EEG Abnormalities Are Associated With Poorer Depressive Symptom Outcomes With Escitalopram and Venlafaxine-XR, but Not Sertraline: Results From the Multicenter Randomized iSPOT-D Study

Abnormal Electroencephalogram Findings and Its Correlation With Clinical Features From Pediatric Patients in Psychiatric Clinic

Objective: We aimed to evaluate the occurrence of electroencephalogram (EEG) abnormalities in pediatric patients attending an outpatient psychiatry clinic at a tertiary center. We examined the rates of abnormalities and specific findings based on demographics, specific diagnoses, and clinical severity. Methods: This study included pediatric patients who underwent EEG at the outpatient psychiatry clinic. Patient demographics, psychiatric diagnosis, intellectual disability, intelligent quotient (IQ) score, family history of psychiatric disorders, and Clinical Global Impression-Severity (CGI-S) score were obtained through retrospective electronic health record analysis. The rate of EEG abnormalities was calculated, and specific abnormal findings were reviewed. Relationships between the rate of EEG abnormalities and diagnosis, severity, IQ, and age at EEG examination were analyzed. Results: Of 319 patients who underwent EEG, 21.3% (68 patients) of patients exhibited abnormalities, including background abnormalities (14.7%, 47 patients), interictal epileptiform discharges (IEDs) (10.3%, 33 patients), and a slow posterior dominant rhythm (3.8%, 10 patients). The frontal region was the most commonly affected area. Neurodevelopmental disorders (NDDs) had the most frequent abnormalities (29.8%), followed by anxiety (16.7%), sleep (14.3%), mood (11.7%), psychotic (5%), and conduct disorders (0%). Disease severity did not correlate with the rate of EEG abnormalities. Adjusted for age, sex, severity, and family history, patients with EEG abnormalities exhibited lower IQ scores. Conclusion: EEG abnormalities were common in pediatric patients with psychiatric disorders, with background abnormalities detected as frequently as IEDs. Disease severity was not associated with EEG abnormality, while IQ scores showed a negative correlation.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

EEG abnormalities are not associated with poor antidepressant treatment outcome - A NeuroPharm study

EEG brain abnormalities, such as slowing and isolated epileptiform discharges (IEDs), has previously been associated with non-response to antidepressant treatment with escitalopram and venlafaxine, suggesting a potential need for treatment with anticonvulsant property in some patients. The current study aims to replicate the reported association of EEG abnormality and treatment outcomes in an open-label trial of escitalopram for major depressive disorder (MDD) and explore its relationship to mood and cognition. Pretreatment, 6 min eyes-closed resting-state 256-channel EEG was recorded in 91 patients with MDD (age 18-57) who were treated with 10-20 mg escitalopram for 12 weeks; patients could switch to duloxetine after four weeks. A certified clinical neurophysiologist rated the EEGs. IED and EEG slowing was seen in 13.2%, and in 6.6% there were findings with unclear significance (i.e., Wicket spikes and theta activity). We saw no group-difference in remission or response rates after 8 and 12 weeks of treatment or switching to duloxetine. Patients with EEG abnormalities had higher pretreatment mood disturbances driven by greater anger (p=.039) and poorer verbal memory (p=.012). However, EEG abnormality was not associated with improved mood or verbal memory after treatment. Our findings should be interpreted in light of the rarity of EEG abnormalities and the sample size. While we cannot confirm that EEG abnormalities are associated with non-response to treatment, including escitalopram, abnormal EEG activity is associated with poor mood and verbal memory. The clinical utility of EEG abnormality in antidepressant treatment selection needs careful evaluation before deciding if useful for clinical implementation.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study

BACKGROUND

Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.

METHODS

The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored.

RESULTS

Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.

CONCLUSIONS

This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

Isolated epileptiform activity in children and adolescents: prevalence, relevance, and implications for treatment

In the field of psychiatry diagnoses are primarily based on the report of symptoms from either the patient, parents, or both, and a psychiatrist's observations. A psychiatric diagnosis is currently the most widely used basis for medication selection and the brain is seldom investigated directly as a source of those symptoms. This study addresses the request from the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC) for scientific research into neurological abnormalities that can be linked to psychiatric symptoms for the purpose of predicting medication response. One such neurological abnormality that has been the focus of many studies over the last three decades is isolated epileptiform discharges (IEDs) in children and adolescents without seizures. We conducted a systematic review of the literature to determine prevalence rates of IEDs within diagnostic categories. We then compared the prevalence of IEDs in the selected literature to our IRB-approved data archive. Our study found a consistent high prevalence of IEDs specifically for ADHD (majority > 25%) and ASD (majority > 59%), and consistent low prevalence rates were found for Depression (3%). If children and adolescents have failed multiple medication attempts, and more than one-third of them have IEDs, then an EEG would be justified within the RDoC paradigm.

