The Effect of Neurofeedback on Eating Behaviour

NCT ID: NCT02148770

Last Updated: 2016-05-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

50 participants

Study Classification

INTERVENTIONAL

Study Start Date

2014-11-30

Study Completion Date

2017-05-31

Brief Summary

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Neuroimaging is becoming increasingly common to investigate the neural networks underlying eating behaviour and food preference in normal-weight and obese humans. It has been observed that obese in comparison to lean individuals display altered activation patterns in networks of brain areas involved in reward, emotion and cognitive control. Interestingly, obese individuals who are capable of losing weight appear to have a stronger connectivity between areas related to food value and to the control of eating behaviour. The same areas are also associated with healthy food choices. It has been suggested that activation in the prefrontal control areas indirectly modulate valuation-related activity. Based on this, brain-related intervention strategies to support weight loss and long-lasting weight maintenance are of particular interest. Hence, we first want to examine the effect on eating behaviour of neurofeedback training-induced up-regulation of functional connectivity between reward- and impulse-related brain areas as a pilot, and second we want to examine up-regulation of the activity of prefrontal control brain areas.

Detailed Description

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Primary objective: We want to investigate whether the training-induced up-regulation of the dorsal prefrontal cortex inhibits eating behaviour.

Study design: A parallel design. Half of the participants will learn to up-regulate activity of the dorsolateral prefrontal cortex (dlPFC), while the other participants will participate in sham-training sessions. Adherence to experimental conditions will be assigned randomly, based on the participants' enrolment in the study, balanced by gender and binge eating classification.

Study population: 50 overweight and obese (BMI 25-40 kg/m2), but otherwise healthy individuals, 18-65 years old.

Intervention: All participants will participate in a screening day, followed by one neurofeedback session day and a follow-up day. During the neurofeedback session, participants will undergo a 45 min real-time-fMRI-brain-computer-interface scan in order to learn to up-regulate dlPFC activation.

Main study parameters/endpoints:

1. The ability to up-regulate dlPFC activity.
2. Respective effects on eating behaviour. Nature and extent of the burden and risks associated with participation: Participants will be scanned once (fMRI). Functional MRI is a safe and non-invasive technique.

Conditions

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Obesity Eating Behaviour

Keywords

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rt-fmri self-control eating behaviour overweight neurofeedback

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

TREATMENT

Blinding Strategy

SINGLE

Participants

Study Groups

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Neurofeedback

Neurofeedback training: Up-regulation of DLPFC.

Group Type EXPERIMENTAL

Neurofeedback

Intervention Type DEVICE

Networks involved in eating behaviour can be modified by neurofeedback training. We will perform a neurofeedback task using the technology of fMRI-based Brain Computer Interface (BCI). BCI approaches based on real-time fMRI (rtfMRI) allow voluntary regulation of brain regions. For the rtfMRI, a well-established setup will be used which translates the blood oxygen level dependent (BOLD) signal of a specific brain region of interest into a visual signal (e.g. moving bar) in real time using brain voyager® and matlab. The study will include 1 training-sessions In the up-regulation condition subjects will learn to up regulate their dlPFC. In the sham-condition subjects are get the same instructions, however they will receive sham feedback.

Neurofeedback SHAM

Neurofeedback training: Sham-regulation of DLPFC.

Group Type SHAM_COMPARATOR

Neurofeedback

Intervention Type DEVICE

Networks involved in eating behaviour can be modified by neurofeedback training. We will perform a neurofeedback task using the technology of fMRI-based Brain Computer Interface (BCI). BCI approaches based on real-time fMRI (rtfMRI) allow voluntary regulation of brain regions. For the rtfMRI, a well-established setup will be used which translates the blood oxygen level dependent (BOLD) signal of a specific brain region of interest into a visual signal (e.g. moving bar) in real time using brain voyager® and matlab. The study will include 1 training-sessions In the up-regulation condition subjects will learn to up regulate their dlPFC. In the sham-condition subjects are get the same instructions, however they will receive sham feedback.

