponsored study of individuals who failed a single course of antidepressants, applied simulation modeling to compare costs of rTMS therapy to multiple serial medication trials and suggested that rTMS may cost less in over the course of the patients' lifetime (23).

Adolescence is not only a time when the incidence of depression increases (24), but also a period of substantial social, emotional, and biological development. These developmental changes may contribute to risk factors and mechanisms underlying adolescent depression (25). Synapses in the adolescent brain are highly dynamic; new synapses are formed and others eliminated at higher rates than seen in adults (26, 27). Proposed developmental models of the increased risk of psychopathology in adolescence point to a mismatch in the growth of brain networks supporting emotional reactivity and regulation. Compared to brain networks subserving emotion regulation, those pertaining to emotional reactivity develop more rapidly (28, 29). The prefrontal cortex (PFC) is a key node in the emotion regulation network underlying complex cognitive tasks such as inhibition, working memory, cognitive control, and attention. The PFC undergoes age-dependent functional changes well into late adolescence and early adulthood (30–32). Structural neuroimaging shows decreases in total gray matter PFC volume starting at around 11–12 years old (33, 34). This decrease is thought to be associated with synaptic pruning (27). In contrast, imaging metrics of myelination, axon density and white matter volume in frontal regions, show relatively linear increases across adolescence (35–39). It is postulated that an imbalance of the immature PFC and the more mature frontal subcortical systems regulating emotional reactivity might lead to a predominance of “bottom-up” emotional reactivity (40–44).

On a molecular level, these adolescent neurodevelopmental changes are thought to result from fluctuations in neurotransmitter concentration and receptor expression. Notably, fluctuations in neurotransmitter systems, such as GABA (45, 46), NMDA (47), and dopamine (48, 49) have profound impact on neural signaling in regions pertaining to emotion regulation. GABAergic and glutamatergic systems in the PFC and cingulate cortices have a direct impact on the excitability and plasticity in regions subserving emotion regulation (50). Fluctuating dopamine levels and emergence of dopamine receptor-mediated facilitation of NMDA (glutamate) receptor transmission and GABAergic interneuron excitability both have been proposed as a mechanism of increased sensitivity to rewards, novelty, or other salient stimuli (51–53). These cellular and molecular changes in adolescence lead to imbalances in excitation and inhibition, changes in cortical plasticity and connectivity, and less effective transferring of information between critical emotion processing brain regions (50, 54).

The pathophysiology that underlies adolescent depression may differ from that in adults (54). Studies in clinical samples and animal models suggest that these aberrant maturational processes contribute to adolescent depression (55). In one magnetic resonance spectroscopy (MRS) study, adolescents with depression showed decreased levels of GABA in the anterior cingulate cortex (ACC), as compared to healthy adolescents. Furthermore, this difference was specifically related to anhedonic symptoms (56). Another study found that symptom severity in both adults and adolescents with depression correlated with GABA and glutamate + glutamine (Glx) concentrations in the PFC (57, 58).

Neurodevelopmental changes and pathophysiology need to be considered when designing trials of novel targeted therapeutics, such as rTMS. In the next section we review the literature on rTMS. We suggest that future rTMS protocols may benefit from applying neurodevelopmentally-informed approaches of modulating aberrant brain networks and neurotransmitters to treat adolescent depression.

Repetitive Transcranial Magnetic Stimulation in Adult and Adolescent Major Depressive Disorder

TMS is a non-invasive neuromodulation technique that is increasingly utilized in clinics and laboratories world-wide to study and treat a range of neurological and psychiatric disorders. In research, TMS can be applied in single pulses to depolarize a small population of neurons in a targeted brain region. Single-pulse TMS can be used to measure cortical excitability, study central motor conduction time, or the cortical silent period (a measure of intracortical inhibition), or map effective connectivity between the stimulated region and other brain regions (59). TMS can also be applied in pairs of pulses (i.e., paired-pulse stimulation); two pulses are presented in rapid succession to study intracortical inhibition and facilitation (60, 61).

During rTMS, trains of regularly repeating TMS pulses are applied at various stimulation frequencies (e.g., 1, 5, 10 Hz) and patterns [e.g., Theta Burst Stimulation (TBS) (62) or Quadrapulse Stimulation (QPS) (63)]. Compared to paired-pulse or single-pulse stimulation protocols, rTMS pulses temporally summate to produce longer lasting changes in neural activity (64). Stimulation frequencies 1 Hz or lower are thought to produce local cortical inhibition while those 5 Hz or higher are thought to generate local cortical excitation (64, 65). There are also specific patterned forms of rTMS including intermittent theta burst stimulation (iTBS) and continuous theta burst stimulation (cTBS). iTBS and cTBS protocols lead to long-lasting facilitation and suppression of cortical excitability, respectively. Compared to 30 min or more for the standard 10 Hz rTMS procedures, a single session of cTBS and iTBS takes ~40 s and 3 min, respectively.

At a system-level, rTMS modulates excitability in targeted regions of stimulation (66–69) and exerts broader effects across networks connected to those regions (70–74). Thus, in adolescents with depression, rTMS applied to PFC could mitigate some regional prefrontal pathophysiology and aberrant functional connectivity between PFC and the limbic system. Furthermore, if successful, modulating these systems during adolescence, a critical period of PFC maturation, could potentially generate longer term clinical benefits than seen in adults. However, the degree and direction of neurophysiological effect of rTMS are influenced by the state of excitability of the targeted cortical region and the degree of functional connectivity across the targeted network (75, 76). In addition, as noted above, there is considerable inter-individual variability at the symptom and pathophysiological level. Thus, it is important to characterize the current brain state in terms of local cortical excitability and network connectivity in order to determine the optimal treatment protocol for a given individual. As will be described below, identifying the optimal target and protocol for a given individual remains theoretical due to the complex etiology of adolescent depression.

Safety and Mechanisms of Action of rTMS in Adult and Adolescent MDD

The safety of TMS in clinical practice and research has been evaluated through multiple meta-analyses (77–80). Safety guidelines have also been disseminated by the International Federation of Clinical Neurophysiology (81–83). Widespread application of several TMS protocols, across diverse populations and devices, show a low incidence of Adverse Events (AEs) (84). This safety record led to FDA clearance of rTMS for the treatment of adult MDD and adult obsessive-compulsive disorder (OCD) in 2008 and 2018, respectively.

Initial rTMS trials in depression were based on theory that the clinical symptoms might arise from an imbalance between PFC hypometabolism and the limbic system (85). Early studies aimed to increase excitability in regions of PFC that were thought to influence regulation of the limbic system (66, 86–88). Subsequent blood oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) studies suggest that rTMS modulates both activity and connectivity of the targeted region and related networks (74, 89–94). Of note, the cellular and molecular mechanism of action of rTMS is still under investigation (95). At a system level, the Human Connectome Project (96, 97) has led to development of resting state functional connectivity (RSFC) signatures that are being proposed as individualized rTMS targets (98–101). It has also been suggested that some of the changes seen on fMRI following rTMS may result from modulations in GABA and glutamatergic systems (2, 57, 102–106).

As compared to the adult literature, the data on the safety of rTMS in adolescent depression are lacking. However, the data that exist suggest a similar safety profile in older children and adolescents as compared to adults. Allen et al. (107) conducted a systematic review of TMS safety in pediatric populations (including healthy volunteers and youth with neurological and/or psychiatric disorders) in 2017. Forty-two single-pulse and/or paired-pulse TMS studies (n = 1,205) and 26 rTMS studies (n = 360) were reviewed. Adverse event rates ranged from 3.4 to 10.11% and varied based on the patient population being studied, the form of TMS being applied, and the number of sessions applied. Those with known neurological disorders or those receiving epileptogenic medications for psychiatric disorders were more at risk of adverse events. Similarly, adverse events were more common in high-frequency and/or high intensity rTMS protocols and protocols that involved a higher number of sessions (107). In 2020, Zewdie et al. (108), who run a pediatric brain stimulation clinical research program, published a report on their experience with the safety and tolerability of TMS in a cohort of 384 youth (108). The individuals in this report included healthy volunteers (n = 118), patients with perinatal stroke (n = 101), patients with mild traumatic brain injury (n = 121), and patients with neuropsychiatric disorders (n = 37). They report no serious adverse events and excellent tolerability despite over a hundred patients who were at greater risk for seizure due to a neurological condition. As with previous reports, Zewdie et al. note that the most common side-effects were transient headache and neck pain. The authors conclude that standard TMS paradigms, including single pulse, paired-pulse, and rTMS, should be considered minimum risk and provide a safety and tolerability evaluation form for use in this population.

In 2015, Krishnan et al. conducted a safety review involving 35 studies (n = 322) focused on the use of rTMS in children and adolescents with range of conditions (109). Fifteen studies reported no adverse events or that the treatment was “well-tolerated,” without specifying adverse events. The most common adverse events reported were headache (11.5% of patients) and scalp discomfort (2.5%). A third pediatric TMS safety study was conducted by Hong and colleagues in 2015 and focused specifically on the safety and tolerability a novel rTMS paradigm, namely TBS (110). This retrospective analysis (n = 76) reported adverse events in 10.5% of TBS sessions including: headache, arm/hand/other pain, numbness/tingling, and weakness (110). The rate and severity of adverse events reported in this study did not differ between those that received TBS and a comparator group of 89 youth who received single- and paired-pulse TMS. Similar rates were also reported for active and sham (placebo) TBS (110).

More serious adverse events in pediatric studies have been rare (occurring in ~1–2% of participants). Two cases of syncope in children with pediatric stroke and six cases of seizures (four in adolescents with depression, one in an adolescent with migraines, and one in an adolescent with schizophrenia) have been reported (111–116). Factors that could have increased the risk of these serious adverse events include: concomitant medication use (in four of the cases), alcohol withdrawal (in one case), and clinical disorders associated with increased risk of syncope and seizure (i.e., pediatric stroke and migraine, respectively). More details about these case reports can be found in Table 1.

The most recent International Federation of Clinical Neurophysiology TMS safety guidelines indicate that the extant pediatric literature “provide reassurance regarding the safety of these techniques” in pediatric populations (82). However, as noted above, this “reassurance” is based on far less data than in the adult literature. Furthermore, the neurodevelopmental processes ongoing in children and adolescence, compounded by the pathophysiological processes affecting those with neuropsychiatric and neurodevelopmental disorders require consideration (117, 118). As it relates to adolescent depression, the aforementioned neurotransmitter fluctuations and potentially aberrant functional connectivity may result in an altered neurophysiological state (as compared to the adult brain). Thus, evaluating and adjusting for the physiological state of the brain may both reduce the risk of adverse events as well as potentially increase the intended effects.

Efficacy of rTMS in Adult and Adolescent MDD

Efficacy of rTMS in Adult MDD

In addition to publishing safety guidelines, the International Federation of Clinical Neurophysiology has also published a series of evidence-based guidelines on the therapeutic use of rTMS (119, 120). To develop these guidelines, experts in the field evaluated the level of evidence of rTMS efficacy for a number of indications. Consistent with the FDA label and based on a number of large-scale clinical trials (121–123), high-frequency (10 Hz) rTMS to the left dorsolateral prefrontal cortex (DLPFC) achieved a “Level A” (definite efficacy) for adult MDD. Since its initial FDA clearance, in 2008, multiple TMS devices and protocols have also received clearance for adult MDD. Consensus guidelines have also been established by the National Network of Depression Centers and the American Psychiatric Association Council on Research (124). Multiple meta-analyses of thousands of individuals have concluded that rTMS is safe, tolerable, and leads to a reduction in depressive symptoms in otherwise treatment resistant adult patients with MDD. Although the safety and tolerability of rTMS is consistent across trials, effect sizes vary greatly based on a number of factors including: rTMS parameters (e.g., intensity, location, and stimulation protocol) and interindividual factors such as brain size, shape, and neurophysiological state.

Intensity of stimulation is typically set in relation to the individual's motor threshold (MT). The MT is the stimulator output that is required to produce a contraction of the thumb or fingers half the time when applied to the primary motor cortex “hotspot.” MT is used as a proxy of the intensity of stimulation necessary to activate other regions of cortex. Intensity of rTMS typically ranges from 80 to 120% of MT. At these intensity levels, current models indicate that standard coils induce an electrical field that can reach 2–3 cm from the scalp (125). Consistent with the FDA label for adult MDD, the DLPFC target is often approximated from measurements on the scalp (126). However, some studies have also used structural or functional MRI combined with a frameless stereotaxic neuronavigation system to target specific regions of interest (65, 127). Targeting rTMS based on fMRI and/or diffusion tensor imaging (DTI) mapping of an individual's brain network (128, 129) tends to result in larger effect sizes as compared to scalp-based approaches (127, 130, 131). Typical stimulation frequencies vary from 1 to 20 Hz. The most common frequency used for adult MDD is 10-Hz. rTMS applied to the DLPFC at 10 Hz frequency per the FDA label is associated with an average of 30% response rate, compared with 10.4% with sham (placebo) rTMS, with an effect size of 0.55 (132–134) and a pooled odds ratio of response or remission of 3.3 (132). Furthermore, according to a recent meta-analysis, 66.5% of individuals who respond to the initial rTMS course have sustained response after 3 months, with responder rates decreasing to 52.9% after 6 months, and 46.3% of individuals still maintaining response 1 year after the initial treatment course (135).

