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NCT Number: NCT07420322

Real-World NeuroModulate: Investigating Non-Invasive Brain Stimulation for Neuropsychiatric Disorders at TUM

In this study, patients diagnosed with a depressive syndrome and with a clinical indication for treatment will receive an acute course of transcranial magnetic stimulation (TMS). The treatment consists of 20 TMS sessions administered over a period of four weeks (five sessions per week).

Magnetic resonance imaging (MRI) will be performed at four time points: prior to the start of treatment, after 10 treatment sessions, after completion of all 20 sessions, and at a three-month follow-up after the end of treatment. The MRI data will include different imaging modalities to assess treatment-related neurobiological changes.

The primary objective is to investigate changes in neuroimaging markers over the course of TMS treatment and to examine their associations with clinical response and behavioral as well as clinical outcome measures. Another key goal of the study is the identification of markers allowing for early prediction of treatment response to TMS and, additionally, for the prediction of relapse at follow-up.

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Key information

Conditions

Depressive Syndrom Depressive Disorder Mental Disorders Mood Disorders

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

University Hospital Technical University of Munich

München, 81675, Germany

Location status: Recruiting

Location contact

Ulrike Vogelmann, MD

CONTACT

[email protected]

+498941406882

About this study

This study investigates the neurobiological effects of standard intermittent theta burst stimulation (iTBS) applied to the left dorsolateral prefrontal cortex (DLPFC) in patients with a depressive syndrome undergoing routine clinical TMS treatment. The study follows a prospective, naturalistic longitudinal design and focuses on the association between iTBS-induced clinical change and repeated multimodal magnetic resonance imaging (MRI) measurements. The primary scientific objective is the identification of imaging-correlates of treatment response, as well as the identification of biomarkers that allow for early prediction of treatment response and later relapse following iTBS.

Patients who receive a clinical indication for TMS as part of routine psychiatric care undergo an acute treatment course consisting of 20 iTBS sessions administered over four consecutive weeks with five sessions per week. The treatment protocol is fixed to standard intermittent theta burst stimulation applied over the left DLPFC. The clinical decision to initiate TMS is made independently of study participation.

The severity of depressive symptoms is assessed at baseline (prior to the first session), as well as after two weeks and after four weeks of treatment, using both self-report and clinician-rated scales (HDRS and MADRS, as well as the BDI). Response is defined as at least a 50% reduction in symptoms, operationalized as a minimum 50% decrease in the respective scale scores. The MADRS is used to classify participants as responders versus non-responders. In addition, sociodemographic data, personality characteristics (degree of sensation-seeking personality), and history of traumatization (CTQ) are collected at baseline.

Prior to treatment initiation, the individual resting motor threshold (rMT) is determined by stimulation of the primary motor cortex, using either visual detection of motor evoked responses or surface electromyography. The stimulation intensity for iTBS is set at 120 % of the individual rMT. In cases where this intensity is not tolerated, e. g. due to pain, the intensity will be reduced to 80% of the individual rMT.

The iTBS protocol consists of bursts of three pulses at 50 Hz repeated at intervals of 200 ms (5 Hz). Each train lasts 2 seconds and is followed by an 8-second inter-train interval. One iTBS session has a total duration of 3 minutes and 9 seconds and comprises 600 pulses. One single iTBS session is delivered per treatment day throughout the four-week acute stimulation phase. No accelerated, high-intensity, or multi-session-per-day protocols are applied.

The stimulation target, the left DLPFC, is localized by a standardized scalp-based localization approach using the BeamF3 algorithm.

Multimodal MRI is acquired at four predefined time points: prior to the start of iTBS (baseline), after 10 stimulation sessions (mid-treatment), after completion of all 20 stimulation sessions (post-treatment), and at a three-month follow-up after the end of the acute stimulation phase. All MRI scans are performed on 3 Tesla clinical MRI systems. The total scan duration per session is approximately 60 minutes. No intravenous contrast agents are administered.

The MRI protocol includes structural, diffusion, perfusion, relaxometry and functional MR imaging. The initial scan also includes a T2* and fluid-attenuated inversion recovery (FLAIR) sequence to detect potential clinically relevant pathologies.

