Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study

Predictive modeling of antidepressant efficacy based on cognitive neuropsychological theory

BACKGROUND

We aimed to develop a clinical predictive model based on the cognitive neuropsychological (CNP) theory and machine-learning to examine SSRI efficacy in the treatment of MDD.

METHODS

Baseline assessments including clinical symptoms (HAMD, HAMA, BDI, and TEPS scores), negative biases (NEO-PI-R-N and NCPBQ scores), sociodemographic characteristics (social support and SES), and a 5-min eye-opening resting-state EEG were completed by 69 participants with first-episode major depressive disorder (MDD) and 36 healthy controls. The clinical symptoms and negative bias were again assessed after an 8-week treatment of depression with selective serotonin reuptake inhibitors (SSRIs). A multi-modality machine-learning model was developed to predict the effectiveness of SSRI antidepressants.

RESULTS

At baseline, we observed significant differences between MDD patients and healthy controls in terms of social support, clinical symptoms, and negative bias characteristics (p < 0.001). A negative association was found (p < 0.05) between neuroticism and alpha asymmetry in both the central and central-parietal areas, as well as between negative cognitive processing bias and alpha asymmetry in the parietal region. Compared to responders, non-responders exhibited less negative cognitive processing bias and greater alpha asymmetry in both central and central-parietal regions. Importantly, we developed a multi-modality machine-learning model with 83 % specificity using the above salient features.

CONCLUSIONS

Research results support the CNP theory of depression treatment. To some extent, the multimodal clinical model constructed based on the CNP theory effectively predicted the efficacy of this treatment in this population.

LIMITATIONS

Small sample and only focus on the mechanisms of delayed-onset SSRI treatment.

Accelerated TMS - moving quickly into the future of depression treatment

Accelerated TMS is an emerging application of Transcranial Magnetic Stimulation (TMS) aimed to reduce treatment length and improve response time. Extant literature generally shows similar efficacy and safety profiles compared to the FDA-cleared protocols for TMS to treat major depressive disorder (MDD), yet accelerated TMS research remains at a very early stage in development. The few applied protocols have not been standardized and vary significantly across a set of core elements. In this review, we consider nine elements that include treatment parameters (i.e., frequency and inter-stimulation interval), cumulative exposure (i.e., number of treatment days, sessions per day, and pulses per session), individualized parameters (i.e., treatment target and dose), and brain state (i.e., context and concurrent treatments). Precisely which of these elements is critical and what parameters are most optimal for the treatment of MDD remains unclear. Other important considerations for accelerated TMS include durability of effect, safety profiles as doses increase over time, the possibility and advantage of individualized functional neuronavigation, use of biological readouts, and accessibility for patients most in need of the treatment. Overall, accelerated TMS appears to hold promise to reduce treatment time and achieve rapid reduction in depressive symptoms, but at this time significant work remains to be done. Rigorous clinical trials combining clinical outcomes and neuroscientific measures such as electroencephalogram, magnetic resonance imaging and e-field modeling are needed to define the future of accelerated TMS for MDD.

Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.

Serotonin 4 Receptor Brain Binding in Major Depressive Disorder and Association With Memory Dysfunction

Importance

The cerebral serotonin 4 (5-HT4) receptor is a promising novel target for treatment of major depressive disorder (MDD), and pharmacological stimulation of the 5-HT4 receptor has been associated with improved learning and memory in healthy individuals.

Objective

To map the neurobiological signatures of patients with untreated MDD compared with healthy controls and to examine the association between cerebral 5-HT4 receptor binding and cognitive functions in the depressed state.

Design, Setting, and Participants

This case-control study used baseline data from the NeuroPharm clinical depression trial in Denmark. Adult participants included antidepressant-free outpatients with a current moderate to severe depressive episode and healthy controls. All participants completed positron emission tomography (PET) scanning with [11C]SB207145 for quantification of brain 5-HT4 receptor binding, but only the patients underwent cognitive testing. Data analyses were performed from January 21, 2020, to April 22, 2022.

Main Outcomes and Measures

The main study outcome was the group difference in cerebral 5-HT4 receptor binding between patients with MDD and healthy controls. In addition, the association between 5-HT4 receptor binding and verbal memory performance in the patient group was tested. Other cognitive domains (working memory, reaction time, emotion recognition bias, and negative social emotions) were assessed as secondary outcomes.

