EEG Biomarkers for a Precision-Medicine Approach to Noninvasive Brain Stimulation for Major Depressive Disorder

EEG Biomarkers for a Precision-Medicine Approach to Noninvasive Brain Stimulation for Major Depressive Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review EEG Biomarkers for a Precision-Medicine Approach to Noninvasive Brain Stimulation for Major Depressive Disorder Rubén Romero-Marín, Davide Cappon, Javier Solana-Sánchez, David Bartrés-Faz, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7771697/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Major Depressive Disorder (MDD) is a prevalent and debilitating psychiatric condition with significant rates of treatment resistance. Non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), has emerged as a promising option for individuals unresponsive to pharmacological interventions. However, a substantial proportion of patients still fail to achieve meaningful clinical improvement, underscoring the need for reliable biomarkers to predict treatment response. Electroencephalography (EEG) and TMS-EEG have been increasingly explored as promising predictive tools due to their ability to assess cortical excitability, connectivity, and neuroplasticity. The evidence gathered from 18 high-quality studies highlights the relevance of EEG and TMS-EEG biomarkers in predicting outcomes of NIBS in MDD. Resting-state EEG studies emphasize the importance of spectral power alterations, alpha asymmetry, and connectivity patterns, while TMS-EEG studies underline the role of TMS-evoked potentials (TEPs), particularly the N100 and N45 components, in forecasting therapeutic response. While these findings suggest significant potential, methodological variability, small sample sizes, and differing stimulation protocols limit their immediate clinical translation. However, these biomarkers provide a solid foundation for implementing precision medicine. Prior EEG or TMS-EEG assessments can play a valuable role in guiding the personalization of NIBS treatment strategies. The systematic integration of these neurophysiological biomarkers into clinical practice could maximize therapeutic efficacy and reduce non-response rates, paving the way for more precise and effective interventions in depression treatment. Cognitive Neuroscience eeg tms biomarkers depression nibs Figures Figure 1 Figure 2 Figure 3 Introduction Major depressive disorder (MDD) is a prevalent and disabling psychiatric condition, affecting approximately 280 million people worldwide ( 1 ). Despite the availability of various pharmacological and psychotherapeutic treatments, approximately 20–40% of patients suffer from chronic depressive episodes ( 2 ), fail to achieve meaningful relief and become medication-resistant ( 3 – 5 ). Emerging evidence suggests that MDD symptoms arise from rapidly evolving pathological brain states within specific neural circuits ( 6 , 7 ). Non-invasive brain stimulation (NIBS) techniques, including transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), can precisely target these circuits, disrupting the maladaptive, self-sustaining activity and – perhaps due to such mechanisms - alleviating depressive symptoms even in chronic cases ( 8 – 10 ). Early clinical studies suggested that repetitive TMS (rTMS) targeting the dorsolateral prefrontal cortex is effective in medication-resistant cases ( 11 ), leading to rigorous clinical trials and eventually the clinical adoption and regulatory approval of rTMS for medication resistant MDD ( 12 – 14 ). Current treatment protocols show that approximately 60% of patients experience significant improvement—with 30% achieving full remission after a six-week rTMS course ( 13 , 15 , 16 ), and accelerated protocols reaching remission in as little as one week ( 17 , 18 ). However, approximately 40% of patients do not respond as expected to treatment. Poorer outcomes are especially common among those with more severe baseline symptoms ( 19 ), as well as in cases involving altered network connectivity and imbalances in neural excitability and inhibition (to be discussed in the following sections). These findings underscore the importance of adopting a precision-medicine approach ( 20 ) that adjusts treatment based on each individual’s biological profile to enhance therapeutic effectiveness and a greater understanding of NIBS mechanisms. Electroencephalography (EEG), whether used alone or in combination with TMS (TMS/EEG), has emerged as a powerful tool for understanding the complex dynamics involved in the pathophysiology of MDD ( 21 ). This approach enables researchers to examine how alterations in neural circuitry contribute to depressive symptoms, offering valuable insights into the underlying mechanisms of the disorder. TMS/EEG leverages the focal stimulation capabilities of TMS alongside the high temporal resolution of EEG, allowing researchers to capture the immediate neural responses evoked by TMS ( 22 , 23 ). When a single TMS pulse (spTMS) is applied, it depolarizes cortical neurons, triggering synaptic activations that are recorded as TMS-evoked potentials (TEPs) in the EEG. These responses provide direct, real-time measures of cortical excitability and connectivity, offering invaluable insights into the dynamic interactions within brain networks ( 24 – 30 ). By characterizing the unique spatiotemporal and physiological profiles of individual patients, TMS/EEG can help identify predictive biomarkers that indicate who is most likely to benefit from NIBS. Such biomarkers could not only pave the way for personalized treatment strategies but also have the potential to reduce nonresponse rates, optimize therapeutic protocols, and lower the economic and clinical burdens associated with ineffective interventions ( 31 ). Some have argued that EEG and TMS-EEG hold considerable promise as predictive biomarkers for MDD interventions ( 32 – 34 ). In the present paper, we provide an updated review of the current literature on EEG and TMS/EEG biomarkers as predictors of NIBS response in MDD. While EEG-based measures and TMS-EEG metrics hold considerable promise as predictive biomarkers, further research is needed to validate their clinical utility. Through a systematic review of the literature, we identify both encouraging findings and major inconsistencies that underscore the need for more rigorous and standardized research. Our goal is to move beyond speculation and outline the specific gaps that must be addressed before EEG and TMS-EEG can reliably inform personalized NIBS interventions. Ultimately, we propose a framework of key neurophysiological determinants that should guide future investigations and help bring the field closer to clinically actionable biomarkers. Methods 3.1 Search strategy PubMed and Scopus were searched between May 1st, 2024 and October 1st, 2024 for publications studying markers of treatment response in patients with MDD. Search terms for both databases were (Markers) AND (Treatment Response) AND (Depressive Disorder) AND ((EEG) OR (electroencephalography) OR (TMS) OR (NIBS) OR (tDCS) OR (tACS) OR (tES)). Results were filtered to only include studies reported in English with a date range between 2019-2024. Two study authors (RR, GC) conducted an independent literature search using pre-defined inclusion and exclusion criteria. Conflicts between authors were resolved by discussion. Approved studies were then moved to data extraction. 3.2 Inclusion criteria The included studies focused on subjects with unipolar MDD who received noninvasive brain stimulation treatments. To be eligible, studies were required to report EEG or TMS-EEG measurements taken before, during, or after the treatment. Additionally, only studies that demonstrated a moderate or higher level of quality of evidence, as assessed following the selection process, were considered. 3.3 Exclusion criteria Case studies, review articles, protocols, posters, and conference abstracts were excluded from the analysis. Studies involving animal populations, healthy subjects, bipolar depression, or conditions other than unipolar MDD were also excluded. Additionally, studies that did not include any therapeutic interventions or utilized pharmacological treatments instead of NIBS were not considered. Finally, only studies that reported EEG or TMS-EEG measures were included; those without these measurements were excluded. 3.4 Quality of evidence assessment Quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology (35). The quality of studies was categorized into four levels: high, moderate, low, and very low. Studies rated as 'high' were randomized, double-blinded, and placebo-controlled; 'moderate' studies were randomized but not blinded; 'low' studies were non-randomized but included a placebo or control group; and 'very low' studies were non-randomized without a placebo or control group. To ensure focus on higher quality and more reliable designs, studies rated as 'very low' were excluded (36). Results 4.1 Study selection A summary of the study selection process, including inclusion and exclusion criteria, is provided in Figure 1. Please insert Figure 1 here 4.2 Included study characteristics The search terms initially yielded 138 studies, which were reduced to 126 after removing duplicates. During the primary screening, 83 studies were excluded based on title or abstract for not aligning with the focus of the review, leaving 43 studies for full-text evaluation. Of these, 25 studies were excluded due to not meeting the inclusion criteria — for example, studies using neuroimaging modalities other than EEG or TMS-EEG, involving interventions not related to NIBS, or examining populations or outcomes outside the scope of this review. In addition, six studies were excluded following a quality assessment that rated them as “very low” due to methodological limitations such as small sample sizes, lack of control groups, or absence of randomization. Ultimately, 18 studies were included in the qualitative synthesis. Quality ratings for these studies are detailed in Tables 1 and 2. The following sections are organized chronologically by year of publication. 4.3 Resting EEG studies Eleven studies explored EEG markers of treatment response to brain stimulation therapies (Table 1), utilizing a wide range of quantitative EEG measures. Please insert Table 1 here Studies that explored the association between EEG power and the response to repetitive transcranial magnetic stimulation (rTMS) revealed that higher baseline power in different frequency bands was associated with increased treatment success. Specifically, different studies found that individuals with better responses to the rTMS intervention had increased beta power (37,38), higher alpha power (39,40), increased theta relative power (41), and low gamma power (40). Additionally an individual alpha frequency (IAF) closer to 10 Hz showed greater clinical improvement after 10 Hz rTMS (42). However, two studies showed contradictory results, showing an association between lower beta power (39), alpha power (43) and better clinical outcomes. Among the reviewed studies, only two have investigated prefrontal theta cordance, both reporting significant findings differentiating responders from non-responders (see supplementary material for details) (37,38). And only one study examined EEG microstates, finding that rTMS treatment led to an increase in microstate MS-2 occurrence and coverage, and a decrease in MS-3 (44). However, this study did not assess whether microstate features could predict clinical response to rTMS. In terms of connectivity, better response to the intervention was associated with increased alpha and beta band coherence (45), as well as decreased theta band coherence (46), and an increase of Alpha Spectral Correlation (αSC) after treatment (47). Non-Linear Features combined with machine learning achieved an impressive accuracy predicting treatment response (37,38). Increases in alpha current source density current source density (CSD) were also significantly associated with clinical improvement (45). These examples illustrate the diversity of EEG metrics explored to date, with researchers often selecting specific features of interest rather than employing comprehensive or standardized approaches. While data-driven methods, including machine learning, offer promising avenues to identify complex biomarker patterns, they remain difficult to interpret and compare across studies due to high variability in input features, sample characteristics, and the algorithms applied. This heterogeneity poses a significant barrier to drawing clear conclusions about EEG-based predictors and calls for more standardized frameworks and validation efforts. 4.4 TMS-EEG studies Seven studies have reported TMS-EEG markers of treatment response to NIBS therapies in MDD (Table 2), primarily using TEP components, which involve examining features such as peak amplitude, peak latency, and slope. Additionally, one study analyzed activity and connectivity patterns using Significant Current Density (SCD) and Significant Current Scattering (SCS) methods. Please insert Table 2 here Six studies have examined the effects of therapeutic TMS on TEP components. Higher baseline N45 (48) and N100 (49,50) amplitudes were linked to improvements in depression symptoms. In contrast, a smaller baseline P60 component predicted better response to treatment (48). Regarding the changes after treatment, a decrease in P30 (51), N45 and N100 (50,52) amplitude correlated with improved clinical outcomes. However, N45 results are inconsistent, as some have found an increase with treatment (50). In terms of slope, treatment responders exhibited a steeper negative N100 slope for single pulses and a steeper positive slope for paired pulses at baseline compared to non-responders (53). Additionally, a decrease in SCS was found to be positively correlated with improvements in depression severity following rTMS (24). Please insert Figure 2 here Please insert Figure 3 here Discussion This systematic review that synthesized the growing body of evidence on resting-state EEG and TMS-EEG biomarkers in depression—especially in treatment‐resistant depression (TRD)—demonstrates both exciting promise and important challenges. Recent studies have begun to identify specific, quantifiable metrics that may predict treatment response to NIBS. 5.1 Key Findings and Overall Trends Resting-state EEG Alpha Band Power Resting‐state EEG studies consistently reveal alterations in spectral power and connectivity in depressed populations. For example, depressed individuals often exhibit heightened synchrony in the theta and alpha bands (54,55), a phenomenon that may reflect abnormal thalamocortical connectivity (56). Multiple studies consistently report that greater baseline alpha power —especially over frontal and parietal regions— has been associated with better responses to rTMS (57–61). Some work suggests that increased alpha power may reflect lower arousal and reduced serotonergic activity (38), though discrepancies remain, with other studies failing to find significant differences or even reporting decreased alpha power in depressed cohorts (62–65). Further complicating the picture, post-treatment changes in alpha power are inconsistent, with some studies reporting decreases (55,66) and others noting increases (67). These mixed results likely reflect interindividual differences and methodological variations, such as pre-treatment stress levels influencing sympathetic activation. Alpha Asymmetry and Entrainment Evidence also points to the relevance of frontal alpha asymmetry, with left frontal regions often showing differential activity compared to the right. Since EEG alpha power is an inverse index of cortical activity (68), reduced alpha at right compared to left frontal sites (i.e., greater right-sided activation) has been associated with withdrawal motivation (69), negative affect (70–72), as well as reports of more intense negative emotions (73,74). In MDD, studies have shown that alpha activity in the left frontal region typically are lower than that of the right (75–77). This asymmetry has been linked to the dysfunction of the left DLPFC, a key target in depression treatment (78,79), and may help predict treatment outcomes depending on the stimulation site. Specifically, for rTMS targeted over the lateral prefrontal cortex (LPFC), greater left-sided alpha activity correlated negatively with clinical improvement, while stimulation over the medial prefrontal cortex (MPFC) showed a positive correlation with clinical improvement (40). This is consistent with PET studies reporting decreased frontal metabolism (80–83) and Voxel-Based Morphometry (VBM) studies revealing reduced gray matter volume in these regions (84), although the relationship between structure and cortical excitability remains to be fully clarified (85). In addition, the observation that an IAF near 10 Hz appears to be optimal, suggesting that aligning stimulation frequency with the brain’s intrinsic rhythm (via entrainment) may enhance neuroplasticity and clinical outcomes (42,86). The Arnold tongue model provides a theoretical framework for understanding how the frequency and amplitude of external stimuli interact with intrinsic oscillations to promote synchronization (86). According to this model, entrainment peaks when external stimulation—be it rTMS or tACS—precisely aligns with the brain’s natural rhythm (87). This perfect synchronization drives powerful neuroplasticity, paving the way for lasting clinical and behavioral gains (42). However, inconsistent findings, likely due to individual differences in intrinsic alpha frequency (IAF) (47,88,89), and the sparse documentation of rTMS effects on these oscillatory patterns in MDD beyond isolated case studies (90,91), call for further investigation. Other resting-state EEG frequency bands Investigations into other frequency bands have also provided valuable insights. Elevated baseline beta power has generally been associated with poorer treatment outcomes, as it may indicate greater depression severity (37,59), although some findings suggest that increased beta