The purpose of the study is to develop a more personalized brain stimulation using repetitive transcranial magnetic stimulation or rTMS approach to treat major depressive disorder (MDD). The investigators had previously developed a personalized rTMS treatment by examining the effectiveness of different rTMS frequency from 5 to 18 Hertz (Hz). The optimal treatment frequency is termed resonance frequency and varies across individuals. There has not been a systematic method to identify the best stimulation frequency in an individual-specific way. In this project, the investigators will identify and compare 3 rTMS frequencies, all targeting the brain region called left dorsolateral prefrontal cortex (DLPFC): 1 that engages brain circuit connectivity the most- the investigators call this resonant frequency (RF)-max (RF-max), 1 that is the lowest ranked resonance frequency called RF-min and 1 that is standard of care treatment - rTMS of 10 Hz as a point of comparison.

This study is a multi-sites project that will be conducted at UCLA and Butler hospital. This study will enroll 84 participants with MDD over the course of 4.5 years.

Participants will undergo a brain imaging scan or magnetic resonance imaging (MRI), 3 electroencephalograms (or EEG, a measurement of electrical activity of the brain), to identify rTMS resonance frequencies (RFs), 3 sessions of different resonant frequencies of rTMS in combination with EEG, totaling up to 7 in person visits. Participation will take up to 4 weeks.

Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but response rates are highly variable across individuals. Clinical improvement with rTMS is believed to reflect engagement of a target mood-regulating circuit. This innovative study aims to enhance target circuit engagement through stimulation at an individual's optimal resonant frequency (RF). Circuit connectivity is maintained by oscillations at one or more RFs specific to that circuit for that individual. Personalizing stimulation frequency is an opportunity to optimize repetitive transcranial magnetic stimulation (rTMS) effectiveness, but there has been no method to identify optimal RF for rTMS treatment in a patient-specific way. The investigators developed a novel rTMS-electroencephalography (TMS-EEG) "interrogation" method that identifies RFs by examining the degree of resonance induced by stimulation frequencies from 5-18 Hz for a target circuit originating in left dorsolateral prefrontal cortex (DLPFC). Frequencies are then ranked based on resonance properties (degree to which they increase connectivity in the target circuit) and the highest-ranked frequency is identified. The pilot data suggest that MDD treatment at maximum RF (rTMSRF-MAX) rapidly modifies connectivity and leads to early symptom improvement, providing preliminary support for a RF-based personalized approach to rTMS.

This project will provide a mechanistic understanding and validation of the RF approach through three aims and hypotheses:

1. Establish reliability of a method for RF determination in a DLPFC-based target circuit. H1: An individual's RF median values and rankings will be stable across repeated measurements.
2. Demonstrate superior neurophysiologic engagement of the target circuit with rTMSRF-MAX stimulation. H2: rTMSRF-MAX will be associated with greater increases in connectivity in a left DLPFC-based target circuit than rTMS at the lowest ranked resonant frequency (rTMSRF-MIN).
3. Contrast the neuroanatomic distribution and degree of target circuit engagement by rTMS RF-MAX and rTMS RF-MIN stimulation. H3: Whole-brain EEG source localization will display a distinct distribution and degree of regional brain activation following rTMSRF-MAX and rTMSRF-MIN, with rTMSRF-MAX better engaging circuits previously reported to be related to antidepressant response.

rTMS typically is administered to the left dorsolateral prefrontal cortex (DLPFC) at a frequency of 10 Hz and in the naturalistic setting, response rates range from 41-56%. There is an urgent unmet need to improve outcomes of rTMS treatment for MDD, as the patients who receive it are the most chronically and severely ill with the highest risk for completed suicide.