Several approaches have been taken to address relapse and recurrence after initial rTMS response. These include various maintenance rTMS schedules as well as additional courses of rTMS treatments during periods of relapse. Though a number of studies have explored various maintenance rTMS schedules [see (136)], protocols vary across studies and clinics. Proposed maintenance regimen involve an initial tapering of sessions over the course of 3-4 weeks from five sessions per week down to one session every week and eventually one session every 2 or 3 weeks for many months to several years depending on the individual [see (137)]. Though this regimen may provide optimal protection against relapse, there may be alternative options for maintenance that are less burdensome including introduction and/or modifications of antidepressant medication or psychotherapy.

The effects of rTMS are not simply a matter of the stimulation parameters, but also how the stimulation is received and processed in the brain. The specific degree and location of stimulation of the targeted brain region depends upon the individually unique structural and functional architecture of each individual's brain. Given the putative mechanism of action of rTMS is the modulation of functional networks and the known interindividual structural and functional heterogeneity of these networks, the efficacy of rTMS depends upon the accurate targeting of the network of interest in that individual. Furthermore, although high-frequency and iTBS protocols typically lead to increased excitability of the targeted region, recent studies report considerable inter-individual and intra-individual (state-dependent) variability in cortical response, especially outside of the primary motor cortex (138, 139). Thus, efficacy can be optimized through careful characterization of both the functional network architecture and neurophysiological state.

Efficacy of rTMS in Adolescent MDD

Most of what is known about the effects of rTMS on the brain are based on adult studies. Less than 1,000 children and adolescents are represented in the published rTMS literature. Without FDA clearance, commercial marketing of rTMS for any pediatric indications is prohibited. While adolescents and children may be studied under an approved research protocol, any other use of TMS in individuals under age 21 is “off-label” in the United States.

The first published review on rTMS in adolescent depression (6) was based on two case series (140, 141) and one open-label trial (142) (n = 14). With so few data, no meaningful conclusions could be made about efficacy. The authors suggested that the optimal stimulation parameters for adolescents might differ from those in adults. A subsequent review (3) included another open-label trial (143), a case report of an induced seizure in an adolescent (111), an open-label trial where depressive symptoms were evaluated in adolescents with Tourette syndrome (8), and a secondary analysis on the previous open-label trial. Despite the expanded number of publications, the rTMS literature was comprised of only 22 (inclusive of the one seizure report) adolescents with a primary diagnosis of MDD. A third review, a decade after the first (4), added three additional case reports (112, 113, 144), a case report of an individual with autism spectrum disorder and co-morbid depression who received rTMS for depressive symptoms (9), a case series (106), an open-label study (58), and three secondary analyses on previous datasets (145–147). By 2017, despite more publications, <50 adolescents with primary MDD had received rTMS and no studies included a placebo control group. The next review (2), in 2019, included two additional open-label trials (148, 149), more than doubling the previous total number of depressed adolescents treated with rTMS. This review focused on the effects of rTMS on GABAergic and glutamatergic neurotransmission and concluded by calling for larger, neurodevelopmentally-informed studies. The most recent review (5), included one additional case series (150), one new retrospective analysis of clinical data (151), and two more secondary analyses of existing datasets (152, 153) (n = ~150). The authors (5) conclude by highlighting flawed study designs, calling for sham-controlled RCTs to properly assess the efficacy of rTMS.

Since the publication of the most recent review article, there is now one sham-controlled RCT of rTMS in adolescents with MDD (7) and two additional retrospective analyses of clinical data (154, 155). With the addition of these new data, the adolescent depression rTMS literature now encompasses 20 publications (Table 2) representing 12 unique datasets (n = ~280 adolescent participants with MDD). Despite the calls for neurodevelopmentally-informed trials, the parameters have mimicked those in the adult studies. Intensity of stimulation has ranged from 80 to 120% of motor threshold. Protocols have largely followed the FDA clearance (for adults) applying 10 Hz unilaterally to the left DLPFC. Two studies took a different path. One (151) compared 1 Hz, unilateral right DLPFC stimulation with a bilateral (left followed by right) stimulation and another applied bilateral TBS stimulation (148). As for sample characteristics, studies have taken a conservative approach, enrolling relatively older adolescents (average age of 17.15), small sample sizes (7/12 datasets with sample sizes n ≤ 10), and open-label designs or active stimulation case reports/retrospective analyses of clinical data (11/12 datasets). The sole exception is the large-scale RCT that enrolled 103 adolescents ~18% of whom were age 12–14 years old (7).

Acknowledging the limitations of the method, open-label trials and case series/retrospective analyses of clinical data were generally positive, with large effect sizes open-label trials: Hedges's g_av = 2.39 and Glass's Δ_pre = 3.01; case reports/retrospective analyses: Hedges's g_av = 2.06 and Glass's Δ_pre = 3.48, [see (158) for explanation of effect size measures]. Average reduction in depressive symptoms for the open-label trials and case series/retrospective analyses equals 40 and 51%, respectively (with study averages ranging from 23 to 71%). Open-label trials and case series/retrospective analyses inherently inflate effect sizes and are influenced by regression to the mean, investigator biases, and confounding the effect of the active treatment with placebo effects (159). The only large-scale RCT of rTMS in adolescent depression yielded an effect size near zero (Hedges's g = 0.10). The active and sham rTMS groups showed comparable response (41.7% for the active and 36.4% for the sham group) and remission rates (29.2% for the active and 29.0% for the sham group) (7).

Sham TMS coils have been used in rTMS trials as an analog to pill placebo in drug trials. Sham coils do not apply a magnetic field but mimic the auditory, visual, and (in some cases) sensory experience of rTMS (160). The largest placebo effects have been seen in trials employing “physical placebo interventions” (e.g., a sham TMS coil) with subjective patient-related outcomes (e.g., self or parent reports or clinician administered interviews) (161). A meta-analysis of 61 studies using rTMS sham-controls for adult depression found a placebo effect size of 0.8 with the magnitude of the effect positively correlated with year of publication (162). Increasing placebo response over time may be an outgrowth of public/media attention, technologically more sophisticated devices (such as neuronavigation), enrollment of patients who are not “treatment resistant” (163, 164), and improved disguising of sham coils (165). The RCT in adolescents resulted in a sham effect size of 1.15 and a response rate of 36.4% (not significantly lower than the active rTMS effect size of 1.27 and response rate of 41.7%) (7). This higher placebo response rate in adolescents is also present in drug trials for adolescent depression (166) where placebo responses rates range from 24 to 60% (167–174).

To determine the efficacy of rTMS for adolescent depression, future RCTs will have to address the higher placebo effect rate. Aside from the conventional “active vs. sham” design alternative approaches such as inclusion of a placebo run-in, giving an active comparator, or applying statistical analysis techniques such as Growth Mixture Modeling (GMM) to capture unobserved subject heterogeneity in trajectories may be considered. The last approach requires dense collection of outcome measures over the course of rTMS and during follow-up. GMM was used to analyze data from an antidepressant trial; the placebo response trajectory deviated from the two active drug response trajectories (175). This technique has also been used to evaluate whether biomarkers, such as quantitative electroencephalography (qEEG), can predict antidepressant response (176).

Beyond the placebo response rate, other features in the study design for the RCT may have contributed to the failed outcome, including: the choice of the “5-cm rule” targeting strategy, broad inclusion criteria, within group variability in response, and insufficient sample size. The rTMS protocol, targeting strategy, frequency of stimulation, and dosage influence effect size in rTMS trials. Optimizing these factors in a neurodevelopmentally informed way could therefore increase effect sizes.

Limitations of the Existing Literature and Proposed Resolutions

Impact of Stimulation Parameters on Effect Size

The majority of clinical trials for both adult and adolescent depression have applied 10 Hz left hemisphere DLPFC rTMS. However, a meta-analysis of rTMS RCTs, encompassing over 4,000 patients and 81 trials, concluded that sequential bilateral stimulation to the right (1 Hz) and left (10 Hz) DLPFC was the most effective method (177). Only two adolescent depression trials used bilateral stimulation. Though safety and efficacy are the primary goals of novel treatment development, feasibility and tolerability also need to be considered. The standard 10 Hz protocol is burdensome for the patient and provider; daily sessions require over half an hour (up to an hour for bilateral stimulation protocols) and at least 6 weeks of treatment. Use of iTBS protocols can reduce this burden with a single session of iTBS taking only 3 min, allowing for accelerated protocols and shorter treatment courses (62).

iTBS protocols were designed to closely mimic endogenous theta/gamma rhythms of the brain and induce long-term potentiation-like (LTP-like) plasticity. Though early iTBS studies targeted primary motor cortex, this protocol has also been applied to other brain regions including the DLPFC. iTBS over the DLPFC has been shown to induce long-term changes in local cortical excitability (178, 179), reduce GABA and glutamate/glutamine levels, and alter network connectivity (180). On a behavioral level, iTBS led to improved performance on the ability to inhibit automatic responses and working memory tasks in a small study of healthy volunteers (181). In a non-inferiority study iTBS and 10 Hz rTMS both reduced symptoms of depression in adults with similar safety, tolerability and efficacy (182). A meta-analysis of adult studies showed a response rate of iTBS of 35.6% (42/118) vs. 17.5% (18/103) with sham, a pooled odds ratio of response and remission of 2.7 and 1.9, respectively, and an effect size of 1.0 (183). Though promising in adults, iTBS has not been evaluated in an RCT in adolescents and faces the challenge of the aforementioned higher sham response rates. However, the mechanism of action of iTBS may be particularly well-suited to the intrinsic neuroplasticity of the adolescent brain. Furthermore, the ability of iTBS to modulate aberrant neurotransmitter function and pathological connectivity is well-matched to the reported DLPFC pathophysiology of adolescent depression.

Different rTMS targeting strategies yield differences in precision, reliability, and effect size. In six of 12 studies (Table 2) of adolescent depression the targeting strategy was scalp-based, four used MRI-guidance, and two provide no data. Scalp-based targeting is unreliable and imprecise for localizing DLPFC (184–188). MRI-based neuronavigation used for rTMS shows larger effect sizes (127, 130, 131). For treatment of depression, one study in 51 depressed adults found a moderately larger effect for MRI-based neuronavigation than standard targeting (Hedges g_s = 0.64) (127). Even when using MRI-based neuronavigation, there are uncertainties about the optimal target for treating depression. Most studies target DLPFC, a large cortical area that is functionally connected to the Frontoparietal Network (also known as the Central Executive Network), the Default Mode Network, and the Salience Network (93, 189). All of these networks may be affected in depression and could be influenced by DLPFC stimulation. Using a standard “figure of 8 coil,” a shift of as little as 0.5–1 cm can differentially affect one or more of these networks (190). Furthermore, there is a large variation in individual functional brain circuitry (191). Thus, especially during adolescence when these networks are in flux, using patient-specific functional neuronavigated rTMS to precisely target and modulate one or more of these networks, could lead to larger effect sizes (192).

Larger effect sizes are also seen in protocols that apply more sessions and more pulses per session (193–195). A meta-analysis of number of sessions and pulses/session to treat depression found the average effect size increased from 0.43 to 2.74 when the number of sessions increased from 5 to 20; the maximum mean effect size (5.47) was seen with 20 or more sessions and more than 1,200 pulses/session (193). However, only five of the 12 studies of adolescent depression applied 20 or more sessions. Increasing the number of sessions per day above the convention of one session daily would increase efficiency. The conventional procedure results in a standard course of treatment of 6 weeks or more. Accelerated protocols with 2–10 session per day applying the same number of total sessions appear to show equivalent safety and efficacy (157, 195–199) with the pace of improvement showing a direct relationship with the cumulative number of sessions. Additionally, neuroimaging studies find that, like standard protocols, accelerated protocols result in changes on neurochemical and functional connectivity biomarkers of depression (103, 200). When applying more than one session daily, intersession interval influences additive effects. Basic research studies on LTP find that the level of LTP is doubled when a second TBS train is applied after 60 min, but if applied after only 10, 30, or 40 min there was no cumulative effect (201). That being said, neurodevelopmental factors could affect optimal intersession intervals and has yet to be determined. Piecing these protocol parameters together, a proposal for the most favorable balance of feasibility, efficacy and efficiency is up to five sessions/day, a 60-min intersession interval, and at least 20 total sessions (202). Such a treatment course can be completed in 1 week (instead of 6 weeks for once daily treatments). The value of swift antidepressant interventions becomes particularly relevant during the COVID pandemic, when access to therapy can be hindered and also adherence to longer therapy is a greater problem (203).

