Use of clinical neurophysiology for the selection of medication in the treatment of major depressive disorder: the state of the evidence

Affective disorders and the loudness dependence of the auditory evoked potential: Serotonin and beyond

Identifying additional noninvasive biomarkers for affective disorders, such as unipolar major depressive disorder (MDD) and bipolar disorder (BD), could aid in the diagnosis and treatment of these prevalent and debilitating neuropsychiatric conditions. One such candidate biomarker is the loudness dependence of the auditory evoked potential (LDAEP), an event-related potential that measures responsiveness of the auditory cortex to different intensities of sound. The LDAEP has been associated with MDD and BD, including therapeutic response to particular classes of antidepressant drugs, while also correlating with several other neuropsychiatric disorders. It has been suggested that increased values of the LDAEP indicate low central serotonergic neurotransmission, further implicating this EEG measure in depression. Here, we briefly review the literature on the LDAEP in affective disorders, including its association with serotonergic signaling, as well as with that of other neurotransmitters such as dopamine. We summarize key findings on the LDAEP and the genetics of these neurotransmitters, as well as prediction of response to particular classes of antidepressants in MDD, including SSRIs versus noradrenergic agents. The possible relationship between this EEG measure and suicidality is addressed. We also briefly analyze acute pharmacologic studies of serotonin and/or dopamine precursor depletion and the LDAEP. In conclusion, the existing literature suggests that serotonin and norepinephrine may modulate the LDAEP in an opposing manner, and that this event-related marker may be of use in predicting response to chronic treatment with particular pharmacologic agents in the context of affective disorders, such as MDD and BD, including in the presence of suicidality.

Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study

We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.

Relationship of loudness-dependent auditory evoked potentials with change-related cortical responses

Previous studies have suggested that change-related cortical responses are phenomena similar to the onset response and could be applied to the loudness dependence of auditory evoked potential (LDAEP) paradigm. In the present study, we examined the relationship between LDAEP and the change-related response using electroencephalography findings in 50 healthy subjects. There were five conditions (55, 65, 75, 85, and 95 dB) for LDAEP and five similar conditions (abrupt sound pressure increase from 70 to 75, 80, 85, 90, and 95 dB) for the change-related response. Both the onset and abrupt sound pressure increase evoked a triphasic response with peaks at approximately 50 (P50), 100 (N100), and 200 (P200) ms. We calculated the peak-to-peak amplitudes for P50/N100 and N100/P200. Medians and slopes for P50/N100 and N100/P200 amplitudes were calculated and compared between the two measures. Results revealed a significant correlation for both the slope and median for P50/N100 (r = 0.36, 0.37, p = 1.0 × 10⁻², 7.9 × 10⁻³), N100/P200 (r = 0.40, 0.34, p = 4.0 × 10⁻³, 1.6 × 10⁻²), and P50/N100/P200 (r = 0.36, 0.35, p = 1.0 × 10⁻², 1.3 × 10⁻²). These results suggested that the change-related response and LDAEP shared generation mechanisms at least partially.

Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls: A narrative review

This narrative review brings together the findings regarding the differences in the auditory event-related potentials (ERPs) between patients with depressive disorder and non-depressed control subjects. These studies' results can inform us of the possible alterations in sensory-cognitive processing in depressive disorders and the potential of using these ERPs in clinical applications. Auditory P3, mismatch negativity (MMN) and loudness dependence of auditory evoked potentials (LDAEP) were the subjects of the investigation. A search in PubMed yielded 84 studies. The findings of the reviewed studies were not highly consistent, but some patterns could be identified. For auditory P3b, the common findings were attenuated amplitude and prolonged latency among depressed patients. Regarding auditory MMN, especially the amplitude of duration deviance MMN was commonly attenuated, and the amplitude of frequency deviance MMN was increased in depressed patients. In LDAEP studies, generally, no differences between depressed patients and non-depressed controls were reported, although some group differences concerning specific depression subtypes were found. This review posits that future research should investigate whether certain stimulus conditions are particularly efficient at separating depressed and non-depressed participant groups. Future studies should contrast responses in different subpopulations of depressed patients, as well as different clinical groups (e.g., depressive disorder and anxiety disorder patients), to investigate the specificity of the auditory ERP alterations for depressive disorders.

Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy

The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.

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.