could reflect preserved reward processing (92). Significant coherence observed in the beta band has been associated with cortical excitability, suggesting that increased baseline coherence in large-scale networks may predict better outcomes from rTMS treatment (45). Additionally, frontal delta power, for example, is lower in responders (37,39,59), suggesting reduced temporoparietal dysfunction that influences emotional arousal (93). High frontocentral theta activity at baseline is linked to poorer outcomes and greater depression severity (59,88,94), though some studies report no significant differences (37,38,95). Moreover, higher medial low-gamma power, on the other hand, predicts better outcomes—possibly reflecting enhanced attentional capacity and cognitive control (40,96–98). Furthermore, increases in baseline connectivity, particularly in the alpha and theta bands, were linked to better treatment outcomes (46), especially in fronto-parietal regions, in line with other studies (99). Theta activity in prefrontal areas has been associated with the default mode network, which plays a role in high-level control over perception (100,101). Lower theta connectivity was linked with better antidepressant outcomes, possibly reflecting reduced top-down control (102). Additionally, lower connectivity in the salience network, which helps in switching from the DMN to the CEN, correlated with antidepressant responses (46). Higher αSC among responders suggest increased functional coupling between brain regions (47). Lastly, theta cordance in the prefrontal cortex holds promise as a predictor of clinical improvement following rTMS treatment, although statistical significance has not been consistently observed across studies, likely due to variations in study designs, sample sizes, and rTMS protocols (37,38). EEG Microstates and non-linear features Clinical improvements correlated with changes in EEG microstates (MS) MS-2 and MS-3 after rTMS treatment, with MS-2 increasing and MS-3 decreasing in occurrence (44). These changes suggest that rTMS modulate large-scale network alterations (103). Specifically, MS-2 has been linked to BOLD activation in the dorsal ACC, inferior frontal cortices, and right insula (103), regions associated with the reward circuit and improvements in anhedonia (104). Conversely, MS-3 showed reduced occurrence post-TMS, involving frontal and parietal cortices implicated in attention and cognitive control networks (103,105). While depression is often linked to hypoconnectivity in cognitive control networks, some patients exhibit hyperconnectivity, potentially contributing to rumination, which correlates with the DMN (104,106). Non-linear EEG features, when integrated with machine learning models, demonstrate strong predictive power, (37–39) yielding high predictive accuracies. These complex dynamics effectively capture subtle changes in brain activity associated with NIBS response in depression. Notably, the higher complexity dimension (CD) observed in non-responders (39) suggests increased dimensionality, reflecting a broader distribution of neural oscillations with lower synchrony (107). This elevated CD may indicate neural isolation (108) and disorganized spiking activity (109), signaling more atypical brain activity in non-responders compared to responders. TMS-EEG Biomarkers and Cortical Inhibition TMS-Evoked Potentials (TEPs) The field of TMS-EEG biomarkers has yielded significant insights into TEP components, particularly emphasizing the N100 component. Higher baseline N100 amplitudes were predictive of greater improvements in depressive symptoms (49,50). The N100 component reflects GABA B receptor-mediated slow inhibitory processes (49). Deficiencies in GABA (110–113) and reduced size and density of GABAergic interneurons in the DLPFC in MDD patients (114) suggest that patients with higher baseline N100 amplitudes may have more intact inhibitory circuitry, allowing greater responsiveness to stimulation. Conversely, a lower baseline N100 amplitude could indicate impaired inhibitory circuitry. However, a significant reduction in N100 amplitude post-treatment among responders, was also observed (49,50,52), and the implications of these findings are in contradiction with the theory of GABA deficiency. Steeper negative slope for single pulses and a steeper positive N100 slope for paired pulses in responders (53), potentially driven by larger P60 amplitudes in the left hemisphere, suggests an interplay between GABA B receptor-mediated inhibition and glutamatergic receptor excitation, potentially reflecting sensory processing differences of the TMS pulse (53). The N100 slope has been associated with cognitive performance in working memory (115,116) and has also been linked to antidepressant response in bipolar depression, with changes indicating long-term potentiation effects related to sleep deprivation (117). Larger baseline N45 amplitudes correlating with improved clinical outcomes (48), indicating that greater cortical inhibition might enhance therapeutic effects through synaptic modulation and improved balance between excitation and inhibition. Post-treatment increases in N45 amplitude were noted among responders (50), suggesting enhanced GABA A receptor-mediated inhibition, and a more robust and functional network of GABAergic interneurons enhances synaptic modulation capacity, facilitating therapeutic effects by modulating cortical excitation and maintaining a balance between cortical excitation and inhibition. An increased baseline level of inhibition in the left DLPFC may create a neurophysiological environment that is more responsive to the synaptic effects of treatment, potentially facilitating changes in neuronal plasticity (48). These modifications may help normalize cortical activity in patients with depression. However, these findings contrast with results related to the N100 component, suggesting that different inhibitory mechanisms may contribute to symptom improvement. The N45 component is linked to GABA A receptor activity (118,119) and may also be modulated by the glutamatergic system (118), whereas the N100 component is associated with GABA B receptor activity (119). If changes in N45 amplitude reflect the dynamic interplay between cortical excitation and inhibition, particularly due to its sensitivity to NMDA receptor antagonists and positive allosteric modulators of GABA A receptors (118,119), this could explain the contrasting responses of N45 and N100 in treatment responders. Evidence indicates that an imbalance between cortical excitation and inhibition plays a key role in MDD pathophysiology (6,120,121). Thus, alterations in N45 and N100 amplitudes observed in treatment responders may reflect rTMS-induced restoration of this balance (50). However, opposite findings (52) show a decrease in N45, suggesting concordance with the N100-associated changes seen in symptom improvement. This points to potential complexity in how these biomarkers respond to treatment, indicating multiple pathways to clinical improvement. The P60 amplitude, indicative of excitatory glutamatergic neurotransmission (122) showed that lower baseline amplitudes predicted greater clinical improvement (48), aligning with the hypothesis that reduced excitatory neurotransmission could indicate a more favorable treatment response due to heightened cortical inhibition. This aligns with observations of increased N45 amplitudes and improved outcomes, suggesting that a robust inhibitory system may support symptom improvement. A decrease in P30 amplitude following rTMS (51), suggest that treatment may reduce prefrontal intracortical inhibition, which could contribute to clinical improvements in depressive symptoms due that that 10 Hz prefrontal stimulation suppressed the intracranial P30 evoked response (123,124), particularly in the stimulated network. Reduction in P30 amplitude may indicate a decrease in GABA-Aergic inhibition (119). Thus, rTMS might promote neural facilitation by decreasing prefrontal cortical inhibition. However, P30 amplitude is also associated with glutamatergic excitatory neurotransmission (116,125). Consequently, the reduction in P30 amplitude could suggest that a decrease in excitability might facilitate cortical inhibition. When considered alongside the observation that a stronger inhibitory system is linked to symptom improvement in depression, this suggests that a less robust inhibitory system may also function more effectively under conditions of reduced excitability. Reductions in the effective connectivity signal ( SCS ) correlated with symptom improvement in depression (24), point to the possibility that subgenual cinguale cortex (SGC) and DLPFC interconnectivity may be partly modulated by GABAergic neurotransmission (126). The DLPFC plays a central role in MDD and is the primary target in many depression treatments. Several studies have focused on the DLPFC, measuring baseline and post-treatment changes due to the association between depressive symptoms and disrupted activity and connectivity in brain regions involved in mood and cognition (7). Specifically, the left DLPFC has been consistently linked to depression symptomatology, as it is typically hypoactive in depression; an increase in its activity often corresponds with antidepressant response (127). Conversely, the right DLPFC tends to be hyperactive in MDD, contributing to dysregulated connectivity and control within the limbic system (7). 5.2 Gaps in Knowledge and Critical Evaluation These findings indicate that both resting EEG and TMS-EEG provide direct measures of cortical excitability and inhibition, offering valuable mechanistic insights into how NIBS modulates brain circuits in MDD, and suggesting promising biomarkers for treatment response. Differences in alpha and beta power, and certain (inhibitory’) TEPs are especially intriguing, with N100 and N45 predicting responses to treatments. Despite the encouraging findings. Many studies suffer from small sample sizes, diverse study designs, and varying NIBS protocols, complicating cross-study comparisons and limiting the generalizability of results. Small sample sizes in some studies lead to inconsistencies in results, and the diversity in study designs, NIBS protocols, and outcome measures complicates cross-study comparisons and limits the ability to draw firm conclusions. Specifically, the influence of pharmacological treatments, because each patient with depression takes a different treatment, and each treatment has different mechanisms of action that can cause EEG activity alterations and interfere with the neuronal response to NIBS. This is a confounding factor that we must take into consideration when interpreting results of the different studies. Also, the absence of standardized methods for EEG and TMS-EEG analysis further restricts the generalizability of these findings. Although some biomarkers show promise in predicting treatment response, their clinical application remains uncertain due to a lack of validation in independent cohorts and real-world settings. Additionally, there is an imbalance in the distribution of NIBS techniques among the included studies. Despite conducting a comprehensive search encompassing all NIBS modalities, most of the identified studies utilized rTMS, while tES was comparatively underrepresented. This disparity limits the generalizability of our findings across different neuromodulation approaches. Future research should aim to determine whether the identified markers are replicable and valid across all NIBS techniques to enhance their broader applicability. Additionally, further studies might also explore less-tapped EEG measures (e.g. like dynamic connectivity patterns) to uncover new insights, and should aim to conduct larger, more standardized studies that integrate multimodal approaches, including EEG, TMS-EEG, and neuroimaging, to create a more comprehensive understanding of treatment response. Developing reliable, individualized predictive models will be essential to advancing personalized treatment strategies for depression. Moreover, longitudinal studies examining the effects of NIBS on EEG and TMS-EEG biomarkers could provide insights into how brain networks adapt over time and how these changes correlate with long-term clinical outcomes. 5.3 Framework of Key Determinants for Future Investigation To move toward more targeted and effective NIBS interventions for MDD, future research should focus on the following key determinants: i) Conducting larger, multicenter studies with standardized protocols and analytical methods to validate promising biomarkers across diverse populations and real-world settings. ii) Incorporating longitudinal designs to evaluate how NIBS-induced changes in EEG and TMS-EEG biomarkers correlate with long-term clinical outcomes. iii) Exploring the interaction between NIBS-induced neurophysiological changes and individual genetic, epigenetic, and neurochemical profiles to identify personalized treatment predictors. iv) Enhancing computational modeling and machine learning approaches to integrate multimodal data (e.g., EEG, fMRI, behavioral measures) and refine predictive models of treatment response. v) Investigating the role of brain network dynamics in NIBS efficacy, focusing on functional and structural connectivity alterations that mediate antidepressant effects. In conclusion, resting-state EEG and TMS-EEG offer unique and clinically relevant insights into the neurophysiological mechanisms underlying MDD and treatment response. Rather than remaining exploratory tools, these modalities should be systematically incorporated into the clinical workflow to guide NIBS interventions. Addressing current methodological challenges and advancing multimodal integration will further strengthen their role in enabling precision medicine, ultimately improving treatment outcomes and reducing non-response in depression care. References Institute of Health Metrics and Evaluation. Institute for Health Metrics and Evaluation 2021 [cited 2024 Nov 7]. Global Health Data Exchange (GHDx). Available from: https://vizhub.healthdata.org/gbd-results/ Nemeroff CB (2007) The burden of severe depression: a review of diagnostic challenges and treatment alternatives. J Psychiatr Res 41(3–4):189–206 Carvalho L, de Arruda F (2016) W. Association between anxiety and depression symptoms with pathological personality traits. Psicol Desde El Caribe. ;33(2):No Pagination Specified-No Pagination Specified Fava GA, Ruini C, Belaise C (2007) The concept of recovery in major depression. Psychol Med 37(3):307–317 Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D et al (2006) Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 163(11):1905–1917 Luscher B, Shen Q, Sahir N (2011) The GABAergic deficit hypothesis of major depressive disorder. Mol Psychiatry 16(4):383–406 Stolz LA, Kohn JN, Smith SE, Benster LL, Appelbaum LG (2023) Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci 13(11):1570 Downar J, Siddiqi SH, Mitra A, Williams N, Liston C (2024) Mechanisms of Action of TMS in the Treatment of Depression. Curr Top Behav Neurosci 66:233–277 Razza LB, Palumbo P, Moffa AH, Carvalho AF, Solmi M, Loo CK et al (2020) A systematic review and meta-analysis on the effects of transcranial direct current stimulation in depressive episodes. Depress Anxiety 37(7):594–608 Xu X, Xu M, Su Y, Cao TV, Nikolin S, Moffa A et al (2023) Efficacy of Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Psychological Interventions: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Brain Sci 13(12):1665 Pascual-Leone A, Rubio B, Pallardó F, Catalá MD (1996) Rapid-rate transcranial magnetic stimulation of left dorsolateral prefrontal cortex in drug-resistant depression. Lancet Lond Engl 348(9022):233–237 George MS (2010) Transcranial magnetic stimulation for the treatment of depression. Expert Rev Neurother 10(11):1761–1772 O’Reardon JP, Solvason HB, Janicak PG, Sampson S, Isenberg KE, Nahas Z et al (2007) Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol Psychiatry 62(11):1208–1216 Perera T, George MS, Grammer G, Janicak PG, Pascual-Leone A, Wirecki TS (2016) The Clinical TMS Society Consensus Review and Treatment Recommendations for TMS Therapy for Major Depressive Disorder. Brain Stimul Basic Transl Clin Res Neuromodulation 9(3):336–346 Blumberger DM, Vila-Rodriguez F, Thorpe KE, Feffer K, Noda Y, Giacobbe P et al (2018) Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): a randomised non-inferiority trial. Lancet 391(10131):1683–1692 George MS, Lisanby SH, Avery D, McDonald WM, Durkalski V, Pavlicova M et al (2010) Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial. Arch Gen Psychiatry 67(5):507–516 Cole EJ, Phillips AL, Bentzley BS, Stimpson KH, Nejad R, Barmak F et al (2022) Stanford Neuromodulation Therapy (SNT): A Double-Blind Randomized Controlled Trial. Am J Psychiatry 179(2):132–141 Li CT, Chen MH, Juan CH, Huang HH, Chen LF, Hsieh JC et al (2014) Efficacy of prefrontal theta-burst stimulation in refractory depression: a randomized sham-controlled study. Brain 137(7):2088–2098 Lyons M, Delgadillo J (2024) A systematic review of predictors and moderators of treatment response in psychological interventions for persisting forms of depression. Br J Clin Psychol. ;bjc.12513. Cappon DB, Pascual-Leone A (2024) Toward Precision Noninvasive Brain Stimulation. Am J Psychiatry 181(9):795–805 de Aguiar Neto FS, Rosa JLG (2019) Depression biomarkers using non-invasive EEG: A review. Neurosci Biobehav Rev 105:83–93 Farzan F, Vernet M, Shafi MMD, Rotenberg A, Daskalakis ZJ, Pascual-Leone A Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography. Front Neural Circuits [Internet]. 2016 Sep 22 [cited 2025 Mar 17];10. Available from: https://www.frontiersin.org/journals/neural-circuits/articles/ 10.3389/fncir.2016.00073/full Tremblay S, Rogasch NC, Premoli I, Blumberger DM, Casarotto S, Chen R et al (2019) Clinical utility and prospective of TMS-EEG. