Ignite Creation Date:
2025-12-25 @ 12:13 AM
Ignite Modification Date:
2025-12-31 @ 3:20 PM
Study NCT ID:
NCT06729658
Status:
COMPLETED
Last Update Posted:
2025-05-31
First Post:
2024-08-27
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Dopamine and Brain Computer Interface
Sponsor:
Organization:
Raw JSON
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'Tyrosine'}]}}, 'protocolSection': {'designModule': {'phases': ['EARLY_PHASE1'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'INVESTIGATOR']}, 'primaryPurpose': 'TREATMENT', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Double-blinded randomized placebo-controlled intervention with 2 arms: BCI intervention with Placebo; BCI intervention with Levodopa'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 22}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2017-10-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2024-08-06', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-05-27', 'studyFirstSubmitDate': '2024-08-27', 'studyFirstSubmitQcDate': '2024-12-10', 'lastUpdatePostDateStruct': {'date': '2025-05-31', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-12-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-02-27', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Changes in brain structure as assessed by MTsat', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes by comprehensive assessment of brain tissue properties, allowing for sensitive detection of subtle neuroplastic changes across magnetization transfer saturation (MTsat) before and after the intervention.'}, {'measure': 'Changes in brain structure as assessed by PD', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes by comprehensive assessment of brain tissue properties, allowing for sensitive detection of subtle neuroplastic changes across proton density (PD) before and after the intervention.'}, {'measure': 'Changes in brain structure as assessed by R1', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes by comprehensive assessment of brain tissue properties, allowing for sensitive detection of subtle neuroplastic changes across longitudinal transverse relaxation rate R1 before and after the intervention.'}, {'measure': 'Changes in brain structure as assessed by R2*', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes by comprehensive assessment of brain tissue properties, allowing for sensitive detection of subtle neuroplastic changes across effective transverse relaxation rate R2\\* before and after the intervention.'}, {'measure': 'White matter changes as assessed by DWI (FA)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes across fractional anisotropy (FA) before and after the intervention.'}, {'measure': 'White matter changes as assessed by DWI (MD)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes across mean diffusivity (MD) before and after the intervention.'}, {'measure': 'White matter changes as assessed by DWI (AD)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes across axial diffusivity (AD) before and after the intervention.'}, {'measure': 'White matter changes as assessed by DWI (RD)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes across radial diffusivity (RD) before and after the intervention.'}, {'measure': 'White matter changes as assessed by DWI (g-ratio)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying structural changes assessed by ratio of the inner axonal diameter to the total outer diameter (g-ratio) before and after the intervention.'}, {'measure': 'Functional connectivity changes due to neuroplasticity (rs-fMRI)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying functional changes by comprehensive assessment of brain connectivity properties using resting-state fMRI before and after the intervention.'}, {'measure': 'Functional and structural brain changes due to neuroplasticity (t-fMRI)', 'timeFrame': 'Total of 4 MRIs: 1 MRI 1 week before the intervention, 1 MRI the day before the intervention week, 1 MRI 1 day after the intervention week, and 1 MRI 1 week after.', 'description': 'Characterization of underlying functional changes by comprehensive assessment of brain activity and connectivity properties using task-based fMRI before and after the intervention.'