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.

EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial

Using pre-treatment biomarkers to guide patients to the preferred antidepressant medication treatment could be a promising approach to enhance its current modest response and remission rates. This open-label prospective study assessed the feasibility of using such pre-treatment biomarkers, by using previously identified EEG features (paroxysmal activity; alpha peak frequency; frontal alpha asymmetry) to inform the clinician in selecting among three different antidepressants (ADs; escitalopram, sertraline, venlafaxine) as compared to Treatment As Usual (TAU). EEG data were obtained from 195 outpatients with major depressive disorder prior to eight weeks of AD treatment. Primary outcome measure was the percentage change between before and after treatment on the Beck Depression Inventory-II (BDI-II). We compared TAU and EEG-informed prescription through AN(C)OVAs. Recruitment started with patients receiving TAU to establish baseline effectiveness, after which we recruited patients receiving EEG-informed prescription. 108 patients received EEG-informed prescription and 87 patients received TAU. Clinicians and patients were satisfied with the protocol. Overall, 70 (65%) of the EEG-informed clinicians followed recommendations (compared to 52 (60%) following prescriptions in the TAU group), establishing feasibility. We here confirm that treatment allocation informed by EEG variables previously reported in correlational studies, was feasible.

Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium

OBJECTIVE

The aim of the current study was to attempt to replicate the finding that the individual alpha frequency (IAF) as well as the absolute difference between IAF and 10 Hz stimulation frequency (IAF-prox) is related to treatment outcome.

METHODS

Correlations were performed to investigate the relationship between IAF-prox and percentage symptom improvement in a sample of 153 patients with major depressive disorder treated with 10 Hz (N = 59) to the left dorsolateral prefrontal cortex (DLPFC) or 1 Hz (N = 94) to the right DLPFC repetitive Transcranial Magnetic Stimulation (rTMS).

RESULTS

There was a significant negative correlation between IAF-prox and the percentage of symptom improvement only for the 10 Hz group. Curve fitting models revealed that there was a quadratic association between IAF and treatment response in the 10 Hz group, with a peak at 10 Hz IAF.

CONCLUSION

The main result of Corlier and colleagues was replicated, and the findings suggest that the distance between 10 Hz stimulation frequency and the IAF may influence clinical outcome in a non-linear manner.

SIGNIFICANCE

rTMS is often administered at a frequency of 10 Hz, which is the center of the EEG alpha frequency band. The results can make a significant contribution to optimizing the clinical application of rTMS.

QEEG - spectral power density of brain regions in predicting risk, resistance and resilience for bipolar disorder: A comparison of first degree relatives and unrelated healthy subjects

BACKGROUND

Temperament stems from the brain circuitry. Genetic differences among people are attributable to differences in neurophysiological function. Affective temperament is proposed endophenotype for bipolar affective disorder. QEEG - spectral power density is thought to be an index of general affective and cognitive brain activity. The association of spectral power density with types of affective temperament may enlighten endophenotypes for bipolar affective disorder disposition.

METHOD

TEMPS-A scale and rest QEEG were done on 25 euthymic patients, their healthy first degree relatives (n = 25) and 25 unrelated healthy control subjects. All patients were on lithium maintenance therapy.

RESULTS

F4 and T4 delta wave activity were similar between patients and first degree relatives, while Pz alpha activity was similar in first degree relatives and unrelated healthy subjects (p = 0.025, p = 0.001, p = 0.010). Cyclothymic and hyperthymic temperament scores were similar between patients and first degree relatives but higher than unrelated healthy subjects (p = 0.015, p = 0.010). F7 beta and F7-O2 high beta power were correlated with hyperthymic and irritable temperaments respectively in bipolar subjects (r = 0.439, 0.387; 0.405, 0.364; 0.226, 0.351). T3-F4-T4 delta powers were correlated with cyclothymic temperament in patients and their first degree relatives (r = 0.443, 0.420, 505). Pz alpha power and hyperthymic temperament were inversely correlated in first degree relatives and unrelated healthy subjects (r = -0.256 and -0.311).

CONCLUSION

Medial temporal network may be associated with bipolar affective disorder heritability. On the other hand, left dorsolateral prefrontal beta and high beta activities may be a neural marker for disorder resistance together with right occipital high beta power.

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.

OBJECTIVE

To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019.

EXPOSURES

Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180).

MAIN OUTCOMES AND MEASURES

The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index.

RESULTS

The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]).

CONCLUSIONS AND RELEVANCE

This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00693849.

Normalization of EEG in depression after antidepressant treatment with sertraline? A preliminary report

BACKGROUND

MDD patients with abnormal EEG patterns seem more likely to be non-responsive to the antidepressants escitalopram and venlafaxine, but not sertraline, than patients without EEG abnormalities. This finding suggests that patients with both MDD and abnormal EEGs may differentially respond to antidepressant treatment. In the current study, we investigated whether depressed patients with an abnormal EEG show a normalization of the EEG related to antidepressant treatment and response and whether such effect is drug specific, and whether having had early life stress (ELS) increases the chance of abnormal activity.