Within-Group Variability: Impact of Inter-individual Heterogeneity and Intra-individual Brain State on Effect Size

Clinical heterogeneity may contribute to reduced therapeutic response in studies of adolescents (204, 205). Adolescent patients present with a diverse range of symptoms (205), clinical courses (206), and responses to treatment (16, 204, 207, 208). We and others have thus suggested that underlying pathophysiology may account for the observed clinical heterogeneity (209). Given this heterogeneity, one would not expect to find a single “one size fits all” optimal treatment for adolescent depression (210). To address clinical heterogeneity, one could increase the sample size, allowing for subgroup analyses, or focus on a narrower phenotype.

Attempts have been made to define clinical subtypes and brain-network-based biotypes, but these have been difficult to replicate (211). One promising symptom domain for targeted treatment is anhedonia/dysphoria. Anhedonic/dysphoric symptoms seem to be reliably associated with PFC-cingulate network dysfunction (212–215) and have been shown to be particularly responsive to neuronavigated rTMS to the DLPFC (100). This raises the possibility that reducing sample heterogeneity by enrolling with primarily anhedonic/dysphoric symptoms might increase power to observe an effect. Such an approach was successfully used in a recent pharmacological trial (216).

A different approach is to focus treatment on suicidal ideation, the most serious risk to patient safety. The literature on efficacy of rTMS for suicidality paints a mixed picture. A pooled analysis of 19 depressed adolescents who received open-label rTMS showed decreased suicidal ideation over the course of treatment (153). This decrease in suicidality, corresponded to an overall decrease in severity of depression symptoms (153). A retrospective analysis of 332 depressed adult patients who received rTMS also reported improvements in suicidality (217) and a review (n = 593) and naturalistic study (n = 43) of rTMS in adult MDD found consistent improvements in depressive symptoms and suicidality in open-label trials. These positive findings are contrasted with results from sham-controlled trials that have failed to show significant group differences in the reduction of suicidal ideation (210). While it is possible that, compared to conventional pharmacotherapy, rTMS might be a safe, faster means of reducing suicidal ideation; larger, sham-controlled RCTs employing the most current sophisticated methods will be needed to conclusively demonstrate efficacy. Furthermore, the immediate therapeutic effects of ketamine may soon become the standard for speed of reducing suicidality against which all other interventions must be compared.

For both safety and feasibility, most adult and adolescent rTMS studies have allowed participants to continue their current medications (218). Safety reviews suggest that rTMS in those receiving stable doses of antidepressant medication does not increase the risk of adverse events (82); however, this increases within-group variability in neurochemical state and decreases statistical power. To control for variability in neurochemical state, investigators could enroll only participants who have been withdrawn from all psychotropic medications; however, withdrawing symptomatic patients from their medications introduces safety concerns of increased suicidal ideation and withdrawal-related side-effects. It demands close medical monitoring. A more feasible approach would be to require participants to maintain a steady medication dose and to apply a within-subject model controlling for baseline severity as the primary outcome measure. While this would not eliminate variability across participants, it reduces the effects of neurochemical brain state on the primary outcome measure. Combining rTMS and pharmacological treatment is another novel multimodal intervention being developed in adults that could be extended to adolescents (219). Notably, recent data suggest that combining antidepressant medication with a course of rTMS may in fact have a greater benefit in adolescents than in adults (150).

In order to increase tolerability of the treatment, adolescent studies have allowed patients to read, watch TV, or listen to music during rTMS sessions. The difference in behavioral engagement/arousal this causes is another potential source of within-group variability. While these distraction strategies are common in everyday practice with rTMS, they are a concern for treatment trials. Factors such as attention, arousal and mood state have been shown to affect modulation of excitability by rTMS (220–223). Thus, if the adolescent is even passively engaged in an unrelated activity, this may impact the effect of the rTMS. Applying shorter stimulation protocols (e.g., iTBS) may reduce the need for co-occurring activities to make the session more tolerable. Alternatively, one can transiently modify the patient's cognitive state by presenting stimuli or engage them in a task that engages the same brain networks as the rTMS target. In this way, rTMS can be combined with the behavioral task in order to amplify the impact on the targeted network (76). Studies of rTMS for post-traumatic stress disorder (PTSD), smoking cessation, and OCD have shown increased treatment response when the participant's symptoms were provoked [e.g., by asking questions about thoughts, images, or impulses related to their obsessions or compulsions or asking the patient to perform a task related to their symptoms (224)] immediately prior to rTMS stimulation (225–227). One could also consider pairing stimulation with concurrent behavioral interventions such as CBT (228).

The Importance of Imaging Biomarkers in rTMS Treatment Development

Given the significant time-investment necessary for rTMS treatment, many have sought to identify early predictors of later response. A recent retrospective study of 101 patients who received 4 weeks of rTMS treatment found that a lack of clinical response at the midway point predicted non-response with 88% accuracy (229). However, this still requires the patient to undergo 10 treatment sessions. Identifying intrinsic neurophysiological signatures or measures that can be obtained after one or two rTMS sessions would be preferable (229). Brain imaging techniques are increasingly capable of obtaining measures of brain function at cellular/molecular and network levels (96, 97, 230, 231). MRS can yield measures of GABA and glutamate neurotransmitter functioning (230, 231). BOLD fMRI and RSFC (96, 97) have been proposed as tools for neuroimaging biomarkers. RSFC as measured by fMRI is stable across development (232, 233) and reproducible (234) at a group level. RSFC in adolescent depression has been shown to differentiate (at a group-, not at the individual-level) symptom severity (235), symptom domains (213, 236, 237), onset (238), and course of disease (239, 240). Furthermore, RSFC in fronto-parietal, cingulo-opercular/ventral attention, and default mode networks has been used to predict individual differences in adolescent brain maturity and executive functioning (241). Within individuals, however, most commonly used sequences are too short to produce reliable RSFC measures (242), but with longer sequences (243, 244) or novel sequence types (244) it may be a reliable measure. rTMS trials in adolescent depression would benefit from dense collection of reliable neuroimaging measures at baseline, post-treatment, and follow-up. At baseline, neuroimaging measures may allow investigators to customize treatment to the unique neurodevelopmental state of each adolescent's developing cortex. Though group data appear promising, the benefit of using RSFC profiles for patient-specific treatment decisions is currently only theoretical.

Neuroimaging measures can also serve as predictive biomarkers for response to rTMS. Multiple studies have linked intrinsic functional connectivity between the targeted region of PFC and ACC, anterior insula and striatum with later response to rTMS (74, 125, 200, 245–254) and other antidepressant treatments (101, 255–258). Interleaved rTMS/fMRI has shown acute effects on BOLD activation of both local and distal brain regions immediately following 1 Hz DLPFC rTMS in adults with MDD (89). Though research is promising in this area, replication and prospective examination of these predictive biomarkers will be critical prior to clinical implementation.

To facilitate clinical translation of such biomarkers, the National Institute of Mental Health (NIMH) has introduced “fast-fail” trials. This initiative employs an experimental therapeutics platform to quickly identify devices, protocols or compounds that should be considered for more extensive testing (259). These types of trials may be especially useful in adolescent depression where there is a clear urgent public health need for novel therapeutics, but still a great deal of uncertainty in the neural targets and biomarkers that reliably predict treatment response. The “fast-fail” initiative has recently led researchers to report successful target-engagement of a novel kappa-opioid receptor antagonist for the treatment of anhedonia in adult MDD (216). Thus, extending this approach to adolescents is both timely and feasible.

Conclusions

Treating adolescent depression is fraught with the challenges of heterogeneity of the clinical phenotype, the high placebo response rate, and the breadth of neurodevelopmental changes during puberty. While the efficacy of rTMS for adults with treatment resistant MDD is supported by multiple, adequately powered RCTs, evidence in adolescent depression is scanty and has many limitations. The safety profile of rTMS for adolescent depression appears to be desirable but many parameters, including the most favorable approaches, have yet to be determined.

rTMS is worthy of further investigation for this vulnerable population. We propose that future adolescent depression rTMS trials: (1) be designed with 20 or more sessions (2) select a narrow clinical phenotype (e.g., select for anhedonia); (3) test for the possibility of individualization by including neuroimaging biomarkers (e.g., RSFC); (4) employ an experimental therapeutics approach (5) allow for robust inferences by being adequately controlled and powered with realistic effect size estimates. Such trials, if successful, may establish paradigms for larger, pivotal trials that clarify whether rTMS is an effective treatment for adolescent depression. Furthermore, they are likely to advance our understanding of the pathophysiology of this disorder.

References

• 1.

  HallettM. Transcranial magnetic stimulation: a primer. Neuron. (2007) 55:187–99. 10.1016/j.neuron.2007.06.026

  • CrossRef
  • Google Scholar

• 2.

  CroarkinPEMacMasterFP. Transcranial magnetic stimulation for adolescent depression. Child Adolesc Psychiatr Clin N Am. (2019) 28:33–43. 10.1016/j.chc.2018.07.003

  • CrossRef
  • Google Scholar

• 3.

  DonaldsonAEGordonMSMelvinGABartonDAFitzgeraldPB. Addressing the needs of adolescents with treatment resistant depressive disorders: a systematic review of rTMS. Brain Stimul. (2014) 7:7–12. 10.1016/j.brs.2013.09.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4.

  MagaviLRRetiIMVasaRA. A review of repetitive transcranial magnetic stimulation for adolescents with treatment-resistant depression. Int Rev Psychiatry. (2017) 29:79–88. 10.1080/09540261.2017.1300574

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5.

  HettDRogersJHumpstonCMarwahaS. Repetitive transcranial magnetic stimulation (rtms) for the treatment of depression in adolescence: a systematic review. J Affect Disord. (2020) 278:460–9. 10.1016/j.jad.2020.09.058

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6.

  CroarkinPEWallCAMcClintockSMKozelFAHusainMMSampsonSM. The emerging role for repetitive transcranial magnetic stimulation in optimizing the treatment of adolescent depression. J ECT. (2010) 26:323–9. 10.1097/YCT.0b013e3181dd17eb

  • CrossRef
  • Google Scholar

• 7.

  CroarkinPEElmaadawiAZAaronsonSTSchrodtGRJrHolbertRCVerdolivaSet al. Left prefrontal transcranial magnetic stimulation for treatment-resistant depression in adolescents: a double-blind, randomized, sham-controlled trial. Neuropsychopharmacology. (2020) 46:462–9. 10.1038/s41386-020-00829-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8.

  LeKLiuLSunMHuLXiaoN. Transcranial magnetic stimulation at 1 Hertz improves clinical symptoms in children with Tourette syndrome for at least 6 months. J Clin Neurosci. (2013) 20:257–62. 10.1016/j.jocn.2012.01.049

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9.

  CristanchoPAkkineniKConstantinoJNCarterARO'ReardonJP. Transcranial magnetic stimulation in a 15-year-old patient with autism and comorbid depression. J ECT. (2014) 30:e46–7. 10.1097/YCT.0000000000000156

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10.

  FriedrichMJ. Depression is the leading cause of disability around the world. JAMA. (2017) 317:1517. 10.1001/jama.2017.3826

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11.

  DiseaseGBDInjuryIPrevalenceC. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. (2017) 390:1211–59. 10.1016/S0140-6736(17)32154-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12.

  PrattLABrodyDJ. Depression in the U.S. household population, 2009-2012. NCHS Data Brief . (2014) 1–8.

  • Google Scholar

• 13.

  CostelloEJMustilloSErkanliAKeelerGAngoldA. Prevalence and development of psychiatric disorders in childhood and adolescence. Arch Gen Psychiatry. (2003) 60:837–44. 10.1001/archpsyc.60.8.837

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14.

  BrentDAPerperJAMoritzGAllmanCFriendARothCet al. Psychiatric risk factors for adolescent suicide: a case-control study. J Am Acad Child Adolesc Psychiatry. (1993) 32:521–9. 10.1097/00004583-199305000-00006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15.

  TureckiGBrentDA. Suicide and suicidal behaviour. Lancet. (2016) 387:1227–39. 10.1016/S0140-6736(15)00234-2

  • CrossRef
  • Google Scholar

• 16.

  CuijpersPStringarisAWolpertM. Treatment outcomes for depression: challenges and opportunities. Lancet Psychiatry. (2020) 7:925–7. 10.1016/S2215-0366(20)30036-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17.