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 130(5):802–844 Hadas I, Sun Y, Lioumis P, Zomorrodi R, Jones B, Voineskos D et al (2019) Association of Repetitive Transcranial Magnetic Stimulation Treatment With Subgenual Cingulate Hyperactivity in Patients With Major Depressive Disorder. JAMA Netw Open 2(6):e195578 Ilmoniemi RJ, Virtanen J, Ruohonen J, Karhu J, Aronen HJ, Näätänen R et al (1997) Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. NeuroReport 8(16):3537–3540 Ilmoniemi RJ, Kicić D (2010) Methodology for combined TMS and EEG. Brain Topogr 22(4):233–248 Massimini M, Ferrarelli F, Huber R, Esser SK, Singh H, Tononi G (2005) Breakdown of Cortical Effective Connectivity During Sleep. Science 309(5744):2228–2232 Ozdemir RA, Tadayon E, Boucher P, Sun H, Momi D, Ganglberger W et al (2021) Cortical responses to noninvasive perturbations enable individual brain fingerprinting. Brain Stimulat 14(2):391–403 Thut G, Pascual-Leone A (2010) Integrating TMS with EEG: How and what for? Brain Topogr 22(4):215–218 Thut G, Pascual-Leone A (2010) A review of combined TMS-EEG studies to characterize lasting effects of repetitive TMS and assess their usefulness in cognitive and clinical neuroscience. Brain Topogr 22(4):219–232 Farzan F (2024) Transcranial Magnetic Stimulation–Electroencephalography for Biomarker Discovery in Psychiatry. Biol Psychiatry 95(6):564–580 Jin MX, Qin PP, Xia AWL, Kan RLD, Zhang BBB, Tang AHP et al (2024) Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies. Neurosci Biobehav Rev. ;162 Klooster D, Voetterl H, Baeken C, Arns M (2024) Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies. Biol Psychiatry 95(6):553–563 Strafella R, Chen R, Rajji TK, Blumberger DM, Voineskos D (2022) Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review. Front Hum Neurosci 16:940759 Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE et al (2008) GRADE: Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 336(7653):1106–1110 Aguayo-Albasini JL, Flores-Pastor B, Soria-Aledo VGRADE, System (2014) Classification of Quality of Evidence and Strength of Recommendation. Cir Esp Engl Ed 92(2):82–88 Ebrahimzadeh E, Fayaz F, Rajabion L, Seraji M, Aflaki F, Hammoud A et al (2023) Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder. Front Syst Neurosci 17:919977 Ebrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H (2024) Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Res - Neuroimaging. ;337 Hasanzadeh F, Mohebbi M, Rostami R (2019) Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. J Affect Disord 256:132–142 Zangen A, Zibman S, Tendler A, Barnea-Ygael N, Alyagon U, Blumberger DM et al (2023) Pursuing personalized medicine for depression by targeting the lateral or medial prefrontal cortex with Deep TMS. JCI Insight. ;8(4) Voetterl H, Miron JP, Mansouri F, Fox L, Hyde M, Blumberger DM et al (2021) Investigating EEG biomarkers of clinical response to low frequency rTMS in depression. J Affect Disord Rep 6:100250 Roelofs C, Krepel N, Corlier J, Carpenter L, Fitzgerald PB, Zj D et al Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium. Clin Neurophysiol Off J Int Fed Clin Neurophysiol [Internet]. 2021 Feb [cited 2024 Oct 7];132(2). Available from: https://pubmed.ncbi.nlm.nih.gov/33243617/ Alexander M, Alagapan S, Lugo C, Mellin J, Lustenberger C, Rubinow D et al (2019) Double-blind, randomized pilot clinical trial targeting alpha oscillations with transcranial alternating current stimulation (tACS) for the treatment of major depressive disorder (MDD). Transl Psychiatry. ;9 Gold MC, Yuan S, Tirrell E, Kronenberg EF, Kang JWD, Hindley L et al (2022) Large-scale EEG neural network changes in response to therapeutic TMS. Brain Stimulat 15(2):316–325 Cook IA, Wilson AC, Corlier J, Leuchter AF (2019) Brain Activity and Clinical Outcomes in Adults With Depression Treated With Synchronized Transcranial Magnetic Stimulation: An Exploratory Study. Neuromodulation J Int Neuromodulation Soc 22(8):894–897 Godfrey K, Muthukumaraswamy SD, Stinear CM, Hoeh NR (2024) Resting-state EEG connectivity recorded before and after rTMS treatment in patients with treatment-resistant depression. Psychiatry Res - Neuroimaging. ;338 Corlier J, Wilson A, Hunter AM, Vince-Cruz N, Krantz D, Levitt J et al (2019) Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder. Cereb Cortex N Y NY 29(12):4958–4967 Dhami P, Moreno S, Croarkin PE, Blumberger DM, Daskalakis ZJ, Farzan F (2023) Baseline markers of cortical excitation and inhibition predict response to theta burst stimulation treatment for youth depression. Sci Rep. ;13(1) Sheen JZ, Mazza F, Momi D, Miron JP, Mansouri F, Russell T et al (2024) N100 as a response prediction biomarker for accelerated 1 Hz right DLPFC-rTMS in major depression. J Affect Disord 363:174–181 Strafella R, Momi D, Zomorrodi R, Lissemore J, Noda Y, Chen R et al (2023) Identifying Neurophysiological Markers of Intermittent Theta Burst Stimulation in Treatment-Resistant Depression Using Transcranial Magnetic Stimulation–Electroencephalography. Biol Psychiatry 94(6):454–465 Eshel N, Keller CJ, Wu W, Jiang J, Mills-Finnerty C, Huemer J et al (2020) Global connectivity and local excitability changes underlie antidepressant effects of repetitive transcranial magnetic stimulation. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 45(6):1018–1025 Voineskos D, Blumberger DM, Rogasch NC, Zomorrodi R, Farzan F, Foussias G et al (2021) Neurophysiological effects of repetitive transcranial magnetic stimulation (rTMS) in treatment resistant depression. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 132(9):2306–2316 Bailey NW, Hoy KE, Sullivan CM, Allman B, Rogasch NC, Daskalakis ZJ et al (2023) Concurrent transcranial magnetic stimulation and electroencephalography measures are associated with antidepressant response from rTMS treatment for depression. J Affect Disord Rep 14:100612 Fingelkurts AA, Fingelkurts AA, Rytsälä H, Suominen K, Isometsä E, Kähkönen S (2007) Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Hum Brain Mapp 28(3):247–261 Leuchter AF, Cook IA, Jin Y, Phillips B (2013) The relationship between brain oscillatory activity and therapeutic effectiveness of transcranial magnetic stimulation in the treatment of major depressive disorder. Front Hum Neurosci [Internet]. Feb 26 [cited 2024 Oct 25];7. Available from: https://www.frontiersin.org/journals/human-neuroscience/articles/ 10.3389/fnhum.2013.00037/full Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H et al (2007) Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biol Psychiatry 62(5):429–437 Bruder GE, Stewart JW, Tenke CE, McGrath PJ, Leite P, Bhattacharya N et al (2001) Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant22. Biol Psychiatry 49(5):416–425 Bruder GE, Sedoruk JP, Stewart JW, McGrath PJ, Quitkin FM, Tenke CE (2008) Electroencephalographic Alpha Measures Predict Therapeutic Response to a Selective Serotonin Reuptake Inhibitor Antidepressant: Pre- and Post-Treatment Findings. Biol Psychiatry 63(12):1171–1177 Knott V, Mahoney C, Kennedy S, Evans K (2000) Pre-treatment EEG and it’s relationship to depression severity and paroxetine treatment outcome. Pharmacopsychiatry 33(6):201–205 Lebiecka K, Zuchowicz U, Wozniak-Kwasniewska A, Szekely D, Olejarczyk E, David O Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation. Front Physiol [Internet]. 2018 Sep 28 [cited 2024 Oct 25];9. Available from: https://www.frontiersin.org/journals/physiology/articles/ 10.3389/fphys.2018.01385/full Suffin SC, Emory WH (1995) Neurometric Subgroups in Attentional and Affective Disorders and Their Association with Pharmacotherapeutic Outcome. Clin Electroencephalogr 26(2):76–83 Arns M, Gordon E, Boutros NN (2017) EEG Abnormalities Are Associated With Poorer Depressive Symptom Outcomes With Escitalopram and Venlafaxine-XR, but Not Sertraline: Results From the Multicenter Randomized iSPOT-D Study. Clin EEG Neurosci 48(1):33–40 Jiang H, Popov T, Jylänki P, Bi K, Yao Z, Lu Q et al (2016) Predictability of depression severity based on posterior alpha oscillations. Clin Neurophysiol 127(4):2108–2114 Newson JJ, Thiagarajan TC EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Front Hum Neurosci [Internet]. 2019 Jan 9 [cited 2024 Oct 25];12. Available from: https://www.frontiersin.org/journals/human-neuroscience/articles/ 10.3389/fnhum.2018.00521/full Widge AS, Rodriguez CI, Carpenter LL, Kalin NH, McDonald W, Nemeroff CB (2019) EEG biomarkers for treatment response prediction in major depressive illness. Am J Psychiatry 176(1):82 Fingelkurts AA, Fingelkurts AA (2015) Altered Structure of Dynamic Electroencephalogram Oscillatory Pattern in Major Depression. Biol Psychiatry 77(12):1050–1060 Zrenner B, Zrenner C, Gordon PC, Belardinelli P, McDermott EJ, Soekadar SR et al (2020) Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-triggered TMS. Brain Stimul Basic Transl Clin Res Neuromodulation 13(1):197–205 Cook IA, O’Hara R, Uijtdehaage SH, Mandelkern M, Leuchter AF (1998) Assessing the accuracy of topographic EEG mapping for determining local brain function. Electroencephalogr Clin Neurophysiol 107(6):408–414 Sutton SK, Davidson RJ (1997) Prefrontal Brain Asymmetry: A Biological Substrate of the Behavioral Approach and Inhibition Systems. Psychol Sci 8(3):204–210 Jacobs GD, Snyder D (1996) Frontal brain asymmetry predicts affective style in men. Behav Neurosci 110(1):3–6 Schaffer CE, Davidson RJ, Saron C (1983) Frontal and parietal electroencephalogram asymmetry in depressed and nondepressed subjects. Biol Psychiatry 18(7):753–762 Tomarken AJ, Davidson RJ, Wheeler RE, Doss RC (1992) Individual differences in anterior brain asymmetry and fundamental dimensions of emotion. J Pers Soc Psychol 62(4):676–687 Papousek I, Weiss EM, Schulter G, Fink A, Reiser EM, Lackner HK (2014) Prefrontal EEG alpha asymmetry changes while observing disaster happening to other people: Cardiac correlates and prediction of emotional impact. Biol Psychol 103:184–194 Wheeler RE, Davidson RJ, Tomarken AJ (1993) Frontal brain asymmetry and emotional reactivity: A biological substrate of affective style. Psychophysiology 30(1):82–89 Allen JJ, Reznik SJ (2015) Frontal EEG asymmetry as a promising marker of depression vulnerability: summary and methodological considerations. Curr Opin Psychol 4:93–97 Henriques JB, Davidson RJ (1990) Regional brain electrical asymmetries discriminate between previously depressed and healthy control subjects. J Abnorm Psychol 99(1):22–31 Stewart JL, Coan JA, Towers DN, Allen JJB (2014) Resting and task-elicited prefrontal EEG alpha asymmetry in depression: Support for the capability model. Psychophysiology 51(5):446–455 Galynker II, Cai J, Ongseng F, Finestone H, Dutta E, Serseni D (1998) Hypofrontality and negative symptoms in major depressive disorder. J Nucl Med Off Publ Soc Nucl Med 39(4):608–612 Koenigs M, Grafman J (2009) The functional neuroanatomy of depression: Distinct roles for ventromedial and dorsolateral prefrontal cortex. Behav Brain Res 201(2):239–243 Baxter LR (1989) Reduction of Prefrontal Cortex Glucose Metabolism Common to Three Types of Depression. Arch Gen Psychiatry 46(3):243 Baxter LR (1991) Pet Studies of Cerebral Function in Major Depression and Obsessive-CompulsiveDisorder: the Emerging Prefrontal Cortex Consensus. Ann Clin Psychiatry 3(2):103–109 George MS, Ketter TA, Post RM (1994) Prefrontal cortex dysfunction in clinical depression. Depression 2(2):59–72 Mayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA et al (1999) Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry 156(5):675–682 Bora E, Fornito A, Pantelis C, Yücel M (2012) Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies. J Affect Disord 138(1–2):9–18 Graterol Pérez JA, Guder S, Choe C, un, Gerloff C, Schulz R (2022) Relationship Between Cortical Excitability Changes and Cortical Thickness in Subcortical Chronic Stroke. Front Neurol 13:802113 Fröhlich F (2015) Chapter 3 - Experiments and models of cortical oscillations as a target for noninvasive brain stimulation. In: Bestmann S, editor. Progress in Brain Research [Internet]. Elsevier; [cited 2024 Oct 25]. pp. 41–73. (Computational Neurostimulation; vol. 222). Available from: https://www.sciencedirect.com/science/article/pii/S0079612315001314 Notbohm A, Kurths J, Herrmann CS (2016) Modification of Brain Oscillations via Rhythmic Light Stimulation Provides Evidence for Entrainment but Not for Superposition of Event-Related Responses. Front Hum Neurosci 10:10 Arns M, Drinkenburg WH, Fitzgerald PB, Kenemans JL (2012) Neurophysiological predictors of non-response to rTMS in depression. Brain Stimulat 5(4):569–576 Krepel N, Sack AT, Kenemans JL, Fitzgerald PB, Drinkenburg WH, Arns M (2018) Non-replication of neurophysiological predictors of non-response to rTMS in depression and neurophysiological data-sharing proposal. Brain Stimulat 11(3):639–641 Pellicciari MC, Ponzo V, Caltagirone C, Koch G (2017) Restored Asymmetry of Prefrontal Cortical Oscillatory Activity after Bilateral Theta Burst Stimulation Treatment in a Patient with Major Depressive Disorder: A TMS-EEG Study. Brain Stimul Basic Transl Clin Res Neuromodulation 10(1):147–149 Vanneste S, Ost J, Langguth B, De Ridder D (2014) TMS by double-cone coil prefrontal stimulation for medication resistant chronic depression: A case report. Neurocase 20(1):61–68 Koshiyama D, Kirihara K, Usui K, Tada M, Fujioka M, Morita S et al (2020) Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation. J Affect Disord 265:416–422 Heller W, Etienne MA, Miller GA (1995) Patterns of perceptual asymmetry in depression and anxiety: Implications for neuropsychological models of emotion and psychopathology. J Abnorm Psychol 104(2):327–333 Corey-Lisle PK, Nash R, Stang P, Swindle R (2004) Response, partial response, and nonresponse in primary care treatment of depression. Arch Intern Med 164(11):1197–1204 Cook IA, Leuchter AF, Witte E, Abrams M, Uijtdehaage SH, Stubbeman W et al (1999) Neurophysiologic predictors of treatment response to fluoxetine in major depression. Psychiatry Res 85(3):263–273 Alyagon U, Shahar H, Hadar A, Barnea-Ygael N, Lazarovits A, Shalev H et al (2020) Alleviation of ADHD symptoms by non-invasive right prefrontal stimulation is correlated with EEG activity. NeuroImage Clin 26:102206 Fitzgerald PJ, Watson BO (2018) Gamma oscillations as a biomarker for major depression: an emerging topic. Transl Psychiatry 8(1):1–7 Roh SC, Kim JS, Kim S, Kim Y, Lee SH (2020) Frontal Alpha Asymmetry Moderated by Suicidal Ideation in Patients with Major Depressive Disorder: A Comparison with Healthy Individuals. Clin Psychopharmacol Neurosci 18(1):58–66 Mitoma R, Tamura S, Tateishi H, Mitsudo T, Tanabe I, Monji A et al (2022) Oscillatory brain network changes after transcranial magnetic stimulation treatment in patients with major depressive disorder. J Affect Disord Rep 7:100277 Hillebrand A, Tewarie P, van Dellen E, Yu M, Carbo EWS, Douw L et al (2016) Direction of information flow in large-scale resting-state networks is frequency-dependent. Proc Natl Acad Sci 113(14):3867–3872 Scheeringa R, Bastiaansen MCM, Petersson KM, Oostenveld R, Norris DG, Hagoort P (2008) Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int J Psychophysiol 67(3):242–251 Godfrey K, Muthukumaraswamy SD, Stinear CM, Hoeh N (2022) Decreased salience network fMRI functional connectivity following a course of rTMS for treatment-resistant depression. J Affect Disord 300:235–242 Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52(4):1162–1170 Williams LM (2017) 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 34(1):9–24 Michel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage 180:577–593 Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015) Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry 72(6):603–611 Ibáñez-Molina AJ, Lozano V, Soriano MF, Aznarte JI, Gómez-Ariza CJ, Bajo MT EEG Multiscale Complexity in Schizophrenia During Picture Naming. Front Physiol [Internet]. 2018 Sep 7 [cited 2024 Oct 31];9. Available from: https://www.frontiersin.org/journals/physiology/articles/ 10.3389/fphys.2018.01213/full Friston KJ, Tononi G, Sporns O, Edelman GM (1995) Characterising the complexity of neuronal interactions. Hum Brain Mapp 3(4):302–314 Takahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K et al (2010) Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. NeuroImage 51(1):173–182 Gold BI, Bowers MB, Roth RH, Sweeney DW (1980) GABA levels in CSF of patients with psychiatric disorders. Am J Psychiatry 137(3):362–364 Hasler G, van der Veen JW, Tumonis T, Meyers N, Shen J, Drevets WC (2007) Reduced prefrontal glutamate/glutamine and gamma-aminobutyric acid levels in major depression determined using proton magnetic resonance spectroscopy. Arch Gen Psychiatry 64(2):193–200 Price RB, Shungu DC, Mao X, Nestadt P, Kelly C, Collins KA et al (2009) Amino acid neurotransmitters assessed by proton magnetic resonance spectroscopy: relationship to treatment resistance in major depressive disorder. Biol Psychiatry 65(9):792–800 Schür RR, Draisma LWR, Wijnen JP, Boks MP, Koevoets MGJC, Joëls M et al (2016) Brain GABA levels across psychiatric disorders: A systematic literature review and meta-analysis of (1) H-MRS studies. Hum Brain Mapp 37(9):3337–3352 Rajkowska G, O’Dwyer G, Teleki Z, Stockmeier CA, Miguel-Hidalgo JJ (2007) GABAergic neurons immunoreactive for calcium binding proteins are reduced in the prefrontal cortex in major depression. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 32(2):471–482 Rogasch NC, Daskalakis ZJ, Fitzgerald PB (2015) Cortical inhibition of distinct mechanisms in the dorsolateral prefrontal cortex is related to working memory performance: A TMS–EEG study. Cortex 64:68–77 Rogasch NC, Fitzgerald PB (2013) Assessing cortical network properties using TMS–EEG. Hum Brain Mapp 34(7):1652 Canali P, Sferrazza Papa G, Casali AG, Schiena G, Fecchio M, Pigorini A et al (2014) Changes of cortical excitability as markers of antidepressant response in bipolar depression: preliminary data obtained by combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Bipolar Disord 16(8):809–819 Belardinelli P, König F, Liang C, Premoli I, Desideri D, Müller-Dahlhaus F et al (2021) TMS-EEG signatures of glutamatergic neurotransmission in human cortex. Sci Rep 11(1):8159 Premoli I, Castellanos N, Rivolta D, Belardinelli P, Bajo R, Zipser C et al (2014) TMS-EEG Signatures of GABAergic Neurotransmission in the Human Cortex. J Neurosci 34(16):5603–5612 Kinjo M, Wada M, Nakajima S, Tsugawa S, Nakahara T, Blumberger DM et al (2021) Transcranial magnetic stimulation neurophysiology of patients with major depressive disorder: a systematic review and meta-analysis. Psychol Med 51(1):1–10 Voineskos D, Blumberger DM, Zomorrodi R, Rogasch NC, Farzan F, Foussias G et al (2019) Altered Transcranial Magnetic Stimulation-Electroencephalographic Markers of Inhibition and Excitation in the Dorsolateral Prefrontal Cortex in Major Depressive Disorder. Biol Psychiatry 85(6):477–486 Cheng H Understanding data analysis aspects of TMS-EEG in clinical study: a mini review and a case study with open dataset. 