Predictive Value of Slow and Fast EEG Oscillations for Methylphenidate Response in ADHD

Transcranial alternating current stimulation in affecting cognitive impairment in psychiatric disorders: a review

Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation method that, through its manipulation of endogenous oscillations, can affect cognition in healthy adults. Given the fact that both endogenous oscillations and cognition are impaired in various psychiatric diagnoses, tACS might represent a suitable intervention. We conducted a search of Pubmed and Web of Science databases and reviewed 27 studies where tACS is used in psychiatric diagnoses and cognition change is evaluated. TACS is a safe and well-tolerated intervention method, suitable for multiple-sessions protocols. It can be administered at home, individualized according to the patient''s anatomical and functional characteristics, or used as a marker of disease progression. The results are varying across diagnoses and applied protocols, with some protocols showing a long-term effect. However, the overall number of studies is small with a great variety of diagnoses and tACS parameters, such as electrode montage or used frequency. Precise mechanisms of tACS interaction with pathophysiological processes are only partially described and need further research. Currently, tACS seems to be a feasible method to alleviate cognitive impairment in psychiatric patients; however, a more robust confirmation of efficacy of potential protocols is needed to introduce it into clinical practise.

Variations in Theta/Beta Ratio and Cognitive Performance in Subpopulations of Subjects with ADHD Symptoms: Towards Neuropsychological Profiling for Patient Subgrouping

ADHD is a neurodevelopmental disorder appearing in childhood but remaining in many cases in adults. There are both pharmacological and non-pharmacological approaches to treating ADHD, but they do not have the same efficacy in all subjects. Better knowledge of the neurophysiological basis of this disorder will allow for the design of more effective treatments. Studies performing qEEG analysis in children suggest the existence of subgroups of ADHD patients with different neurophysiological traits. There are fewer studies in adults, who might have undergone plastic changes allowing them to cope with ADHD symptoms along with brain maturation. Herein, we study cognitive performance and the theta/beta ratio in young adults with ADHD symptoms. We found that subjects with ADHD symptoms and low working memory performance (n = 30) present higher theta/beta ratios than controls (n = 40) at O2 and T6 in the eyes-closed condition, as well as a tendency toward a higher theta/beta ratio at O1 and Cz. Subjects with ADHD and high working memory performance (n = 50) do not differ from the controls in their theta/beta ratios at any derivation. Our results suggest that neuropsychological profiling could be useful for patient subgrouping. Further research will allow for the distinction of neuropsychological profiles and their neurophysiological correlates, leading to a better classification of ADHD subtypes, thus improving treatment selection.

Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy

Sub-scalp Implantable Telemetric EEG (SITE) devices are under development for the treatment of epilepsy. However, beyond epilepsy, continuous EEG analysis could revolutionize the management of patients suffering from all types of brain disorders. This article reviews decades of foundational EEG research, collected from short-term routine EEG studies of common neurological and behavioral disorders, that may guide future SITE management and research. Established quantitative EEG methods, like spectral EEG power density calculation combined with state-of-the-art machine learning techniques applied to SITE data, can identify new EEG biomarkers of neurological disease. From distinguishing syncopal events from seizures to predicting the risk of dementia, SITE-derived EEG biomarkers can provide clinicians with real-time information about diagnosis, treatment response, and disease progression.

Electrophysiological and Clinical Predictors of Methylphenidate, Guanfacine, and Combined Treatment Outcomes in Children With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

The combination of d-methylphenidate and guanfacine (an α-2A agonist) has emerged as a potential alternative to either monotherapy in children with attention-deficit/hyperactivity disorder (ADHD), but it is unclear what predicts response to these treatments. This study is the first to investigate pretreatment clinical and electroencephalography (EEG) profiles as predictors of treatment outcome in children randomized to these different medications.

METHOD

A total of 181 children with ADHD (aged 7-14 years; 123 boys) completed an 8-week randomized, double-blind, comparative study with d-methylphenidate, guanfacine, or combined treatments. Pretreatment assessments included ratings on ADHD, anxiety, and oppositional behavior. EEG activity from cortical sources localized within midfrontal and midoccipital regions was measured during a spatial working memory task with encoding, maintenance, and retrieval phases. Analyses tested whether pretreatment clinical and EEG measures predicted treatment-related change in ADHD severity.

RESULTS

Higher pretreatment hyperactivity-impulsivity and oppositional symptoms and lower anxiety predicted greater ADHD improvements across all medication groups. Pretreatment event-related midfrontal beta power predicted treatment outcome with combined and monotherapy treatments, albeit in different directions. Weaker beta modulations predicted improvements with combined treatment, whereas stronger modulation during encoding and retrieval predicted improvements with d-methylphenidate and guanfacine, respectively. A multivariate model including EEG and clinical measures explained twice as much variance in ADHD improvement with guanfacine and combined treatment (R2= 0.34-0.41) as clinical measures alone (R2 = 0.14-.21).

CONCLUSION

We identified treatment-specific and shared predictors of response to different pharmacotherapies in children with ADHD. If replicated, these findings would suggest that aggregating information from clinical and brain measures may aid personalized treatment decisions in ADHD.

CLINICAL TRIAL REGISTRATION INFORMATION

Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder; https://clinicaltrials.gov; NCT00429273.

Treatment biomarkers for ADHD: Taking stock and moving forward

The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.

Personalization of pharmacological treatments for ADHD: Why it is advisable and possible options to achieve it

Abstract:

Attention-deficit hyperactivity disorder is a neurodevelopmental disorder diagnosed primarily in children, although it is also present in adults. Patients present inattention, impulsivity, and hyperactivity symptoms that create difficulties in their daily lives. Pharmacological treatment with stimulants or non-stimulants is used most commonly to reduce ADHD symptoms. Although generally effective and safe, pharmacological treatments have different effects among patients, including lack of response and adverse reactions. The reasons for these differences are not fully understood, but they may derive from the highly diverse etiology of ADHD. Strategies to guide optimal pharmacological treatment selection on the basis of individual patients’ physiological markers are being developed. In this review, we describe the main pharmacological ADHD treatments used and their main drawbacks. We present alternatives under study that would allow the customization of pharmacological treatments to overcome these drawbacks and achieve more reliable improvement of ADHD symptoms.

[EEG predictors of therapeutic response in psychiatry]

The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.

EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome

Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied. Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site. Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS. Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.