}], 'secondaryOutcomes': [{'measure': 'BCI classification accuracy', 'timeFrame': '1 week', 'description': 'Change in BCI classification accuracy. The BCI accuracy is calculated after each session and it is defined as the number of correctly classified trials divided by the number of total trials.'}, {'measure': 'Time needed to achieve above chance-level BCI accuracy.', 'timeFrame': '1 week', 'description': 'Time in days needed to achieve above chance-level BCI accuracy.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Dopamine', 'Brain Computer Interface', 'Motor control', 'Neuroplasticity', 'MRI', 'Quantitative MRI', 'Diffusion MRI'], 'conditions': ['Healthy Participants']}, 'referencesModule': {'references': [{'pmid': '21250374', 'type': 'BACKGROUND', 'citation': 'Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJL, editors. Global Burden of Disease and Risk Factors. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006. Available from http://www.ncbi.nlm.nih.gov/books/NBK11812/'}, {'pmid': '22208122', 'type': 'BACKGROUND', 'citation': 'Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, Piccione F, Birbaumer N. Brain-computer interface in stroke: a review of progress. Clin EEG Neurosci. 2011 Oct;42(4):245-52. doi: 10.1177/155005941104200410.'}, {'pmid': '25489973', 'type': 'BACKGROUND', 'citation': 'Soekadar SR, Birbaumer N, Slutzky MW, Cohen LG. Brain-machine interfaces in neurorehabilitation of stroke. Neurobiol Dis. 2015 Nov;83:172-9. doi: 10.1016/j.nbd.2014.11.025. Epub 2014 Dec 7.'}, {'pmid': '4272516', 'type': 'BACKGROUND', 'citation': 'Bernheimer H, Birkmayer W, Hornykiewicz O, Jellinger K, Seitelberger F. Brain dopamine and the syndromes of Parkinson and Huntington. Clinical, morphological and neurochemical correlations. J Neurol Sci. 1973 Dec;20(4):415-55. doi: 10.1016/0022-510x(73)90175-5. No abstract available.'}, {'pmid': '18320725', 'type': 'BACKGROUND', 'citation': 'Arias-Carrion O, Poppel E. Dopamine, learning, and reward-seeking behavior. Acta Neurobiol Exp (Wars). 2007;67(4):481-8. doi: 10.55782/ane-2007-1664.'}, {'pmid': '21144997', 'type': 'BACKGROUND', 'citation': 'Bromberg-Martin ES, Matsumoto M, Hikosaka O. Dopamine in motivational control: rewarding, aversive, and alerting. Neuron. 2010 Dec 9;68(5):815-34. doi: 10.1016/j.neuron.2010.11.022.'}, {'pmid': '15935475', 'type': 'BACKGROUND', 'citation': "Cools R. Dopaminergic modulation of cognitive function-implications for L-DOPA treatment in Parkinson's disease. Neurosci Biobehav Rev. 2006;30(1):1-23. doi: 10.1016/j.neubiorev.2005.03.024. Epub 2005 Jun 1."}, {'pmid': '12570364', 'type': 'BACKGROUND', 'citation': 'Saint-Cyr JA. Frontal-striatal circuit functions: context, sequence, and consequence. J Int Neuropsychol Soc. 2003 Jan;9(1):103-27. doi: 10.1017/s1355617703910125.'}, {'pmid': '17919725', 'type': 'BACKGROUND', 'citation': 'Seger CA. How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neurosci Biobehav Rev. 2008;32(2):265-78. doi: 10.1016/j.neubiorev.2007.07.010. Epub 2007 Aug 12.'}, {'pmid': '6357357', 'type': 'BACKGROUND', 'citation': 'Beninger RJ. The role of dopamine in locomotor activity and learning. Brain Res. 1983 Oct;287(2):173-96. doi: 10.1016/0165-0173(83)90038-3.'}, {'pmid': '2648975', 'type': 'BACKGROUND', 'citation': 'Wise RA, Rompre PP. Brain dopamine and reward. Annu Rev Psychol. 1989;40:191-225. doi: 10.1146/annurev.ps.40.020189.001203.'}, {'pmid': '9054347', 'type': 'BACKGROUND', 'citation': 'Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997 Mar 14;275(5306):1593-9. doi: 10.1126/science.275.5306.1593.'}, {'pmid': '21469956', 'type': 'BACKGROUND', 'citation': 'Gerfen CR, Surmeier DJ. Modulation of striatal projection systems by dopamine. Annu Rev Neurosci. 2011;34:441-66. doi: 10.1146/annurev-neuro-061010-113641.'}, {'pmid': '16926036', 'type': 'BACKGROUND', 'citation': 'Colzato LS, van Wouwe NC, Hommel B. Feature binding and affect: emotional modulation of visuo-motor integration. Neuropsychologia. 2007 Jan 28;45(2):440-6. doi: 10.1016/j.neuropsychologia.2006.06.032. Epub 2006 Aug 22.'