  DwyerJBStringarisABrentDABlochMH. Annual research review: defining and treating pediatric treatment-resistant depression. J Child Psychol Psychiatry. (2020) 61:312–32. 10.1111/jcpp.13202

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18.

  KennardBSilvaSVitielloBCurryJKratochvilCSimonsAet al. Remission and residual symptoms after short-term treatment in the Treatment of Adolescents with Depression Study (TADS). J Am Acad Child Adolesc Psychiatry. (2006) 45:1404–11. 10.1097/01.chi.0000242228.75516.21

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19.

  KennardBDEmslieGJMayesTLHughesJL. Relapse and recurrence in pediatric depression. Child Adolesc Psychiatr Clin N Am. (2006) 15:1057–79, xi. 10.1016/j.chc.2006.05.003

  • CrossRef
  • Google Scholar

• 20.

  EmslieGJKennardBDMayesTLNightingale-TeresiJCarmodyTHughesCWet al. Fluoxetine versus placebo in preventing relapse of major depression in children and adolescents. Am J Psychiatry. (2008) 165:459–67. 10.1176/appi.ajp.2007.07091453

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21.

  BirmaherBBrentDIssuesAWGoQBernetWBuksteinOWalterHet al. Practice parameter for the assessment and treatment of children and adolescents with depressive disorders. J Am Acad Child Adolesc Psychiatry. (2007) 46:1503–26. 10.1097/chi.0b013e318145ae1c

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22.

  KonigHKonigHHKonnopkaA. The excess costs of depression: a systematic review and meta-analysis. Epidemiol Psychiatr Sci. (2019) 29:e30. 10.1017/S2045796019000180

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23.

  VoigtJCarpenterLLeuchterA. Cost effectiveness analysis comparing repetitive transcranial magnetic stimulation to antidepressant medications after a first treatment failure for major depressive disorder in newly diagnosed patients - a lifetime analysis. PLoS ONE. (2017) 12:e0186950. 10.1371/journal.pone.0186950

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24.

  BeesdoKHoflerMLeibenluftELiebRBauerMPfennigA. Mood episodes and mood disorders: patterns of incidence and conversion in the first three decades of life. Bipolar Disord. (2009) 11:637–49. 10.1111/j.1399-5618.2009.00738.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25.

  HankinBL. Depression during childhood and adolescence. In: DerubeisRJStrunkDR, editors. Oxford Handbook of Mood Disorders. New York, NY: Oxford University Press (2017). p. 276–86. 10.1093/oxfordhb/9780199973965.013.24

  • CrossRef
  • Google Scholar

• 26.

  GanWBKwonEFengGSanesJRLichtmanJW. Synaptic dynamism measured over minutes to months: age-dependent decline in an autonomic ganglion. Nat Neurosci. (2003) 6:956–60. 10.1038/nn1115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27.

  ZuoYLinAChangPGanWB. Development of long-term dendritic spine stability in diverse regions of cerebral cortex. Neuron. (2005) 46:181–9. 10.1016/j.neuron.2005.04.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28.

  CaseyBJonesRMSomervilleLH. Braking and accelerating of the adolescent brain. J Res Adolesc. (2011) 21:21–33. 10.1111/j.1532-7795.2010.00712.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29.

  ErnstMPineDSHardinM. Triadic model of the neurobiology of motivated behavior in adolescence. Psychol Med. (2006) 36:299–312. 10.1017/S0033291705005891

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30.

  LewisDA. Development of the prefrontal cortex during adolescence: insights into vulnerable neural circuits in schizophrenia. Neuropsychopharmacology. (1997) 16:385–98. 10.1016/S0893-133X(96)00277-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31.

  SowellERPetersonBSThompsonPMWelcomeSEHenkeniusALTogaAW. Mapping cortical change across the human life span. Nat Neurosci. (2003) 6:309–15. 10.1038/nn1008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32.

  LunaBGarverKEUrbanTALazarNASweeneyJA. Maturation of cognitive processes from late childhood to adulthood. Child Dev. (2004) 75:1357–72. 10.1111/j.1467-8624.2004.00745.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33.

  GogtayNThompsonPM. Mapping gray matter development: implications for typical development and vulnerability to psychopathology. Brain Cogn. (2010) 72:6–15. 10.1016/j.bandc.2009.08.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34.

  GogtayNGieddJNLuskLHayashiKMGreensteinDVaituzisACet al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci USA. (2004) 101:8174–9. 10.1073/pnas.0402680101

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35.

  GieddJNBlumenthalJJeffriesNOCastellanosFXLiuHZijdenbosAet al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. (1999) 2:861–3. 10.1038/13158

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36.

  LenrootRKGogtayNGreensteinDKWellsEMWallaceGLClasenLSet al. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. Neuroimage. (2007) 36:1065–73. 10.1016/j.neuroimage.2007.03.053

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37.

  LebelCBeaulieuC. Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci. (2011) 31:10937–47. 10.1523/JNEUROSCI.5302-10.2011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38.

  HasanKMSankarAHalphenCKramerLABrandtMEJuranekJet al. Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. Neuroreport. (2007) 18:1735–9. 10.1097/WNR.0b013e3282f0d40c

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39.

  ListonCWattsRTottenhamNDavidsonMCNiogiSUlugAMet al. Frontostriatal microstructure modulates efficient recruitment of cognitive control. Cereb Cortex. (2006) 16:553–60. 10.1093/cercor/bhj003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40.

  ListonCMcEwenBSCaseyBJ. Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proc Natl Acad Sci USA. (2009) 106:912–7. 10.1073/pnas.0807041106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41.

  DurstonSDavidsonMCTottenhamNGalvanASpicerJFossellaJAet al. A shift from diffuse to focal cortical activity with development. Dev Sci. (2006) 9:1–8. 10.1111/j.1467-7687.2005.00454.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42.

  CaseyBJTrainorRJOrendiJLSchubertABNystromLEGieddJNet al. A developmental functional MRI study of prefrontal activation during performance of a go-no-go task. J Cogn Neurosci. (1997) 9:835–47. 10.1162/jocn.1997.9.6.835

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43.

  StevensMCKiehlKAPearlsonGDCalhounVD. Functional neural networks underlying response inhibition in adolescents and adults. Behav Brain Res. (2007) 181:12–22. 10.1016/j.bbr.2007.03.023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44.

  CaseyBJGetzSGalvanA. The adolescent brain. Dev Rev. (2008) 28:62–77. 10.1016/j.dr.2007.08.003

  • CrossRef
  • Google Scholar

• 45.

  ThomasesDRCassDKTsengKY. Periadolescent exposure to the NMDA receptor antagonist MK-801 impairs the functional maturation of local GABAergic circuits in the adult prefrontal cortex. J Neurosci. (2013) 33:26–34. 10.1523/JNEUROSCI.4147-12.2013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46.

  HednerTIversenKLundborgP. Central GABA mechanisms during postnatal development in the rat: neurochemical characteristics. J Neural Transm. (1984) 59:105–18. 10.1007/BF01255409

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47.

  WangHXGaoWJ. Cell type-specific development of NMDA receptors in the interneurons of rat prefrontal cortex. Neuropsychopharmacology. (2009) 34:2028–40. 10.1038/npp.2009.20

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48.

  ErnstMDanieleTFrantzK. New perspectives on adolescent motivated behavior: attention and conditioning. Dev Cogn Neurosci. (2011) 1:377–89. 10.1016/j.dcn.2011.07.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49.

  WahlstromDWhiteTLucianaM. Neurobehavioral evidence for changes in dopamine system activity during adolescence. Neurosci Biobehav Rev. (2010) 34:631–48. 10.1016/j.neubiorev.2009.12.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50.

  CaballeroAGranbergRTsengKY. Mechanisms contributing to prefrontal cortex maturation during adolescence. Neurosci Biobehav Rev. (2016) 70:4–12. 10.1016/j.neubiorev.2016.05.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51.

  ChambersRATaylorJRPotenzaMN. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am J Psychiatry. (2003) 160:1041–52. 10.1176/appi.ajp.160.6.1041

  • CrossRef
  • Google Scholar

• 52.

  O'DonnellP. Adolescent maturation of cortical dopamine. Neurotox Res. (2010) 18:306–12. 10.1007/s12640-010-9157-3

  • CrossRef
  • Google Scholar

• 53.

  TsengKYO'DonnellP. Dopamine modulation of prefrontal cortical interneurons changes during adolescence. Cereb Cortex. (2007) 17:1235–40. 10.1093/cercor/bhl034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54.

  SturmanDAMoghaddamB. The neurobiology of adolescence: changes in brain architecture, functional dynamics, and behavioral tendencies. Neurosci Biobehav Rev. (2011) 35:1704–12. 10.1016/j.neubiorev.2011.04.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55.

  DumanRSSanacoraGKrystalJH. Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron. (2019) 102:75–90. 10.1016/j.neuron.2019.03.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56.

  GabbayVMaoXKleinRGElyBABabbJSPanzerAMet al. Anterior cingulate cortex gamma-aminobutyric acid in depressed adolescents: relationship to anhedonia. Arch Gen Psychiatry. (2012) 69:139–49. 10.1001/archgenpsychiatry.2011.131

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 57.

  DubinMJMaoXBanerjeeSGoodmanZLapidusKAKangGet al. Elevated prefrontal cortex GABA in patients with major depressive disorder after TMS treatment measured with proton magnetic resonance spectroscopy. J Psychiatry Neurosci. (2016) 41:E37–45. 10.1503/jpn.150223

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58.

  CroarkinPENakoneznyPAWallCAMurphyLLSampsonSMFryeMAet al. Transcranial magnetic stimulation potentiates glutamatergic neurotransmission in depressed adolescents. Psychiatry Res Neuroimaging. (2016) 247:25–33. 10.1016/j.pscychresns.2015.11.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59.

  ChenRCrosDCurraADi LazzaroVLefaucheurJPMagistrisMRet al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol. (2008) 119:504–32. 10.1016/j.clinph.2007.10.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60.

  RothwellJC. Paired-pulse investigations of short-latency intracortical facilitation using TMS in humans. Electroencephalogr Clin Neurophysiol Suppl. (1999) 51:113–9.

  • Pubmed Abstract
  • Google Scholar

• 61.

  KujiraiTCaramiaMDRothwellJCDayBLThompsonPDFerbertAet al. Corticocortical inhibition in human motor cortex. J Physiol. (1993) 471:501–19. 10.1113/jphysiol.1993.sp019912

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62.

  HuangYZEdwardsMJRounisEBhatiaKPRothwellJC. Theta burst stimulation of the human motor cortex. Neuron. (2005) 45:201–6. 10.1016/j.neuron.2004.12.033

  • CrossRef
  • Google Scholar

• 63.

  HamadaMUgawaY. Quadripulse stimulation–a new patterned rTMS. Restor Neurol Neurosci. (2010) 28:419–24. 10.3233/RNN-2010-0564

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 64.

  HallettM. Transcranial magnetic stimulation and the human brain. Nature. (2000) 406:147–50. 10.1038/35018000

  • CrossRef
  • Google Scholar

• 65.

  AlemanA. Use of repetitive transcranial magnetic stimulation for treatment in psychiatry. Clin Psychopharmacol Neurosci. (2013) 11:53–9. 10.9758/cpn.2013.11.2.53

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66.

  Pascual-LeoneARubioBPallardoFCatalaMD. Rapid-rate transcranial magnetic stimulation of left dorsolateral prefrontal cortex in drug-resistant depression. Lancet. (1996) 348:233–7. 10.1016/S0140-6736(96)01219-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67.

  NahasZLomarevMRobertsDRShastriALorberbaumJPTenebackCet al. Unilateral left prefrontal transcranial magnetic stimulation (TMS) produces intensity-dependent bilateral effects as measured by interleaved BOLD fMRI. Biol Psychiatry. (2001) 50:712–20. 10.1016/S0006-3223(01)01199-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68.

  KimbrellTALittleJTDunnRTFryeMAGreenbergBDWassermannEMet al. Frequency dependence of antidepressant response to left prefrontal repetitive transcranial magnetic stimulation (rTMS) as a function of baseline cerebral glucose metabolism. Biol Psychiatry. (1999) 46:1603–13. 10.1016/S0006-3223(99)00195-X

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69.

  FitzgeraldPBSritharanADaskalakisZJde CastellaARKulkarniJEganG. A functional magnetic resonance imaging study of the effects of low frequency right prefrontal transcranial magnetic stimulation in depression. J Clin Psychopharmacol. (2007) 27:488–92. 10.1097/jcp.0b013e318151521c

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70.

