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Status unknownNCT02148770Updated May 30, 2016

The Effect of Neurofeedback on Eating Behaviour

An interventional study of Neurofeedback in Obesity and Eating Behaviour, sponsored by University Hospital Tuebingen. Status unknown at 1 site in Germany. Open to participants aged 18 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2016-05-30.

Sponsored by University Hospital Tuebingen · Not applicable, Interventional, and Treatment

The sponsor has not verified this record recently (last verified Jun 2015), so the status shown — last known as Recruiting — may be out of date.
Phase
Not applicable
Study type
Interventional
Enrollment
50
Allocation
Randomized
Ages
18 Years to 65 Years
Sex
All
01

Study summary

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.

Read the detailed description

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.
02

Conditions studied

  • Obesity
  • Eating Behaviour

Keywords

  • rt-fmri
  • self-control
  • eating behaviour
  • overweight
  • neurofeedback
03

In context

Lead sponsor

University Hospital Tuebingen is the lead sponsor of 476 studies on the registry; 104 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 65 Years
Sexes eligible
All
Accepts healthy volunteers
Yes

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

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
05

Study design

Phase
Not applicable
Primary purpose
Treatment
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Single (Participant)
Enrollment
50 participants (estimated)

Study arms

  • Experimental
    Neurofeedback

    Neurofeedback training: Up-regulation of DLPFC.

    Device: Neurofeedback

  • Sham comparator
    Neurofeedback SHAM

    Neurofeedback training: Sham-regulation of DLPFC.

    Device: Neurofeedback

Interventions

  • DeviceNeurofeedback

    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.

    Also known as: fMRI-based Brain Computer Interface (BCI), Neurofeedback training, rtfMRI

06

What researchers measure

Primary outcomes

  1. Activity in the dlPFC during the training-session

    Differences in dlPFC activity between baseline and after up-regulation during the neurofeedback training session, as well as the difference between the treatment and the sham groups (ANCOVA approach).

    Time frame: 1 day

Secondary outcomes

  1. Food intake

    Snack consumption during the snack test, comparing pre vs post neurofeedback session, and between the two groups (ANCOVA approach).

    Time frame: 4 weeks

  2. Preferred food (healthy or unhealthy food).

    Differences in food choice (healthy vs unhealthy) pre compared to post neurofeedback session and between the two groups (ANCOVA approach).

    Time frame: 4 weeks

  3. Weight

    Difference in weight before and after training

    Time frame: 4 weeks

07

Study locations

1 of 1 sites recruiting
  • UKT and MPI
    Tuebingen, 72076, Germany
    • Manfred Hallschmid, PhD · Contact · Manfred.Hallschmid@uni-tuebingen.de · +49 7071 29-8825
    • Maartje Spetter, PhD · Sub investigator
    • Manfred Hallschmid, PhD · Principal investigator
    • Ralf Veit, PhD · Sub investigator
    Recruiting
08

References and documents

Publications

  • 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. PubMed 16289548 ↗
  • 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. PubMed 18274615 ↗
  • 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. PubMed 28131758 ↗

Individual participant data

Plan to share: Yes — We will share the data inside the EU-consortium

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on May 30, 2016, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT02148770
Lead sponsor
University Hospital Tuebingen
Responsible party
Sponsor
First posted
May 28, 2014
Start date
Nov 2014
Primary completion
May 2017 (estimated)
Completion
May 2017 (estimated)
Last update
May 30, 2016

Study contacts

Manfred Hallschmid, PhD
Contact
Manfred.Hallschmid@uni-tuebingen.de
+49 7071 29-8825
Maartje Spetter, PhD
Contact
Maartje.Spetter@uni-tuebingen.de
+49 7071 29-81193
Manfred Hallschmid, PhD
principal investigator · University Tuebingen

Oversight

Data monitoring committee
No
View the source record on ClinicalTrials.gov ↗

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This study is status unknown, as verified in Jun 2015. You cannot join it, but the record below documents what was studied.

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