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Depressive SyndromNCT07420322

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

An observational study of Depressive Syndrom, run by Technical University of Munich at 1 site in Germany.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record
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Trial summary

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). Read more about this study

Who is it for?
Ages 18 years and olderHealthy volunteers can take part
Conditions the study is for
Depressive Syndrom
What is being tested?
iTBS Treatment
How many people
100 participants
Where
1 siteGermany
Study dates
15 Aug 2024to 15 Aug 2026
How long will I take part?
4 Months
Phase
Observational
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • Healthy volunteers accepted
  • Adult, Older adult

Study population: The study population consists of adult patients receiving clinical treatment with non-invasive brain stimulation for depressive symptoms within routine inpatient or outpatient psychiatric care. Participants are recruited through the clinical brain stimulation service and include individuals with a primary depressive episode (unipolar or bipolar) or predominant depression-associated symptoms within other psychiatric disorders.

Who may be able to join

  • 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

Who cannot join

  • 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
Show all 9Show fewer
  • 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)

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Germany1 site, 1 recruiting
  • University Hospital Technical University of Munich

    München, 81675

    Ulrike Vogelmann, MD, +498941406882, ulrike.vogelmann@mri.tum.de

    Recruiting

Other trials in Germany

About this study

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.

Read the detailed description

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.

Being tested
iTBS Treatment
Keywords
Depressive Disorder, TMS, rTMS, iTBS, MRI
Medical subject headings
Depressive Disorder

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • iTBS TreatmentOther

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

Study groups in NCT07420322
Study groupRoleWhat they receive
Patients with depressive Syndrom receiving regular iTBS treatmentPatients with depressive Syndrom receiving regular iTBS treatment over 4 weeks, no relevant personality disorder
Receives: Other: iTBS Treatment

What is being measured

The primary outcome is the main question the study is designed to answer.

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

    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.

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

Secondary outcomes (5)
  1. Difference in connectome trajectory between Responders and Non-Responders across multi-modal connectomes.

    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.

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

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

    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.

    Measured: Data generated at enrollment.

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

    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.

    Measured: Enrollment to two weeks after therapy start.

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

    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.

    Measured: Data generated at enrollment.

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

    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.

    Measured: Enrollment to two weeks after therapy start.

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted19 Feb 2026
  2. Study start15 Aug 2024
  3. Primary completion15 Jul 2026 (estimated)
  4. Study completion15 Aug 2026 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
100 participants (estimated)
Target duration
4 Months
IDs, dates and publications
ClinicalTrials.gov ID
NCT07420322
Sponsor's protocol ID
2024-220_3-S-CB
Status last verified
Feb 2026
First submitted
6 Jan 2026
Last updated
19 Feb 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No

Questions about this trial

What is this clinical trial studying?

An observational study of Depressive Syndrom, run by Technical University of Munich at 1 site in Germany.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 4 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: looking for participants now, as of the sponsor's last update on 19 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

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).). It is what the study was designed to measure, not a finding.

Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Feb 2026.

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