Improving Visual Perception and Visuo-motor Learning With Neurofeedback of Brain Network Interaction.

Healthy Clinical Trial

— NF-BrainNet  

Official title:

Improving Visual Perception and Visuo-motor Learning With Neurofeedback of Brain Network Interaction.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05732649
• Other study ID #: 2022-00976
• Status: Recruiting
• Phase: N/A
• Start date: May 1, 2023
• Est. completion date: May 31, 2025

Study information

• Verified date: November 2023
• Source: Insel Gruppe AG, University Hospital Bern
• Contact: Adrian Guggisberg, Prof. Dr.
• Phone: +41316323081
• Email: adrian.guggisberg[@]insel.ch
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Neuroscience has long focused on understanding brain activity during task performance. As a result, current training methods aim to maximize brain activation during a trained task. However, new evidence shows that this may not be an efficient way to go. Human subjects achieve maximum performance only when the brain network is in a state of high spontaneous interaction and communication between brain regions before training or, in other words, in a state of high "network communication." In this case, minimal effort is required during the task. This requires new learning strategies aimed at inducing higher network communication prior to task execution. The investigators have previously shown that healthy people can learn to increase network communication of motor areas (i.e., the areas that control movement) when they receive real-time feedback on their current activity, which is known as neurofeedback. In neurofeedback, subjects receive continuous feedback about the state of their brain activity in a present moment. Through this feedback, they can learn to change their own brain activity.

The aim of the present study is to validate neurofeedback as a new treatment approach for inducing high network communication at rest (i.e., when participants are not engaged in a task), and to test whether this heightened network communication can enhance visual perception and motor learning.

Description:

As the investigators have seen in a pilot study, participants are unable to improve the network communication of visual brain regions (i.e., regions that process light stimuli) through neurofeedback when the feedback is perceived with the eyes. Therefore, the goal of Experiment 1 is to find an alternative form of feedback through which participants can efficiently increase network communication. Specifically, participants will receive neurofeedback in the form of a sound, a vibration on the skin, or both. For neurofeedback as sound, the investigators will use relaxing sounds which have previously been found to enhance network communication. For neurofeedback as vibration, electrical or vibrotactile stimulators will stimulate both hands and feet, as previous research has found a positive effect on brain network communication. The sensory stimulation intensity will be modulated based on the current level of network communication between the target brain area and the rest of the brain. Thus, greater communication leads to reduced feedback intensity. The idea is that subjects learn to maintain states of high communication without sensory feedback. Subjects will receive the instruction to lower the feedback level without indication of any particular strategy of mental imagery, as the investigators have previously observed that no specific mental imagery task is able to enhance alpha-band FC without feedback. Finally, to explore effects on the behavioral level, the investigators will additionally assess visual perception at the beginning and at the end of each session, similarly as in a previous study.

In Experiment 2, the neurofeedback modality from Experiment 1 is adopted to test whether increasing network communication through neurofeedback can lead to improved visuo-motor learning. Visuo-motor learning will be measured with the mirror-drawing task because the investigators have evidence for feasibility from a previous study and because it represents a good model for re-learning as needed in clinics.

In both experiments, participants will undergo magnetic resonance imaging (MRI). This MRI will increase the precision of neurofeedback.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 65
• Est. completion date: May 31, 2025
• Est. primary completion date: May 31, 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Signed informed consent

• Age at least 18 years old

• Normal or corrected-to-normal vision

• No neurological or psychiatric diseases

• No regular consumption of benzodiazepines or neuroleptics

Exclusion Criteria:

• Any surgical intervention to the brain

• Drug or alcohol abuse

• Presence of non-MRI safe metal in the body

Related Conditions & MeSH terms

• Healthy

Intervention

Behavioral:
• Auditory neurofeedback
Network interaction measured with EEG at visual areas will be coupled with the intensity of a sound.
• Tactile neurofeedback
Network interaction measured with EEG at visual areas will be coupled with the intensity of tactile stimulation (i.e., electrical or vibrotactile stimulators applied on both hands and feet).
• Auditory and tactile neurofeedback
Network interaction measured with EEG at visual areas will be coupled with the intensity of a sound and tactile stimulation.
• Neurofeedback
Participants train to decrease the intensity of a sensory stimulation (defined in Experiment 1) that is coupled with the network interaction at a specific brain region.