Interventions

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Neurofeedback

Networks involved in eating behaviour can be modified by neurofeedback training. We will perform a neurofeedback task using the technology of fMRI-based Brain Computer Interface (BCI). BCI approaches based on real-time fMRI (rtfMRI) allow voluntary regulation of brain regions. For the rtfMRI, a well-established setup will be used which translates the blood oxygen level dependent (BOLD) signal of a specific brain region of interest into a visual signal (e.g. moving bar) in real time using brain voyager® and matlab. The study will include 1 training-sessions In the up-regulation condition subjects will learn to up regulate their dlPFC. In the sham-condition subjects are get the same instructions, however they will receive sham feedback.

Intervention Type DEVICE

Other Intervention Names

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fMRI-based Brain Computer Interface (BCI) Neurofeedback training rtfMRI

Eligibility Criteria

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Inclusion Criteria

* Healthy male and female subjects
* Age 18-65 years at start of the study
* Body Mass Index (BMI) between 25 and 40 kg/m2
* Informed consent to study protocol
* Willingness to be informed about chance findings of pathology and approval of the disclosure of this information to the general physician (see Informed Consent)
* Fulfilment of the criteria for blood donors according to the "Richtlinien zur Gewinnung von Blut und Blutbestandteilen und zur Anwendung von Blutprodukten", in particular Hb ≥ 135 g/l (8,37 mmol/l; Bundesärztekammer 2010)

Exclusion Criteria

* Subjects who have a non-removable metal object in or at their body, such as, for ex-ample:

* Heart pace-maker
* Artificial heart valve
* Metal prosthesis
* Metallic implants (screws, plates from operations, etc.)
* Metal splinters / grenade fragments
* Non-removable dental braces
* Acupuncture needles
* Insulin pump
* Intraport, etc.
* In field strengths of over 1.0 T also: tattoos, eye lid-shadow
* Current weight loss regimens
* Limited temperature perception and/or increased sensitivity to warming of the body
* Pathological hearing ability or an increased sensitivity to loud noises
* Claustrophobia
* Lack of ability to give informed consent
* Operation less than three month ago
* Simultaneous participation in other studies
* Acute illness or infection during the last 4 weeks
* Neurological disorder or injury
* Moderate or severe head injury
* Severe psychotic illness
* Intake of antidepressants / antipsychotics
* Participation in other studies with blood withdrawals or blood donation in previous and subsequent 2 months
Minimum Eligible Age

18 Years

Maximum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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University Hospital Tuebingen

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Manfred Hallschmid, PhD

Role: PRINCIPAL_INVESTIGATOR

University Tuebingen

Locations

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UKT and MPI

Tübingen, , Germany

Site Status RECRUITING

Countries

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Germany

Central Contacts

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Manfred Hallschmid, PhD

Role: CONTACT

Phone: +49 7071 29-8825

Email: [email protected]

Maartje Spetter, PhD

Role: CONTACT

Phone: +49 7071 29-81193

Email: [email protected]

Facility Contacts

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Manfred Hallschmid, PhD

Role: primary

References

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Weiskopf N, Scharnowski F, Veit R, Goebel R, Birbaumer N, Mathiak K. Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J Physiol Paris. 2004 Jul-Nov;98(4-6):357-73. doi: 10.1016/j.jphysparis.2005.09.019. Epub 2005 Nov 10.

Reference Type BACKGROUND
PMID: 16289548 (View on PubMed)

Sitaram R, Caria A, Veit R, Gaber T, Rota G, Kuebler A, Birbaumer N. FMRI brain-computer interface: a tool for neuroscientific research and treatment. Comput Intell Neurosci. 2007;2007:25487. doi: 10.1155/2007/25487.

Reference Type BACKGROUND
PMID: 18274615 (View on PubMed)

Spetter MS, Malekshahi R, Birbaumer N, Luhrs M, van der Veer AH, Scheffler K, Spuckti S, Preissl H, Veit R, Hallschmid M. Volitional regulation of brain responses to food stimuli in overweight and obese subjects: A real-time fMRI feedback study. Appetite. 2017 May 1;112:188-195. doi: 10.1016/j.appet.2017.01.032. Epub 2017 Jan 25.

Reference Type DERIVED
PMID: 28131758 (View on PubMed)

Related Links

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http://www.braintrainproject.eu/

Overview of main EU-project.

Other Identifiers

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646/2013BO2

Identifier Type: -

Identifier Source: org_study_id