}, {'pmid': '18714325', 'type': 'BACKGROUND', 'citation': 'Jenner P. Molecular mechanisms of L-DOPA-induced dyskinesia. Nat Rev Neurosci. 2008 Sep;9(9):665-77. doi: 10.1038/nrn2471.'}, {'pmid': '23494615', 'type': 'BACKGROUND', 'citation': 'Ramos-Murguialday A, Broetz D, Rea M, Laer L, Yilmaz O, Brasil FL, Liberati G, Curado MR, Garcia-Cossio E, Vyziotis A, Cho W, Agostini M, Soares E, Soekadar S, Caria A, Cohen LG, Birbaumer N. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013 Jul;74(1):100-8. doi: 10.1002/ana.23879. Epub 2013 Aug 7.'}, {'pmid': '25120465', 'type': 'BACKGROUND', 'citation': 'Ang KK, Guan C, Phua KS, Wang C, Zhou L, Tang KY, Ephraim Joseph GJ, Kuah CW, Chua KS. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng. 2014 Jul 29;7:30. doi: 10.3389/fneng.2014.00030. eCollection 2014.'}, {'pmid': '27990240', 'type': 'BACKGROUND', 'citation': 'Cho W, Sabathiel N, Ortner R, Lechner A, Irimia DC, Allison BZ, Edlinger G, Guger C. Paired Associative Stimulation Using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot Study. Eur J Transl Myol. 2016 Jun 6;26(3):6132. doi: 10.4081/ejtm.2016.6132. eCollection 2016 Jun 13.'}, {'pmid': '20718931', 'type': 'BACKGROUND', 'citation': 'Caria A, Weber C, Brotz D, Ramos A, Ticini LF, Gharabaghi A, Braun C, Birbaumer N. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology. 2011 Apr;48(4):578-82. doi: 10.1111/j.1469-8986.2010.01117.x. Epub 2010 Aug 16.'}, {'pmid': '24590225', 'type': 'BACKGROUND', 'citation': 'Mukaino M, Ono T, Shindo K, Fujiwara T, Ota T, Kimura A, Liu M, Ushiba J. Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. J Rehabil Med. 2014 Apr;46(4):378-82. doi: 10.2340/16501977-1785.'}, {'pmid': '16458595', 'type': 'BACKGROUND', 'citation': 'Birbaumer N. Brain-computer-interface research: coming of age. Clin Neurophysiol. 2006 Mar;117(3):479-83. doi: 10.1016/j.clinph.2005.11.002. Epub 2006 Feb 2. No abstract available.'}, {'pmid': '9469657', 'type': 'BACKGROUND', 'citation': 'Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett. 1997 Dec 19;239(2-3):65-8. doi: 10.1016/s0304-3940(97)00889-6.'}, {'pmid': '20726844', 'type': 'BACKGROUND', 'citation': 'Johansson BB. Current trends in stroke rehabilitation. A review with focus on brain plasticity. Acta Neurol Scand. 2011 Mar;123(3):147-59. doi: 10.1111/j.1600-0404.2010.01417.x. Epub 2010 Aug 19.'}, {'pmid': '25076886', 'type': 'BACKGROUND', 'citation': 'Young BM, Nigogosyan Z, Walton LM, Song J, Nair VA, Grogan SW, Tyler ME, Edwards DF, Caldera K, Sattin JA, Williams JC, Prabhakaran V. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface. Front Neuroeng. 2014 Jul 15;7:26. doi: 10.3389/fneng.2014.00026. eCollection 2014.'}, {'pmid': '27695404', 'type': 'BACKGROUND', 'citation': 'Young BM, Stamm JM, Song J, Remsik AB, Nair VA, Tyler ME, Edwards DF, Caldera K, Sattin JA, Williams JC, Prabhakaran V. Brain-Computer Interface Training after Stroke Affects Patterns of Brain-Behavior Relationships in Corticospinal Motor Fibers. Front Hum Neurosci. 2016 Sep 16;10:457. doi: 10.3389/fnhum.2016.00457. eCollection 2016.'}, {'pmid': '22645108', 'type': 'BACKGROUND', 'citation': 'Varkuti B, Guan C, Pan Y, Phua KS, Ang KK, Kuah CW, Chua K, Ang BT, Birbaumer N, Sitaram R. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair. 2013 Jan;27(1):53-62. doi: 10.1177/1545968312445910. Epub 2012 May 29.'}]}, 'descriptionModule': {'briefSummary': 'The use of Brain-Computer Interface system (BCI system) allows for the detection of neurophysiological signals on the surface of the head and provides feedback to subjects or patients. For patients with neurological disorders who have severe motor deficits, self-generated brain signals can be translated, for example, into orthosis-supported movement of the paralyzed limb. Another possibility is to translate the brain signal into peripheral electrostimulation (functional electrical stimulation, FES), which generates muscle contraction and thus movement.\n\nFundamentally, BCI technology can be used as a replacement therapy when no recovery of motor function is expected. Another important application lies in improving motor training, relearning, and initiating movements. In the latter case, it is hoped that BCI training will stimulate neuroplastic mechanisms that lead to functional improvement.