See Upcoming Public Defences

FULLY DIGITAL - NO PUBLIC : PhD Defence Nikita van der Vinne | EEG biomarkers in depression - Paving the way for stratified Psychiatry

EEG biomarkers in depression - Paving the way for stratified Psychiatry

Due to the COVID-19 crisis measures the PhD defence of Nikita van der Vinne will take place online without the presence of an audience.

The PhD defence can be followed by a live stream.

Nikitia van der Vinne is a PhD student in the research group Clinical Neurophysiology (CNPH). Her supervisor is M.J.A.M. van Putten from the Faculty of Science and Technology.

The symptoms of major depressive disorder (MDD) include a depressed mood most of the day (nearly every day), loss of interest in daily activities, weight gain or diet independent weight loss, fatigue, and feelings of worthlessness or guilt (as determined by the Diagnostic and Statistical Manual of Mental Disorders 5). MDD is commonly treated with antidepressant medication (AD), next to psychotherapies like cognitive behavioral therapy. Clinical efficacy rates unfortunately only reach 37% remission after a first AD prescription, with declining remission rates after each consecutive AD trial.

The primary aim of this dissertation was to investigate the value of neurophysiological biomarkers measured by the EEG, in the prognosis of treatment outcome in depression. Wishing to improve treatment outcome, we described our first steps towards the implementation of an EEG biomarker informed protocol. We zoomed in on detailed characteristics of biomarkers that proved to be promising. We attempted to utilize automated processes for fast, professionalized EEG assessments. We developed a protocol in which all knowledge on biomarker informed AD prescription was implemented, and performed a feasibility trial. We also compared protocol outcomes with the results of a control group.

Chapter 2 provides an up to date meta-analysis on the diagnostic value of the biomarker frontal alpha asymmetry (FAA) in MDD, and the evaluations of discrepancies in a large cross-sectional dataset. Sixteen studies were included (MDD: n = 1883, controls: n = 2161). The main result was a non-significant, negligible ES, demonstrating limited diagnostic value of FAA in MDD. The high degree of heterogeneity across studies indicates covariate influence, as was confirmed by cross-sectional analyses.

Chapter 3 explores the stability of biomarker FAA, that was demonstrated in earlier, smaller studies. In patients with MDD, FAA did not change significantly after eight weeks of treatment (n = 453, p = .234), nor did we find associations with age, sex, depression severity, or change in depression severity. We demonstrate that FAA is a stable trait, robust to time, state and pharmacological status. This confirms FAA stability.

Chapter 4 explores whether depressed patients with an abnormal EEG show a normalization of the EEG related to AD treatment and response, and whether such effect is drug specific. In fifty-seven patients with subclinical EEG abnormalities, the EEGs did not normalize significantly more with sertraline compared to the other ADs, escitalopram and venlafaxine. However, response rates in patients with normalized EEG taking sertraline were 5.2 times (significantly) larger than in subjects treated with escitalopram/venlafaxine.

Chapter 5 shows the utilization of computed features, to improve the assessment of abnormal EEGs in the depressed population, and compare them to our previous methods. The computed features CNN probability, the dominant frequency, and the tBSI all successfully showed good performance in identifying the specific and notably “light” abnormalities. A random forest model containing the combined features, trained on predicting treatment outcome per AD drug, did not reliably predict treatment outcome.

Chapter 6 evaluates the results from a first prospective feasibility trial. The EEG biomarkers abnormal EEG activity, alpha peak frequency, and FAA were prospectively used for EEG informed prescription of ADs. Seventy patients were stratified to AD based on their EEG biomarkers, 52 patients received AD treatment as usual. In general, both professionals and stratified patients were satisfied with the new protocol and practical implementation proved to be feasible, with better symptom improvement in patients who received EEG informed prescribed ADs using EEG biomarkers.

To our knowledge, this is the first attempt to elevate the treatment of depression through these biomarkers, which not only shows our stratification protocol is non-inferior: patients actually show a modest increase in symptom improvement. The proposed protocol therefore not only makes our methods easily translatable to clinical practice, it bears the promise of a small but much needed achievement of higher treatment standards, in a new form of neuropsychiatric health care for depression.