Changes in functional connectivity after theta-burst transcranial magnetic stimulation for post-traumatic stress disorder: a machine-learning study.

Intermittent theta burst stimulation (iTBS) is a novel treatment approach for post-traumatic stress disorder (PTSD), and recent neuroimaging work indicates that functional connectivity profiles may be able to identify those most likely to respond. However, prior work has relied on functional magnetic resonance imaging, which is expensive and difficult to scale. Alternatively, electroencephalography (EEG) represents a different approach that may be easier to implement in clinical practice. To this end, we acquired an 8-channel resting-state EEG signal on participants before (n = 47) and after (n = 43) randomized controlled trial of iTBS for PTSD (ten sessions, delivered at 80% of motor threshold, 1,800 pulses, to the right dorsolateral prefrontal cortex). We used a cross-validated support vector machine (SVM) to track changes in EEG functional connectivity after verum iTBS stimulation. We found that an SVM classifier was able to successfully separate patients who received active treatment vs. sham treatment, with statistically significant findings in the Delta band (1-4 Hz, p = 0.002). Using Delta coherence, the classifier was 75.0% accurate in detecting sham vs. active iTBS, and observed changes represented an increase in functional connectivity between midline central/occipital and a decrease between frontal and central regions. The primary limitations of this work are the sparse electrode system and a modest sample size. Our findings raise the possibility that EEG and machine learning may be combined to provide a window into mechanisms of action of TMS, with the potential that these approaches can inform the development of individualized treatment methods.

Laminar dependence of neuronal correlations in visual cortex.

Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30-50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.

Functional connectivity and tuning curves in populations of simultaneously recorded neurons.

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay function.

Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.

Examining the neural mechanisms of rTMS: a naturalistic pilot study of acute and serial effects in pharmacoresistant depression.

Introduction: Previous studies have demonstrated the effectiveness of therapeutic repetitive transcranial magnetic stimulation (rTMS) to treat pharmacoresistant depression. Nevertheless, these trials have primarily focused on the therapeutic and neurophysiological effects of rTMS following a long-term treatment course. Identifying brain-based biomarkers of early rTMS therapeutic response remains an important unanswered question. In this pilot study, we examined the effects of rTMS on individuals with pharmacoresistant depression using a graph-based method, called Functional Cortical Networks (FCN), and serial electroencephalography (EEG). We hypothesized that changes in brain activity would occur early in treatment course. Methods: A total of 15 patients with pharmacoresistant depression underwent five rTMS sessions (5Hz over the left dorsolateral prefrontal cortex, 120%MT, up to 4,000 pulses/session). Five participants received additional rTMS treatment, up to 40 sessions. Resting EEG activity was measured at baseline and following every five sessions, using 64-channel EEG, for 10 minutes with eyes closed. An FCN model was constructed using time-varying graphs and motif synchronization. The primary outcome was acute changes in weighted-node degree. Secondary outcomes included serial FFT-based power spectral analysis and changes in depressive symptoms measured by the 9-Item Patient Health Questionnaire (PHQ-9) and the 30-item Inventory of Depressive Symptoms-Self Report (IDS-SR). Results: We found a significant acute effect over the left posterior area after five sessions, as evidenced by an increase in weighted-node degree of 37,824.59 (95% CI, 468.20 to 75,180.98) and a marginal enhancement in the left frontal region (t (14) = 2.0820, p = 0.056). One-way repeated measures ANOVA indicated a significant decrease in absolute beta power over the left prefrontal cortex (F (7, 28) = 2.37, p = 0.048) following ten rTMS sessions. Furthermore, a significant clinical improvement was observed following five rTMS sessions on both PHQ-9 (t (14) = 2.7093, p = 0.017) and IDS-SR (t (14) = 2.5278, p = 0.024) and progressed along the treatment course. Discussion: Our findings suggest that FCN models and serial EEG may contribute to a deeper understanding of mechanisms underlying rTMS treatment. Additional research is required to investigate the acute and serial effects of rTMS in pharmacoresistant depression and assess whether early EEG changes could serve as predictors of therapeutic rTMS response.