  BeynelLPowersJPAppelbaumLG. Effects of repetitive transcranial magnetic stimulation on resting-state connectivity: a systematic review. Neuroimage. (2020) 211:116596. 10.1016/j.neuroimage.2020.116596

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71.

  HawcoCVoineskosANSteevesJKEDickieEWVivianoJDDownarJet al. Spread of activity following TMS is related to intrinsic resting connectivity to the salience network: a concurrent TMS-fMRI study. Cortex. (2018) 108:160–72. 10.1016/j.cortex.2018.07.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72.

  TangYJiaoXWangJZhuTZhouJQianZet al. Dynamic functional connectivity within the fronto-limbic network induced by intermittent theta-burst stimulation: a pilot study. Front Neurosci. (2019) 13:944. 10.3389/fnins.2019.00944

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73.

  AlkhasliISakreidaKMottaghyFMBinkofskiF. Modulation of fronto-striatal functional connectivity using transcranial magnetic stimulation. Front Hum Neurosci. (2019) 13:190. 10.3389/fnhum.2019.00190

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74.

  FoxMDBucknerRLWhiteMPGreiciusMDPascual-LeoneA. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry. (2012) 72:595–603. 10.1016/j.biopsych.2012.04.028

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75.

  SilvantoJPascual-LeoneA. State-dependency of transcranial magnetic stimulation. Brain Topogr. (2008) 21:1–10. 10.1007/s10548-008-0067-0

  • CrossRef
  • Google Scholar

• 76.

  SchienaGMaggioniEPozzoliSBrambillaP. Transcranial magnetic stimulation in major depressive disorder: response modulation and state dependency. J Affect Disord. (2020) 266:793–801. 10.1016/j.jad.2020.02.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77.

  MachiiKCohenDRamos-EstebanezCPascual-LeoneA. Safety of rTMS to non-motor cortical areas in healthy participants and patients. Clin Neurophysiol. (2006) 117:455–71. 10.1016/j.clinph.2005.10.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78.

  LooCKMcFarquharTFMitchellPB. A review of the safety of repetitive transcranial magnetic stimulation as a clinical treatment for depression. Int J Neuropsychopharmacol. (2008) 11:131–47. 10.1017/S1461145707007717

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79.

  JanicakPGO'ReardonJPSampsonSMHusainMMLisanbySHRadoJTet al. Transcranial magnetic stimulation in the treatment of major depressive disorder: a comprehensive summary of safety experience from acute exposure, extended exposure, and during reintroduction treatment. J Clin Psychiatry. (2008) 69:222–32. 10.4088/JCP.v69n0208

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80.

  DobekCEBlumbergerDMDownarJDaskalakisZJVila-RodriguezF. Risk of seizures in transcranial magnetic stimulation: a clinical review to inform consent process focused on bupropion. Neuropsychiatr Dis Treat. (2015) 11:2975–87. 10.2147/NDT.S91126

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81.

  WassermannEM. Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the International Workshop on the Safety of Repetitive Transcranial Magnetic Stimulation, June 5-7, 1996. Electroencephalogr Clin Neurophysiol. (1998) 108:1–16. 10.1016/S0168-5597(97)00096-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82.

  RossiSAntalABestmannSBiksonMBrewerCBrockmöllerJet al. Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: expert guidelines. J Clin Neurophysiol. (2020) 132:269–306. 10.1016/j.clinph.2020.10.003

  • CrossRef
  • Google Scholar

• 83.

  Rossi S Hallett M Rossini PM Pascual-Leone A Safety of TMSCG. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol. (2009) 120:2008–39. 10.1016/j.clinph.2009.08.016

  • CrossRef
  • Google Scholar

• 84.

  LernerAJWassermannEMTamirDI. Seizures from transcranial magnetic stimulation 2012-2016: results of a survey of active laboratories and clinics. Clin Neurophysiol. (2019) 130:1409–16. 10.1016/j.clinph.2019.03.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85.

  GeorgeMSKetterTAPostRM. Prefrontal cortex dysfunction in clinical depression. Depression. (1994) 2:59–72. 10.1002/depr.3050020202

  • CrossRef
  • Google Scholar

• 86.

  GeorgeMSWassermannEMWilliamsWACallahanAKetterTABasserPet al. Daily repetitive transcranial magnetic stimulation (rTMS) improves mood in depression. Neuroreport. (1995) 6:1853–6. 10.1097/00001756-199510020-00008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87.

  GeorgeMSWassermannEMKimbrellTALittleJTWilliamsWEDanielsonALet al. Mood improvement following daily left prefrontal repetitive transcranial magnetic stimulation in patients with depression: a placebo-controlled crossover trial. Am J Psychiatry. (1997) 154:1752–6. 10.1176/ajp.154.12.1752

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88.

  GeorgeMS. Why would you ever want to? Toward understanding the antidepressant effect of prefrontal rTMS. Hum Psychopharmacol. (1998) 15:307–13. 10.1002/(SICI)1099-1077(199807)13:5<307::AIDHUP1>3.0.CO;2-9

  • CrossRef
  • Google Scholar

• 89.

  LiXNahasZKozelFAAndersonBBohningDEGeorgeMS. Acute left prefrontal transcranial magnetic stimulation in depressed patients is associated with immediately increased activity in prefrontal cortical as well as subcortical regions. Biol Psychiatry. (2004) 55:882–90. 10.1016/j.biopsych.2004.01.017

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90.

  PerssonJStruckmannWGingnellMFallmarDBodenR. Intermittent theta burst stimulation over the dorsomedial prefrontal cortex modulates resting-state connectivity in depressive patients: a sham-controlled study. Behav Brain Res. (2020) 394:112834. 10.1016/j.bbr.2020.112834

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91.

  TikMHoffmannASladkyRTomovaLHummerANavarro de LaraLet al. Towards understanding rTMS mechanism of action: Stimulation of the DLPFC causes network-specific increase in functional connectivity. Neuroimage. (2017) 162:289–96. 10.1016/j.neuroimage.2017.09.022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92.

  FoxMDHalkoMAEldaiefMCPascual-LeoneA. Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS). Neuroimage. (2012) 62:2232–43. 10.1016/j.neuroimage.2012.03.035

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93.

  AndersonRJHoyKEDaskalakisZJFitzgeraldPB. Repetitive transcranial magnetic stimulation for treatment resistant depression: re-establishing connections. Clin Neurophysiol. (2016) 127:3394–405. 10.1016/j.clinph.2016.08.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94.

  ListonCChenACZebleyBDDrysdaleATGordonRLeuchterBet al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry. (2014) 76:517–26. 10.1016/j.biopsych.2014.01.023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95.

  GeorgeMSShortEBKernsSE. Brain stimulation treatments for depression. In: DerubeisRJStrunkDR editors. Oxford Handbook of Mood Disorders. New York: Oxford University Press (2017). p. 398–410. 10.1093/oxfordhb/9780199973965.013.34

  • CrossRef
  • Google Scholar

• 96.

  GlasserMFSmithSMMarcusDSAnderssonJLAuerbachEJBehrensTEet al. The human connectome project's neuroimaging approach. Nat Neurosci. (2016) 19:1175–87. 10.1038/nn.4361

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97.

  JbabdiSSotiropoulosSNHaberSNVan EssenDCBehrensTE. Measuring macroscopic brain connections in vivo. Nat Neurosci. (2015) 18:1546–55. 10.1038/nn.4134

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98.

  DownarJGeraciJSalomonsTVDunlopKWheelerSMcAndrewsMPet al. Anhedonia and reward-circuit connectivity distinguish nonresponders from responders to dorsomedial prefrontal repetitive transcranial magnetic stimulation in major depression. Biol Psychiatry. (2014) 76:176–85. 10.1016/j.biopsych.2013.10.026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99.

  DrysdaleATGrosenickLDownarJDunlopKMansouriFMengYet al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. (2017) 23:28–38. 10.1038/nm.4246

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100.

  SiddiqiSHTaylorSFCookeDPascual-LeoneAGeorgeMSFoxMD. Distinct symptom-specific treatment targets for circuit-based neuromodulation. Am J Psychiatry. (2020) 177:435–46. 10.1176/appi.ajp.2019.19090915

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101.

  WilliamsLM. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress Anxiety. (2017) 34:9–24. 10.1002/da.22556

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102.

  CroarkinPENakoneznyPAHusainMMMeltonTBuyukduraJSKennardBDet al. Evidence for increased glutamatergic cortical facilitation in children and adolescents with major depressive disorder. JAMA Psychiatry. (2013) 70:291–9. 10.1001/2013.jamapsychiatry.24

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103.

  BaekenCLefaucheurJPVan SchuerbeekP. The impact of accelerated high frequency rTMS on brain neurochemicals in treatment-resistant depression: insights from (1)H MR spectroscopy. Clin Neurophysiol. (2017) 128:1664–72. 10.1016/j.clinph.2017.06.243

  • CrossRef
  • Google Scholar

• 104.

  LuborzewskiASchubertFSeifertFDanker-HopfeHBrakemeierELSchlattmannPet al. Metabolic alterations in the dorsolateral prefrontal cortex after treatment with high-frequency repetitive transcranial magnetic stimulation in patients with unipolar major depression. J Psychiatr Res. (2007) 41:606–15. 10.1016/j.jpsychires.2006.02.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105.

  Vidal-PineiroDMartin-TriasPFalconCBargalloNClementeICValls-SoleJet al. Neurochemical modulation in posteromedial default-mode network cortex induced by transcranial magnetic stimulation. Brain Stimul. (2015) 8:937–44. 10.1016/j.brs.2015.04.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106.

  YangXRKirtonAWilkesTCPradhanSLiuIJaworskaNet al. Glutamate alterations associated with transcranial magnetic stimulation in youth depression: a case series. J ECT. (2014) 30:242–7. 10.1097/YCT.0000000000000094

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107.

  AllenCHKlugerBMBuardI. Safety of transcranial magnetic stimulation in children: a systematic review of the literature. Pediatr Neurol. (2017) 68:3–17. 10.1016/j.pediatrneurol.2016.12.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108.

  ZewdieECiechanskiPKuoHCGiuffreAKahlCKingRet al. Safety and tolerability of transcranial magnetic and direct current stimulation in children: prospective single center evidence from 3.5 million stimulations. Brain Stimul. (2020) 13:565–75. 10.1016/j.brs.2019.12.025

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109.

  KrishnanCSantosLPetersonMDEhingerM. Safety of noninvasive brain stimulation in children and adolescents. Brain Stimul. (2015) 8:76–87. 10.1016/j.brs.2014.10.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110.

  HongYHWuSWPedapatiEVHornPSHuddlestonDALaueCSet al. Safety and tolerability of theta burst stimulation vs. single and paired pulse transcranial magnetic stimulation: a comparative study of 165 pediatric subjects. Front Hum Neurosci. (2015) 9:29. 10.3389/fnhum.2015.00029

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111.

  HuSHWangSSZhangMMWangJWHuJBHuangMLet al. Repetitive transcranial magnetic stimulation-induced seizure of a patient with adolescent-onset depression: a case report and literature review. J Int Med Res. (2011) 39:2039–44. 10.1177/147323001103900552

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112.

  ChiramberroMLindbergNIsometsaEKahkonenSAppelbergB. Repetitive transcranial magnetic stimulation induced seizures in an adolescent patient with major depression: a case report. Brain Stimul. (2013) 6:830–1. 10.1016/j.brs.2013.02.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113.

  CullenKRJasbergSNelsonBKlimes-DouganBLimKOCroarkinPE. Seizure induced by deep transcranial magnetic stimulation in an adolescent with depression. J Child Adolesc Psychopharmacol. (2016) 26:637–41. 10.1089/cap.2016.0070

  • CrossRef
  • Google Scholar

• 114.

  WangTHuangLXuHYanLWuQShenFet al. Seizure induced by repetitive transcranial magnetic stimulation in an adolescent with migraine with aura. Brain Stimul. (2018) 11:1380–1. 10.1016/j.brs.2018.07.052

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115.

  PurushothamASinhaVKGoyalNTikkaSK. Intermittent theta burst stimulation induced seizure in a child with schizophrenia: a case report. Brain Stimul. (2018) 11:1415–6. 10.1016/j.brs.2018.09.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116.

  KallelLBrunelinJ. A case report of transcranial magnetic stimulation-related seizure in a young patient with major depressive disorder receiving accelerated transcranial magnetic stimulation. J ECT. (2020) 36:e31–e2. 10.1097/YCT.0000000000000666

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117.

  ObermanLMEnticottP (editors.). Neurotechnology and Brain Stimulation in Pediatric Psychiatric and Neurodevelopmental Disorders. 1st Edition. London: Elsevier (2019).

  • Google Scholar

• 118.