METHODS

Baseline and week 8 EEGs and depression symptoms were extracted from a large multicenter study (iSPOT-D, n = 1008) where depressed patients were randomized to escitalopram, sertraline, or venlafaxine-XR treatment. We calculated Odds Ratios of EEG normalization and depression response in patients with an abnormal EEG at baseline, comparing sertraline versus other antidepressants.

RESULTS

Fifty seven patients with abnormal EEGs were included. EEGs did not normalize significantly more with sertraline compared to other antidepressants (OR = 1.9, p = .280). However, patients with a normalized EEG taking sertraline were 5.2 times more likely to respond than subjects taking other antidepressants (p = .019). ELS was not significantly related to abnormal activity.

LIMITATIONS

Neurophysiological recordings were limited in time (two times 2-minute EEGs) and statistical power (n = 57 abnormal EEGs).

CONCLUSIONS

Response rates in patients with normalized EEG taking sertraline were significantly larger than in subjects treated with escitalopram/venlafaxine. This adds to personalized medicine and suggests a possible drug repurposing for sertraline.

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

OBJECTIVE

Reducing unsuccessful treatment trials could improve depression treatment. Quantitative EEG (QEEG) may predict treatment response and is being commercially marketed for this purpose. The authors sought to quantify the reliability of QEEG for response prediction in depressive illness and to identify methodological limitations of the available evidence.

METHOD

The authors conducted a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio. Raters also judged each article on indicators of good research practice.

RESULTS

In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed a sensitivity of 0.72 (95% CI=0.67-0.76) and a specificity of 0.68 (95% CI=0.63-0.73). The logarithm of the diagnostic odds ratio was 1.89 (95% CI=1.56-2.21), and the area under the receiver operator curve was 0.76 (95% CI=0.71-0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias. Most studies did not use ideal practices.

CONCLUSIONS

QEEG does not appear to be clinically reliable for predicting depression treatment response, as the literature is limited by underreporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of previous findings. Until these limitations are remedied, QEEG is not recommended for guiding selection of psychiatric treatment.

QEEG in affective disorder: about to be a biomarker, endophenotype and predictor of treatment response

QEEG is a relatively easy to apply, cost effective method among many electrophysiologic and functional brain imaging techniques used to assess individuals for diagnosis and determination of the most suitable treatment. Its temporal resolution provides an important advantage. Many specific EEG indicators play a role in the differential diagnosis of neuropsychiatric disorders. QEEG has advantages over EEG in the dimensional approach to symptomatology of psychiatric disorders. The prognostic value of EEG has a long history. Slow wave EEG rhythm has been reported as a predictor and measure of clinical improvement under ECT. The induction level in delta band activity predicts the long term effect of ECT. Current studies focus on the predictive power of EEG on response to pharmacotherapy and somatic treatments other than ECT. This paper discusses either QEEG can be a biomarker and/or an endophenotype in affective disorders, if it has diagnostic and prognostic value and if it can contribute to personalized treatment design, through a review of relevant literature.

The potential of predictive analytics to provide clinical decision support in depression treatment planning

PURPOSE OF REVIEW

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD).

RECENT FINDINGS

Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem.

SUMMARY

Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Retrospective Analysis of Nonepileptic Patients With Isolated Epileptiform Discharges Treated With Anticonvulsants

Many antiepileptic drugs (AEDs) have been tested on nonepileptic patients with a variety of diagnoses. The Food and Drug Administration has only approved certain AEDs for a small number of psychiatric conditions. There are few studies of nonepileptic patients that recommend an empirical trial of AEDs when isolated epileptiform discharges (IEDs) are identified in the electroencephalogram (EEG). However, no trials have been published. The purpose of this study is to evaluate the outcome of treating nonepileptic patients with AEDs when IEDs are present. Refractory cases were reviewed from a multidisciplinary practice whose EEG readings contained IEDs and were subsequently medicated with anticonvulsants by the clinic's psychiatrist. The psychiatrist's progress notes were assessed to determine the impact of adding anticonvulsants based on parent reports, teacher reports, and clinical observation. The final sample was composed of 76 refractory cases. Of the 76 patients treated with anticonvulsants, the majority were found to be improved in follow-up progress notes: 65 improved (85.53%), 6 unchanged (7.89%), and 5 more severe (6.58%). These observational findings suggest that further studies will be needed to show that IEDs may predict positive treatment outcome to anticonvulsant medication and act as a step toward an evidence-based treatment. Also, EEG screening may prove to be useful for refractory cases regardless of age, gender, or diagnosis.

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.