Fronto-central resting-state 15-29Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder.

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre-to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder.

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5 Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre- to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Neural signature of the perceptual decision in the neural population responses of the inferior temporal cortex.

Rapid categorization of visual objects is critical for comprehending our complex visual world. The role of individual cortical neurons and neural populations in categorizing visual objects during passive vision has previously been studied. However, it is unclear whether and how perceptually guided behaviors affect the encoding of stimulus categories by neural population activity in the higher visual cortex. Here we studied the activity of the inferior temporal (IT) cortical neurons in macaque monkeys during both passive viewing and categorization of ambiguous body and object images. We found enhanced category information in the IT neural population activity during the correct, but not wrong, trials of the categorization task compared to the passive task. This encoding enhancement was task difficulty dependent with progressively larger values in trials with more ambiguous stimuli. Enhancement of IT neural population information for behaviorally relevant stimulus features suggests IT neural networks' involvement in perceptual decision-making behavior.

Feedforward and feedback interactions between visual cortical areas use different population activity patterns.

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.

Disentangling the flow of signals between populations of neurons.

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.

Repetitive Transcranial Magnetic Stimulation for Treatment-Resistant Depression: Recent Critical Advances in Patient Care.

PURPOSE: Transcranial magnetic stimulation (TMS) is an evidence-based treatment for pharmacoresistant major depressive disorder (MDD). In the last decade, the field has seen significant advances in the understanding and use of this new technology. This review aims to describe the large, randomized controlled studies leading to the modern use of rTMS for MDD. It also includes a special section briefly discussing the use of these technologies during the COVID-19 pandemic. RECENT FINDINGS: Several new approaches and technologies are emerging in this field, including novel approaches to reduce treatment time and potentially yield new approaches to optimize and maximize clinical outcomes. Of these, theta burst TMS now has evidence indicating it is non-inferior to standard TMS and provides significant advantages in administration. Recent studies also indicate that neuroimaging and related approaches may be able to improve TMS targeting methods and potentially identify those patients most likely to respond to stimulation. SUMMARY: While new data is promising, significant research remains to be done to individualize and optimize TMS procedures. Emerging new approaches, such as accelerated TMS and advanced targeting methods, require additional replication and demonstration of real-world clinical utility. Cautious administration of TMS during the pandemic is possible with careful attention to safety procedures.

Transcranial magnetic stimulation for post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder. While current treatment options are effective for some, many individuals fail to respond to first-line psychotherapies and pharmacotherapy. Transcranial magnetic stimulation (TMS) has emerged over the past several decades as a noninvasive neuromodulatory intervention for psychiatric disorders including depression, with mounting evidence for its safety, tolerability, and efficacy in treating PTSD. While several meta-analyses of TMS for PTSD have been published to date showing large effect sizes on PTSD overall, there is marked variability between studies, making it difficult to draw simple conclusions about how best to treat patients. The following review summarizes over 20 years of the existing literature on TMS as a PTSD treatment, and includes nine randomized controlled trials and many other prospective studies of TMS monotherapy, as well as five randomized controlled trials investigating TMS combined with psychotherapy. While the majority of studies utilize repetitive TMS targeted to the right dorsolateral prefrontal cortex (DLPFC) at low frequency (1 Hz) or high frequency (10 or 20 Hz), others have used alternative frequencies, targeted other regions (most commonly the left DLPFC), or trialed different stimulation protocols utilizing newer TMS modalities such as synchronized TMS and theta-burst TMS (TBS). Although it is encouraging that positive outcomes have been shown, there is a paucity of studies directly comparing available approaches. Biomarkers, such as functional imaging and electroencephalography, were seldomly incorporated yet remain crucial for advancing our knowledge of how to predict and monitor treatment response and for understanding mechanism of action of TMS in this population. Effects on PTSD are often sustained for up to 2-3 months, but more long-term studies are needed in order to understand and predict duration of response. In short, while TMS appears safe and effective for PTSD, important steps are needed to operationalize optimal approaches for patients suffering from this disorder.