  KirtonAGilbertDL (editors.). Pediatric Brain Stimulation: Mapping and Modulating the Developing Brain. London: Elsevier (2016).

  • Google Scholar

• 119.

  LefaucheurJPAlemanABaekenCBenningerDHBrunelinJDi LazzaroVet al. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): an update (2014-2018). Clin Neurophysiol. (2020) 131:474–528. 10.1016/j.clinph.2020.02.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120.

  LefaucheurJPAndre-ObadiaNAntalAAyacheSSBaekenCBenningerDHet al. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Clin Neurophysiol. (2014) 125:2150–206. 10.1016/j.clinph.2014.05.021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121.

  AveryDHIsenbergKESampsonSMJanicakPGLisanbySHMaixnerDFet al. Transcranial magnetic stimulation in the acute treatment of major depressive disorder: clinical response in an open-label extension trial. J Clin Psychiatry. (2008) 69:441–51. 10.4088/JCP.v69n0315

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122.

  LisanbySHHusainMMRosenquistPBMaixnerDGutierrezRKrystalAet al. Daily left prefrontal repetitive transcranial magnetic stimulation in the acute treatment of major depression: clinical predictors of outcome in a multisite, randomized controlled clinical trial. Neuropsychopharmacology. (2009) 34:522–34. 10.1038/npp.2008.118

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123.

  O'ReardonJPSolvasonHBJanicakPGSampsonSIsenbergKENahasZet al. Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol Psychiatry. (2007) 62:1208–16. 10.1016/j.biopsych.2007.01.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124.

  McClintockSMRetiIMCarpenterLLMcDonaldWMDubinMTaylorSFet al. Consensus recommendations for the clinical application of repetitive Transcranial Magnetic Stimulation (rTMS) in the treatment of depression. J Clin Psychiatry. (2018) 79:16cs10905. 10.4088/JCP.16cs10905

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125.

  FoxMDLiuHPascual-LeoneA. Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. Neuroimage. (2013) 66:151–60. 10.1016/j.neuroimage.2012.10.082

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126.

  ConnollyKRHelmerACristanchoMACristanchoPO'ReardonJP. Effectiveness of transcranial magnetic stimulation in clinical practice post-FDA approval in the United States: results observed with the first 100 consecutive cases of depression at an academic medical center. J Clin Psychiatry. (2012) 73:e567–73. 10.4088/JCP.11m07413

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127.

  FitzgeraldPBHoyKMcQueenSMallerJJHerringSSegraveRet al. A randomized trial of rTMS targeted with MRI based neuro-navigation in treatment-resistant depression. Neuropsychopharmacology. (2009) 34:1255–62. 10.1038/npp.2008.233

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128.

  GordonEMLaumannTOGilmoreAWNewboldDJGreeneDJBergJJet al. Precision functional mapping of individual human brains. Neuron. (2017) 95:791–807 e7. 10.1016/j.neuron.2017.07.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129.

  LaumannTOGordonEMAdeyemoBSnyderAZJooSJChenMYet al. Functional system and areal organization of a highly sampled individual human brain. Neuron. (2015) 87:657–70. 10.1016/j.neuron.2015.06.037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130.

  SackATCohen KadoshRSchuhmannTMoerelMWalshVGoebelR. Optimizing functional accuracy of TMS in cognitive studies: a comparison of methods. J Cogn Neurosci. (2009) 21:207–21. 10.1162/jocn.2009.21126

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131.

  BeynelLAppelbaumLGLuberBCrowellCAHilbigSALimWet al. Effects of online repetitive transcranial magnetic stimulation (rTMS) on cognitive processing: a meta-analysis and recommendations for future studies. Neurosci Biobehav Rev. (2019) 107:47–58. 10.1016/j.neubiorev.2019.08.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132.

  BerlimMTvan den EyndeFTovar-PerdomoSDaskalakisZJ. Response, remission and drop-out rates following high-frequency repetitive transcranial magnetic stimulation (rTMS) for treating major depression: a systematic review and meta-analysis of randomized, double-blind and sham-controlled trials. Psychol Med. (2014) 44:225–39. 10.1017/S0033291713000512

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133.

  GaynesBNLloydSWLuxLGartlehnerGHansenRABrodeSet al. Repetitive transcranial magnetic stimulation for treatment-resistant depression: a systematic review and meta-analysis. J Clin Psychiatry. (2014) 75:477–89; quiz 89. 10.4088/JCP.13r08815

  • CrossRef
  • Google Scholar

• 134.

  SlotemaCWBlomJDHoekHWSommerIE. Should we expand the toolbox of psychiatric treatment methods to include Repetitive Transcranial Magnetic Stimulation (rTMS)? A meta-analysis of the efficacy of rTMS in psychiatric disorders. J Clin Psychiatry. (2010) 71:873–84. 10.4088/JCP.08m04872gre

  • CrossRef
  • Google Scholar

• 135.

  SenovaSCotovioGPascual-LeoneAOliveira-MaiaAJ. Durability of antidepressant response to repetitive transcranial magnetic stimulation: systematic review and meta-analysis. Brain Stimul. (2019) 12:119–28. 10.1016/j.brs.2018.10.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136.

  RachidF. Maintenance repetitive transcranial magnetic stimulation (rTMS) for relapse prevention in with depression: a review. Psychiatry Res. (2018) 262:363–72. 10.1016/j.psychres.2017.09.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137.

  ChangJChuYRenYLiCWangYChuXP. Maintenance treatment of transcranial magnetic stimulation (TMS) for treatment-resistant depression patients responding to acute TMS treatment. Int J Physiol Pathophysiol Pharmacol. (2020) 12:128–33.

  • Pubmed Abstract
  • Google Scholar

• 138.

  HoogendamJMRamakersGMDi LazzaroV. Physiology of repetitive transcranial magnetic stimulation of the human brain. Brain Stimul. (2010) 3:95–118. 10.1016/j.brs.2009.10.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139.

  SiebnerHRRothwellJ. Transcranial magnetic stimulation: new insights into representational cortical plasticity. Exp Brain Res. (2003) 148:1–16. 10.1007/s00221-002-1234-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140.

  LooCMcFarquharTWalterG. Transcranial magnetic stimulation in adolescent depression. Australas Psychiatry. (2006) 14:81–5. 10.1111/j.1440-1665.2006.02251.x

  • CrossRef
  • Google Scholar

• 141.

  WalterGTormosJMIsraelJAPascual-LeoneA. Transcranial magnetic stimulation in young persons: a review of known cases. J Child Adolesc Psychopharmacol. (2001) 11:69–75. 10.1089/104454601750143483

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142.

  BlochYGrisaruNHarelEVBeitlerGFaivelNRatzoniGet al. Repetitive transcranial magnetic stimulation in the treatment of depression in adolescents: an open-label study. J ECT. (2008) 24:156–9. 10.1097/YCT.0b013e318156aa49

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 143.

  WallCACroarkinPESimLAHusainMMJanicakPGKozelFAet al. Adjunctive use of repetitive transcranial magnetic stimulation in depressed adolescents: a prospective, open pilot study. J Clin Psychiatry. (2011) 72:1263–9. 10.4088/JCP.11m07003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144.

  SegevASpellunJBlochY. Anxiety as a central outcome measure in an adolescent with major depressive disorder treated with repetitive transcranial magnetic stimulation. J ECT. (2014) 30:e54–5. 10.1097/YCT.0000000000000183

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 145.

  WallCACroarkinPEMaroney-SmithMJHaugenLMBaruthJMFryeMAet al. Magnetic resonance imaging-guided, open-label, high-frequency repetitive transcranial magnetic stimulation for adolescents with major depressive disorder. J Child Adolesc Psychopharmacol. (2016) 26:582–9. 10.1089/cap.2015.0217

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 146.

  WallCACroarkinPEMcClintockSMMurphyLLBandelLASimLAet al. Neurocognitive effects of repetitive transcranial magnetic stimulation in adolescents with major depressive disorder. Front Psychiatry. (2013) 4:165. 10.3389/fpsyt.2013.00165

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 147.

  CroarkinPEWallCANakoneznyPABuyukduraJSHusainMMSampsonSMet al. Increased cortical excitability with prefrontal high-frequency repetitive transcranial magnetic stimulation in adolescents with treatment-resistant major depressive disorder. J Child Adolesc Psychopharmacol. (2012) 22:56–64. 10.1089/cap.2011.0054

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 148.

  DhamiPKnyahnytskaYAtluriSLeeJCourtneyDBCroarkinPEet al. Feasibility and clinical effects of theta burst stimulation in youth with major depressive disorders: an open-label trial. J Affect Disord. (2019) 258:66–73. 10.1016/j.jad.2019.07.084

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 149.

  MacMasterFPCroarkinPEWilkesTCMcLellanQLangevinLMJaworskaNet al. Repetitive transcranial magnetic stimulation in youth with treatment resistant major depression. Front Psychiatry. (2019) 10:170. 10.3389/fpsyt.2019.00170

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 150.

  ZhangTZhuJXuLTangXCuiHWeiYet al. Add-on rTMS for the acute treatment of depressive symptoms is probably more effective in adolescents than in adults: evidence from real-world clinical practice. Brain Stimul. (2019) 12:103–9. 10.1016/j.brs.2018.09.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 151.

  RosenichEGillSClarkePPatersonTHahnLGalletlyC. Does rTMS reduce depressive symptoms in young people who have not responded to antidepressants?Early Interv Psychiatry. (2019) 13:1129–35. 10.1111/eip.12743

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 152.

  SonmezAIKucukerMULewisCPKollaBPDoruk CamsariDVande VoortJLet al. Improvement in hypersomnia with high frequency repetitive transcranial magnetic stimulation in depressed adolescents: preliminary evidence from an open-label study. Prog Neuropsychopharmacol Biol Psychiatry. (2020) 97:109763. 10.1016/j.pnpbp.2019.109763

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 153.

  CroarkinPENakoneznyPADengZDRomanowiczMVoortJLVCamsariDDet al. High-frequency repetitive TMS for suicidal ideation in adolescents with depression. J Affect Disord. (2018) 239:282–90. 10.1016/j.jad.2018.06.048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 154.

  ZhangTZhuJWangJTangYXuLTangXet al. An open-label trial of adjuvant high-frequency left prefrontal repetitive transcranial magnetic stimulation for treating suicidal ideation in adolescents and adults with depression. J ECT. (2020) 10.1097/YCT.0000000000000739. [Epub ahead of print].

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 155.

  ZhangLZhuJZhangTJiaQHuiLZhuHet al. Comparative efficacy of add-on rTMS in treating the somatic and psychic anxiety symptoms of depression comorbid with anxiety in adolescents, adults, and elderly patients-A real-world clinical application. J Affect Disord. (2020) 276:305–11. 10.1016/j.jad.2020.05.151

  • CrossRef
  • Google Scholar

• 156.

  MayerGAviramSWalterGLevkovitzYBlochY. Long-term follow-up of adolescents with resistant depression treated with repetitive transcranial magnetic stimulation. J ECT. (2012) 28:84–6. 10.1097/YCT.0b013e318238f01a

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 157.

  SonmezAICamsariDDNandakumarALVoortJLVKungSLewisCPet al. Accelerated TMS for depression: a systematic review and meta-analysis. Psychiatry Res. (2019) 273:770–81. 10.1016/j.psychres.2018.12.041

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 158.

  LakensD. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. (2013) 4:863. 10.3389/fpsyg.2013.00863

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 159.

  HowardJPShun-ShinMJHartleyABhattDLKrumHFrancisDP. Quantifying the 3 biases that lead to unintentional overestimation of the blood pressure-lowering effect of renal denervation. Circ Cardiovasc Qual Outcomes. (2016) 9:14–22. 10.1161/CIRCOUTCOMES.115.002533

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 160.

  DavisNJGoldEPascual-LeoneABracewellRM. Challenges of proper placebo control for non-invasive brain stimulation in clinical and experimental applications. Eur J Neurosci. (2013) 38:2973–7. 10.1111/ejn.12307

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 161.

  HrobjartssonAGotzschePC. Placebo interventions for all clinical conditions. Cochrane Database Syst Rev. (2010) 2010:CD003974. 10.1002/14651858.CD003974.pub3

  • CrossRef
  • Google Scholar

• 162.

  RazzaLBMoffaAHMorenoMLCarvalhoAFPadbergFFregniFet al. A systematic review and meta-analysis on placebo response to repetitive transcranial magnetic stimulation for depression trials. Prog Neuropsychopharmacol Biol Psychiatry. (2018) 81:105–13. 10.1016/j.pnpbp.2017.10.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 163.

  BrunoniARLopesMKaptchukTJFregniF. Placebo response of non-pharmacological and pharmacological trials in major depression: a systematic review and meta-analysis. PLoS ONE. (2009) 4:e4824. 10.1371/journal.pone.0004824

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 164.