2024 [cited 2024 Mar 19]; Available from: https://rgdoi.net/10.13140/RG.2.2.23310.37449 Huang Y, Hajnal B, Entz L, Fabó D, Herrero JL, Mehta AD et al (2019) Intracortical Dynamics Underlying Repetitive Stimulation Predicts Changes in Network Connectivity. J Neurosci 39(31):6122 Keller CJ, Huang Y, Herrero JL, Fini ME, Du V, Lado FA et al (2018) Induction and Quantification of Excitability Changes in Human Cortical Networks. J Neurosci 38(23):5384 Ferreri F, Pasqualetti P, Määttä S, Ponzo D, Ferrarelli F, Tononi G et al (2011) Human brain connectivity during single and paired pulse transcranial magnetic stimulation. NeuroImage 54(1):90–102 Medalla M, Barbas H (2012) The Anterior Cingulate Cortex May Enhance Inhibition of Lateral Prefrontal Cortex Via m2 Cholinergic Receptors at Dual Synaptic Sites. J Neurosci 32(44):15611–15625 Cappon D, den Boer T, Jordan C, Yu W, Lo A, LaGanke N et al (2022) Safety and Feasibility of Tele-Supervised Home-Based Transcranial Direct Current Stimulation for Major Depressive Disorder. Front Aging Neurosci 13:765370 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Med 18(3):e1003583 Tables Table 1 Resting EEG outcomes in brain stimulation studies include the type of treatment and EEG measures, baseline findings (capturing data before treatment initiation or during the initial stages), and post-treatment findings (reflecting changes observed after treatment completion). These included power analysis (such as absolute and relative power, IAF as well as FAA), cordance and microstates. Additionally, various connectivity metrics, including envelope correlation and αSC and coherence, were employed to assess inter-regional brain synchronization. As well as Non-linear features, such as PE, FD, LZC, CD, and KFD, were also included. References N Treatment Quality assessment Measures Baseline significative findings Change in measure post treatment Ebrahimzadeh et al., 2024 106 rTMS Moderate Power Analysis; Cordance; Non-Linear Features ↑β power; Combined features - Godfrey et al., 2024 28 rTMS Moderate Power Analysis; Connectivity ↓θ connectivity θ connectivity↑ Ebrahimzadeh et al., 2023 88 rTMS Moderate Power Analysis; Cordance; Non-Linear Features ↑β power; ↓CD, ↓LZC β power↓, CD↓, LZC↓, θ cordance↓ Zangen et al., 2023 169 dTMS High Power Analysis ↑α left; ↑Low-γ power; Combined features - Gold et al., 2022 49 rTMS Moderate Microstates - MS-2↑, MS-3↓ Voetterl et al., 2021 39 rTMS Moderate Power Analysis ↑θ relative power α absolute power↑ Hasanzadeh et al., 2019 46 rTMS Moderate Power Analysis; Non-Linear Features ↓β power; ↑α power; ↑CD - Corlier et al., 2019 109 rTMS High Connectivity - αSC↑ Roelofs et al., 2019 153 rTMS Moderate Power Analysis (IAF) IAF ≈ 10Hz - Alexander et al., 2019 32 tACS High Power Analysis - Left α power↓ (10 Hz tACS only) Cook et al., 2019 16 sTMS High Power Analysis; Connectivity; Non-Linear Features ↑α coherence; ↑β coherence CSD↑ Table 2 TMS-EEG outcomes for brain stimulation studies References N Treatment Quality assessment Measure Baseline significative findings Change in measure post treatment Sheen et al., 2024 23 rTMS Moderate TEP ↑N100 - Dhami et al., 2023 43 TBS Moderate TEP ↑N45; ↓P60 - Strafella et al., 2023 98 iTBS Moderate TEP ↑N100 N100↓; N45↑ Bailey et al., 2023 39 rTMS Moderate TEP ↑sp ↓pp N100 Slope - Voineskos et al., 2021 30 rTMS High TEP - N45↓; N100↓ Eshel et al., 2020 33 rTMS High TEP - P30↓ Hadas et al., 2019 26 rTMS High Connectivity - SCS↓ Additional Declarations The authors declare potential competing interests as follows: G.C., J.S-S. R.R-M. and D.B.-F are partially supported by the Marató of TV3 (grant 202129.30 and 202211.30). C.G. and J.S.-S. are partially supported by the Spanish Ministry of Science and Innovation (PID2022-139298OA-C22). D.B.-F. is funded by the Spanish Ministry Science and Innovation (Ref: PID2022-137234OB-I00), and an ICREA Academia 2024 research grant from the Catalan Government. D. C. was partly supported by the National Institutes of Health (R01AG076708), the NARSAD-Brain and Behavior Research Foundation (30772), and the Bright Focus Foundation (A2024027S). Dr. A. P-L is partly supported by grants from the National Institutes of Health (R01AG076708), Jack Satter Foundation, and BrightFocus Foundation. Dr. A. P-L serves as a paid member of the scientific advisory boards for Neuroelectrics, Magstim Inc., TetraNeuron, BitBrain and AscenZion. He is co-founder of TI solutions and co-founder and chief medical officer of Linus Health. Dr. A. P- L is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging, and applications of noninvasive brain stimulation in various neurological disorders, as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia. Supplementary Files 2SA1.docx Supplementary material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7771697","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":524154288,"identity":"b00b6ed8-b06c-4cb6-9da4-9b9022cc2c3f","order_by":0,"name":"Rubén Romero-Marín","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBADOdK1GDMwMDM2kKQlsYFoLeazDx/d8HGPTfr8iPzjj27U3MljYAeK4NMicy4t7eaMZ2m5G28kMzbnHHtWzMCTlnYDnxYJHh6z2zwHDudunAHSwnY4sUGCx4ywlj8HDqcbgrX8I1YLw4HDCfISQC25bURpYUu72XMgzXADz2PD2bl9hxPbCPuF+diNHwds5OXbEx98zvl2OLGf/fAxvFrgwOAAlMFGlHIQkG8gWukoGAWjYBSMNAAA3H9Nsz1ZmY0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0358-0015","institution":"Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Spain","correspondingAuthor":true,"prefix":"","firstName":"Rubén","middleName":"","lastName":"Romero-Marín","suffix":""},{"id":524170527,"identity":"c7cd8a63-98f3-43e9-ad72-9b3239eccdcc","order_by":1,"name":"Davide Cappon","email":"","orcid":"https://orcid.org/0000-0002-0326-2191","institution":"Department of Neurology, Harvard Medical School, Boston, MA, USA","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Cappon","suffix":""},{"id":524170528,"identity":"d0230c80-1670-4aac-8d3a-dfc9dc22d7ff","order_by":2,"name":"Javier Solana-Sánchez","email":"","orcid":"https://orcid.org/0000-0003-0880-7856","institution":"Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Spain","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Solana-Sánchez","suffix":""},{"id":524170529,"identity":"0e42b211-2481-4ba1-9f75-e4a24bef45b3","order_by":3,"name":"David Bartrés-Faz","email":"","orcid":"https://orcid.org/0000-0001-6020-4118","institution":"Departament de Medicina, Facultat de Medicina i Ciències de la Salut, i Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Bartrés-Faz","suffix":""},{"id":524170530,"identity":"a484fa29-4c98-4068-8288-98f95144153f","order_by":4,"name":"Álvaro Pascual-Leone","email":"","orcid":"https://orcid.org/0000-0001-8975-0382","institution":"Department of Neurology, Harvard Medical School, Boston, MA, USA","correspondingAuthor":false,"prefix":"","firstName":"Álvaro","middleName":"","lastName":"Pascual-Leone","suffix":""},{"id":524170531,"identity":"f4c83637-0ccd-4a5e-a2a5-50e39f1d4e12","order_by":5,"name":"Gabriele Cattaneo","email":"","orcid":"https://orcid.org/0000-0002-7411-6829","institution":"Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Spain","correspondingAuthor":false,"prefix":"","firstName":"Gabriele","middleName":"","lastName":"Cattaneo","suffix":""}],"badges":[],"createdAt":"2025-10-03 08:40:03","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7771697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7771697/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92857625,"identity":"f7610490-d99b-46de-b595-6dbcdf07ef04","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1313146,"visible":true,"origin":"","legend":"","description":"","filename":"EEGBiomarkersforaPrecisionMedicineApproachtoNoninvasiveBrainStimulationforMDD.docx","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/9a7398faa7b4f872039ca9b4.docx"},{"id":92857620,"identity":"21f35499-717a-4b8b-a0a4-30138b0d7a94","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7771697.json","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/ea929a3340da702144d227a4.json"},{"id":92857628,"identity":"76ce5689-03d6-43ce-ae24-d25e9afb37bf","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":236837,"visible":true,"origin":"","legend":"","description":"","filename":"rs77716970enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/c2ee14fb3304b716a5551d13.xml"},{"id":92858177,"identity":"0b7a4ed7-587b-455e-af18-a2683267e978","added_by":"auto","created_at":"2025-10-06 11:53:11","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152452,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/f8b23d0e984d90687bb0f4a7.png"},{"id":92857626,"identity":"37c562ed-31c2-4824-9554-f4d7579cfe72","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":397326,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/2f9b7445ab9a3415483f3165.png"},{"id":92857631,"identity":"b4629c5c-8f13-4f52-97e9-e693d0180ad4","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":524501,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/0b596479a270ce67576953b8.png"},{"id":92858176,"identity":"9209372b-b93d-46e0-acaa-d71790914097","added_by":"auto","created_at":"2025-10-06 11:53:11","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49282,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/b3e6c72623af11471ccb56db.png"},{"id":92858179,"identity":"0135ec0c-de6f-430f-8071-ec4b2ac43c7d","added_by":"auto","created_at":"2025-10-06 11:53:11","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57044,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/7c3bd6987a641fe54931cd2f.png"},{"id":92857633,"identity":"3fc120a2-46ca-4926-b630-aa93d7cf3423","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69671,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/6b73ad81050cf515afba977f.png"},{"id":92857630,"identity":"c5fd1ead-0c8e-4d22-bcb5-0769741f3ef5","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":235782,"visible":true,"origin":"","legend":"","description":"","filename":"rs77716970structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/7342a3ab9ac55922975efcc0.xml"},{"id":92857634,"identity":"5d2efd1f-9d43-491a-b9ab-6cfd1cddc392","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":248891,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/3e529b764849bc707b068c4a.html"},{"id":92857623,"identity":"f37556b9-5e16-4209-9024-29f290eff1d4","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61814,"visible":true,"origin":"","legend":"\u003cp\u003eProvides detailed information on the study selection process, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (128).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/b409bfa5a5b24ae3187525f2.png"},{"id":92858178,"identity":"e6633b0a-d258-45e2-976d-985d19d8c986","added_by":"auto","created_at":"2025-10-06 11:53:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517060,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline TMS-EEG markers predict response to bilateral DLPFC intermittent TBS treatment for youth depression (2 groups receiving respectively 2-week TBS, 10 sessions, or 4-week TBS, 20 sessions applied bilateral DLPFC. A) Topoplot analysis displays the p-values of the TEP × time interaction, highlighting significant electrodes with a white asterisk. B) Boxplot shows the distribution of amplitude values across participants for an exemplary electrode. C) Predictive line plots split participants into three groups based on their TEP amplitude, illustrating their predicted treatment response trajectory. Error bars indicate standard error of the mean. Adapted from Dhami et al., (2023) (48).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/677a0b9d9a4ebe52f7461600.png"},{"id":92858175,"identity":"a356a46b-218f-4b86-a81f-978829ac40a6","added_by":"auto","created_at":"2025-10-06 11:53:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":915322,"visible":true,"origin":"","legend":"\u003cp\u003eTMS-EEG markers (single and paired pulse) of response to 6 weeks of active 10Hz or sham rTMS targeting the left DLPFC or bilateral DLPFC. A) TEPs from F3 in response to single pulse LDLPFC stimulation from each group at baseline (red controls, blue responders, green non-responders). B) TEPs from F3 in response to paired pulse LDLPFC stimulation from each group at baseline (red controls, blue responders, green non-responders). C), D) Steeper N100 slopes in responders suggest that cortical inhibition dynamics may distinguish treatment responders from non-responders. Adapted from Bailey et al., (2023) (53).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/3ad3f1c2661e425d17e18fb5.png"},{"id":92859285,"identity":"642101dd-6b65-487a-82b4-2b5e4110046a","added_by":"auto","created_at":"2025-10-06 12:01:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2280893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/c5d71d20-299d-4665-b4c5-2103ac59d228.pdf"},{"id":92857621,"identity":"a9c88f52-50ec-4d4a-b3d9-f3b1d2c3b88b","added_by":"auto","created_at":"2025-10-06 11:45:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":104833,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary material\u003c/p\u003e","description":"","filename":"2SA1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7771697/v1/7068c379d7615101286f3943.docx"}],"financialInterests":"The authors declare potential competing interests as follows: G.C., J.S-S. R.R-M. and D.B.-F are partially supported by the Marató of TV3 (grant 202129.30 and 202211.30). C.G. and J.S.-S. are partially supported by the Spanish Ministry of Science and Innovation (PID2022-139298OA-C22).\n\nD.B.-F. is funded by the Spanish Ministry Science and Innovation (Ref: PID2022-137234OB-I00), and an ICREA Academia 2024 research grant from the Catalan Government. \n\nD. C. was partly supported by the National Institutes of Health (R01AG076708), the NARSAD-Brain and Behavior Research Foundation (30772), and the Bright Focus Foundation (A2024027S).\n\nDr. A. P-L is partly supported by grants from the National Institutes of Health (R01AG076708), Jack Satter Foundation, and BrightFocus Foundation.\n\nDr. A. P-L serves as a paid member of the scientific advisory boards for Neuroelectrics, Magstim Inc., TetraNeuron, BitBrain and AscenZion. He is co-founder of TI solutions and co-founder and chief medical officer of Linus Health.\n\nDr. A. P- L is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging, and applications of noninvasive brain stimulation in various neurological disorders, as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEEG Biomarkers for a Precision-Medicine Approach to Noninvasive Brain Stimulation for Major Depressive Disorder\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a prevalent and disabling psychiatric condition, affecting approximately 280\u0026nbsp;million people worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite the availability of various pharmacological and psychotherapeutic treatments, approximately 20\u0026ndash;40% of patients suffer from chronic depressive episodes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), fail to achieve meaningful relief and become medication-resistant (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that MDD symptoms arise from rapidly evolving pathological brain states within specific neural circuits (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Non-invasive brain stimulation (NIBS) techniques, including transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), can precisely target these circuits, disrupting the maladaptive, self-sustaining activity and \u0026ndash; perhaps due to such mechanisms - alleviating depressive symptoms even in chronic cases (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEarly clinical studies suggested that repetitive TMS (rTMS) targeting the dorsolateral prefrontal cortex is effective in medication-resistant cases (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), leading to rigorous clinical trials and eventually the clinical adoption and regulatory approval of rTMS for medication resistant MDD (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCurrent treatment protocols show that approximately 60% of patients experience significant improvement\u0026mdash;with 30% achieving full remission after a six-week rTMS course (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and accelerated protocols reaching remission in as little as one week (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, approximately 40% of patients do not respond as expected to treatment. Poorer outcomes are especially common among those with more severe baseline symptoms (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), as well as in cases involving altered network connectivity and imbalances in neural excitability and inhibition (to be discussed in the following sections). These findings underscore the importance of adopting a precision-medicine approach (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) that adjusts treatment based on each individual\u0026rsquo;s biological profile to enhance therapeutic effectiveness and a greater understanding of NIBS mechanisms.