ADHD Electrophysiological Subtypes and Implications in Transcranial Direct-current Stimulation

NCT ID: NCT01649232

Last Updated: 2024-05-09

Study Results

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Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

60 participants

Study Classification

OBSERVATIONAL

Study Start Date

2012-06-30

Study Completion Date

2012-12-31

Brief Summary

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In the present study the aim is to examine whether transcranial direct-current stimulation (tDCS) generated excitability changes and induce modifications of functional cortical architecture in Attention Deficit Hyperactivity Disorder (ADHD) patients. To achieve this, the investigators used an event-related potential (ERP) analysis based on 20 channel EEG recordings in ADHD subjects before and after bipolar tDCS-anode stimulation over F3/F4 or T5/T6 or P4/P3, during resting state and measure clinical scores and visual CPT tasks changes. Time courses and topography of independent component visual ERPs were compared before and after tDCS.

Detailed Description

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Important advances in the understanding of ADHD pathophysiology, such fMRI studies showing a focal frontotemporal loops dysfunction in brain activity, suggest that frontal brain stimulation might be helpful for the treatment of ADHD. In a recent study of Lyon's university with tDCS they concluded that tDCS is "cheaper and easier-use than transcranial magnetic stimulation (TMS) and the impact on symptomatology seems larger (impact on negative symptoms of schizophrenic patients) and longer (at least 3 months duration) than that TMS currently permits. It is possible that tDCS could in the future be used at home by patients themselves. The efficacy of tDCS depends of parameters like electrode position and current strength.

In this trial, The investigators investigated the effects of 12 days of anodal stimulation of the left dorsolateral prefrontal cortex in ten patients with ADHD (aged 8 +/- 3 years). tDCS was applied through a saline-soaked pair of surface sponge electrodes (35 cm2). The anode electrode was placed over F3/F4 or T5/T6 or P4/P3(based on the 10-20 International EEG System) of each subject. The cathode was placed over the contralateral mastoid area. A constant current between 1.1 and 2.0 mA was applied for 25 min/day (administered for 12 alternated days).

Prior to the first session, ADHD subjects were asked to complete and return a series of questionnaires, including the Conners Brief Symptom Inventory, a health history questionnaire, and the QEEG questionnaire. Subjects were then tested in a first session which lasted approximately three hours. During this period, a comprehensive structured clinical interview was carried out, comprising of an assessment of current and past ADHD symptoms, the history of problems at school, the past psychiatric history (including drug and medication use), as well as past and present comorbidities. Subsequently, EEG data was acquired. EEG data was first recorded while the subject was in eyes-closed and eyes-open resting conditions, lasting four minutes each. Then data was recorded while subjects performed a visual continuous performance task (VCPT). The VCPT took approximately 22 minutes to complete. In addition, subjects randomly performed either an auditory or an emotional continuous performance task.

The control group had a shortened procedure. Subjects were tested in a single session lasting approximately two and a half hours. During this period, a series of questionnaires (Brief Symptom Inventory, Health History questionnaire, Current Symptoms Scales) were filled out and thereafter, EEG data was acquired. Subsequently, a working memory task, which is not relevant here, was administered.

EEG was recorded using a Mitsar 201 19-channel electroencephalographic system. The input signals referenced to the linked ears were filtered between 0.5 and 50 Hz and digitized at a sampling rate of 250 Hz. Impedance was kept below 5 kOhm for all electrodes. Electrodes were placed according to the International 10-20 system using a electrode cap. Quantitative data was calculated using WinEEG software. Linked ears reference montage was changed to average reference montage prior to data processing. Eye-blink artefacts were corrected by zeroing the activation curves of individual ICA component score responding to eye blinks. In addition, epochs of the filtered electroencephalogram with excessive amplitude (\>100 μV) and/or excessive fast (\>35 μV in 20 to 35 Hz band) and slow (\>50 μV in 0 to 1 Hz band) activity were automatically marked and excluded from further analysis. Finally, EEG was manually inspected to verify artefact removal.

Behavioral task

The VCPT is a modification of the visual two-stimulus GO/NOGO paradigm. Three categories of visual stimuli were selected: 20 pictures of animals, 20 pictures of plants, and 20 pictures of humans (presented together with an artificial "novel" sound). The trials consisted of presentations of pairs of stimuli: animal-animal (GO trials), animal-plant (NOGO trials), plant-plant (IGNORE trials), and plant-human (NOVEL trials). The trials were grouped into four blocks. In each block a unique set of five animal stimuli, five plant stimuli and five human stimuli was selected. Each block consisted of a pseudo-random presentation of 100 stimuli pairs with equal probability for each trial category.

