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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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.
Cohort
Prospective
iTBS Treatment
Other intervention20 iTBS sessions over 4 weeks, lDLPFC (BeamF3), 600Pulses.
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.
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.
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.
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.
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.
Depressive Symptoms assessed with MADRS
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
Depressive Symptoms assessed with BDI II
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
Depressive Symptoms assessed with HDRS
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
Psychosocial functioning assessed by GAF score
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
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