\n\nProblems on the translational path to clinical application are:\n\n* The high interindividual variability between different people regarding learning to control the BCI system;\n* The extent of learning and motor improvement is often limited For this reason, the present study aims to investigate whether dopaminergic influence on the brain affects the effectiveness of using a BCI system in healthy subjects.', 'detailedDescription': 'Aims of the present research project are to assess the effect of dopaminergic modulation on BCI performance in healthy elderly subjects to understand the underlying neurophysiological mechanisms. The perspective lies in the application of this approach for improved motor recovery after stroke.\n\nStroke is one of the most common causes of motor function impairment, and its prevalence is expected to rise due to an aging population. Stroke survivors often experience some level of spontaneous recovery of motor function during the acute stage and reach a functional plateau after which the recovery is generally slow or stagnant. Interestingly, there is emerging evidence indicating that brain-computer interface (BCI) based therapies can induce recovery beyond this plateau.\n\nPharmacological MRI (phMRI) is a new and promising method to study the effects of substances on brain function that can ultimately be used to unravel underlying neurobiological mechanisms behind drug action. Like most of the imaging methods it represents a progress in the investigation of brain disorders and the related function of neurotransmitter pathways in a non-invasive way with respect of the overall neuronal connectivity.\n\nMoreover, it provides an ideal tool for translation to clinical investigations. MRI, while still behind in molecular imaging strategies compared to PET and SPECT, has the advantage to have a high spatial resolution and no need for the injection of a contrast-agent or radio-labeled molecules, thereby avoiding the repetitive exposure to ionising radiations. Functional MRI (fMRI) is extensively used in research and clinical setting, where it is generally combined with a psycho-motor task. phMRI is an adaptation of fMRI enabling the investigation of a specific neurotransmitter system, such as dopamine, under physiological or pathological conditions following activation via administration of a specific challenging drug.\n\nThe importance of the neurotransmitter dopamine (DA) for motor processes has long been known. In patients suffering from the Parkinson\'s disease the dopamine deficiency in the basal ganglia is known to cause strong movement-related deficits.\n\nRecent studies suggest that DA stimulates neuronal structures, which in turn affect extensive brain regions, and thus contributes to various processes of behavioural control: both motor processes of movement control and cognitive processes in the context of perceptual categorisation, reward, motivation, and executive control. For this reason, DA is also referred to as a "teaching signal".\n\nThere\'s emerging evidence, that DA can be effective also for forming new stroke rehabilitation strategies. For a rehabilitation strategy to be effective, it should result in formation of new motor memories, which is anatomically mediated by networks that connect the dorsolateral prefrontal cortex, primary motor cortex, striatum, and the cerebellum. New motor memories are formed and pruned by the processes of synaptic plasticity such as LTP and LTD, which require dopaminergic signaling between the substantia nigra pars compacta and striatal medium spiny neurons in the putamen. Within the motor loops of the basal ganglia, dopaminergic binding to D1Rs facilitate desired movements, whereas binding to D2Rs inhibit undesired movements.\n\nIn addition to its role in motor drive within the basal ganglia, the dopaminergic system also potentiates visuomotor integration, which is the coordination of perceptual and action-related information. At the receptor level, D1Rs are critical for proper visuomotor integration. This system is important for relating visualized environmental information with body position, thus enabling optimal movement planning and correction. Therefore, potentiating the coordination of motor drive and visuomotor integration through dopaminergic therapy may enhance recovery after stroke.