\u003c/p\u003e\u003cp\u003eElectroencephalography (EEG), whether used alone or in combination with TMS (TMS/EEG), has emerged as a powerful tool for understanding the complex dynamics involved in the pathophysiology of MDD (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This approach enables researchers to examine how alterations in neural circuitry contribute to depressive symptoms, offering valuable insights into the underlying mechanisms of the disorder. TMS/EEG leverages the focal stimulation capabilities of TMS alongside the high temporal resolution of EEG, allowing researchers to capture the immediate neural responses evoked by TMS (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). When a single TMS pulse (spTMS) is applied, it depolarizes cortical neurons, triggering synaptic activations that are recorded as TMS-evoked potentials (TEPs) in the EEG. These responses provide direct, real-time measures of cortical excitability and connectivity, offering invaluable insights into the dynamic interactions within brain networks (\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy characterizing the unique spatiotemporal and physiological profiles of individual patients, TMS/EEG can help identify predictive biomarkers that indicate who is most likely to benefit from NIBS. Such biomarkers could not only pave the way for personalized treatment strategies but also have the potential to reduce nonresponse rates, optimize therapeutic protocols, and lower the economic and clinical burdens associated with ineffective interventions (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome have argued that EEG and TMS-EEG hold considerable promise as predictive biomarkers for MDD interventions (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the present paper, we provide an updated review of the current literature on EEG and TMS/EEG biomarkers as predictors of NIBS response in MDD. While EEG-based measures and TMS-EEG metrics hold considerable promise as predictive biomarkers, further research is needed to validate their clinical utility. Through a systematic review of the literature, we identify both encouraging findings and major inconsistencies that underscore the need for more rigorous and standardized research. Our goal is to move beyond speculation and outline the specific gaps that must be addressed before EEG and TMS-EEG can reliably inform personalized NIBS interventions. Ultimately, we propose a framework of key neurophysiological determinants that should guide future investigations and help bring the field closer to clinically actionable biomarkers.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Search strategy\u003c/h2\u003e\n\u003cp\u003ePubMed and Scopus were searched between May 1st, 2024 and October 1st, 2024 for publications studying markers of treatment response in patients with MDD. Search terms for both databases were (Markers) AND (Treatment Response) AND (Depressive Disorder) AND ((EEG) OR (electroencephalography) OR (TMS) OR (NIBS) OR (tDCS) OR (tACS) OR (tES)). Results were filtered to only include studies reported in English with a date range between 2019-2024. Two study authors (RR, GC) conducted an independent literature search using pre-defined inclusion and exclusion criteria. Conflicts between authors were resolved by discussion. Approved studies were then moved to data extraction.\u003c/p\u003e\n\u003ch2\u003e3.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Inclusion criteria\u003c/h2\u003e\n\u003cp\u003eThe included studies focused on subjects with unipolar MDD who received noninvasive brain stimulation treatments. To be eligible, studies were required to report EEG or TMS-EEG measurements taken before, during, or after the treatment. Additionally, only studies that demonstrated a moderate or higher level of quality of evidence, as assessed following the selection process, were considered.\u003c/p\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Exclusion criteria\u003c/h2\u003e\n\u003cp\u003eCase studies, review articles, protocols, posters, and conference abstracts were excluded from the analysis. Studies involving animal populations, healthy subjects, bipolar depression, or conditions other than unipolar MDD were also excluded. Additionally, studies that did not include any therapeutic interventions or utilized pharmacological treatments instead of NIBS were not considered. Finally, only studies that reported EEG or TMS-EEG measures were included; those without these measurements were excluded.\u003c/p\u003e\n\u003ch2\u003e3.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Quality of evidence assessment\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eQuality of evidence was assessed using the \u003cstrong\u003eGrading of Recommendations Assessment, Development, and Evaluation (GRADE)\u003c/strong\u003e methodology (35). The quality of studies was categorized into four levels: high, moderate, low, and very low. Studies rated as \u0026apos;high\u0026apos; were randomized, double-blinded, and placebo-controlled; \u0026apos;moderate\u0026apos; studies were randomized but not blinded; \u0026apos;low\u0026apos; studies were non-randomized but included a placebo or control group; and \u0026apos;very low\u0026apos; studies were non-randomized without a placebo or control group. To ensure focus on higher quality and more reliable designs, studies rated as \u0026apos;very low\u0026apos; were excluded (36).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e4.1 \u0026nbsp; \u0026nbsp; Study selection\u003c/h2\u003e\n\u003cp\u003eA summary of the study selection process, including inclusion and exclusion criteria, is provided in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Please insert Figure 1 here\u003c/p\u003e\n\u003ch2\u003e4.2 \u0026nbsp; \u0026nbsp; Included study characteristics\u003c/h2\u003e\n\u003cp\u003eThe search terms initially yielded 138 studies, which were reduced to 126 after removing duplicates. During the primary screening, 83 studies were excluded based on title or abstract for not aligning with the focus of the review, leaving 43 studies for full-text evaluation. Of these, 25 studies were excluded due to not meeting the inclusion criteria \u0026mdash; for example, studies using neuroimaging modalities other than EEG or TMS-EEG, involving interventions not related to NIBS, or examining populations or outcomes outside the scope of this review. In addition, six studies were excluded following a quality assessment that rated them as \u0026ldquo;very low\u0026rdquo; due to methodological limitations such as small sample sizes, lack of control groups, or absence of randomization. Ultimately, 18 studies were included in the qualitative synthesis. Quality ratings for these studies are detailed in Tables 1 and 2. The following sections are organized chronologically by year of publication.\u003c/p\u003e\n\u003ch2\u003e4.3 \u0026nbsp; \u0026nbsp; Resting EEG studies\u003c/h2\u003e\n\u003cp\u003eEleven studies explored EEG markers of treatment response to brain stimulation therapies (Table 1), utilizing a wide range of quantitative EEG measures.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Please insert Table 1 here\u003c/p\u003e\n\u003cp\u003eStudies that explored the association between EEG power and the response to \u003cstrong\u003erepetitive transcranial magnetic stimulation (rTMS)\u0026nbsp;\u003c/strong\u003erevealed that higher baseline power in different frequency bands was associated with increased treatment success. Specifically, different studies found that individuals with better responses to the rTMS intervention had increased beta power (37,38), higher alpha power (39,40), increased theta relative power (41), and low gamma power (40). Additionally an individual alpha frequency (IAF) closer to 10 Hz showed greater clinical improvement after 10 Hz rTMS (42). However, two studies showed contradictory results, showing an association between lower beta power (39), alpha power (43) and better clinical outcomes.\u003c/p\u003e\n\u003cp\u003eAmong the reviewed studies, only two have investigated prefrontal theta cordance, both reporting significant findings differentiating responders from non-responders (see supplementary material for details) (37,38). And only one study examined EEG microstates, finding that rTMS treatment led to an increase in microstate MS-2 occurrence and coverage, and a decrease in MS-3 (44). However, this study did not assess whether microstate features could predict clinical response to rTMS.\u003c/p\u003e\n\u003cp\u003eIn terms of connectivity, better response to the intervention was associated with increased alpha and beta band coherence (45), as well as decreased theta band coherence (46), and an increase of Alpha Spectral Correlation (\u0026alpha;SC) after treatment (47).\u003c/p\u003e\n\u003cp\u003eNon-Linear Features combined with machine learning achieved an impressive accuracy predicting treatment response (37,38).\u003c/p\u003e\n\u003cp\u003eIncreases in alpha current source density\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecurrent source density (CSD) were also significantly associated with clinical improvement (45).\u003c/p\u003e\n\u003cp\u003eThese examples illustrate the diversity of EEG metrics explored to date, with researchers often selecting specific features of interest rather than employing comprehensive or standardized approaches. While data-driven methods, including machine learning, offer promising avenues to identify complex biomarker patterns, they remain difficult to interpret and compare across studies due to high variability in input features, sample characteristics, and the algorithms applied. This heterogeneity poses a significant barrier to drawing clear conclusions about EEG-based predictors and calls for more standardized frameworks and validation efforts.\u003c/p\u003e\n\u003ch2\u003e4.4 \u0026nbsp; \u0026nbsp; TMS-EEG studies\u003c/h2\u003e\n\u003cp\u003eSeven studies have reported TMS-EEG markers of treatment response to NIBS therapies in MDD (Table 2), primarily using \u003cstrong\u003eTEP\u003c/strong\u003e components, which involve examining features such as peak amplitude, peak latency, and slope. Additionally, one study analyzed activity and connectivity patterns using \u003cstrong\u003eSignificant Current Density (SCD)\u003c/strong\u003e and \u003cstrong\u003eSignificant Current Scattering (SCS)\u003c/strong\u003e methods.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Please insert Table 2 here\u003c/p\u003e\n\u003cp\u003eSix studies have examined the effects of therapeutic TMS on TEP components. Higher baseline N45 (48) and N100 (49,50) amplitudes were linked to improvements in depression symptoms. In contrast, a smaller baseline P60 component predicted better response to treatment (48).\u003c/p\u003e\n\u003cp\u003eRegarding the changes after treatment, a decrease in P30 (51), N45 and N100 (50,52) amplitude correlated with improved clinical outcomes. However, N45 results are inconsistent, as some have found an increase with treatment (50).\u003c/p\u003e\n\u003cp\u003eIn terms of slope, treatment responders exhibited a steeper negative N100 slope for single pulses and a steeper positive slope for paired pulses at baseline compared to non-responders (53). Additionally, a decrease in SCS was found to be positively correlated with improvements in depression severity following rTMS (24).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Please insert Figure 2 here\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Please insert Figure 3 here\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review that synthesized the growing body of evidence on resting-state EEG and TMS-EEG biomarkers in depression\u0026mdash;especially in treatment‐resistant depression (TRD)\u0026mdash;demonstrates both exciting promise and important challenges. Recent studies have begun to identify specific, quantifiable metrics that may predict treatment response to NIBS.\u003c/p\u003e\n\u003ch2\u003e5.1 \u0026nbsp; \u0026nbsp; Key Findings and Overall Trends \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u003cu\u003eResting-state EEG\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAlpha Band Power\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResting‐state EEG studies consistently reveal alterations in spectral power and connectivity in depressed populations. For example, depressed individuals often exhibit heightened synchrony in the theta and alpha bands (54,55), a phenomenon that may reflect abnormal thalamocortical connectivity \u0026nbsp;(56).\u003c/p\u003e\n\u003cp\u003eMultiple studies consistently report that greater baseline alpha power \u0026mdash;especially over frontal and parietal regions\u0026mdash; has been associated with better responses to rTMS (57\u0026ndash;61). Some work suggests that \u0026nbsp;increased alpha power may reflect lower arousal and reduced serotonergic activity (38), though discrepancies remain, with other studies failing to find significant differences or even reporting decreased alpha power in depressed cohorts (62\u0026ndash;65). Further complicating the picture, post-treatment changes in alpha power are inconsistent, with some studies reporting decreases \u0026nbsp;(55,66) and others noting increases (67). These mixed results likely reflect interindividual differences and methodological variations, such as pre-treatment stress levels influencing sympathetic activation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAlpha Asymmetry and Entrainment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEvidence also points to the relevance of frontal alpha asymmetry, with left frontal regions often showing differential activity compared to the right. Since EEG alpha power is an inverse index of cortical activity (68), reduced alpha at right compared to left frontal sites (i.e., greater right-sided activation) has been associated with \u0026nbsp;withdrawal motivation (69), negative affect (70\u0026ndash;72), as well as reports of more \u0026nbsp;intense negative emotions (73,74). In MDD, studies have shown that alpha activity in the left frontal region typically are lower than that of the right (75\u0026ndash;77). This asymmetry has been linked to the dysfunction of the left DLPFC, a key target in depression treatment (78,79), and may help predict treatment outcomes depending on the stimulation site. Specifically, for rTMS targeted over the lateral prefrontal cortex (LPFC), greater left-sided alpha activity correlated negatively with clinical improvement, while stimulation over the medial prefrontal cortex (MPFC) showed a positive correlation with clinical improvement (40). This is consistent with PET studies reporting decreased frontal metabolism (80\u0026ndash;83) and Voxel-Based Morphometry (VBM) studies revealing reduced gray matter volume in these regions (84), although the relationship between structure and cortical excitability remains to be fully clarified (85).\u003c/p\u003e\n\u003cp\u003eIn addition, the observation that an IAF near 10 Hz appears to be optimal, suggesting that aligning stimulation frequency with the brain\u0026rsquo;s intrinsic rhythm (via entrainment) may enhance neuroplasticity and clinical outcomes (42,86). The Arnold tongue model provides a theoretical framework for understanding how the frequency and amplitude of external stimuli interact with intrinsic oscillations to promote synchronization (86). According to this model, entrainment peaks when external stimulation\u0026mdash;be it rTMS or tACS\u0026mdash;precisely aligns with the brain\u0026rsquo;s natural rhythm (87). This perfect synchronization drives powerful neuroplasticity, paving the way for lasting clinical and behavioral gains (42). However, inconsistent findings, likely due to individual differences in intrinsic alpha frequency (IAF) (47,88,89), and the sparse documentation of rTMS effects on these oscillatory patterns in MDD beyond isolated case studies (90,91), call for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOther resting-state EEG frequency bands\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInvestigations into other frequency bands have also provided valuable insights. Elevated baseline beta power has generally been associated with poorer treatment outcomes, as it may indicate greater depression severity (37,59), although some findings suggest that increased beta could reflect preserved reward processing (92). Significant coherence observed in the beta band has been associated with cortical excitability, suggesting that increased baseline coherence in large-scale networks may predict better outcomes from rTMS treatment (45). Additionally, frontal delta power, for example, is lower in responders (37,39,59), suggesting reduced temporoparietal dysfunction that influences emotional arousal (93). High frontocentral theta activity at baseline is linked to poorer outcomes and greater depression severity (59,88,94), though some studies report no significant differences (37,38,95). Moreover, higher medial low-gamma power, on the other hand, predicts better outcomes\u0026mdash;possibly reflecting enhanced attentional capacity and cognitive control (40,96\u0026ndash;98). Furthermore, increases in baseline connectivity, particularly in the alpha and theta bands, were linked to better treatment outcomes (46), especially in fronto-parietal regions, in line with other studies (99). Theta activity in prefrontal areas has been associated with the default mode network, which plays a role in high-level control over perception (100,101). Lower theta connectivity was linked with better antidepressant outcomes, possibly reflecting reduced top-down control (102). Additionally, lower connectivity in the salience network, which helps in switching from the DMN to the CEN, correlated with antidepressant responses (46). Higher \u0026alpha;SC among responders suggest increased functional coupling between brain regions (47). Lastly, theta cordance in the prefrontal cortex holds promise as a predictor of clinical improvement following rTMS treatment, although statistical significance has not been consistently observed across studies, likely due to variations in study designs, sample sizes, and rTMS protocols (37,38).