Addiction to cigarette smoking and physiological regulation of brain areas - Neuronal mechanims of reward learing and extinction in smokers

• Applicant: Professor Dr. Niels Birbaumer
• Subject Area: Personality Psychology, Clinical and Medical Psychology, Methodology
• Term: from 2010 to 2014
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 178697565

• Final Report Year: 2014

Final Report Abstract

The project investigated brain processes which could explain why smoking tobacco constitutes such a resistant to change addiction despite its negligable subjective and emotional effects. In addition, on the basis of these brain mechanisms new methods of non-pharmacological treatment were devoloped and tested. In the course of these experiments with heavy smokers, light smokers and non-smokers some new insights in the phenomenon were gained: Self-regulation and self-control of your own brain activity which underlies also the self-regulation of behavior and attention improves considerably for minutes and hours after smoking a cigarette. Smokers can adapt the focus of their attention much more flexible to new environmental demands. In the experiments subjects learned to influence and regulate voluntarily the electric and metabolic (blood flow) activity of the frontal brain areas responsible for attention and concentration: they observed their own brain activity on a computer screen and the computer rewards them for the demanded brain changes with praise and small amounts of money. Already after minutes of training smokers learn this whereas non-smokers need much more training time. These neurotechnologies are called neurofeedback or brain computer interfaces (BCI). After these investigations smokers not motivated to quit smoking learned in a magnetic resonance imaging scanner (MRI) to down-regulate brain blood-flow of the insula region of their brain. The insula region is the critical brain region where all the bodily processes responsible for emotional responding converge. The smokers watched the intensity of their blood flow in this brain region on a thermometer on a screen and were rewarded for reductions of "temperature" (brain blood-flow). Despite the fact that these persons had not the slightest motivation to quit, their desire to smoke a cigarette was substantially reduced in parallel with the learned reduction of insula activity. In summary, the research of this project allows a new understanding and conceptualization of the development of addictions and opened the door for new strategies of treatment not possible without the inclusion of brain processes.

Publications

• (2012). Acquired control of ventral premotor cortex activity by feedback training: An exploratory real-time fRMI and TMS study. Neurorehabilitation and Neural Repair, 26, 3, 256-265
  Sitaram, R., Veit, R., Stevens, B., Caria, A., Gerloff, C., Birbaumer, N., Hummel, F.
  
• (2012). BCIs that use metabolic signals. In: J. Wolpaw, E.W. Wolpaw (Eds.): Brain-computer Interfaces: Principles and Practice. Oxford University Press
  Sitaram, R., Lee, S., Birbaumer, N.
  
• (2012). Real-time fMRI: a tool for local brain regulation. Neuroscientist 18, 5, 487-501
  Caria, A., Sitaram, R., Birbaumer, N.
  
• (2013). Learned regulation of brain metabolism. Trends in Cognitive Sciences, TICS, 17, 6, 295-302
  Birbaumer, N., Ruiz, S., Sitaram, R.
  
• (2013). Neurofeedback-mediated self-regulation of the dopaminergic midbrain. Neuroimage, 83, 817-825
  Sulzer, J., Sitaram, R., Blefari, M.L., Kollias, S., Birbaumer, N., Stephan, K.E., Luft, A., Gassert, R.
  
• (2013). Real-time fMRI neurofeedback: Progress and challenges. Neuroimage, 76, 386-399
  Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M.L., Bruehl, A.B., Cohen, L.G., deCharms, R.C, Gassert, R., Goebel, R., Herwig, U., LaConte, S., Linden, D., Luft, A.. Seifritz, E., Sitaram, R.
  
• (2014). Neurofeedback and Brain-Computer-Inferfaces. In: D. I. Mostofsky (Ed ): The Handbook of Behavioral Medicine, 1st ed. Chapter 15, pp. 275-312
  Wyckoff, S., Birbaumer, N.
  
• (2014). Real-time fMRI brain computer Interfaces: Self-regulation of single brain regions to networks. Biological Psychology, 95, 4-20
  Ruiz, S., Buyukturkoglu, K., Rana, M., Birbaumer, N., Sitaram, R.
  
• (2016). Real-time subject-independent pattern classification of overt and covert movements from fNIRS signals. PLoS ONE 11(7): e0159959
  Zaidi, A., Robinson, N., Rana, M., Prasad, V., Cuntai, G., Birbaumer, N., Sitaram, R.