\n\nDrugs that increase the availability of central nervous system neurotransmitters (dopamine, noradrenaline, serotonin, and acetylcholine) have been shown to exert a facilitatory effect on neuroplasticity. With this in mind, investigators have studied the effects of amphetamines, selective serotonin reuptake inhibitors, donepezil, psychostimulants such as methylphenidate, and dopaminergic agents on motor recovery after stroke. Of the aforementioned drugs, only levodopa has been shown to enhance the induction of LTP-like plasticity, practice-dependent plasticity, and motor recovery after stroke in human subjects. In addition, levodopa has a safe side effect profile and is not a controlled substance.\n\nThe most common side effect of levodopa is dyskinesia, followed by nausea, then hallucinations and dizziness. Also, there is some risk of levodopa induced dyskinesia in patients with Parkinson\'s disease. However, these severe side-effects generally enroll after long-term (i.e., years) intake of the drug. In addition, the risk in patients with other conditions, such as stroke, is estimated to be much lower. In fact, levodopa has been used in numerous studies that focus on motor recovery in stroke survivors without any reports of dyskinesia nor other minor or major side effects. "The literature" concludes that treating stroke survivors with levodopa is unlikely to cause levodopa-induced dyskinesia, unless there is comorbid basal ganglia damage or Parkinson\'s disease.\n\nThe basis of BCIs functioning is the translation of neural activity directly recorded from the subject into real-time feedback in order to train consistent brain activation patterns associated with specific mental states. Neural activity can be detected through invasive (ECoG/iEEG), or non-invasive (EEG, MEG, real-time fMRI, or NIRS) methods. The majority of studies deploy EEG based non-invasive BCIs, as they are relatively easy and fast to operate, and have good temporal and spatial characteristics, thus can be used safely and effectively to elicit functional gains in stroke survivors with persistent motor deficits and may enhance the efficacy of concurrent or associated therapies, even after individuals reach a functional plateau using traditional therapies.\n\nCurrently, the majority of BCIs that target restoration of motor function are based on motor imagery (MI). Such systems are not reliant on actual movements, but rather use the mental process of imagination of a movement. The main reason is that MI leads to the activation of the same brain areas as actual movement. Problems that arise with the motor imagery without any feedback are the lack of control of the activity as well as the lack of motivation. Using a BCI, motor imagery can be measured in real-time, thus making it possible to provide real-time feedback to the subject. Furthermore, the coupling of BCI devices with MI triggered functional electrical stimulation (FES) allows for resynchronisation of cortical activation, peripheral activation, and sensory feedback. In addition to this, some studies have argued for inclusion of virtual reality for immediate visual feedback. Combination of virtual reality based action observation and FES feedback may potentiate the motor function improvement as subjects interact with the real-time on-screen avatar. Thus, it is possible to close the circuit: the motor imagery is detected by the system, and FES is applied to the targeted muscle to help the participant carry out the movement. At the same time, an avatar performs the exact same movement (in synchrony with FES), which is displayed on the participant screen in real-time. Hence, in addition to performing the physical movement which contributes to the success of the therapy, the areas of the sensory cortex are also activated synchronously with the motor imagery via the afferent nerve impulses. This leads to the stimulation of the Hebbian plasticity, which states that neurones which are repeatedly stimulated together create common connections. This is thought to induce use-dependent plasticity and facilitate functional recovery. Such strengthening of central-peripheral connections via complimentary technologies has the potential to enhance motor function recovery through induced use-dependent plasticity and facilitate post-stroke functional recovery.