ADHD: Reviewing the Causes and Evaluating Solutions

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder in which patients present inattention, hyperactivity, and impulsivity. The etiology of this condition is diverse, including environmental factors and the presence of variants of some genes. However, a great diversity exists among patients regarding the presence of these ADHD-associated factors. Moreover, there are variations in the reported neurophysiological correlates of ADHD. ADHD is often treated pharmacologically, producing an improvement in symptomatology, albeit there are patients who are refractory to the main pharmacological treatments or present side effects to these drugs, highlighting the importance of developing other therapeutic options. Different non-pharmacological treatments are in this review addressed, finding diverse results regarding efficacy. Altogether, ADHD is associated with different etiologies, all of them producing changes in brain development, leading to the characteristic symptomatology of this condition. Given the heterogeneous etiology of ADHD, discussion is presented about the convenience of personalizing ADHD treatment, whether pharmacological or non-pharmacological, to reach an optimum effect in the majority of patients. Approaches to personalizing both pharmacological therapy and neurofeedback are presented.

The relationship between auditory evoked potentials and symptoms of attention-deficit/hyperactivity disorder in adult patients with major depressive disorder

OBJECTIVES

Loudness dependence of auditory evoked potential (LDAEP) has been proposed as a biological marker for central serotonergic activity in depressive illness. A recent study has suggested that serotonin plays an important role in impulsivity and emotional sensitivity that are prominent clinical manifestations in attention deficit and hyperactivity disorder (ADHD). The objective of this study was to examine the association between LDAEP and ADHD symptoms in major depressive disorder (MDD).

METHODS

A total of 60 participants (40 subjects with MDD and 20 healthy controls) aged >18 years who had LDAEPs performed during electroencephalograms were included in this study. ADHD symptoms, depressive, and anxiety symptoms were evaluated. Psychological characteristics and event-related potentials (ERP) were compared among three groups: depression with ADHD symptoms, depression without ADHD symptoms, and healthy controls.

RESULTS

MDD subjects with ADHD symptoms (N = 20) showed significantly lower LDAEP levels than those without ADHD symptoms (N = 20) and healthy controls (N = 20). LDAEP differences between MDD subjects without ADHD symptoms and healthy controls were not statistically significant. In partial correlation analyses adjusted for age and sex, significant correlations of psychological scales of depression, ADHD symptoms, and LDAEPs were found.

CONCLUSION

Results of the present study suggest that LDAEP can reflect adult ADHD symptoms in MDD. Auditory evoked potential appears to be a promising candidate as an evaluation tool for inattention and poor impulse control as well as emotional sensitivity.

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha₁/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha₂ being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha₂ and frontopolar alpha₁. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.

Data mining EEG signals in depression for their diagnostic value

Background

Quantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models.

Methods

This paper uses a data mining methodology for classifying EEGs of 53 MDD patients and 43 HVs. This included: (a) pre-processing the data, including cleaning and normalization, applying Linear Discriminant Analysis (LDA) to map the features into a new feature space; and applying Genetic Algorithm (GA) to identify the most significant features; (b) building predictive models using the Decision Tree (DT) algorithm to discover rules and hidden patterns based on the reduced and mapped features; and (c) evaluating the models based on the accuracy and false positive values on the EEG data of MDD and HV participants. Two categories of experiments were performed. The first experiment analyzed each frequency band individually, while the second experiment analyzed the bands together.

Results

Application of LDA and GA markedly reduced the total number of utilized features by ≥ 50 % and, with all frequency bands analyzed together, the model showed average classification accuracy (MDD vs. HV) of 80 %. The best results from model testing with additional test EEG recordings from 9 MDD patients and 35 HV individuals demonstrated an accuracy of 80 % and showed an average sensitivity of 70 %, a specificity of 76 %, and a positive (PPV) and negative predictive value (NPV) of 74 and 75 %, respectively.

Conclusions

These initial findings suggest that the proposed automated EEG analytical approach could be a useful adjunctive diagnostic approach in clinical practice.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-015-0227-6) contains supplementary material, which is available to authorized users.