Results

A total of 90 patients with untreated MDD (mean [SD] age, 27.1 [8.2] years; 64 women [71.1%]) and 91 healthy controls (mean [SD] age, 27.1 [8.0] years; 55 women [60.4%]) were included in the analysis. Patients with current MDD had significantly lower cerebral 5-HT4 receptor binding than healthy controls (-7.0%; 95% CI, -11.2 to -2.7; P = .002). In patients with MDD, there was a correlation between cerebral 5-HT4 receptor binding and verbal memory (r = 0.29; P = .02).

Conclusions and Relevance

Results of this study show that cerebral 5-HT4 receptor binding was lower in patients with MDD than in healthy controls and that the memory dysfunction in patients with MDD was associated with lower cerebral 5-HT4 receptor binding. The cerebral 5-HT4 receptor is a promising treatment target for memory dysfunction in patients with MDD.

Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort

BACKGROUND

Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.

METHODS

All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [¹¹C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.

DISCUSSION

The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.

TRIAL REGISTRATION

Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Association between the loudness dependence of auditory evoked potential, serotonergic neurotransmission and treatment outcome in patients with depression

Previous studies have suggested that the loudness dependence of auditory evoked potential (LDAEP) is associated with the effectiveness of antidepressant treatment in patients with major depressive disorders (MDD). Furthermore, both LDAEP and the cerebral serotonin 4 receptor (5-HT₄R) density is inversely related to brain serotonin levels. We included 84 patients with MDD and 22 healthy controls to examined the association between LDAEP and treatment response and its association with cerebral 5-HT₄R density. Participants underwent both EEG and 5-HT₄R neuroimaging with [¹¹C]SB207145 PET. Thirty-nine patients with MDD were re-examined after 8 weeks of treatment with selective serotonin reuptake inhibitors/serotonin noradrenaline reuptake inhibitor (SSRI/SNRI). We found that the cortical source of LDAEP was higher in untreated patients with MDD compared to healthy controls (p=0.03). Prior to SSRI/SNRI treatment, subsequent treatment responders had a negative association between LDAEP and depressive symptoms and a positive association between scalp LDAEP and symptom improvement at week 8. This was not found in source LDAEP. In healthy controls, we found a positive correlation between both scalp and source LDAEP and cerebral 5-HT₄R binding but that was not observed in patients with MDD. We did not see any changes in scalp and source LDAEP in response to SSRI/SNRI treatment. These results support a theoretical framework where both LDAEP and cerebral 5-HT₄R are indices of cerebral 5-HT levels in healthy individuals while this association seems to be disrupted in MDD. The combination of the two biomarkers may be useful for stratifying patients with MDD. Clinical Trials Registration:https://clinicaltrials.gov/ct2/show/NCT02869035?draw=1Registration number: NCT0286903.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BACKGROUND

Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS

We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS

We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION

We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

EEG-Update

Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.

Toward biophysical markers of depression vulnerability

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Generalizability of treatment outcome prediction in major depressive disorder using structural MRI: A NeuroPharm study

Brain morphology has been suggested to be predictive of drug treatment outcome in major depressive disorders (MDD). The current study aims at evaluating the performance of pretreatment structural brain magnetic resonance imaging (MRI) measures in predicting the outcome of a drug treatment of MDD in a large single-site cohort, and, importantly, to assess the generalizability of these findings in an independent cohort. The random forest, boosted trees, support vector machines and elastic net classifiers were evaluated in predicting treatment response and remission following an eight week drug treatment of MDD using structural brain measures derived with FastSurfer (FreeSurfer). Models were trained and tested within a nested cross-validation framework using the NeuroPharm dataset (n = 79, treatment: escitalopram); their generalizability was assessed using an independent clinical dataset, EMBARC (n = 64, treatment: sertraline). Prediction of antidepressant treatment response in the Neuropharm cohort was statistically significant for the random forest (p = 0.048), whereas none of the models could significantly predict remission. Furthermore, none of the models trained using the entire NeuroPharm dataset could significantly predict treatment outcome in the EMBARC dataset. Although our primary findings in the NeuroPharm cohort support some, but limited value in using pretreatment structural brain MRI to predict drug treatment outcome in MDD, the models did not generalize to an independent cohort suggesting limited clinical applicability. This study emphasizes the importance of assessing model generalizability for establishing clinical utility.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Stratified psychiatry: Tomorrow's precision psychiatry?

Here we review the paradigm-change from one-size-fits-all psychiatry to more personalized-psychiatry, where we distinguish between 'precision psychiatry' and 'stratified psychiatry'. Using examples in Depression and ADHD we argue that stratified psychiatry, using biomarkers to facilitate patients to best 'on-label' treatments, is a more realistic future for implementing biomarkers in clinical practice.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.