The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI.
Christoph KrausManfred KlöblMartin TikBastian AuerThomas VanicekNicole GeissbergerDaniela Melitta PfabiganAndreas HahnMichael WoletzKatharina PaulArkadiusz KomorowskiSiegfried KasperChristian WindischbergerClaus LammRupert LanzenbergerPublished in: Molecular psychiatry (2018)
Functional magnetic resonance imaging (fMRI) successfully disentangled neuronal pathophysiology of major depression (MD), but only a few fMRI studies have investigated correlates and predictors of remission. Moreover, most studies have used clinical outcome parameters from two time points, which do not optimally depict differential response times. Therefore, we aimed to detect neuronal correlates of response and remission in an antidepressant treatment study with 7 T fMRI, potentially harnessing advances in detection power and spatial specificity. Moreover, we modeled outcome parameters from multiple study visits during a 12-week antidepressant fMRI study in 26 acute (aMD) patients compared to 36 stable remitted (rMD) patients and 33 healthy control subjects (HC). During an electrical painful stimulation task, significantly higher baseline activity in aMD compared to HC and rMD in the medial thalamic nuclei of the pulvinar was detected (p = 0.004, FWE-corrected), which was reduced by treatment. Moreover, clinical response followed a sigmoid function with a plateau phase in the beginning, a rapid decline and a further plateau at treatment end. By modeling the dynamic speed of response with fMRI-data, perigenual anterior cingulate activity after treatment was significantly associated with antidepressant response (p < 0.001, FWE-corrected). Temporoparietal junction (TPJ) baseline activity significantly predicted non-remission after 2 antidepressant trials (p = 0.005, FWE-corrected). The results underline the importance of the medial thalamus, attention networks in MD and antidepressant treatment. Moreover, by using a sigmoid model, this study provides a novel method to analyze the dynamic nature of response and remission for future trials.
Keyphrases
- major depressive disorder
- magnetic resonance imaging
- functional connectivity
- end stage renal disease
- resting state
- ejection fraction
- chronic kidney disease
- clinical trial
- computed tomography
- rheumatoid arthritis
- machine learning
- intensive care unit
- magnetic resonance
- disease activity
- hepatitis b virus
- electronic health record
- blood brain barrier
- bipolar disorder
- deep brain stimulation
- brain injury
- respiratory failure