  LamRWChanPWilkins-HoMYathamLN. Repetitive transcranial magnetic stimulation for treatment-resistant depression: a systematic review and metaanalysis. Can J Psychiatry. (2008) 53:621–31. 10.1177/070674370805300909

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 165.

  FergussonDGlassKCWaringDShapiroS. Turning a blind eye: the success of blinding reported in a random sample of randomised, placebo controlled trials. BMJ. (2004) 328:432. 10.1136/bmj.328.74327.37952.631667.EE

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 166.

  MeisterRAbbasMAntelJPetersTPanYBingelUet al. Placebo response rates and potential modifiers in double-blind randomized controlled trials of second and newer generation antidepressants for major depressive disorder in children and adolescents: a systematic review and meta-regression analysis. Eur Child Adolesc Psychiatry. (2020) 29:253–73. 10.1007/s00787-018-1244-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 167.

  KellerMBRyanNDStroberMKleinRGKutcherSPBirmaherBet al. Efficacy of paroxetine in the treatment of adolescent major depression: a randomized, controlled trial. J Am Acad Child Adolesc Psychiatry. (2001) 40:762–72. 10.1097/00004583-200107000-00010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 168.

  EmslieGJHeiligensteinJHWagnerKDHoogSLErnestDEBrownEet al. Fluoxetine for acute treatment of depression in children and adolescents: a placebo-controlled, randomized clinical trial. J Am Acad Child Adolesc Psychiatry. (2002) 41:1205–15. 10.1097/00004583-200210000-00010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 169.

  WagnerKDAmbrosiniPRynnMWohlbergCYangRGreenbaumMSet al. Efficacy of sertraline in the treatment of children and adolescents with major depressive disorder: two randomized controlled trials. JAMA. (2003) 290:1033–41. 10.1001/jama.290.8.1033

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 170.

  MarchJSilvaSPetryckiSCurryJWellsKFairbankJet al. Fluoxetine, cognitive-behavioral therapy, and their combination for adolescents with depression: Treatment for Adolescents With Depression Study (TADS) randomized controlled trial. JAMA. (2004) 292:807–20. 10.1001/jama.292.7.807

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 171.

  BerardRFongRCarpenterDJThomasonCWilkinsonC. An international, multicenter, placebo-controlled trial of paroxetine in adolescents with major depressive disorder. J Child Adolesc Psychopharmacol. (2006) 16:59–75. 10.1089/cap.2006.16.59

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 172.

  von KnorringALOlssonGIThomsenPHLemmingOMHultenA. A randomized, double-blind, placebo-controlled study of citalopram in adolescents with major depressive disorder. J Clin Psychopharmacol. (2006) 26:311–5. 10.1097/01.jcp.0000219051.40632.d5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 173.

  UsalaTClavennaAZuddasABonatiM. Randomised controlled trials of selective serotonin reuptake inhibitors in treating depression in children and adolescents: a systematic review and meta-analysis. Eur Neuropsychopharmacol. (2008) 18:62–73. 10.1016/j.euroneuro.2007.06.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 174.

  BridgeJABirmaherBIyengarSBarbeRPBrentDA. Placebo response in randomized controlled trials of antidepressants for pediatric major depressive disorder. Am J Psychiatry. (2009) 166:42–9. 10.1176/appi.ajp.2008.08020247

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 175.

  MuthenBBrownHCHunterAMCookIALeuchterAF. General approaches to analysis course: applying growth mixture modeling to randomized trials of depression medication. In: ShroutPE editor. Causality and Psychopathology: Finding the Determinants of Disorders and their Cures. Washington, DC: American Psychiatric Publishing (2010). p. 159–78. 10.1093/oso/9780199754649.003.0012

  • CrossRef
  • Google Scholar

• 176.

  HunterAMMuthenBOCookIALeuchterAF. Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder. J Psychiatr Res. (2010) 44:90–8. 10.1016/j.jpsychires.2009.06.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 177.

  BrunoniARChaimaniAMoffaAHRazzaLBGattazWFDaskalakisZJet al. Repetitive transcranial magnetic stimulation for the acute treatment of major depressive episodes: a systematic review with network meta-analysis. JAMA Psychiatry. (2017) 74:143–52. 10.1001/jamapsychiatry.2016.3644

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 178.

  ChungSWLewisBPRogaschNCSaekiTThomsonRHHoyKEet al. Demonstration of short-term plasticity in the dorsolateral prefrontal cortex with theta burst stimulation: a TMS-EEG study. Clin Neurophysiol. (2017) 128:1117–26. 10.1016/j.clinph.2017.04.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 179.

  LeeCWWuHFChuMCChungYJMaoWCLiCTet al. Mechanism of intermittent theta-burst stimulation in synaptic pathology in the prefrontal cortex in an antidepressant-resistant depression rat model. Cereb Cortex. (2021) 31:575–90. 10.1093/cercor/bhaa244

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 180.

  IwabuchiSJRaschkeFAuerDPLiddlePFLankappaSTPalaniyappanL. Targeted transcranial theta-burst stimulation alters fronto-insular network and prefrontal GABA. Neuroimage. (2017) 146:395–403. 10.1016/j.neuroimage.2016.09.043

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 181.

  Viejo-SoberaRRedolar-RipollDBoixadosMPalausMValero-CabreAMarronEM. Impact of prefrontal theta burst stimulation on clinical neuropsychological tasks. Front Neurosci. (2017) 11:462. 10.3389/fnins.2017.00462

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 182.

  BlumbergerDMVila-RodriguezFThorpeKEFefferKNodaYGiacobbePet al. Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): a randomised non-inferiority trial. Lancet. (2018) 391:1683–92. 10.1016/S0140-6736(18)30295-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 183.

  BerlimMTMcGirrARodrigues Dos SantosNTremblaySMartinsR. Efficacy of theta burst stimulation (TBS) for major depression: an exploratory meta-analysis of randomized and sham-controlled trials. J Psychiatr Res. (2017) 90:102–9. 10.1016/j.jpsychires.2017.02.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 184.

  HerwigUPadbergFUngerJSpitzerMSchonfeldt-LecuonaC. Transcranial magnetic stimulation in therapy studies: examination of the reliability of “standard” coil positioning by neuronavigation. Biol Psychiatry. (2001) 50:58–61. 10.1016/S0006-3223(01)01153-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 185.

  SparingRBuelteDMeisterIGPausTFinkGR. Transcranial magnetic stimulation and the challenge of coil placement: a comparison of conventional and stereotaxic neuronavigational strategies. Hum Brain Mapp. (2008) 29:82–96. 10.1002/hbm.20360

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 186.

  NauczycielCHellierPMorandiXBlestelSDrapierDFerreJCet al. Assessment of standard coil positioning in transcranial magnetic stimulation in depression. Psychiatry Res. (2011) 186:232–8. 10.1016/j.psychres.2010.06.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 187.

  NikolinSD'SouzaOVulovicVAlonzoAChandNDongVet al. comparison of site localization techniques for brain stimulation. J ECT. (2019) 35:127–32. 10.1097/YCT.0000000000000537

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 188.

  TrappNTBrussJKing JohnsonMUitermarktBDGarrettLHeinzerlingAet al. Reliability of targeting methods in TMS for depression: Beam F3 vs. 5.5 cm. Brain Stimul. (2020) 13:578–81. 10.1016/j.brs.2020.01.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 189.

  KaiserRHWhitfield-GabrieliSDillonDGGoerFBeltzerMMinkelJet al. Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology. (2016) 41:1822–30. 10.1038/npp.2015.352

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 190.

  O'SheaJWalshV. Transcranial magnetic stimulation. Curr Biol. (2007) 17:R196–9. 10.1016/j.cub.2007.01.030

  • CrossRef
  • Google Scholar

• 191.

  GordonEMLaumannTOAdeyemoBPetersenSE. Individual variability of the system-level organization of the human brain. Cereb Cortex. (2017) 27:386–99. 10.1093/cercor/bhv239

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 192.

  SaleMVMattingleyJBZaleskyACocchiL. Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation. Neurosci Biobehav Rev. (2015) 57:187–98. 10.1016/j.neubiorev.2015.09.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 193.

  TengSGuoZPengHXingGChenHHeBet al. High-frequency repetitive transcranial magnetic stimulation over the left DLPFC for major depression: session-dependent efficacy: a meta-analysis. Eur Psychiatry. (2017) 41:75–84. 10.1016/j.eurpsy.2016.11.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 194.

  FitzgeraldPBHoyKEElliotDMcQueenSWambeekLEDaskalakisZJ. Exploring alternative rTMS strategies in non-responders to standard high frequency left-sided treatment: a switching study. J Affect Disord. (2018) 232:79–82. 10.1016/j.jad.2018.02.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 195.

  SchulzeLFefferKLozanoCGiacobbePDaskalakisZJBlumbergerDMet al. Number of pulses or number of sessions? An open-label study of trajectories of improvement for once-vs. twice-daily dorsomedial prefrontal rTMS in major depression. Brain Stimul. (2018) 11:327–36. 10.1016/j.brs.2017.11.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 196.

  FitzgeraldPBHoyKEElliotDSusan McQueenRNWambeekLEDaskalakisZJ. Accelerated repetitive transcranial magnetic stimulation in the treatment of depression. Neuropsychopharmacology. (2018) 43:1565–72. 10.1038/s41386-018-0009-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 197.

  ChenLHudaibARHoyKEFitzgeraldPB. Efficacy, efficiency and safety of high-frequency repetitive transcranial magnetic stimulation applied more than once a day in depression: a systematic review. J Affect Disord. (2020) 277:986–96. 10.1016/j.jad.2020.09.035

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 198.

  ColeEJStimpsonKHBentzleyBSGulserMCherianKTischlerCet al. Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression. Am J Psychiatry. (2020) 177:716–26. 10.1176/appi.ajp.2019.19070720

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 199.

  RachidF. Accelerated transcranial magnetic stimulation for the treatment of patients with depression: a review. Asian J Psychiatr. (2019) 40:71–5. 10.1016/j.ajp.2019.02.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 200.

  BaekenCMarinazzoDWuGRVan SchuerbeekPDe MeyJMarchettiIet al. Accelerated HF-rTMS in treatment-resistant unipolar depression: insights from subgenual anterior cingulate functional connectivity. World J Biol Psychiatry. (2014) 15:286–97. 10.3109/15622975.2013.872295

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 201.

  LynchGKramarEABabayanAHRumbaughGGallCM. Differences between synaptic plasticity thresholds result in new timing rules for maximizing long-term potentiation. Neuropharmacology. (2013) 64:27–36. 10.1016/j.neuropharm.2012.07.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 202.

  ThomsonACSackAT. How to design optimal accelerated rTMS protocols capable of promoting therapeutically beneficial metaplasticity. Front Neurol. (2020) 11:599918. 10.3389/fneur.2020.599918

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 203.

  MorenoCWykesTGalderisiSNordentoftMCrossleyNJonesNet al. How mental health care should change as a consequence of the COVID-19 pandemic. Lancet Psychiatry. (2020) 7:813–24. 10.1016/S2215-0366(20)30307-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 204.

  BreslauJGilmanSESteinBDRuderTGmelinTMillerE. Sex differences in recent first-onset depression in an epidemiological sample of adolescents. Transl Psychiatry. (2017) 7:e1139. 10.1038/tp.2017.105

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 205.

  ChenJYuJZhangLLiXZhangJ. Etiological heterogeneity of symptom dimensions of adolescent depression. Psych J. (2014) 3:254–63. 10.1002/pchj.62

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 206.

  YaroslavskyIPettitJWLewinsohnPMSeeleyJRRobertsRE. Heterogeneous trajectories of depressive symptoms: adolescent predictors and adult outcomes. J Affect Disord. (2013) 148:391–9. 10.1016/j.jad.2012.06.028

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 207.

  MojtabaiROlfsonMHanB. National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics. (2016) 138:e20161878. 10.1542/peds.2016-1878

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 208.

  StringarisA. Editorial: what is depression?J Child Psychol Psychiatry. (2017) 58:1287–9. 10.1111/jcpp.12844

  • CrossRef
  • Google Scholar

• 209.

  NielsonDMKerenHO'CallaghanGJacksonSMDoukaIVidal-RibasPet al. Great expectations: a critical review of and suggestions for the study of reward processing as a cause and predictor of depression. Biol Psychiatry. (2020) 89:134–4310.1101/2020.03.04.975136

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 210.

  BozzayMLPrimackJBarredoJPhilipNS. Transcranial magnetic stimulation to reduce suicidality - a review and naturalistic outcomes. J Psychiatr Res. (2020) 125:106–12. 10.1016/j.jpsychires.2020.03.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 211.

