Personalized Medicine in ADHD and Depression: Use of Pharmaco-EEG

Actigraphy-Derived Sleep Profiles of Children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) over Two Weeks-Comparison, Precursor Symptoms, and the Chronotype

Although sleep problems are common in children with ADHD, their extent, preceding risk factors, and the association between neurocognitive performance and neurobiological processes in sleep and ADHD, are still largely unknown. We examined sleep variables in school-aged children with ADHD, addressing their intra-individual variability (IIV) and considering potential precursor symptoms as well as the chronotype. Additionally, in a subgroup of our sample, we investigated associations with neurobehavioral functioning (n = 44). A total of 57 children (6-12 years) with (n = 24) and without ADHD (n = 33) were recruited in one center of the large ESCAlife study to wear actigraphs for two weeks. Actigraphy-derived dependent variables, including IIV, were analyzed using linear mixed models in order to find differences between the groups. A stepwise regression model was used to investigate neuropsychological function. Overall, children with ADHD showed longer sleep onset latency (SOL), higher IIV in SOL, more movements during sleep, lower sleep efficiency, and a slightly larger sleep deficit on school days compared with free days. No group differences were observed for chronotype or sleep onset time. Sleep problems in infancy predicted later SOL and the total number of movements during sleep in children with and without ADHD. No additional effect of sleep problems, beyond ADHD symptom severity, on neuropsychological functioning was found. This study highlights the importance of screening children with ADHD for current and early childhood sleep disturbances in order to prevent long-term sleep problems and offer individualized treatments. Future studies with larger sample sizes should examine possible biological markers to improve our understanding of the underlying mechanisms.

Sensory processing and P300 event-related potential correlates of stimulant response in children with attention-deficit/hyperactivity disorder: A critical review

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with considerable impairment in psychiatric and functional domains. Although stimulant medication can reduce core symptoms of inattention, hyperactivity, and impulsivity, a subgroup of patients does not respond to this intervention. A precision medicine approach has been proposed, whereby biomarkers are used to identify an effective treatment approach for a given individual. This review synthesizes the existing literature on event-related potential (ERP) correlates of stimulant response in children diagnosed with ADHD, with the goal of evaluating the potential for ERP to inform precision medicine care in this population. Forty-three articles were examined and results tentatively suggest that stimulant medications normalize the amplitude of the P300 component, and this is also associated with behavioral improvement. In contrast, results generally indicate that stimulants do not significantly alter early processing components, although there are some exceptions to this finding. Implications for research, theory, and clinical work are considered and concrete recommendations for future directions are provided. While recognizing limitations of existing literature (e.g., homogenous samples, variable methodologies), we conclude that ERP methods represent a promising approach for precision medicine care of patients with ADHD.

Examining the heterogeneity of treatment patterns in attention deficit hyperactivity disorder among children and adolescents in the Texas Medicaid population: modeling suboptimal treatment response

Objectives: To examine suboptimal responses (SR) in attention deficit hyperactivity disorder (ADHD) among pediatric patients in the Texas Medicaid program receiving osmotic-release oral system methylphenidate (OROS-MPH) or lisdexamfetamine (LDX) and apply an SR prediction model to identify patients most likely to experience an SR to either OROS-MPH or LDX therapies. Methods: A retrospective cohort study was conducted using Texas Medicaid claims data of ADHD children and adolescents (6-17 years of age) initiating OROS-MPH or LDX. Primary SR endpoints were drug discontinuation, switching, and augmentation 12-months post-ADHD drug initiation. Logistic regression models were developed to predict SR to OROS-MPH and LDX in 1:1 matched groups of children and adolescent cohorts. Results: A total of 3,633 children and 1,611 adolescents were matched for each cohort. SR was observed among more children (76.4% vs 72.3%; p < 0.001) and adolescents (82.7% vs 78.2%; p = 0.002) initiating OROS-MPH compared to LDX. Patient sub-groups with the highest predicted risk of OROS-MPH SR experienced significantly lower observed SR rates (p < 0.05) when initiating LDX (children: 80.6% for OROS-MPH vs 75.8% for LDX; OR = 0.75, 95% CI = 0.60-0.94; adolescents: 87.2% for OROS-MPH vs 80.6% for LDX; OR = 0.61, 95% CI = 0.41-0.89). For patients with highest predicted SR rates to LDX, observed SR rates were not significantly different between patients initiating LDX or OROS-MPH. Conclusions: This study demonstrated how a personalized medicine approach using administrative claims data can be used to identify sub-groups of child and adolescent ADHD patients with different risks for suboptimal response with OROS-MPH or LDX in a Medicaid population.