QEEG Theta Cordance in the Prediction of Treatment Outcome to Prefrontal Repetitive Transcranial Magnetic Stimulation or Venlafaxine ER in Patients With Major Depressive Disorder

The aims of this double-blind study were to assess and compare the efficacy of quantitative electroencephalographic (QEEG) prefrontal theta band cordance in the prediction of response to 4-week, right, prefrontal, 1-Hz repetitive transcranial magnetic stimulation (rTMS) or venlafaxine ER in patients with major depressive disorder (MDD). Prefrontal QEEG cordance values of 50 inpatients (25 subjects in each group) completing 4 weeks of the study were obtained at baseline and after 1 week of treatment. Depressive symptoms were assessed using Montgomery-Åsberg Depression Rating Scale (MADRS) at baseline and at week 1 and 4. Treatment response was defined as a ≥50% reduction in baseline MADRS total score. All responders (n = 9) and 6 of 16 nonresponders in the rTMS group had reduced cordance at week 1 (P < .01). Reduction of theta cordance value at week 1 was detected in all responders (n = 10) to venlafaxine ER, but only in 4 of 15 nonresponders (P = .005). The comparison of the areas under the curve of cordance change for prediction of response between rTMS (0.75) and venlafaxine ER (0.89) treated groups yielded no significant difference (P = .27). Our study indicates that prefrontal QEEG cordance is a promising tool not only for predicting the response to certain antidepressants but also to rTMS treatment, with comparable predictive efficacy for both therapeutic interventions.

Prediction of long-term treatment response to selective serotonin reuptake inhibitors (SSRIs) using scalp and source loudness dependence of auditory evoked potentials (LDAEP) analysis in patients with major depressive disorder

BACKGROUND

Animal and clinical studies have demonstrated that the loudness dependence of auditory evoked potentials (LDAEP) is inversely related to central serotonergic activity, with a high LDAEP reflecting weak serotonergic neurotransmission and vice versa, though the findings in humans have been less consistent. In addition, a high pretreatment LDAEP appears to predict a favorable response to antidepressant treatments that augment the actions of serotonin. The aim of this study was to test whether the baseline LDAEP is correlated with response to long-term maintenance treatment in patients with major depressive disorder (MDD).

METHODS

Scalp N1, P2 and N1/P2 LDAEP and standardized low resolution brain electromagnetic tomography-localized N1, P2, and N1/P2 LDAEP were evaluated in 41 MDD patients before and after they received antidepressant treatment (escitalopram (n = 32, 10.0 ± 4.0 mg/day), sertraline (n = 7, 78.6 ± 26.7 mg/day), and paroxetine controlled-release formulation (n = 2, 18.8 ± 8.8 mg/day)) for more than 12 weeks. A treatment response was defined as a reduction in the Beck Depression Inventory (BDI) score of >50% between baseline and follow-up.

RESULTS

The responders had higher baseline scalp P2 and N1/P2 LDAEP than nonresponders (p = 0.017; p = 0.036). In addition, changes in total BDI score between baseline and follow-up were larger in subjects with a high baseline N1/P2 LDAEP than those with a low baseline N1/P2 LDAEP (p = 0.009). There were significantly more responders in the high-LDAEP group than in the low-LDAEP group (p = 0.041).

CONCLUSIONS

The findings of this study reveal that a high baseline LDAEP is associated with a clinical response to long-term antidepressant treatment.

TMS by double-cone coil prefrontal stimulation for medication resistant chronic depression: a case report

A double-cone coil with large angled windings has been developed to modulate deeper brain areas such as the anterior cingulate cortex (ACC). Abnormal resting state activity in the pregenual ACC (pgACC), dorsal ACC (dACC) and subgenual ACC (sgACC) has been observed in depression. A patient with medication resistant chronic depression received ten sessions of transcranial magnetic stimulation (TMS) (10 Hz, 2000 stimuli/session) using a double-cone coil placed over the supplementary motor area, targeting the anterior cingulate. Source localized EEG recordings were conducted pre- and post-TMS. The Beck Depression Inventory (BDI-II) improved by 27%, and the two subscales of the Hospital Anxiety Depression Scale (HADS), namely depression (40%) and anxiety (33%) improved as well. Along with the clinical improvement eletrophysiological resting state activity changed in the dACC and sgACC in this patient in comparison to a normative group. The results of this case report further support the involvement of pgACC, dACC and sgACC activity in the pathophysiology of depression and indicate that modulation of neural activity in this area by high frequency TMS with a double-cone coil might represent a new promising approach in the treatment of medication resistant chronic depression.

