ADHD, Healthy
Conditions
Brief summary
EEG neurofeedback (NFB) may represent a new therapeutic opportunity for ADHD, a neuropsychiatric disorder characterized by attentional deficits and high impulsivity. Recent research of the Geneva group has demonstrated the ability of ADHD patients to control specific features of their EEG (notably alpha desynchronization) and that this control was associated with reduced impulsivity. In addition, alterations in EEG brain microstates (i.e., recurrent stable periods of short duration) have been described in adult ADHD patients, potentially representing a biomarker of the disorder. The present study aims to use neurofeedback to manipulate EEG microstates in ADHD patients and healthy controls, in order to observe the effects on neurophysiological, clinical and behavioural parameters.
Detailed description
Neurofeedback (NFB) is a broadly used method that enables individuals to self-regulate one or more neurophysiological parameters. In the case of electroencephalography (EEG) the parameters most often used so far are slow cortical potentials (SCPs), coherence training and frequency training. Protocols based on these measures have been applied to many clinical populations exhibiting abnormal EEG patterns including schizophrenia, insomnia, dyslexia, drug addiction, autistic spectrum disorder and attention deficit/hyperactivity disorder (ADHD). Today, the most widely used neurofeedback protocol for the ADHD population is based on the theta/beta ratio (TBR). However more recent studies have failed to replicate this finding of elevated TBR as a diagnostic feature in ADHD, which was also confirmed in a meta-analysis. These divergent results motivate the need for research to explore new markers to diagnose and treat ADHD. In a recent study, Férat and colleagues proposed EEG microstate analysis as a new framework to study ADHD. Microstate analysis models spontaneous EEG as a sequence of states defined by recurring appearance of a given distribution of scalp potentials. The authors observed a significantly increased contribution of one specific state commonly referred to microstate D in the ADHD population compared to healthy subjects. This state is often associated with attentional functions and brain regions in the dorsal attention networks are involved . It would therefore be interesting to study the causal link between this microstate and attention by manipulating this biomarker with neurofeedback. In this context, a recent study by Hernandez and colleagues has already demonstrated that healthy participants were able to control such brain microstates by neurofeedback. The aim of the present study is to test whether patients with ADHD are also capable of self-regulating their microstate dynamics. In the light of recent findings on EEG microstate and the ADHD population, the hypothesis is that microstate D could be a potential functional biomarker of ADHD. To test it, the proposal is to modulate this microstate using a neurofeedback training protocol directly targeting microstate parameters. According to the main hypothesis, changes in microstate parameters should be correlated with change in attentional and impulsive behaviour. To answer this question, a two-session study was designed, where participants will perform a continuous performance task (CPT) before and after 30 minutes of microstate-based neurofeedback training. During one of the sessions participants will be trained to upregulate microstate parameters, while during the other one, they will be trained to downregulate the same parameters. Intra- and across-section statistical contrasts, both in terms of brain activity changes and behavioural performance, should provide evidence to evaluate the impact of microstate changes relative to behaviour. In addition, and according to a large number of studies on ERP components in ADHD patients the recording of event related potentials (ERPs) during the behavioural task could help us understand the neurophysiological changes linked to attention and impulsivity measures.
Interventions
DEVICENeurofeedback
Neurofeedback training during which participant will be asked to change the size of a bar using different strategies to vary the parameters of its current brain's states (neurofeedback training) computed on the realtime EEG signals.
Sponsors
University of Geneva, Switzerland
University Hospital, Geneva
Nader Perroud
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
BASIC_SCIENCE
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
18 Years to 50 Years
Healthy volunteers
Yes
Inclusion criteria
ADHD POPULATION GROUP A subject will be eligible if all the following criteria apply: * Age: between 18-50 years * Gender: male and female * Health: general good health and normal or corrected-to-normal visual acuity * Patients clinically able to stop the following psychotropic medications for 48h: psychostimulants, benzodiazepines * Having provided written informed written consent A subject will not be eligible if any of the following criteria apply: * Past or current history of a clinically significant central nervous system disorder, including structural brain abnormalities; cerebrovascular disease; history of other neurological disease, epilepsy, stroke or head trauma (defined as loss of consciousness \> 5 min or requiring hospitalization) * Impaired vision (normal or corrected acuity below 20/40) * Medical illness (e.g., cardiovascular disease, renal failure, hepatic dysfunction) * Comorbidities with current psychiatric disorders (bipolar disorder, borderline personality disorder, major depressive disorder, anxiety disorder) including substance use disorder as defined by the DIGS. HEALTHY POPULATION GROUP A subject will be eligible if all of the following criteria apply: * Age: between 18-50 years * Gender: male and female * Health: general good health and normal or corrected-to-normal visual acuity * Having provided written informed written consent A subject will not be eligible if any of the following criteria apply: * Past or current history of ADHD * Past or current history of main psychiatric disorders (bipolar disorder, borderline personality disorder, major depressive disorder, anxiety disorder), including substance use disorder as defined by the DIGS. * Past or current history of a clinically significant central nervous system disorder, including structural brain abnormalities; cerebrovascular disease; history of other neurological disease, including epilepsy, stroke or head trauma (defined as loss of consciousness \> 5 min or requiring hospitalization) * Impaired vision (normal or corrected acuity below 20/40) * Medical illness (e.g., cardiovascular disease, renal failure, hepatic dysfunction)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in microstate coverage during rest | Change within session week 1 (session 2) and week 2 (session 2) | Difference in EEG microstate time coverage (%) between rest periods for each session (session 2, session 3) independently. |
| Change in microstate coverage during training | Change within session at week 1 (session 2) and week 2 (session 2) | Difference in EEG microstate time coverage (%) between training and rest periods for each session (session 2, session 3) independently. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlations between EEG microstate time coverage (%) and task performance: error rates (%) and reaction time. | Within session at week 1 (session 2) and week 2 (session 2) | — |
| Change in EEG Event Related potentiels before and after neurofeedback training. | Within session at week 1 (session 2) and week 2 (session 2) | For each condition (Go/NoGo) we will investigate differences in Global map dissimilarity (GMD), amplitude and microstate segmentation between pre and post neurofeedback training tasks. |
Countries
Switzerland
Contacts
Primary ContactNader Perroud, Professor
Backup ContactMarie-Pierre Deiber, PhD
Outcome results
None listed