The task was to press a button as fast as possible in response to GO trials.

According to the task design, two preparatory sets were distinguished in the trials. In the "Continue set" a picture of an animal is presented as the first stimulus and the subject is supposed to prepare to respond. In the "Discontinue set" a picture of a plant is presented as the first stimulus and the subject does not need to prepare to respond.

During the task, subjects were seated in a comfortable chair, 1.5 m in front of a computer screen. The stimuli were presented on a 17 inch monitor using the Psytask (Mitsar Ltd.) software.

The primary outcome was change in score on the QEEG Rating Scale (AMEN questionnaire). The ERP and questionnaire/behavioural assessments will be made at baseline (before stimulation)and 3 months after stimulation.

This study involved 30 subjects, all aged between 7 and 13. All have been diagnosed with ADHD by a medical professional.

Conditions

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ADHD ADD

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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active tDCS

The patients with ADHD received electro-stimulation at 20 sessions with 2 mAmp 1 session per day alternative days. The investigators used an ERP analysis derived of 20 channel EEG recordings during resting state and visual CPT to define the tDCS site and polarity at refractory ADHD patients to conventional treatments. Time courses, topography and amplitude of ERPs, correlated with clinical scores, were compared with the controls average (data base)to guide the selection of personal tDCS parameters. The following relation shown how many patients were submitted to intervention in each electrode, according to their polarity: Anodal tDCS: T5, T6, etc. Cathodal tDCS: T5, T6, etc.

Active tDCS

Intervention Type DEVICE

tDCS applied to left dorsolateral prefrontal scalp area through a saline-soaked pair of surface sponge electrodes (35 cm2). The anode electrode was placed over F3 (based on the 10-20 International EEG System) of each subject. The cathode was placed over the contralateral mastoid area. A constant current of 1.1 mA was applied for 25 min/day (administered for 12 alternated days).

controls

Healthy people that not receive tDCS

No interventions assigned to this group

Interventions

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Active tDCS

tDCS applied to left dorsolateral prefrontal scalp area through a saline-soaked pair of surface sponge electrodes (35 cm2). The anode electrode was placed over F3 (based on the 10-20 International EEG System) of each subject. The cathode was placed over the contralateral mastoid area. A constant current of 1.1 mA was applied for 25 min/day (administered for 12 alternated days).

Intervention Type DEVICE

Other Intervention Names

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Chattanooga Iontophoresis

Eligibility Criteria

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Inclusion Criteria

1. ADHD diagnosis.
2. Age between 7 and 65 years.
3. Comorbidities were no reason for subject exclusion.

Exclusion Criteria

1. Presence of psychosis.
2. Subjects taking medication,they had refrained from taking methylphenidate during 24 hours before testing.
3. Subjects taking other psychotropics were not included in the study.
4. Subjects which had suffered of a head injury with subsequent loss of consciousness, and subjects suffering from neurological or systemic medical diseases were excluded from the study.
Minimum Eligible Age

8 Years

Maximum Eligible Age

68 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Spanish Foundation for Neurometrics Development

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Moises Aguilar Domingo, PhD

Role: STUDY_CHAIR

Brainmech Foundation

Locations

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New Remedies

Liverpool, , United Kingdom

Site Status

Countries

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United Kingdom

References

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Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera GM. Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear Biomed Phys. 2011 Jul 19;5:5. doi: 10.1186/1753-4631-5-5.

Reference Type RESULT
PMID: 21771289 (View on PubMed)

Mueller A, Candrian G, Kropotov JD, Ponomarev VA, Baschera GM. Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomed Phys. 2010 Jun 3;4 Suppl 1(Suppl 1):S1. doi: 10.1186/1753-4631-4-S1-S1.

Reference Type RESULT
PMID: 20522259 (View on PubMed)

Bledsoe JC, Xiao C, Chaovalitwongse A, Mehta S, Grabowski TJ, Semrud-Clikeman M, Pliszka S, Breiger D. Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment. J Atten Disord. 2020 Sep;24(11):1547-1556. doi: 10.1177/1087054716649666. Epub 2016 May 26.

Reference Type RESULT
PMID: 27231214 (View on PubMed)

Mikolas P, Vahid A, Bernardoni F, Suss M, Martini J, Beste C, Bluschke A. Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Sci Rep. 2022 Jul 28;12(1):12934. doi: 10.1038/s41598-022-17126-x.

Reference Type RESULT
PMID: 35902654 (View on PubMed)

Other Identifiers

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vpradtdcs0102012

Identifier Type: -

Identifier Source: org_study_id

NCT01755793

Identifier Type: -

Identifier Source: nct_alias

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