One-year clinical outcomes following theta burst stimulation for post-traumatic stress disorder.

Theta burst transcranial magnetic stimulation (TBS) is a potential new treatment for post-traumatic stress disorder (PTSD). We previously reported active intermittent TBS (iTBS) was associated with superior clinical outcomes for up to 1-month, in a sample of fifty veterans with PTSD, using a crossover design. In that study, participants randomized to the active group received a total of 4-weeks of active iTBS, or 2-weeks if randomized to sham. Results were superior with greater exposure to active iTBS, which raised the question of whether observed effects persisted over the longer-term. This study reviewed naturalistic outcomes up to 1-year from study endpoint, to test the hypothesis that greater exposure to active iTBS would be associated with superior outcomes. The primary outcome measure was clinical relapse, defined as any serious adverse event (e.g., suicide, psychiatric hospitalization, etc.,) or need for retreatment with repetitive transcranial magnetic stimulation (rTMS). Forty-six (92%) of the initial study's intent-to-treat participants were included. Mean age was 51.0 ± 12.3 years and seven (15.2%) were female. The group originally randomized to active iTBS (4-weeks active iTBS) demonstrated superior outcomes at one year compared to those originally randomized to sham (2-weeks active iTBS); log-rank ChiSq = 5.871, df = 1, p = 0.015; OR = 3.50, 95% CI = 1.04-11.79. Mean days to relapse were 296.0 ± 22.1 in the 4-week group, and 182.0 ± 31.9 in the 2-week group. When used, rTMS retreatment was generally effective. Exploratory neuroimaging revealed default mode network connectivity was predictive of 1-year outcomes (corrected p < 0.05). In summary, greater accumulated exposure to active iTBS demonstrated clinically meaningful improvements in the year following stimulation, and default mode connectivity could be used to predict longer-term outcomes.

Mapping PTSD symptoms to brain networks: a machine learning study.

Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = 50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R2) for total PCL-5 score was 0.29 and the R2 for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, -0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p = 0.030) as well as intrusion and avoidance scores (p = 0.002 and p = 0.034). It was not able to predict cognition and arousal scores (p = 0.412 and p = 0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.

Differentiating Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features from Classic Papillary Thyroid Carcinoma: Analysis of Cytomorphologic Descriptions Using a Novel Machine-Learning Approach.

BACKGROUND: Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples. METHODS: Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs). RESULTS: SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance, P < 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC. CONCLUSIONS: This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis.

Transcranial Direct Current Stimulation in Psychiatry: What Psychiatrists Need to Know.

Transcranial direct current stimulation (tDCS) is emerging as a potential treatment for a host of neuropsychiatric disorders. Data appear to indicate that tDCS applied over frontal or prefrontal brain regions may reduce symptoms of major depression, yet results have been mixed. Early studies showed promise, but recent work failed to replicate earlier results. The decision whether to use tDCS is further affected by the complex regulatory environment; no tDCS devices are cleared by the U.S. Food and Drug Administration for clinical use. Older systems have grandfathered regulatory approval for treating mood, anxiety, and insomnia, although they have not demonstrated efficacy in rigorous trials. Furthermore, as the field of noninvasive brain stimulation advances, various side effects and contraindications are increasingly recognized. Over the last few years, research and consumer use of tDCS have outpaced education, thus providing little guidance for clinicians and trainees about how to understand tDCS. Therefore, this focused review includes those items psychiatric clinicians and trainees most need to understand tDCS, including basic electrical and neurophysiological principles, a brief review of efficacy data in major depressive disorder, and suggested guidelines about how to manage patients using tDCS.

Cortical Areas Interact through a Communication Subspace.