\n\nThere is evidence of changes in brain activation and functional connectivity (FC) in stroke patients receiving BCI based rehabilitation therapies. They can potentially result in an increase of FC between the inferior parietal lobe and the supplementary motor area (SMA), as well as between the anterior cingulate cortex and the SMA, positively correlated with gains in Fugl-Meyer scores. Moreover, FC increases were observed between the ipsilesional thalamus and the contralesional cingulate, contralateral paracentral lobule, and the bilateral precuneus.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Age: between 18 and 80 years old at the time of signing the consent form\n* BCI naïve\n* MRI compatible\n* Participation in a detailed discussion on the explanation of the experiment\n* Signing of consent to participate in each experiment\n\nExclusion Criteria:\n\n* Sensory deficits (visual and auditory)\n* Wernicke's or global aphasia\n* Strong spasticity\n* Neurological and/or psychiatric diseases\n* Severe pre-existing lung or heart diseases; Gastrointestinal diseases; Malignant disease\n* Thyroid diseases\n* Taking other medications\n* Narrow angle glaucoma\n* Non-age-related otological diseases\n* Stimulators (cardiac, neuro, etc.)\n* Participation in a similar study\n* Fractures or lesions in the upper extremities\n* Preceding neurosurgical procedures\n* Inability to perform the experimental tasks\n* Inability to give consent\n* Have contraindication for magnetic resonance tomography (MRI) (e.g. braces, cardiac pacemakers, metallic implants that might interfere with the MR signal, claustrophobia)\n* Severe attention and drive disorders\n* Alcohol or drug abuse\n* Pregnancy\n* Women in breastfeeding period"}, 'identificationModule': {'nctId': 'NCT06729658', 'acronym': 'BCI_LDOPA', 'briefTitle': 'Dopamine and Brain Computer Interface', 'organization': {'class': 'OTHER', 'fullName': 'Max Planck Institute for Human Cognitive and Brain Sciences'}, 'officialTitle': 'The Effect of Dopaminergic Modulation on Brain Computer Interface Efficacy', 'orgStudyIdInfo': {'id': 'BCI_LDOPA_01'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Interventional group - Levodopa', 'description': 'Arm Description: Participants will receive Levodopa followed by BCI-mediated training for 6 days.', 'interventionNames': ['Drug: Madopar']}, {'type': 'PLACEBO_COMPARATOR', 'label': 'Control group - Placebo', 'description': 'Arm Description: Participants will receive Placebo followed by BCI-mediated training for 6 days.', 'interventionNames': ['Drug: Placebo']}], 'interventions': [{'name': 'Madopar', 'type': 'DRUG', 'otherNames': ['Levodopa'], 'description': 'Experimental group participants will receive Madopar 125mg for 6 days.', 'armGroupLabels': ['Interventional group - Levodopa']}, {'name': 'Placebo', 'type': 'DRUG', 'otherNames': ['A pill without any active components.'], 'description': 'Control group participants will receive placebo for 6 days.', 'armGroupLabels': ['Control group - Placebo']}]}, 'contactsLocationsModule': {'locations': [{'zip': '04103', 'city': 'Leipzig', 'country': 'Germany', 'facility': 'Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences', 'geoPoint': {'lat': 51.33962, 'lon': 12.37129}}], 'overallOfficials': [{'name': 'Arno Villringer, PhD', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Max Planck Institute for Human Cognitive and Brain Sciences'}, {'name': 'Bernhard Sehm, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Max Planck Institute for Human Cognitive and Brain Sciences'}, {'name': 'Khosrov A. Grigoryan, MSc', 'role': 'STUDY_CHAIR', 'affiliation': 'Max Planck Institute for Human Cognitive and Brain Sciences'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Max Planck Institute for Human Cognitive and Brain Sciences', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}