Resting-State Quantitative Electroencephalography Demonstrates Differential Connectivity in Adolescents with Major Depressive Disorder

Background: Biomarkers for psychiatric disorders in children and adolescents are urgently needed. This cross-sectional pilot study investigated quantitative electroencephalogram (qEEG), a promising intermediate biomarker, in pediatric patients with major depressive disorder (MDD) compared with healthy controls (HCs). We hypothesized that youth with MDD would have increased coherence (connectivity) and absolute alpha power in the frontal cortex compared with HC. Methods: qEEG was obtained in adolescents aged 14-17 years with MDD (n = 25) and age- and gender-matched HCs (n = 14). The primary outcome was overall coherence on qEEG in the four frequency bands (alpha, beta, theta, and delta). Other outcomes included frontal-only coherence, overall and frontal-only qEEG power, and clinician-rated measures of anhedonia and anxiety. Results: Average coherence in the theta band was significantly lower in MDD patients versus HCs, and also lower in frontal cortex among MDD patients. Seven node pairs were significantly different or trending toward significance between MDD and HC; all had lower coherence in MDD patients. Average frontal delta power was significantly higher in MDD versus HCs. Conclusions: Brain connectivity measured by qEEG differs significantly between adolescents with MDD and HCs. Compared with HCs, youth with MDD showed decreased connectivity, yet no differences in power in any frequency bands. In the frontal cortex, youth with MDD showed decreased resting connectivity in the alpha and theta frequency bands. Impaired development of a resting-state brain network (e.g., default mode network) in adolescents with MDD may represent an intermediate phenotype that can be assessed with qEEG.

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain’s multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers. This mAChR index yielded higher classification performance than any single EEG biomarker with cross-validated accuracy, sensitivity, specificity and precision ranging from 88–92%. The mAChR index also discriminated healthy elderly from patients with Alzheimer’s disease (AD); however, an index optimized for AD pathophysiology provided a better classification. We conclude that integrating multiple EEG biomarkers can enhance the accuracy of identifying disease or drug interventions, which is essential for clinical trials.

Sleep disturbances in obsessive-compulsive disorder: Association with non-response to repetitive transcranial magnetic stimulation (rTMS)

Background Repetitive transcranial magnetic stimulation (rTMS) is a promising augmentation strategy for treatment-refractory OCD. However, a substantial group still fails to respond. Sleep disorders, e.g. circadian rhythm sleep disorders (CRSD), are highly prevalent in OCD and might mediate treatment response. The aims of the current study were to compare sleep disturbances between OCD patients and healthy subjects as well as between rTMS responders and non-responders, and most importantly to determine sleep-related predictors of rTMS non-response. Methods 22 OCD patients received at least 10 sessions rTMS combined with psychotherapy. Sleep disturbances were measured using questionnaires and actigraphy. Sleep in patients was compared to healthy subjects. Treatment response was defined as >35% reduction on YBOCS. Treatment response prediction models were based on measures of CRSD and insomnia. Results Sleep disturbances were more prevalent in OCD patients than healthy subjects. The OCD group consisted of 12 responders and 10 non-responders. The CRSD model could accurately predict non-response with 83% sensitivity and 63% specificity, whereas the insomnia model could not. Conclusions CRSD is more prevalent in OCD patients than healthy subjects, specifically in rTMS non-responders. Therefore, CRSD may serve as a biomarker for different subtypes of OCD corresponding with response to specific treatment approaches.

Recent advances in predicting responses to antidepressant treatment

Major depressive disorder is one of the leading causes of disability in the world since depression is highly frequent and causes a strong burden. In order to reduce the duration of depressive episodes, clinicians would need to choose the most effective therapy for each individual right away. A prerequisite for this would be to have biomarkers at hand that would predict which individual would benefit from which kind of therapy (for example, pharmacotherapy or psychotherapy) or even from which kind of antidepressant class. In the past, neuroimaging, electroencephalogram, genetic, proteomic, and inflammation markers have been under investigation for their utility to predict targeted therapies. The present overview demonstrates recent advances in all of these different methodological areas and concludes that these approaches are promising but also that the aim to have such a marker available has not yet been reached. For example, the integration of markers from different systems needs to be achieved. With ongoing advances in the accuracy of sensing techniques and improvement of modelling approaches, this challenge might be achievable.