MDD is increasingly conceptualized as a disorder of brain circuitry instead of individual brain regions Lack of clinical response to rTMS treatment is conceptualized as a failure to engage brain circuitry through the site of stimulation. It has become clear that the effects of rTMS propagate beyond the stimulation target and affect a network of regions with strong functional connections to the stimulation site. The therapeutic benefit of rTMS appears to be in part based upon the modulation of widespread network function. The investigators have previously demonstrated that clinical outcome of standard 10 Hz rTMS treatment was most robustly predicted by rTMS-induced changes in electroencephalographic (EEG) network connectivity rather than by changes in oscillatory power. Specifically, The investigators showed that the similarity of the frequency spectra in the alpha band (alpha spectral correlation, a connectivity metric) between the frontal and parietal regions following a single 3,000-pulse session of 10 Hz rTMS was a robust predictor of outcome following a full course of rTMS treatments.

Research to improve rTMS outcomes has focused on a "personalized medicine" approach in which rTMS stimulation parameters are adjusted to improve engagement of brain circuitry on an individual basis. One approach to enhancing circuit engagement has been to adjust the neuroanatomic region to which stimulation is administered. This approach is based upon findings that emphasize the importance of DLPFC connectivity to deeper structures (e.g., subgenual anterior cingulate) and meta-analyses of imaging data suggesting that distinct symptom biotypes of MDD may respond preferentially to different stimulation regions of interest (ROIs) The innovation of this proposal is to explore frequency of stimulation as the basis for personalized rTMS. MDD treatment has been reported to be effective using stimulation frequencies from 1-50 Hz, but some individuals may respond to treatment at one frequency but not another. While personalized ROI targeting is being systematically explored, individualized frequency targeting has remained largely unexplored. This is surprising because one immediate effect of rTMS is entrainment of oscillations in underlying cortex to match the frequency of stimulation. Changes in oscillations rapidly spread through brain networks to related brain regions and may be related to the therapeutic mechanism of rTMS Frequency of stimulation therefore constitutes an important parameter to consider when investigating ways to enhance rTMS efficacy for MDD.

The investigators have hypothesized that rTMS stimulation at an individual's RF may yield greater therapeutic benefit than stimulation at a single common frequency (i.e., 10 Hz) for all patients. The investigators developed a novel method that enables identification of RFs within a specific network for each individual. This method is based upon a TMS-EEG frequency interrogation paradigm that examines the resonance characteristics of frequencies up to 18 Hz in circuits connected to L DLPFC. The investigators used this TMS-EEG interrogation paradigm to determine the maximal RF (RFMAX) in 56 MDD patients prior to starting their first rTMS treatment. While 10 Hz was an RFMAX for some patients, 73% had RFMAX outside of the alpha band and thus distant from the 10 Hz stimulation protocol that comprises a common practice standard for rTMS treatment. This suggests that many patients could potentially benefit from an RF approach to treatment.

The pilot work suggests that RF stimulation induces significant changes in both MDD symptoms and circuit connectivity involving left DLPFC. In order to test the clinical significance of the RF rTMS (rTMS RF) treatment approach, The investigators examined the effects of RFMAX stimulation on early symptom improvement (ESI) in patients with MDD. ESI is commonly seen after 10 daily rTMS treatment sessions for MDD; our group and others have shown that it is associated with a high probability of full clinical response at the end of a full treatment course. In a prospective pilot study, 56 subjects received 10 (once-daily) sessions of rTMS at either maximum RF (rTMSRF-MAX) (N=23) or a frequency that was not the highest-ranked RF (non-RF) (N=33) .Connectivity in a left DLPFC-based circuit (as measured with EEG Spectral Correlation Coefficient [SCC]) showed significantly greater increases with rTMSRF-MAX than non-RF stimulation depressive symptoms (as measured by the Patient Health Questionnaire-9, PHQ-9), improved on average by 32%, significantly more than the group of patients who received standard rTMS treatment (13%, T-test p = 0.03). These preliminary findings suggest that the TMS-EEG interrogation method can be used to identify individual RFs that provide stronger engagement of a target circuit, and that doing so may lead to greater symptom improvement than conventional rTMS stimulation.

Additional preliminary data suggest that RF stimulation reliably activates a specific brain circuit. In the 23 subjects who underwent RF treatment, increased connectivity (measured by EEG spectral correlation coefficients) was seen between the stimulation site in left DLPFC (indicated by the F3 electrode) and electrodes in the bilateral prefrontal and left parietal regions. These regions constitute the empirically-derived target circuit that The investigators will examine in the second aim of our proposal.