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEEG Microstates and non-linear features\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClinical improvements correlated with changes in EEG microstates (MS) MS-2 and MS-3 after rTMS treatment, with MS-2 increasing and MS-3 decreasing in occurrence (44). These changes suggest that rTMS modulate large-scale network alterations (103). Specifically, MS-2 has been linked to BOLD activation in the dorsal ACC, inferior frontal cortices, and right insula (103), regions associated with the reward circuit and improvements in anhedonia (104). Conversely, MS-3 showed reduced occurrence post-TMS, involving frontal and parietal cortices implicated in attention and cognitive control networks (103,105). While depression is often linked to hypoconnectivity in cognitive control networks, some patients exhibit hyperconnectivity, potentially contributing to rumination, which correlates with the DMN (104,106).\u003c/p\u003e\n\u003cp\u003eNon-linear EEG features, when integrated with machine learning models, demonstrate strong predictive power, (37\u0026ndash;39) yielding high predictive accuracies. These complex dynamics effectively capture subtle changes in brain activity associated with NIBS response in depression. Notably, the higher complexity dimension (CD) observed in non-responders (39) suggests increased dimensionality, reflecting a broader distribution of neural oscillations with lower synchrony (107). This elevated CD may indicate neural isolation (108) and disorganized spiking activity (109), signaling more atypical brain activity in non-responders compared to responders.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cu\u003eTMS-EEG Biomarkers and Cortical Inhibition\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTMS-Evoked Potentials (TEPs)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe field of TMS-EEG biomarkers has yielded significant insights into TEP components, particularly emphasizing the N100 component. Higher baseline N100 amplitudes were predictive of greater improvements in depressive symptoms (49,50). The N100 component reflects GABA\u003csub\u003eB\u003c/sub\u003e receptor-mediated slow inhibitory processes (49). Deficiencies in GABA (110\u0026ndash;113) and reduced size and density of GABAergic interneurons in the DLPFC in MDD patients (114) suggest that patients with higher baseline N100 amplitudes may have more intact inhibitory circuitry, allowing greater responsiveness to stimulation. Conversely, a lower baseline N100 amplitude could indicate impaired inhibitory circuitry. However, a significant reduction in N100 amplitude post-treatment among responders, was also observed (49,50,52), and the implications of these findings are in contradiction with the theory of GABA deficiency. Steeper negative slope for single pulses and a steeper positive N100 slope for paired pulses in responders (53), potentially driven by larger P60 amplitudes in the left hemisphere, suggests an interplay between GABA\u003csub\u003eB\u003c/sub\u003e receptor-mediated inhibition and glutamatergic receptor excitation, potentially reflecting sensory processing differences of the TMS pulse (53). The N100 slope has been associated with cognitive performance in working memory (115,116) and has also been linked to antidepressant response in bipolar depression, with changes indicating long-term potentiation effects related to sleep deprivation (117).\u003c/p\u003e\n\u003cp\u003eLarger baseline N45 amplitudes correlating with improved clinical outcomes (48), indicating that greater cortical inhibition might enhance therapeutic effects through synaptic modulation and improved balance between excitation and inhibition. Post-treatment increases in N45 amplitude were noted among responders (50), suggesting enhanced GABA\u003csub\u003eA\u003c/sub\u003e receptor-mediated inhibition, and a more robust and functional network of GABAergic interneurons enhances synaptic modulation capacity, facilitating therapeutic effects by modulating cortical excitation and maintaining a balance between cortical excitation and inhibition. An increased baseline level of inhibition in the left DLPFC may create a neurophysiological environment that is more responsive to the synaptic effects of treatment, potentially facilitating changes in neuronal plasticity (48). These modifications may help normalize cortical activity in patients with depression. However, these findings contrast with results related to the N100 component, suggesting that different inhibitory mechanisms may contribute to symptom improvement. The N45 component is linked to GABA\u003csub\u003eA\u003c/sub\u003e receptor activity (118,119) and may also be modulated by the glutamatergic system (118), whereas the N100 component is associated with GABA\u003csub\u003eB\u003c/sub\u003e receptor activity (119). If changes in N45 amplitude reflect the dynamic interplay between cortical excitation and inhibition, particularly due to its sensitivity to NMDA receptor antagonists and positive allosteric modulators of GABA\u003csub\u003eA\u003c/sub\u003e receptors (118,119), this could explain the contrasting responses of N45 and N100 in treatment responders. Evidence indicates that an imbalance between cortical excitation and inhibition plays a key role in MDD pathophysiology (6,120,121). Thus, alterations in N45 and N100 amplitudes observed in treatment responders may reflect rTMS-induced restoration of this balance (50). However, opposite findings (52) show a decrease in N45, suggesting concordance with the N100-associated changes seen in symptom improvement. This points to potential complexity in how these biomarkers respond to treatment, indicating multiple pathways to clinical improvement.\u003c/p\u003e\n\u003cp\u003eThe P60 amplitude, indicative of excitatory glutamatergic neurotransmission (122) showed that lower baseline amplitudes predicted greater clinical improvement (48), aligning with the hypothesis that reduced excitatory neurotransmission could indicate a more favorable treatment response due to heightened cortical inhibition. This aligns with observations of increased N45 amplitudes and improved outcomes, suggesting that a robust inhibitory system may support symptom improvement.\u003c/p\u003e\n\u003cp\u003eA decrease in P30 amplitude following rTMS (51), suggest that treatment may reduce prefrontal intracortical inhibition, which could contribute to clinical improvements in depressive symptoms due that that 10\u0026thinsp;Hz prefrontal stimulation suppressed the intracranial P30 evoked response (123,124), particularly in the stimulated network. Reduction in P30 amplitude may indicate a decrease in GABA-Aergic inhibition (119). Thus, rTMS might promote neural facilitation by decreasing prefrontal cortical inhibition. However, P30 amplitude is also associated with glutamatergic excitatory neurotransmission (116,125). Consequently, the reduction in P30 amplitude could suggest that a decrease in excitability might facilitate cortical inhibition. When considered alongside the observation that a stronger inhibitory system is linked to symptom improvement in depression, this suggests that a less robust inhibitory system may also function more effectively under conditions of reduced excitability.\u003c/p\u003e\n\u003cp\u003eReductions in the effective connectivity signal (\u003cstrong\u003eSCS\u003c/strong\u003e) correlated with symptom improvement in depression (24), point to the possibility that subgenual cinguale cortex (SGC) and DLPFC interconnectivity may be partly modulated by GABAergic neurotransmission (126). The DLPFC plays a central role in MDD and is the primary target in many depression treatments. Several studies have focused on the DLPFC, measuring baseline and post-treatment changes due to the association between depressive symptoms and disrupted activity and connectivity in brain regions involved in mood and cognition (7). Specifically, the left DLPFC has been consistently linked to depression symptomatology, as it is typically hypoactive in depression; an increase in its activity often corresponds with antidepressant response (127). Conversely, the right DLPFC tends to be hyperactive in MDD, contributing to dysregulated connectivity and control within the limbic system (7).\u003c/p\u003e\n\u003ch2\u003e5.2 \u0026nbsp; \u0026nbsp; Gaps in Knowledge and Critical Evaluation\u003c/h2\u003e\n\u003cp\u003eThese findings indicate that both resting EEG and TMS-EEG provide direct measures of cortical excitability and inhibition, offering valuable mechanistic insights into how NIBS modulates brain circuits in MDD, and suggesting promising biomarkers for treatment response. Differences in alpha and beta power, and certain (inhibitory\u0026rsquo;) TEPs are especially intriguing, with N100 and N45 predicting responses to treatments.\u003c/p\u003e\n\u003cp\u003eDespite the encouraging findings. Many studies suffer from small sample sizes, diverse study designs, and varying NIBS protocols, complicating cross-study comparisons and limiting the generalizability of results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmall sample sizes in some studies lead to inconsistencies in results, and the diversity in study designs, NIBS protocols, and outcome measures complicates cross-study comparisons and limits the ability to draw firm conclusions. Specifically, the influence of pharmacological treatments, because each patient with depression takes a different treatment, and each treatment has different mechanisms of action that can cause EEG activity alterations and interfere with the neuronal response to NIBS. This is a confounding factor that we must take into consideration when interpreting results of the different studies. Also, the absence of standardized methods for EEG and TMS-EEG analysis further restricts the generalizability of these findings. Although some biomarkers show promise in predicting treatment response, their clinical application remains uncertain due to a lack of validation in independent cohorts and real-world settings. Additionally, there is an imbalance in the distribution of NIBS techniques among the included studies. Despite conducting a comprehensive search encompassing all NIBS modalities, most of the identified studies utilized rTMS, while tES was comparatively underrepresented. This disparity limits the generalizability of our findings across different neuromodulation approaches. Future research should aim to determine whether the identified markers are replicable and valid across all NIBS techniques to enhance their broader applicability. Additionally, further studies might also explore less-tapped EEG measures (e.g. like dynamic connectivity patterns) to uncover new insights, and should aim to conduct larger, more standardized studies that integrate multimodal approaches, including EEG, TMS-EEG, and neuroimaging, to create a more comprehensive understanding of treatment response. Developing reliable, individualized predictive models will be essential to advancing personalized treatment strategies for depression. Moreover, longitudinal studies examining the effects of NIBS on EEG and TMS-EEG biomarkers could provide insights into how brain networks adapt over time and how these changes correlate with long-term clinical outcomes.\u003c/p\u003e\n\u003ch2\u003e5.3 \u0026nbsp; \u0026nbsp; Framework of Key Determinants for Future Investigation\u003c/h2\u003e\n\u003cp\u003eTo move toward more targeted and effective NIBS interventions for MDD, future research should focus on the following key determinants:\u003c/p\u003e\n\u003cp\u003ei) Conducting larger, multicenter studies with standardized protocols and analytical methods to validate promising biomarkers across diverse populations and real-world settings.\u003c/p\u003e\n\u003cp\u003eii) Incorporating longitudinal designs to evaluate how NIBS-induced changes in EEG and TMS-EEG biomarkers correlate with long-term clinical outcomes.\u003c/p\u003e\n\u003cp\u003eiii) Exploring the interaction between NIBS-induced neurophysiological changes and individual genetic, epigenetic, and neurochemical profiles to identify personalized treatment predictors.\u003c/p\u003e\n\u003cp\u003eiv) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Enhancing computational modeling and machine learning approaches to integrate multimodal data (e.g., EEG, fMRI, behavioral measures) and refine predictive models of treatment response.\u003c/p\u003e\n\u003cp\u003ev) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Investigating the role of brain network dynamics in NIBS efficacy, focusing on functional and structural connectivity alterations that mediate antidepressant effects.\u003c/p\u003e\n\u003cp\u003eIn conclusion, resting-state EEG and TMS-EEG offer unique and clinically relevant insights into the neurophysiological mechanisms underlying MDD and treatment response. Rather than remaining exploratory tools, these modalities should be systematically incorporated into the clinical workflow to guide NIBS interventions. Addressing current methodological challenges and advancing multimodal integration will further strengthen their role in enabling precision medicine, ultimately improving treatment outcomes and reducing non-response in depression care. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInstitute of Health Metrics and Evaluation. Institute for Health Metrics and Evaluation 2021 [cited 2024 Nov 7]. Global Health Data Exchange (GHDx). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNemeroff CB (2007) The burden of severe depression: a review of diagnostic challenges and treatment alternatives. J Psychiatr Res 41(3\u0026ndash;4):189\u0026ndash;206\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarvalho L, de Arruda F (2016) W. Association between anxiety and depression symptoms with pathological personality traits. Psicol Desde El Caribe. ;33(2):No Pagination Specified-No Pagination Specified\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFava GA, Ruini C, Belaise C (2007) The concept of recovery in major depression. Psychol Med 37(3):307\u0026ndash;317\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D et al (2006) Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 163(11):1905\u0026ndash;1917\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuscher B, Shen Q, Sahir N (2011) The GABAergic deficit hypothesis of major depressive disorder. Mol Psychiatry 16(4):383\u0026ndash;406\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStolz LA, Kohn JN, Smith SE, Benster LL, Appelbaum LG (2023) Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci 13(11):1570\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDownar J, Siddiqi SH, Mitra A, Williams N, Liston C (2024) Mechanisms of Action of TMS in the Treatment of Depression. Curr Top Behav Neurosci 66:233\u0026ndash;277\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRazza LB, Palumbo P, Moffa AH, Carvalho AF, Solmi M, Loo CK et al (2020) A systematic review and meta-analysis on the effects of transcranial direct current stimulation in depressive episodes. Depress Anxiety 37(7):594\u0026ndash;608\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu X, Xu M, Su Y, Cao TV, Nikolin S, Moffa A et al (2023) Efficacy of Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Psychological Interventions: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Brain Sci 13(12):1665\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePascual-Leone A, Rubio B, Pallard\u0026oacute; F, Catal\u0026aacute; MD (1996) Rapid-rate transcranial magnetic stimulation of left dorsolateral prefrontal cortex in drug-resistant depression. Lancet Lond Engl 348(9022):233\u0026ndash;237\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorge MS (2010) Transcranial magnetic stimulation for the treatment of depression. Expert Rev Neurother 10(11):1761\u0026ndash;1772\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Reardon JP, Solvason HB, Janicak PG, Sampson S, Isenberg KE, Nahas Z et al (2007) Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol Psychiatry 62(11):1208\u0026ndash;1216\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerera T, George MS, Grammer G, Janicak PG, Pascual-Leone A, Wirecki TS (2016) The Clinical TMS Society Consensus Review and Treatment Recommendations for TMS Therapy for Major Depressive Disorder. Brain Stimul Basic Transl Clin Res Neuromodulation 9(3):336\u0026ndash;346\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlumberger DM, Vila-Rodriguez F, Thorpe KE, Feffer K, Noda Y, Giacobbe P et al (2018) Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): a randomised non-inferiority trial. Lancet 391(10131):1683\u0026ndash;1692\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorge MS, Lisanby SH, Avery D, McDonald WM, Durkalski V, Pavlicova M et al (2010) Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial. Arch Gen Psychiatry 67(5):507\u0026ndash;516\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCole EJ, Phillips AL, Bentzley BS, Stimpson KH, Nejad R, Barmak F et al (2022) Stanford Neuromodulation Therapy (SNT): A Double-Blind Randomized Controlled Trial. Am J Psychiatry 179(2):132\u0026ndash;141\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi CT, Chen MH, Juan CH, Huang HH, Chen LF, Hsieh JC et al (2014) Efficacy of prefrontal theta-burst stimulation in refractory depression: a randomized sham-controlled study. Brain 137(7):2088\u0026ndash;2098\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLyons M, Delgadillo J (2024) A systematic review of predictors and moderators of treatment response in psychological interventions for persisting forms of depression. Br J Clin Psychol. ;bjc.12513.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappon DB, Pascual-Leone A (2024) Toward Precision Noninvasive Brain Stimulation. Am J Psychiatry 181(9):795\u0026ndash;805\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Aguiar Neto FS, Rosa JLG (2019) Depression biomarkers using non-invasive EEG: A review. Neurosci Biobehav Rev 105:83\u0026ndash;93\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarzan F, Vernet M, Shafi MMD, Rotenberg A, Daskalakis ZJ, Pascual-Leone A Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography. Front Neural Circuits [Internet]. 