Most brain functions involve interactions among multiple, distinct areas or nuclei. For instance, visual processing in primates requires the appropriate relaying of signals across many distinct cortical areas. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Here we investigate how trial-to-trial fluctuations of population responses in primary visual cortex (V1) are related to simultaneously recorded population responses in area V2. Using dimensionality reduction methods, we find that V1-V2 interactions occur through a communication subspace: V2 fluctuations are related to a small subset of V1 population activity patterns, distinct from the largest fluctuations shared among neurons within V1. In contrast, interactions between subpopulations within V1 are less selective. We propose that the communication subspace may be a general, population-level mechanism by which activity can be selectively routed across brain areas.

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS action and developing biomarkers predicting response remain important goals. We applied a combination of machine learning and electroencephalography (EEG), testing whether machine learning analysis of EEG coherence would (1) predict clinical outcomes in individuals with comorbid MDD and PTSD, and (2) determine whether an individual had received a TMS course. METHODS: We collected resting-state 8-channel EEG before and after TMS (5 Hz to the left dorsolateral prefrontal cortex). We used Lasso regression and Support Vector Machine (SVM) to test the hypothesis that baseline EEG coherence predicted the outcome and to assess if EEG coherence changed after TMS. RESULTS: In our sample, clinical response to TMS were predictable based on pretreatment EEG coherence (n = 29). After treatment, 13/29 had more than 50% reduction in MDD self-report score 12/29 had more than 50% reduction in PTSD self-report score. For MDD, area under roc curve was for MDD was 0.83 (95% confidence interval 0.69-0.94) and for PTSD was 0.71 (95% confidence interval 0.54-0.87). SVM classifier was able to accurately assign EEG recordings to pre- and post-TMS treatment. The accuracy for Alpha, Beta, Theta and Delta bands was 75.4 ±â€¯1.5%, 77.4 ±â€¯1.4%, 73.8 ±â€¯1.5%, and 78.6 ±â€¯1.4%, respectively, all significantly better than chance (50%, p < 0.001). LIMITATION: Limitations of this work include lack of sham condition, modest sample size, and a sparse electrode array. Despite these methodological limitations, we found validated and clinically meaningful results. CONCLUSIONS: Machine learning successfully predicted non-response to TMS with high specificity, and identified pre- and post-TMS status using EEG coherence. This approach may provide mechanistic insights and may also become a clinically useful screening tool for TMS candidates.

Pilot Testing of Peak Alpha Frequency Stability During Repetitive Transcranial Magnetic Stimulation.

Over half of those diagnosed with post-traumatic stress disorder (PTSD) have comorbid major depressive disorder (MDD), and rates are even higher among military veterans. Transcranial magnetic stimulation (TMS) may be a safe and efficacious treatment for PTSD, both with and without comorbid MDD. Still, the mechanism of action of TMS is not fully understood, and it remains unclear which stimulation techniques (e.g., target regions, pulse strength/frequency, waveform) optimize treatment for these patients. Recent research indicated that a patient's unique individualized alpha frequency (IAF) may be used to guide brain stimulation treatment, and emerging data suggests that stimulation synchronized to the IAF may be efficacious for MDD. However, to our knowledge there are no studies to date that evaluate the stability of IAF over time in patients with comorbid PTSD and MDD. To this end, we used an eight-lead electroencephalography (EEG) system to record IAF before and after a course of TMS. Stimulation parameters were informed by prior studies of TMS for comorbid PTSD and MDD and included 5 Hz TMS to the left dorsolateral prefrontal cortex, at 120% of motor threshold, 3,000-4,000 pulses per session for up to 40 sessions. We tested whether IAF was changed with a course of TMS therapy and evaluated whether IAF predicted clinical outcomes. We observed no significant changes in IAF from baseline to post-treatment, and there was no relationship between IAF and clinical symptom change. These data demonstrate the stability of IAF with TMS and indicate its utility as a trait marker for future brain stimulation studies. This work does not support the use of IAF as predictor of clinical response to TMS as administered.