Locations

Country	Name	City	State
Switzerland	Division of Neurorehabilitation, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.	Bern

Sponsors (2)

Lead Sponsor	Collaborator
Insel Gruppe AG, University Hospital Bern	University of Bern

Country where clinical trial is conducted

Switzerland, 

References & Publications (9)

Allaman L, Mottaz A, Guggisberg AG. Disrupted resting-state EEG alpha-band interactions as a novel marker for the severity of visual field deficits after brain lesion. Clin Neurophysiol. 2021 Sep;132(9):2101-2109. doi: 10.1016/j.clinph.2021.05.029. Epub 2021 Jun 28. — View Citation

Allaman L, Mottaz A, Kleinschmidt A, Guggisberg AG. Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations. J Neurosci. 2020 Dec 9;40(50):9663-9675. doi: 10.1523/JNEUROSCI.1166-20.2020. Epub 2020 Nov 6. — View Citation

Dubovik S, Pignat JM, Ptak R, Aboulafia T, Allet L, Gillabert N, Magnin C, Albert F, Momjian-Mayor I, Nahum L, Lascano AM, Michel CM, Schnider A, Guggisberg AG. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012 May 15;61(1):249-57. doi: 10.1016/j.neuroimage.2012.03.024. Epub 2012 Mar 13. — View Citation

Freyer F, Reinacher M, Nolte G, Dinse HR, Ritter P. Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline. Front Hum Neurosci. 2012 May 23;6:144. doi: 10.3389/fnhum.2012.00144. eCollection 2012. — View Citation

Guggisberg AG, Dalal SS, Zumer JM, Wong DD, Dubovik S, Michel CM, Schnider A. Localization of cortico-peripheral coherence with electroencephalography. Neuroimage. 2011 Aug 15;57(4):1348-57. doi: 10.1016/j.neuroimage.2011.05.076. Epub 2011 Jun 7. — View Citation

Guggisberg AG, Honma SM, Findlay AM, Dalal SS, Kirsch HE, Berger MS, Nagarajan SS. Mapping functional connectivity in patients with brain lesions. Ann Neurol. 2008 Feb;63(2):193-203. doi: 10.1002/ana.21224. — View Citation

Manuel AL, Guggisberg AG, Theze R, Turri F, Schnider A. Resting-state connectivity predicts visuo-motor skill learning. Neuroimage. 2018 Aug 1;176:446-453. doi: 10.1016/j.neuroimage.2018.05.003. Epub 2018 May 4. Erratum In: Neuroimage. 2019 Jan 15;185:83-84. — View Citation

Mottaz A, Solca M, Magnin C, Corbet T, Schnider A, Guggisberg AG. Neurofeedback training of alpha-band coherence enhances motor performance. Clin Neurophysiol. 2015 Sep;126(9):1754-60. doi: 10.1016/j.clinph.2014.11.023. Epub 2014 Dec 6. — View Citation

Paszkiel S, Dobrakowski P, Lysiak A. The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study. Brain Sci. 2020 Oct 13;10(10):728. doi: 10.3390/brainsci10100728. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Changes in network communication	The primary outcome measure for both experiments will be the change in network communication during neurofeedback measured with electroencephalography (EEG). Network communication will be computed as alpha-band functional connectivity (FC) as described in the investigators' validation papers.	Alpha-band FC will be measured using EEG for 10 minutes (min) before neurofeedback, for ca. 20 min during neurofeedback, and for 10 min after neurofeedback.
Secondary	Visual perception	The secondary outcome measure for experiment 1 will be the behavioral performance in visual perception. Better visual performance results in a higher percentage of detected stimuli either on the left or right half of the screen, as in the investigators' validation paper.	The visual perception task will be given at the start of each session. Then, after 10 min EEG, ca. 20 min neurofeedback, and 10 min EEG the task will be repeated.
Secondary	Visuo-motor learning	The secondary outcome measure for experiment 2 will be the learning gain in a visuo-motor learning task. Specifically, the visuo-motor learning task that will be used is the mirror-drawing task. As in the validation papers, the two variables of interest are the number of errors and the completion time.	A pre-test of 5 min in the mirror-drawing task will be obtained after 10 min EEG and 20 min neurofeedback. Then, after 20 min of training in the task, a post-test of 5 min will be taken.