Structural MRI comprises high-resolution T1-weighted sequences for anatomical reference, cortical thickness and volumetric analyses, and neuronavigation.

Multi-modal relaxometry will be performed by acquiring 6-echo proton density (PD), magnetization transfer saturation (MTsat) and T1 scans, combined with B1 field mapping. From these scans, quantitative tissue maps for R1, R2*, PD and MTsat will be generated using the hMRI toolbox.

Diffusion-weighted imaging (DWI) will be performed to characterize white matter microstructure and structural connectivity. The employed sequence consists of 60 gradient directions at b=2000 s/mm², 30 gradient directions a b=711 s/mm² and 6 interleaved b0 volumes. From these scans, we will generate both microstructural parameter maps using the Neurite Orientation Dispersion and Density Imaging (NODDI) framework as well as diffusion kurtosis imaging (DKI). To investigate connectivity, we will employ constrained spherical deconvolution to generate whole-brain tractography. We will use commonly employed cortical parcellations such as the Destrieux atlas and the Human Connectome Project Multi-Modal Parcellation (HCP-MMP) to generate connectivity matrices. These will then be analyzed using network-based statistics. Furthermore, we will directly analyze fibre orientation distribution functions in a fixel-based approach.

Perfusion imaging is obtained using arterial spin labeling (ASL). This non-invasive technique allows quantification of regional cerebral blood flow (CBF) without the use of contrast agents and enables assessment of iTBS-related changes in cerebral perfusion across cortical and subcortical regions. Specifically, we are employing a multi-delay, hadamard-encoded 3D pseudo-continuous ASL sequence with voxel size of 3 mm isotropic. This sequence further enables measuring the T2 signal progression of labelled protons. From the imaging data, we will generate quantitative CBF maps and arterial transit time maps. We will further use modeling of T2 signal progression to generate blood-brain barrier integrity maps.

Functional MRI is acquired using blood-oxygenation-level-dependent (BOLD) contrast during resting-state conditions. Participants are instructed to remain still, stay awake, and not engage in any specific task. Resting-state fMRI is used to quantify intrinsic functional connectivity within large-scale brain networks, with a particular focus on prefrontal-limbic circuits, default mode network connectivity, salience network dynamics, and frontoparietal control networks. We will generate respective tissue maps quantifying the degree of voxel-wise correlation with network activity for each network. Additionally, we will generate regional homogeneity (ReHo) and amplitude of low frequency fluctuations (ALFF) maps. We will further generate functional connectome matrices using similar parcellations as specified for the DWI approach.

This longitudinal multimodal imaging approach allows between- and within-subject characterization of neurobiological trajectories across the acute iTBS phase and into the follow-up period. By combining repeated structural, diffusion, perfusion, and functional MRI, the study aims to identify neurobiological signatures of iTBS response, mechanisms of action at the network level, and potential predictors of treatment outcomes.

We plan to enroll 100 patients. An interim analysis is planned after at least 60 patients enrolled.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Age 18 years or older

Clinical indication for non-invasive brain stimulation due to a depressive episode (e.g., unipolar or bipolar depression) or predominant depression-associated symptoms (e.g., negative symptoms in schizophrenia-spectrum disorders)

Insufficient response to first-line treatments and/or patient preference for brain stimulation treatment

Capacity and willingness to provide written informed consent

Exclusion criteria

Acute suicidal risk

Presence of a significant neurological disorder or condition, including:

Increased intracranial pressure

Space-occupying brain lesions

History of cerebrovascular accident within the last 12 months

Incidental structural brain abnormalities on MRI requiring further diagnostic clarification

For TMS specifically:

Non-removable ferromagnetic implants in or near the stimulation site (e.g., cochlear implants, implanted neurostimulators, metallic clips or nails)

Other treatment-relevant ferromagnetic implants (e.g., cardiac pacemakers)

Treatment and study plan

iTBS Treatment

Other

20 iTBS sessions over 4 weeks, lDLPFC (BeamF3), 600Pulses.

Primary outcomes

  1. Difference in imaging parameter trajectory between Responders and Non-Responders across multi-modal Tissue Parameter Maps.