Results of these pilot studies provide compelling evidence that interrogating the effects of different frequencies of stimulation at a single neuroanatomic region (left DLPFC) reveals physiological data about a target circuit that may be leveraged to personalize rTMS treatment through selection of a stimulation frequency that leads to early changes in connectivity and symptom improvement. Greater mechanistic understanding and validation of the RF approach is required, however, before it can be relied upon as a clinical method. Findings from this project could significantly advance the goal of a personalized medicine approach to rTMS treatment Study Design The investigators will identify the participant's individual resonant frequencies (RF) by a series of pulse trains between 5-18 Hz at 0.5 Hz intervals, at a total of 27 unique frequencies, delivered in a random order. The total procedure takes 35 minutes. The investigators will conduct this procedure 3 times over the course of 3 days, 1 procedure per day. The sessions will be separated by 48 hours. The investigators will then determine the optimal resonant frequency by ranking all interrogated frequencies based on their degree of elicited connectivity changes within the defined target circuit. The optimal RF (RFMAX) will be the stimulation frequency that ranks highest with regard to connectivity increase (RFMAX). RFMIN is the lowest ranked frequency. Participants will then undergo six rTMS "treatment" sessions: two at RFMAX, two at RFMIN, two at 10 Hz in random order. The two sessions at the same frequency will be administered on the same day, separated by 1h break. The sessions will be separated by 48 hours.

Inclusion Criteria:

• All subjects must be between 21-75 years of age.

• Must have confirmed diagnosis of severe *Major Depressive Disorder (single or recurrent episode) as confirmed by the Mini-International Neuropsychiatric Interview (MINI) or/and a score of 17 or above on the Hamilton Depression Rating Scale (HAMD).

• Failure to respond to a minimum of 2 trials of antidepressant medication.

• Failure to respond from at least two different agent classes.

• Accompanied by at least two evidence-based augmentation therapies (Benzodiazepines do not count).

• Must have a trial of psychotherapy known to be effective in the treatment of MDD of an adequate frequency and duration.

• Subjects are willing and able to adhere to the treatment schedule and required study visits.

  • TMS treatment is clinically-approved only for those suffering from Major Depressive Disorder as their primary diagnosis.

Exclusion Criteria:

• Are mentally or legally incapacitated, unable to give informed consent
• Have an infection or poor skin condition over the scalp where the device will be positioned.
• Have increased risk of seizure because of family history, stroke, or currently use medications that lead to increased risk for seizure.
• Diagnosis of acute or chronic psychotic symptoms or disorders (such as schizophrenia, schizophreniform or schizoaffective disorder) in the current depressive episode.
• Neurological conditions that include epilepsy, cerebrovascular disease, dementia, increased intracranial pressure, having a history of repetitive or severe head trauma, or with primary or secondary tumors in the central nervous system.
• Presence of an implanted magnetic-sensitive medical device present in the body scan, located less than or equal to 30 centimeters from the transcranial magnetic stimulation magnetic coil or other implanted metal items, including but not limited to a cochlear implant, implanted cardioverter defibrillator, pacemaker, vagus nerve stimulator, or metal aneurysm clips or coils, staples, or stents. (Note: Dental amalgam fillings are not affected by the magnetic field and are acceptable for use with transcranial magnetic stimulation and MRI.)
• Current diagnosis of substance use disorder.

Data will be collected from research participants, the final dataset will include demographic, MRI and EEG data, generating 480 datasets and totaling approximately 5 Tb in size. The provided data will be re-processed (following artifact removal). Raw data will be preprocessed for artifact removal using existing toolboxes (such as EEGLAB, fieldtrip) and custom-made code. To protect research participants' identities, all data will be de-identified before making available for sharing. All datasets that can be shared will be deposited in NIMH Data Archive (NDA). All custom code will also be shared using NDA.

Shared data generated from this project will be made available as soon as possible, and no later than at the time of publication or the end of the funding period, whichever comes first. The duration of preservation and sharing of the data will be a minimum of 5 years after the end of the funding period.