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

Rationale Limited research is available on electrophysiological abnormalities such as epileptiform EEG or EEG slowing in depression and its association with antidepressant treatment response. Objectives We investigated the association between EEG abnormalities and antidepressant treatment response in the international Study to Predict Optimized Treatment in Depression (iSPOT-D). Methods Of 1008 participants with major depressive disorder randomized to escitalopram, sertraline, or venlafaxine-XR, 622 completed 8 weeks of treatment per protocol. The study also recruited 336 healthy controls. Treatment response was established after 8 weeks using the 17-item Hamilton Rating Scale for Depression (HRSD₁₇). The resting-state EEG was assessed at baseline with eyes closed. EEG abnormalities including epileptiform activity, EEG slowing, and alpha peak frequency (APF) were scored for all subjects, blind to treatment outcome. Results Patients and controls did not differ in the occurrence of EEG abnormalities. Furthermore, in the per protocol sample the occurrence of epileptiform EEG and EEG slowing (as a combined marker) were associated with a reduced likelihood of responding to escitalopram (P = .019; odds ratio [OR] = 3.56) and venlafaxine-XR (P = .043; OR = 2.76), but not sertraline (OR = 0.73). The response rates for this "any EEG abnormality" groups versus the "no-abnormality" group were 33% and 64% for escitalopram and 41% and 66% for venlafaxine-XR, respectively. A slow APF was associated with treatment response only in the sertraline group (P = .21; d = .027). Conclusions EEG abnormalities are associated with nonresponse to escitalopram and venlafaxine-XR, but not sertraline, whereas a slow APF is associated to response for sertraline only.

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report

It is essential to improve antidepressant treatment of major depressive disorder (MDD) and one way this could be achieved is by reducing the number of treatment steps by employing biomarkers that can predict treatment outcome. This study investigated differences between MDD patients and healthy controls in the P3 and N1 component from the event-related potential (ERP) generated in a standard two-tone oddball paradigm. Furthermore, the P3 and N1 are investigated as predictors for treatment outcome to three different antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D)--a multi-center, international, randomized, prospective practical trial--1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression. P3 and N1 latencies and amplitudes were analyzed using a peak-picking approach and further replicated by using exact low resolution tomography (eLORETA). A reduced P3 was found in MDD patients compared to controls by a peak-picking analysis. This was validated in a temporal global field power analysis. Source density analysis revealed that the difference in cortical activity originated from the posterior cingulate and parahippocampal gyrus. Male non-responders to venlafaxine-XR had significantly smaller N1 amplitudes than responders. This was demonstrated by both analytical methods. Male non-responders to venlafaxine-XR had less activity originating from the left insular cortex. The observed results are discussed from a neural network viewpoint.

Developing a Risk Score to Guide Individualized Treatment Selection in Attention Deficit/Hyperactivity Disorder

OBJECTIVE

To develop a risk score for treatment failure that could potentially be used to individualize treatment selection between lisdexamfetamine dimesylate (LDX) and osmotic-release oral system methylphenidate (OROS-MPH) in children and adolescents with attention deficit/hyperactivity disorder (ADHD).

METHODS

The study used data from patients with ADHD receiving LDX (N = 104) or OROS-MPH (N = 107) in a phase III randomized clinical trial. A prediction model was developed to estimate risk scores for failing OROS-MPH, where treatment failure was defined as less than 25% improvement in the ADHD Rating Scale IV total score from baseline. Patients were ranked by their predicted risks of OROS-MPH failure to define high-risk subpopulations. Outcomes of LDX and OROS-MPH were compared within subpopulations.

RESULTS

The prediction model for OROS-MPH failure selected seven predictors (age, disease duration, and five ADHD Rating Scale IV item scores) and had an in-sample C statistic of 0.860. Among all patients, LDX had a 17% (95% confidence interval 7.1%-27.8%) lower treatment failure rate than that of OROS-MPH; differences in failure rates ranged from 17% to 43% across subpopulations, increasingly enriched for high-risk patients. Similar heterogeneity across subgroups was observed for other efficacy measures.

CONCLUSIONS

In the overall trial population, LDX was associated with a lower rate of treatment failure compared with OROS-MPH in patients with ADHD. A more pronounced benefit of LDX over OROS-MPH was observed among subpopulations with a higher predicted risk of failing OROS-MPH. The present study showed the feasibility of individualizing treatment selection. Future research is needed to prospectively verify these results.

Association between COMT Val158Met genotype and EEG alpha peak frequency tested in two independent cohorts

This study could not confirm the association between the Catechol-O-Methyltransferase Val158Met polymorphism (COMT) and electroencephalographic (EEG) alpha peak frequency (APF) in two independent cohorts of 187 (96 depressed and 91 healthy participants) and 413 healthy participants. If COMT and APF play a role in depression or antidepressant treatment response, they do not have a shared pathway. We emphasize the importance of publishing null-findings for obtaining more accurate overall estimates of genetic effects.