    Time frame: From enrollment to the end of treatment (4 weeks).

    We aim to identify an imaging-based correlate for treatment response. Therefore, we will compare the dynamic of tissue parameter maps (TPMs) between Responders and Non-Responders.

    We will realize this by employing a voxel-wise, flexible factorial statistical framework with two main independent variables, TIME and RESPONDER. TIME will reflect the timepoint of the scan (before treatment, during treatment, after treatment). RESPONDER will denote whether a participant is a reponder or not. The responder status will be defined via the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) before and after treatment (cutoff at least 50% MADRS reduction).

    The main outcome will be generated by testing for the interaction effect of TIME and RESPONDER. This will test for voxel values in which the longitudinal change is different between responders and non-responders.

    This statistical framework will be the main framework for all modalities with voxel-based outcomes.

Secondary outcomes

  1. Difference in connectome trajectory between Responders and Non-Responders across multi-modal connectomes.

    Time frame: From enrollment to the end of treatment (4 weeks).

    We aim to identify an imaging-based correlate for treatment response. Therefore, we will compare the dynamic of connectivity matrices between Responders and Non-Responders. We will realize this by employing a network-based statistical framework with two main independent variables, TIME and RESPONDER. TIME will reflect the timepoint of the scan (before treatment, during treatment, after treatment). RESPONDER will denote whether a participant is a reponder or not. The responder status will be defined via the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) before and after treatment (cutoff at least 50% MADRS reduction). The main outcome will be generated by testing for the interaction effect of TIME and RESPONDER. This will test for network components and graph metrics in which the longitudinal change is different between responders and non-responders. This statistical framework will apply to both functional and structural networks.

  2. Prediction of Responder Status using Structural and Functional Networks at Baseline

    Time frame: Data generated at enrollment.

    We aim to identify a way to predict treatment response using imaging-derived structural and functional networks. Therefore, we will compare the baseline connectivity matrices between Responders and Non-Responders to investigate whether the strength of specific network components predict later treatment response.

    We will realize this by employing a network-based statistical framework with one main independent variable, RESPONDER. RESPONDER will denote whether a participant is a reponder or not. The responder status will be defined via the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) before and after treatment (cutoff at least 50% MADRS reduction).

    The main outcome will be generated by testing for differences in network components and graph metrics between the two levels of RESPONDER. This basic statistical framework will be the same for both structural and functional networks. We will furthermore employ machine-learning-based classifier models.

  3. Prediction of Responder Status using Structural and Functional Networks at Baseline and Mid-Therapy

    Time frame: Enrollment to two weeks after therapy start.

    We aim to identify a way to predict treatment response using imaging-derived structural and functional networks. Therefore, we will compare early changes in connectomes from baseline connectivity matrices to the mid-therapy point (after two weeks) between Responders and Non-Responders. We will realize this by employing a network-based statistical framework with two main independent variables, TIME and RESPONDER. TIME denotes whether a scan is from baseline or the mid-therapy point, RESPONDER will denote whether a participant is a responder or not. The responder status will be defined via the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) before and after treatment (cutoff at least 50% MADRS reduction). The main outcome will be generated by testing for the interaction of TIME and RESPONDER on network components and graph metrics. We will furthermore employ machine-learning-based classifier models.

  4. Prediction of Responder Status using Tissue Parameter Maps at Baseline

    Time frame: Data generated at enrollment.

    We aim to identify a way to predict treatment response using imaging-derived multimodal tissue parameter maps (TPMs). Therefore, we will compare the baseline TPMs between Responders and Non-Responders to investigate whether the regional voxel values predict later treatment response.

    We will realize this by employing a voxel-based statistical framework with one main independent variable, RESPONDER. RESPONDER will denote whether a participant is a reponder or not. The responder status will be defined via the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) before and after treatment (cutoff at least 50% MADRS reduction).

    The main outcome will be generated by testing for differences in voxel values between the two levels of RESPONDER. This basic statistical framework will be the same across all modalities of TPMs.