2016 Sep 22 [cited 2025 Mar 17];10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/neural-circuits/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/neural-circuits/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fncir.2016.00073/full\u003c/span\u003e\u003cspan address=\"10.3389/fncir.2016.00073/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTremblay S, Rogasch NC, Premoli I, Blumberger DM, Casarotto S, Chen R et al (2019) Clinical utility and prospective of TMS-EEG. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 130(5):802\u0026ndash;844\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHadas I, Sun Y, Lioumis P, Zomorrodi R, Jones B, Voineskos D et al (2019) Association of Repetitive Transcranial Magnetic Stimulation Treatment With Subgenual Cingulate Hyperactivity in Patients With Major Depressive Disorder. JAMA Netw Open 2(6):e195578\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIlmoniemi RJ, Virtanen J, Ruohonen J, Karhu J, Aronen HJ, N\u0026auml;\u0026auml;t\u0026auml;nen R et al (1997) Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity. NeuroReport 8(16):3537\u0026ndash;3540\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIlmoniemi RJ, Kicić D (2010) Methodology for combined TMS and EEG. Brain Topogr 22(4):233\u0026ndash;248\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMassimini M, Ferrarelli F, Huber R, Esser SK, Singh H, Tononi G (2005) Breakdown of Cortical Effective Connectivity During Sleep. Science 309(5744):2228\u0026ndash;2232\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzdemir RA, Tadayon E, Boucher P, Sun H, Momi D, Ganglberger W et al (2021) Cortical responses to noninvasive perturbations enable individual brain fingerprinting. Brain Stimulat 14(2):391\u0026ndash;403\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThut G, Pascual-Leone A (2010) Integrating TMS with EEG: How and what for? Brain Topogr 22(4):215\u0026ndash;218\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThut G, Pascual-Leone A (2010) A review of combined TMS-EEG studies to characterize lasting effects of repetitive TMS and assess their usefulness in cognitive and clinical neuroscience. Brain Topogr 22(4):219\u0026ndash;232\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarzan F (2024) Transcranial Magnetic Stimulation\u0026ndash;Electroencephalography for Biomarker Discovery in Psychiatry. Biol Psychiatry 95(6):564\u0026ndash;580\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin MX, Qin PP, Xia AWL, Kan RLD, Zhang BBB, Tang AHP et al (2024) Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies. Neurosci Biobehav Rev. ;162\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlooster D, Voetterl H, Baeken C, Arns M (2024) Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies. Biol Psychiatry 95(6):553\u0026ndash;563\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStrafella R, Chen R, Rajji TK, Blumberger DM, Voineskos D (2022) Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review. Front Hum Neurosci 16:940759\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026uuml;nemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE et al (2008) GRADE: Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 336(7653):1106\u0026ndash;1110\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAguayo-Albasini JL, Flores-Pastor B, Soria-Aledo VGRADE, System (2014) Classification of Quality of Evidence and Strength of Recommendation. Cir Esp Engl Ed 92(2):82\u0026ndash;88\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEbrahimzadeh E, Fayaz F, Rajabion L, Seraji M, Aflaki F, Hammoud A et al (2023) Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder. Front Syst Neurosci 17:919977\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEbrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H (2024) Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Res - Neuroimaging. ;337\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasanzadeh F, Mohebbi M, Rostami R (2019) Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. J Affect Disord 256:132\u0026ndash;142\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZangen A, Zibman S, Tendler A, Barnea-Ygael N, Alyagon U, Blumberger DM et al (2023) Pursuing personalized medicine for depression by targeting the lateral or medial prefrontal cortex with Deep TMS. JCI Insight. ;8(4)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVoetterl H, Miron JP, Mansouri F, Fox L, Hyde M, Blumberger DM et al (2021) Investigating EEG biomarkers of clinical response to low frequency rTMS in depression. J Affect Disord Rep 6:100250\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoelofs C, Krepel N, Corlier J, Carpenter L, Fitzgerald PB, Zj D et al Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium. Clin Neurophysiol Off J Int Fed Clin Neurophysiol [Internet]. 2021 Feb [cited 2024 Oct 7];132(2). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/33243617/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/33243617/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander M, Alagapan S, Lugo C, Mellin J, Lustenberger C, Rubinow D et al (2019) Double-blind, randomized pilot clinical trial targeting alpha oscillations with transcranial alternating current stimulation (tACS) for the treatment of major depressive disorder (MDD). Transl Psychiatry. ;9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGold MC, Yuan S, Tirrell E, Kronenberg EF, Kang JWD, Hindley L et al (2022) Large-scale EEG neural network changes in response to therapeutic TMS. Brain Stimulat 15(2):316\u0026ndash;325\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook IA, Wilson AC, Corlier J, Leuchter AF (2019) Brain Activity and Clinical Outcomes in Adults With Depression Treated With Synchronized Transcranial Magnetic Stimulation: An Exploratory Study. Neuromodulation J Int Neuromodulation Soc 22(8):894\u0026ndash;897\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodfrey K, Muthukumaraswamy SD, Stinear CM, Hoeh NR (2024) Resting-state EEG connectivity recorded before and after rTMS treatment in patients with treatment-resistant depression. Psychiatry Res - Neuroimaging. ;338\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorlier J, Wilson A, Hunter AM, Vince-Cruz N, Krantz D, Levitt J et al (2019) Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder. Cereb Cortex N Y NY 29(12):4958\u0026ndash;4967\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhami P, Moreno S, Croarkin PE, Blumberger DM, Daskalakis ZJ, Farzan F (2023) Baseline markers of cortical excitation and inhibition predict response to theta burst stimulation treatment for youth depression. Sci Rep. ;13(1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheen JZ, Mazza F, Momi D, Miron JP, Mansouri F, Russell T et al (2024) N100 as a response prediction biomarker for accelerated 1 Hz right DLPFC-rTMS in major depression. J Affect Disord 363:174\u0026ndash;181\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStrafella R, Momi D, Zomorrodi R, Lissemore J, Noda Y, Chen R et al (2023) Identifying Neurophysiological Markers of Intermittent Theta Burst Stimulation in Treatment-Resistant Depression Using Transcranial Magnetic Stimulation\u0026ndash;Electroencephalography. Biol Psychiatry 94(6):454\u0026ndash;465\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEshel N, Keller CJ, Wu W, Jiang J, Mills-Finnerty C, Huemer J et al (2020) Global connectivity and local excitability changes underlie antidepressant effects of repetitive transcranial magnetic stimulation. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 45(6):1018\u0026ndash;1025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVoineskos D, Blumberger DM, Rogasch NC, Zomorrodi R, Farzan F, Foussias G et al (2021) Neurophysiological effects of repetitive transcranial magnetic stimulation (rTMS) in treatment resistant depression. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 132(9):2306\u0026ndash;2316\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBailey NW, Hoy KE, Sullivan CM, Allman B, Rogasch NC, Daskalakis ZJ et al (2023) Concurrent transcranial magnetic stimulation and electroencephalography measures are associated with antidepressant response from rTMS treatment for depression. J Affect Disord Rep 14:100612\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFingelkurts AA, Fingelkurts AA, Ryts\u0026auml;l\u0026auml; H, Suominen K, Isomets\u0026auml; E, K\u0026auml;hk\u0026ouml;nen S (2007) Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Hum Brain Mapp 28(3):247\u0026ndash;261\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeuchter AF, Cook IA, Jin Y, Phillips B (2013) The relationship between brain oscillatory activity and therapeutic effectiveness of transcranial magnetic stimulation in the treatment of major depressive disorder. Front Hum Neurosci [Internet]. Feb 26 [cited 2024 Oct 25];7. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/human-neuroscience/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/human-neuroscience/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2013.00037/full\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2013.00037/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H et al (2007) Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biol Psychiatry 62(5):429\u0026ndash;437\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBruder GE, Stewart JW, Tenke CE, McGrath PJ, Leite P, Bhattacharya N et al (2001) Electroencephalographic and perceptual asymmetry differences between responders and nonresponders to an SSRI antidepressant22. Biol Psychiatry 49(5):416\u0026ndash;425\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBruder GE, Sedoruk JP, Stewart JW, McGrath PJ, Quitkin FM, Tenke CE (2008) Electroencephalographic Alpha Measures Predict Therapeutic Response to a Selective Serotonin Reuptake Inhibitor Antidepressant: Pre- and Post-Treatment Findings. Biol Psychiatry 63(12):1171\u0026ndash;1177\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnott V, Mahoney C, Kennedy S, Evans K (2000) Pre-treatment EEG and it\u0026rsquo;s relationship to depression severity and paroxetine treatment outcome. Pharmacopsychiatry 33(6):201\u0026ndash;205\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLebiecka K, Zuchowicz U, Wozniak-Kwasniewska A, Szekely D, Olejarczyk E, David O Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation. Front Physiol [Internet]. 2018 Sep 28 [cited 2024 Oct 25];9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/physiology/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/physiology/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2018.01385/full\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2018.01385/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuffin SC, Emory WH (1995) Neurometric Subgroups in Attentional and Affective Disorders and Their Association with Pharmacotherapeutic Outcome. Clin Electroencephalogr 26(2):76\u0026ndash;83\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArns M, Gordon E, Boutros NN (2017) EEG Abnormalities Are Associated With Poorer Depressive Symptom Outcomes With Escitalopram and Venlafaxine-XR, but Not Sertraline: Results From the Multicenter Randomized iSPOT-D Study. Clin EEG Neurosci 48(1):33\u0026ndash;40\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang H, Popov T, Jyl\u0026auml;nki P, Bi K, Yao Z, Lu Q et al (2016) Predictability of depression severity based on posterior alpha oscillations. Clin Neurophysiol 127(4):2108\u0026ndash;2114\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewson JJ, Thiagarajan TC EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Front Hum Neurosci [Internet]. 2019 Jan 9 [cited 2024 Oct 25];12. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/human-neuroscience/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/human-neuroscience/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnhum.2018.00521/full\u003c/span\u003e\u003cspan address=\"10.3389/fnhum.2018.00521/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWidge AS, Rodriguez CI, Carpenter LL, Kalin NH, McDonald W, Nemeroff CB (2019) EEG biomarkers for treatment response prediction in major depressive illness. Am J Psychiatry 176(1):82\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFingelkurts AA, Fingelkurts AA (2015) Altered Structure of Dynamic Electroencephalogram Oscillatory Pattern in Major Depression. Biol Psychiatry 77(12):1050\u0026ndash;1060\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZrenner B, Zrenner C, Gordon PC, Belardinelli P, McDermott EJ, Soekadar SR et al (2020) Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-triggered TMS. Brain Stimul Basic Transl Clin Res Neuromodulation 13(1):197\u0026ndash;205\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook IA, O\u0026rsquo;Hara R, Uijtdehaage SH, Mandelkern M, Leuchter AF (1998) Assessing the accuracy of topographic EEG mapping for determining local brain function. Electroencephalogr Clin Neurophysiol 107(6):408\u0026ndash;414\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSutton SK, Davidson RJ (1997) Prefrontal Brain Asymmetry: A Biological Substrate of the Behavioral Approach and Inhibition Systems. Psychol Sci 8(3):204\u0026ndash;210\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJacobs GD, Snyder D (1996) Frontal brain asymmetry predicts affective style in men. Behav Neurosci 110(1):3\u0026ndash;6\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchaffer CE, Davidson RJ, Saron C (1983) Frontal and parietal electroencephalogram asymmetry in depressed and nondepressed subjects. Biol Psychiatry 18(7):753\u0026ndash;762\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomarken AJ, Davidson RJ, Wheeler RE, Doss RC (1992) Individual differences in anterior brain asymmetry and fundamental dimensions of emotion. J Pers Soc Psychol 62(4):676\u0026ndash;687\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapousek I, Weiss EM, Schulter G, Fink A, Reiser EM, Lackner HK (2014) Prefrontal EEG alpha asymmetry changes while observing disaster happening to other people: Cardiac correlates and prediction of emotional impact. Biol Psychol 103:184\u0026ndash;194\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWheeler RE, Davidson RJ, Tomarken AJ (1993) Frontal brain asymmetry and emotional reactivity: A biological substrate of affective style. Psychophysiology 30(1):82\u0026ndash;89\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen JJ, Reznik SJ (2015) Frontal EEG asymmetry as a promising marker of depression vulnerability: summary and methodological considerations. Curr Opin Psychol 4:93\u0026ndash;97\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenriques JB, Davidson RJ (1990) Regional brain electrical asymmetries discriminate between previously depressed and healthy control subjects. J Abnorm Psychol 99(1):22\u0026ndash;31\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStewart JL, Coan JA, Towers DN, Allen JJB (2014) Resting and task-elicited prefrontal EEG alpha asymmetry in depression: Support for the capability model. Psychophysiology 51(5):446\u0026ndash;455\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalynker II, Cai J, Ongseng F, Finestone H, Dutta E, Serseni D (1998) Hypofrontality and negative symptoms in major depressive disorder. J Nucl Med Off Publ Soc Nucl Med 39(4):608\u0026ndash;612\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoenigs M, Grafman J (2009) The functional neuroanatomy of depression: Distinct roles for ventromedial and dorsolateral prefrontal cortex. Behav Brain Res 201(2):239\u0026ndash;243\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaxter LR (1989) Reduction of Prefrontal Cortex Glucose Metabolism Common to Three Types of Depression. Arch Gen Psychiatry 46(3):243\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaxter LR (1991) Pet Studies of Cerebral Function in Major Depression and Obsessive-CompulsiveDisorder: the Emerging Prefrontal Cortex Consensus. Ann Clin Psychiatry 3(2):103\u0026ndash;109\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorge MS, Ketter TA, Post RM (1994) Prefrontal cortex dysfunction in clinical depression. Depression 2(2):59\u0026ndash;72\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA et al (1999) Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry 156(5):675\u0026ndash;682\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBora E, Fornito A, Pantelis C, Y\u0026uuml;cel M (2012) Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies. J Affect Disord 138(1\u0026ndash;2):9\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGraterol P\u0026eacute;rez JA, Guder S, Choe C, un, Gerloff C, Schulz R (2022) Relationship Between Cortical Excitability Changes and Cortical Thickness in Subcortical Chronic Stroke. Front Neurol 13:802113\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFr\u0026ouml;hlich F (2015) Chapter 3 - Experiments and models of cortical oscillations as a target for noninvasive brain stimulation. In: Bestmann S, editor. Progress in Brain Research [Internet]. Elsevier; [cited 2024 Oct 25]. pp. 41\u0026ndash;73. (Computational Neurostimulation; vol. 222). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S0079612315001314\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S0079612315001314\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNotbohm A, Kurths J, Herrmann CS (2016) Modification of Brain Oscillations via Rhythmic Light Stimulation Provides Evidence for Entrainment but Not for Superposition of Event-Related Responses. Front Hum Neurosci 10:10\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArns M, Drinkenburg WH, Fitzgerald PB, Kenemans JL (2012) Neurophysiological predictors of non-response to rTMS in depression. Brain Stimulat 5(4):569\u0026ndash;576\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrepel N, Sack AT, Kenemans JL, Fitzgerald PB, Drinkenburg WH, Arns M (2018) Non-replication of neurophysiological predictors of non-response to rTMS in depression and neurophysiological data-sharing proposal. Brain Stimulat 11(3):639\u0026ndash;641\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePellicciari MC, Ponzo V, Caltagirone C, Koch G (2017) Restored Asymmetry of Prefrontal Cortical Oscillatory Activity after Bilateral Theta Burst Stimulation Treatment in a Patient with Major Depressive Disorder: A TMS-EEG Study. Brain Stimul Basic Transl Clin Res Neuromodulation 10(1):147\u0026ndash;149\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVanneste S, Ost J, Langguth B, De Ridder D (2014) TMS by double-cone coil prefrontal stimulation for medication resistant chronic depression: A case report. Neurocase 20(1):61\u0026ndash;68\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoshiyama D, Kirihara K, Usui K, Tada M, Fujioka M, Morita S et al (2020) Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation. J Affect Disord 265:416\u0026ndash;422\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeller W, Etienne MA, Miller GA (1995) Patterns of perceptual asymmetry in depression and anxiety: Implications for neuropsychological models of emotion and psychopathology. J Abnorm Psychol 104(2):327\u0026ndash;333\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorey-Lisle PK, Nash R, Stang P, Swindle R (2004) Response, partial response, and nonresponse in primary care treatment of depression. Arch Intern Med 164(11):1197\u0026ndash;1204\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook IA, Leuchter AF, Witte E, Abrams M, Uijtdehaage SH, Stubbeman W et al (1999) Neurophysiologic predictors of treatment response to fluoxetine in major depression. Psychiatry Res 85(3):263\u0026ndash;273\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlyagon U, Shahar H, Hadar A, Barnea-Ygael N, Lazarovits A, Shalev H et al (2020) Alleviation of ADHD symptoms by non-invasive right prefrontal stimulation is correlated with EEG activity. NeuroImage Clin 26:102206\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFitzgerald PJ, Watson BO (2018) Gamma oscillations as a biomarker for major depression: an emerging topic. Transl Psychiatry 8(1):1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoh SC, Kim JS, Kim S, Kim Y, Lee SH (2020) Frontal Alpha Asymmetry Moderated by Suicidal Ideation in Patients with Major Depressive Disorder: A Comparison with Healthy Individuals. Clin Psychopharmacol Neurosci 18(1):58\u0026ndash;66\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitoma R, Tamura S, Tateishi H, Mitsudo T, Tanabe I, Monji A et al (2022) Oscillatory brain network changes after transcranial magnetic stimulation treatment in patients with major depressive disorder. J Affect Disord Rep 7:100277\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHillebrand A, Tewarie P, van Dellen E, Yu M, Carbo EWS, Douw L et al (2016) Direction of information flow in large-scale resting-state networks is frequency-dependent. Proc Natl Acad Sci 113(14):3867\u0026ndash;3872\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScheeringa R, Bastiaansen MCM, Petersson KM, Oostenveld R, Norris DG, Hagoort P (2008) Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int J Psychophysiol 67(3):242\u0026ndash;251\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodfrey K, Muthukumaraswamy SD, Stinear CM, Hoeh N (2022) Decreased salience network fMRI functional connectivity following a course of rTMS for treatment-resistant depression. J Affect Disord 300:235\u0026ndash;242\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBritz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52(4):1162\u0026ndash;1170\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams LM (2017) 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 34(1):9\u0026ndash;24\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage 180:577\u0026ndash;593\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015) Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry 72(6):603\u0026ndash;611\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIb\u0026aacute;\u0026ntilde;ez-Molina AJ, Lozano V, Soriano MF, Aznarte JI, G\u0026oacute;mez-Ariza CJ, Bajo MT EEG Multiscale Complexity in Schizophrenia During Picture Naming. Front Physiol [Internet]. 2018 Sep 7 [cited 2024 Oct 31];9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/physiology/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/physiology/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2018.01213/full\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2018.01213/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriston KJ, Tononi G, Sporns O, Edelman GM (1995) Characterising the complexity of neuronal interactions. Hum Brain Mapp 3(4):302\u0026ndash;314\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakahashi T, Cho RY, Mizuno T, Kikuchi M, Murata T, Takahashi K et al (2010) Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. NeuroImage 51(1):173\u0026ndash;182\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGold BI, Bowers MB, Roth RH, Sweeney DW (1980) GABA levels in CSF of patients with psychiatric disorders. Am J Psychiatry 137(3):362\u0026ndash;364\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasler G, van der Veen JW, Tumonis T, Meyers N, Shen J, Drevets WC (2007) Reduced prefrontal glutamate/glutamine and gamma-aminobutyric acid levels in major depression determined using proton magnetic resonance spectroscopy. Arch Gen Psychiatry 64(2):193\u0026ndash;200\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrice RB, Shungu DC, Mao X, Nestadt P, Kelly C, Collins KA et al (2009) Amino acid neurotransmitters assessed by proton magnetic resonance spectroscopy: relationship to treatment resistance in major depressive disorder. Biol Psychiatry 65(9):792\u0026ndash;800\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026uuml;r RR, Draisma LWR, Wijnen JP, Boks MP, Koevoets MGJC, Jo\u0026euml;ls M et al (2016) Brain GABA levels across psychiatric disorders: A systematic literature review and meta-analysis of (1) H-MRS studies. Hum Brain Mapp 37(9):3337\u0026ndash;3352\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRajkowska G, O\u0026rsquo;Dwyer G, Teleki Z, Stockmeier CA, Miguel-Hidalgo JJ (2007) GABAergic neurons immunoreactive for calcium binding proteins are reduced in the prefrontal cortex in major depression. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 32(2):471\u0026ndash;482\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRogasch NC, Daskalakis ZJ, Fitzgerald PB (2015) Cortical inhibition of distinct mechanisms in the dorsolateral prefrontal cortex is related to working memory performance: A TMS\u0026ndash;EEG study. Cortex 64:68\u0026ndash;77\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRogasch NC, Fitzgerald PB (2013) Assessing cortical network properties using TMS\u0026ndash;EEG. Hum Brain Mapp 34(7):1652\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCanali P, Sferrazza Papa G, Casali AG, Schiena G, Fecchio M, Pigorini A et al (2014) Changes of cortical excitability as markers of antidepressant response in bipolar depression: preliminary data obtained by combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Bipolar Disord 16(8):809\u0026ndash;819\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelardinelli P, K\u0026ouml;nig F, Liang C, Premoli I, Desideri D, M\u0026uuml;ller-Dahlhaus F et al (2021) TMS-EEG signatures of glutamatergic neurotransmission in human cortex. Sci Rep 11(1):8159\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePremoli I, Castellanos N, Rivolta D, Belardinelli P, Bajo R, Zipser C et al (2014) TMS-EEG Signatures of GABAergic Neurotransmission in the Human Cortex. J Neurosci 34(16):5603\u0026ndash;5612\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKinjo M, Wada M, Nakajima S, Tsugawa S, Nakahara T, Blumberger DM et al (2021) Transcranial magnetic stimulation neurophysiology of patients with major depressive disorder: a systematic review and meta-analysis. Psychol Med 51(1):1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVoineskos D, Blumberger DM, Zomorrodi R, Rogasch NC, Farzan F, Foussias G et al (2019) Altered Transcranial Magnetic Stimulation-Electroencephalographic Markers of Inhibition and Excitation in the Dorsolateral Prefrontal Cortex in Major Depressive Disorder. Biol Psychiatry 85(6):477\u0026ndash;486\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng H Understanding data analysis aspects of TMS-EEG in clinical study: a mini review and a case study with open dataset. 2024 [cited 2024 Mar 19]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rgdoi.net/10.13140/RG.2.2.23310.37449\u003c/span\u003e\u003cspan address=\"https://rgdoi.net/10.13140/RG.2.2.23310.37449\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Hajnal B, Entz L, Fab\u0026oacute; D, Herrero JL, Mehta AD et al (2019) Intracortical Dynamics Underlying Repetitive Stimulation Predicts Changes in Network Connectivity. J Neurosci 39(31):6122\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeller CJ, Huang Y, Herrero JL, Fini ME, Du V, Lado FA et al (2018) Induction and Quantification of Excitability Changes in Human Cortical Networks. J Neurosci 38(23):5384\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreri F, Pasqualetti P, M\u0026auml;\u0026auml;tt\u0026auml; S, Ponzo D, Ferrarelli F, Tononi G et al (2011) Human brain connectivity during single and paired pulse transcranial magnetic stimulation. NeuroImage 54(1):90\u0026ndash;102\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedalla M, Barbas H (2012) The Anterior Cingulate Cortex May Enhance Inhibition of Lateral Prefrontal Cortex Via m2 Cholinergic Receptors at Dual Synaptic Sites. J Neurosci 32(44):15611\u0026ndash;15625\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappon D, den Boer T, Jordan C, Yu W, Lo A, LaGanke N et al (2022) Safety and Feasibility of Tele-Supervised Home-Based Transcranial Direct Current Stimulation for Major Depressive Disorder. Front Aging Neurosci 13:765370\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Med 18(3):e1003583\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Resting EEG outcomes in brain stimulation studies include the type of treatment and EEG measures, baseline findings (capturing data before treatment initiation or during the initial stages), and post-treatment findings (reflecting changes observed after treatment completion). These included power analysis (such as absolute and relative power, IAF as well as FAA), cordance and microstates. Additionally, various connectivity metrics, including envelope correlation and \u0026alpha;SC and coherence, were employed to assess inter-regional brain synchronization. As well as Non-linear features, such as PE, FD, LZC, CD, and KFD, were also included.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline significative findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange in measure post treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEbrahimzadeh et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis; Cordance; Non-Linear Features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026beta; power; Combined features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eGodfrey et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis; Connectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026theta; connectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026theta; connectivity\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eEbrahimzadeh et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis; Cordance; Non-Linear Features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026beta; power; \u0026darr;CD, \u0026darr;LZC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026beta; power\u0026darr;, CD\u0026darr;, LZC\u0026darr;, \u0026theta; cordance\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eZangen et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003edTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026alpha; left; \u0026uarr;Low-\u0026gamma; power; Combined features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eGold et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eMicrostates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eMS-2\u0026uarr;, MS-3\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eVoetterl et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026theta; relative power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026alpha; absolute power\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHasanzadeh et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis; Non-Linear Features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026beta; power; \u0026uarr;\u0026alpha; power; \u0026uarr;CD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCorlier et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eConnectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026alpha;SC\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRoelofs et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis (IAF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eIAF \u0026asymp; 10Hz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eAlexander et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003etACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eLeft \u0026alpha; power\u0026darr; (10 Hz tACS only)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eCook et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003esTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePower Analysis; Connectivity; Non-Linear Features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026alpha; coherence; \u0026uarr;\u0026beta; coherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eCSD\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 TMS-EEG outcomes for brain stimulation studies\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline significative findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange in measure post treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSheen et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eTEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026uarr;N100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eDhami et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eTBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eTEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026uarr;N45; \u0026darr;P60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eStrafella et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eiTBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eTEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026uarr;N100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eN100\u0026darr;; N45\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eBailey et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eTEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026uarr;sp \u0026darr;pp N100 Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eVoineskos et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eTEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eN45\u0026darr;; N100\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eEshel et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eTEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eP30\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eHadas et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003erTMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eConnectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSCS\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"506ac28b-dac0-40bb-aea6-04135aa28beb","identifier":"10.13039/100008666","name":"Fundació la Marató de TV3","awardNumber":"202211-30-31","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institut Guttmann","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eeg, tms, biomarkers, depression, nibs","lastPublishedDoi":"10.21203/rs.3.rs-7771697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7771697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMajor Depressive Disorder (MDD) is a prevalent and debilitating psychiatric condition with significant rates of treatment resistance. Non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), has emerged as a promising option for individuals unresponsive to pharmacological interventions. However, a substantial proportion of patients still fail to achieve meaningful clinical improvement, underscoring the need for reliable biomarkers to predict treatment response. Electroencephalography (EEG) and TMS-EEG have been increasingly explored as promising predictive tools due to their ability to assess cortical excitability, connectivity, and neuroplasticity. The evidence gathered from 18 high-quality studies highlights the relevance of EEG and TMS-EEG biomarkers in predicting outcomes of NIBS in MDD. Resting-state EEG studies emphasize the importance of spectral power alterations, alpha asymmetry, and connectivity patterns, while TMS-EEG studies underline the role of TMS-evoked potentials (TEPs), particularly the N100 and N45 components, in forecasting therapeutic response. While these findings suggest significant potential, methodological variability, small sample sizes, and differing stimulation protocols limit their immediate clinical translation. However, these biomarkers provide a solid foundation for implementing precision medicine. Prior EEG or TMS-EEG assessments can play a valuable role in guiding the personalization of NIBS treatment strategies. The systematic integration of these neurophysiological biomarkers into clinical practice could maximize therapeutic efficacy and reduce non-response rates, paving the way for more precise and effective interventions in depression treatment.\u003c/p\u003e","manuscriptTitle":"EEG Biomarkers for a Precision-Medicine Approach to Noninvasive Brain Stimulation for Major Depressive Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:45:06","doi":"10.21203/rs.3.rs-7771697/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c5bc276-ce98-48a4-b647-e9d032160f76","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55715843,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2025-10-06T11:45:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 11:45:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7771697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7771697","identity":"rs-7771697","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}