The neurobiology of the EEG biomarker as a predictor of treatment response in depression

The management of depression remains a constant challenge in clinical practice. This is largely due to the fact that initial treatments frequently do not lead to remission and recovery. The current treatment approach involves lengthy trial-and-error periods. It would be beneficial to have early reliable predictors to determine whether patients will respond to treatment or not. Electroencephalography (EEG) derived biomarkers namely change in the activity of EEG frequency bands, hemispheric alpha asymmetry, theta cordance, the antidepressant treatment response index (ATR) and evoked potentials have all been shown to predict response to a variety of antidepressant medications. However, the neurobiology in support of this association has been largely unexplored. In this review, we discuss biological mechanisms for each EEG derived biomarker predictive of treatment response. Validating such biomarkers will not only greatly aid clinicians in selecting antidepressant treatment for individual patients but will also provide a critical step in drug discovery.

Electroencephalography-derived biomarkers of antidepressant response

Recent meta-analyses point to the relatively low efficacy of commonly used antidepressant medications. Selecting the most effective medications for depressed subjects having failed previous treatments is especially difficult. There is a clear need for objective biomarkers that could assist and optimize such treatment selection. We will review here a growing body of evidence suggesting that several electroencephalography (EEG)-based methods may be useful for predicting antidepressant response and eventually for guiding clinical treatment decisions. While most of these methods are based on resting-state EEGs (e.g., alpha- and theta-band EEG abnormalities, the combined Antidepressant Response Index (ATR), cordance, referenced EEG), others include EEG source localization and evoked potentials. The limitations of these technologies and the potential clinical uses will also be outlined.

Clinical electrophysiologic assessments and mild traumatic brain injury: state-of-the-science and implications for clinical practice

Conventional and quantitative electroencephalography (EEG and qEEG, respectively) may enhance clinical diagnosis and treatment planning provided to persons with mild traumatic brain injury (mTBI) and postconcussive symptoms. Effective and appropriate use of EEG and qEEG in this context requires expert-level knowledge of these technologies, mTBI, and the differential diagnosis for postconcussive symptoms. A practical and brief review from the perspective of a clinician-scientist engaged principally in the care and study of persons with mTBI therefore may be of use and value to other clinicians and scientists interested in these matters. Toward that end, this article offers an overview of the current applications of conventional EEG and qEEG to the study and clinical evaluation of persons with mTBI. The clinical case definition of TBI, the differential diagnosis of post-injury neuropsychiatric disturbances, and the typical course of recovery following mTBI are reviewed. With this background and context, the strengths and limitations of the literature describing EEG and qEEG studies in this population are considered. The implications of this review on the applications of these electrophysiologic assessments to the clinical evaluation of persons with mTBI and postconcussive symptoms are then considered. Finally, suggestions are offered regarding the design of future studies using these technologies in this population. Although this review may be of interest and value to professionals engaged in clinical or research electrophysiology in their daily work, it is intended to serve more immediately the needs of clinicians less familiar with these types of clinical electrophysiologic assessments.

Subanesthetic dose of ketamine decreases prefrontal theta cordance in healthy volunteers: implications for antidepressant effect

BACKGROUND

Theta cordance is a novel quantitative electroencephalography (QEEG) measure that correlates with cerebral perfusion. A series of clinical studies has demonstrated that the prefrontal theta cordance value decreases after 1 week of treatment in responders to antidepressants and that this effect precedes clinical improvement. Ketamine, a non-competitive antagonist of N-methyl-D-aspartate (NMDA) receptors, has a unique rapid antidepressant effect but its influence on theta cordance is unknown.

METHOD

In a double-blind, cross-over, placebo-controlled experiment we studied the acute effect of ketamine (0.54 mg/kg within 30 min) on theta cordance in a group of 20 healthy volunteers.

RESULTS

Ketamine infusion induced a decrease in prefrontal theta cordance and an increase in the central region theta cordance after 10 and 30 min. The change in prefrontal theta cordance correlated with ketamine and norketamine blood levels after 10 min of ketamine infusion.

CONCLUSIONS

Our data indicate that ketamine infusion immediately induces changes similar to those that monoamineric-based antidepressants induce gradually. The reduction in theta cordance could be a marker and a predictor of the fast-acting antidepressant effect of ketamine, a hypothesis that could be tested in depressive patients treated with ketamine.