  5. Prediction of Responder Status using Tissue Parameter Maps at Baseline and Mid-Therapy

    Time frame: Enrollment to two weeks after therapy start.

    We aim to identify a way to predict treatment response using multi-modal tissue parameter maps (TPMs). Therefore, we will compare early changes in TPMs from baseline to the mid-therapy point (after two weeks) between Responders and Non-Responders. We will realize this by employing a voxel-based statistical framework with two main independent variables, TIME and RESPONDER. TIME denotes whether a scan is from baseline or the mid-therapy point, RESPONDER will denote whether a participant is a responder or not. The responder status will be defined via the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) before and after treatment (cutoff at least 50% MADRS reduction). The main outcome will be generated by testing for the interaction of TIME and RESPONDER on TPM values. This basic statistical framework will be the same for both structural and functional networks.

Other outcomes

  1. Depressive Symptoms assessed with MADRS

    Time frame: MADRS: Measured at baseline, week two, week four during treatment phase and 3 months after the acute treatment phase.

    Major Depression psychiatric symptoms assessed with the Montgomery Asberg Depression Rating Scale (MADRS). MADRS values are interpreted as follows: 0-6 = No depression, 07-19 = mild, 20-34 = moderate, 35-60 = severe.

    Outcome is the Difference in MADRS Score from baseline to post-treatment visit at week four and at follow-up as well as to mid-treatment at week 2

  2. Depressive Symptoms assessed with BDI II

    Time frame: BDI II: Measured at baseline, week two, week four during treatment phase and 3 months after the acute treatment phase.

    Major Depression psychiatric symptoms assessed with the Beck Depression Inventory (BDI-II). Outcome values are interpreted as follows: 0-13 = Minimal depression, 14-19 = Mild depression, 20-28 = Moderate depression, 29-63 = Severe depression

  3. Depressive Symptoms assessed with HDRS

    Time frame: Measured at baseline, week two, week four during treatment phase and 3 months after the acute treatment phase.

    Major Depression psychiatric symptoms assessed with the Hamilton Depression Rating Scale (HADRS). Outcome values are interpreted as follows: 0-7 = No Depression, 08-16 = mild, 17-23 = moderate, ≥ 24 = severe

  4. Psychosocial functioning assessed by GAF score

    Time frame: Measured at baseline, week two, week four during treatment phase and 3 months after the acute treatment phase.

    Global psychosocial functioning will be assessed using the Global Assessment of Functioning (GAF) scale. Outcome values are interpreted as follows:

    91-100 Excellent No symptoms; superior functioning 81-90 Good Minimal symptoms, generally doing well 71-80 Mild Some mild symptoms or difficulties 61-70 Moderate Mild symptoms but generally functioning 51-60 Moderate to serious Moderate symptoms or difficulty in functioning 41-50 Serious Serious symptoms or impairments 31-40 Major impairment Impairment in several areas (e.g., work, school) 21-30 Severe Behavior influenced by delusions, serious communication issues 11-20 Very severe Danger to self/others, gross communication issues 1-10 Extreme Persistent danger of severely hurting self/others 0 Not assessable Insufficient information

  5. Clinical Global Impression assessed by CGI-S

    Time frame: Measured at baseline, week two, week four during treatment phase and 3 months after the acute treatment phase.

    Clinical Global Impression - Severity scale (CGI-S). Outcome values are interpreted as follows:

    • - Normal, not at all ill
    • - Borderline mentally ill
    • - Mildly ill
    • - Moderately ill
    • - Markedly ill
    • - Severely ill
    • - Among the most extremely ill patients

  6. Correlation of Sensation Seeking Personality and Treatment Respose

    Time frame: At baseline and after acute treatment phase (week four)

    The Sensation Seeking Scale is a questionnaire-based instrument that assesses the individual need for sensation (stimulation). The questionnaire is administered at baseline and yields a total score. Subsequently, a potential correlation between treatment response (reduction of depressive symptoms as measured by the MADRS, HDRS, and BDI) and the degree of sensation-seeking personality is examined.

Sponsors and collaborators

Lead sponsor

Technical University of Munich

Other

Registry information

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Feb 19, 2026
Registry last updated
Feb 19, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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