  DingaRSchmaalLPenninxBvan TolMJVeltmanDJvan VelzenLet al. Evaluating the evidence for biotypes of depression: methodological replication and extension of. Neuroimage Clin. (2019) 22:101796. 10.1016/j.nicl.2019.101796

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 212.

  HendersonSEJohnsonARVallejoAIKatzLWongEGabbayV. A preliminary study of white matter in adolescent depression: relationships with illness severity, anhedonia, and irritability. Front Psychiatry. (2013) 4:152. 10.3389/fpsyt.2013.00152

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 213.

  RzepaEMcCabeC. Anhedonia and depression severity dissociated by dmPFC resting-state functional connectivity in adolescents. J Psychopharmacol. (2018) 32:1067–74. 10.1177/0269881118799935

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 214.

  StringarisAVidal-Ribas BelilPArtigesELemaitreHGollier-BriantFWolkeSet al. The Brain's response to reward anticipation and depression in adolescence: dimensionality, specificity, and longitudinal predictions in a community-based sample. Am J Psychiatry. (2015) 172:1215–23. 10.1176/appi.ajp.2015.14101298

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 215.

  PornpattananangkulNLeibenluftEPineDSStringarisA. association between childhood anhedonia and alterations in large-scale resting-state networks and task-evoked activation. JAMA Psychiatry. (2019) 76:624–33. 10.1001/jamapsychiatry.2019.0020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 216.

  KrystalADPizzagalliDASmoskiMMathewSJNurnbergerJJrLisanbySHet al. A randomized proof-of-mechanism trial applying the 'fast-fail' approach to evaluating kappa-opioid antagonism as a treatment for anhedonia. Nat Med. (2020) 26:760–8. 10.1038/s41591-020-0806-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 217.

  AbdelnaimMALangguthBDeppeMMohonkoAKreuzerPMPoepplTBet al. Anti-suicidal efficacy of repetitive transcranial magnetic stimulation in depressive patients: a retrospective analysis of a large sample. Front Psychiatry. (2019) 10:929. 10.3389/fpsyt.2019.00929

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 218.

  PhilipNSDunnerDLDowdSMAaronsonSTBrockDGCarpenterLLet al. Can medication free, treatment-resistant, depressed patients who initially respond to TMS Be maintained off medications? A prospective, 12-month multisite randomized pilot study. Brain Stimul. (2016) 9:251–7. 10.1016/j.brs.2015.11.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 219.

  BestSRDPavelDGHaustrupN. Combination therapy with transcranial magnetic stimulation and ketamine for treatment-resistant depression: a long-term retrospective review of clinical use. Heliyon. (2019) 5:e02187. 10.1016/j.heliyon.2019.e02187

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 220.

  RomeiVBrodbeckVMichelCAmediAPascual-LeoneAThutG. Spontaneous fluctuations in posterior alpha-band EEG activity reflect variability in excitability of human visual areas. Cereb Cortex. (2008) 18:2010–8. 10.1093/cercor/bhm229

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 221.

  FacchiniSMuellbacherWBattagliaFBoroojerdiBHallettM. Focal enhancement of motor cortex excitability during motor imagery: a transcranial magnetic stimulation study. Acta Neurol Scand. (2002) 105:146–51. 10.1034/j.1600-0404.2002.1o004.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 222.

  LiSLatashMLZatsiorskyVM. Effects of motor imagery on finger force responses to transcranial magnetic stimulation. Brain Res Cogn Brain Res. (2004) 20:273–80. 10.1016/j.cogbrainres.2004.03.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 223.

  ScangosKWMakhoulGSSugrueLPChangEFKrystalAD. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat Med. (2021) 27:229–31. 10.1038/s41591-020-01175-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 224.

  TendlerASiskoEBarnea-YgaelNZangenAStorchEA. A method to provoke obsessive compulsive symptoms for basic research and clinical interventions. Front Psychiatry. (2019) 10:814. 10.3389/fpsyt.2019.00814

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 225.

  IsserlesMShalevAYRothYPeriTKutzIZlotnickEet al. Effectiveness of deep transcranial magnetic stimulation combined with a brief exposure procedure in post-traumatic stress disorder–a pilot study. Brain Stimul. (2013) 6:377–83. 10.1016/j.brs.2012.07.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 226.

  Dinur-KleinLDannonPHadarARosenbergORothYKotlerMet al. Smoking cessation induced by deep repetitive transcranial magnetic stimulation of the prefrontal and insular cortices: a prospective, randomized controlled trial. Biol Psychiatry. (2014) 76:742–9. 10.1016/j.biopsych.2014.05.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 227.

  CarmiLTendlerABystritskyAHollanderEBlumbergerDMDaskalakisJet al. Efficacy and safety of deep transcranial magnetic stimulation for obsessive-compulsive disorder: a prospective multicenter randomized double-blind placebo-controlled trial. Am J Psychiatry. (2019) 176:931–8. 10.1176/appi.ajp.2019.18101180

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 228.

  SathappanAVLuberBMLisanbySH. The dynamic duo: combining noninvasive brain stimulation with cognitive interventions. Prog Neuropsychopharmacol Biol Psychiatry. (2019) 89:347–60. 10.1016/j.pnpbp.2018.10.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 229.

  FefferKLeeHHMansouriFGiacobbePVila-RodriguezFKennedySHet al. Early symptom improvement at 10 sessions as a predictor of rTMS treatment outcome in major depression. Brain Stimul. (2018) 11:181–9. 10.1016/j.brs.2017.10.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 230.

  HarrisADSalehMGEddenRA. Edited (1) H magnetic resonance spectroscopy in vivo: Methods and metabolites. Magn Reson Med. (2017) 77:1377–89. 10.1002/mrm.26619

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 231.

  SalehMGOeltzschnerGChanKLPutsNAJMikkelsenMScharMet al. Simultaneous edited MRS of GABA and glutathione. Neuroimage. (2016) 142:576–82. 10.1016/j.neuroimage.2016.07.056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 232.

  JalbrzikowskiMLiuFForanWRoederKDevlinBLunaB. Resting-state functional network organization is stable across adolescent development for typical and psychosis spectrum youth. Schizophr Bull. (2020) 46:395–407. 10.1093/schbul/sbz053

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 233.

  JalbrzikowskiMLiuFForanWKleiLCalabroFJRoederKet al. Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Hum Brain Mapp. (2020) 41:4187–99. 10.1101/812719

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 234.

  MarekSTervo-ClemmensBNielsenANWheelockMDMillerRLLaumannTOet al. Identifying reproducible individual differences in childhood functional brain networks: an ABCD study. Dev Cogn Neurosci. (2019) 40:100706. 10.1016/j.dcn.2019.100706

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 235.

  HoTCConnollyCGHenje BlomELeWinnKZStrigoIAPaulusMPet al. Emotion-dependent functional connectivity of the default mode network in adolescent depression. Biol Psychiatry. (2015) 78:635–46. 10.1016/j.biopsych.2014.09.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 236.

  KuhnSVanderhasseltMADe RaedtRGallinatJ. Why ruminators won't stop: the structural and resting state correlates of rumination and its relation to depression. J Affect Disord. (2012) 141:352–60. 10.1016/j.jad.2012.03.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 237.

  CullenKRWestlundMKKlimes-DouganBMuellerBAHouriAEberlyLEet al. Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry. (2014) 71:1138–47. 10.1001/jamapsychiatry.2014.1087

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 238.

  ClarkDLKonduruNKempABraySBrownECGoodyearBet al. The impact of age of onset on amygdala intrinsic connectivity in major depression. Neuropsychiatr Dis Treat. (2018) 14:343–52. 10.2147/NDT.S145042

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 239.

  ConnollyCGHoTCBlomEHLeWinnKZSacchetMDTymofiyevaOet al. Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression. J Affect Disord. (2017) 207:86–94. 10.1016/j.jad.2016.09.026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 240.

  ScheuerHAlarconGDemeterDVEarlEFairDANagelBJ. Reduced fronto-amygdalar connectivity in adolescence is associated with increased depression symptoms over time. Psychiatry Res Neuroimaging. (2017) 266:35–41. 10.1016/j.pscychresns.2017.05.012

  • CrossRef
  • Google Scholar

• 241.

  CuiZLiHXiaCHLarsenBAdebimpeABaumGLet al. Individual variation in functional topography of association networks in youth. Neuron. (2020) 106:340–53 e8. 10.1016/j.neuron.2020.01.029

  • CrossRef
  • Google Scholar

• 242.

  NobleSScheinostDConstableRT. A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage. (2019) 203:116157. 10.1016/j.neuroimage.2019.116157

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 243.

  GrattonCLaumannTONielsenANGreeneDJGordonEMGilmoreAWet al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron. (2018) 98:439–52 e5. 10.1016/j.neuron.2018.03.035

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 244.

  BirnRMMolloyEKPatriatRParkerTMeierTBKirkGRet al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage. (2013) 83:550–8. 10.1016/j.neuroimage.2013.05.099

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 245.

  AvissarMPowellFIlievaIRespinoMGunningFMListonCet al. Functional connectivity of the left DLPFC to striatum predicts treatment response of depression to TMS. Brain Stimul. (2017) 10:919–25. 10.1016/j.brs.2017.07.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 246.

  BaekenCDupratRWuGRDe RaedtRvan HeeringenK. Subgenual anterior cingulate-medial orbitofrontal functional connectivity in medication-resistant major depression: a neurobiological marker for accelerated intermittent theta burst stimulation treatment?Biol Psychiatry Cogn Neurosci Neuroimaging. (2017) 2:556–65. 10.1016/j.bpsc.2017.01.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 247.

  CashRFHCocchiLAndersonRRogachovAKucyiABarnettAJet al. A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression. Hum Brain Mapp. (2019) 40:4618–29. 10.1002/hbm.24725

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 248.

  CashRFHZaleskyAThomsonRHTianYCocchiLFitzgeraldPB. Subgenual functional connectivity predicts antidepressant treatment response to transcranial magnetic stimulation: independent validation and evaluation of personalization. Biol Psychiatry. (2019) 86:e5–e7. 10.1016/j.biopsych.2018.12.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 249.

  GeRBlumbergerDMDownarJDaskalakisZJDipintoAAThamJCWet al. Abnormal functional connectivity within resting-state networks is related to rTMS-based therapy effects of treatment resistant depression: a pilot study. J Affect Disord. (2017) 218:75–81. 10.1016/j.jad.2017.04.060

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 250.

  GeRDownarJBlumbergerDMDaskalakisZJVila-RodriguezF. Functional connectivity of the anterior cingulate cortex predicts treatment outcome for rTMS in treatment-resistant depression at 3-month follow-up. Brain Stimul. (2020) 13:206–14. 10.1016/j.brs.2019.10.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 251.

  KloosterDCVosINCaeyenberghsKLeemansADavidSBesselingRMet al. Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder. J Psychiatry Neurosci. (2020) 45:243–52. 10.1503/jpn.190088

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 252.

  SalomonsTVDunlopKKennedySHFlintAGeraciJGiacobbePet al. Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder. Neuropsychopharmacology. (2014) 39:488–98. 10.1038/npp.2013.222

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 253.

  WeigandAHornACaballeroRCookeDSternAPTaylorSFet al. Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites. Biol Psychiatry. (2018) 84:28–37. 10.1016/j.biopsych.2017.10.028

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 254.

  EshelNKellerCJWuWJiangJMills-FinnertyCHuemerJet al. Global connectivity and local excitability changes underlie antidepressant effects of repetitive transcranial magnetic stimulation. Neuropsychopharmacology. (2020) 45:1018–25. 10.1038/s41386-020-0633-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 255.

  LenerMSIosifescuDV. In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature. Ann N Y Acad Sci. (2015) 1344:50–65. 10.1111/nyas.12759

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 256.

  PizzagalliDA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology. (2011) 36:183–206. 10.1038/npp.2010.166

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 257.

  BrakowskiJSpinelliSDorigNBoschOGManoliuAHoltforthMGet al. Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research. J Psychiatr Res. (2017) 92:147–59. 10.1016/j.jpsychires.2017.04.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 258.

  Klimes-DouganBWestlund SchreinerMThaiMGunlicks-StoesselMReigstadKCullenKR. Neural and neuroendocrine predictors of pharmacological treatment response in adolescents with depression: a preliminary study. Prog Neuropsychopharmacol Biol Psychiatry. (2018) 81:194–202. 10.1016/j.pnpbp.2017.10.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 259.

  GrabbMCHilleforsMPotterWZ. The NIMH 'Fast-Fail Trials' (FAST) initiative: rationale, promise, and progress. Pharmaceut Med. (2020) 34:233–45. 10.1007/s40290-020-00343-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar