Neurofeedback and networks of depression

David E J Linden, David E J Linden

Abstract

Recent advances in imaging technology and in the understanding of neural circuits relevant to emotion, motivation, and depression have boosted interest and experimental work in neuromodulation for affective disorders. Real-time functional magnetic resonance imaging (fMRI) can be used to train patients in the self regulation of these circuits, and thus complement existing neurofeedback technologies based on electroencephalography (EEG). EEG neurofeedback for depression has mainly been based on models of altered hemispheric asymmetry. fMRI-based neurofeedback (fMRI-NF) can utilize functional localizer scans that allow the dynamic adjustment of the target areas or networks for self-regulation training to individual patterns of emotion processing. An initial application of fMRI-NF in depression has produced promising clinical results, and further clinical trials are under way. Challenges lie in the design of appropriate control conditions for rigorous clinical trials, and in the transfer of neurofeedback protocols from the laboratory to mobile devices to enhance the sustainability of any clinical benefits.

Keywords: emotion; frontal lobe; functional magnetic resonance imaging; limbic system; mood disorder; self-regulation.

Figures

Figure 1.. Basic diagram of a real-time…
Figure 1.. Basic diagram of a real-time functional magnetic resonance imaging brain-computer interface for neurofeedback. Figure courtesy of Isabelle Habes
Figure 2.. Display screen of visual neurofeedback…
Figure 2.. Display screen of visual neurofeedback with an outline of the protocol. The patients trained to increase activity in functionally localized areas during 20-second periods, alternating with 20 second periods of rest. Overall, they did this for 20 minutes each in four weekly sessions. Adapted from ref 43: Subramanian L, Hindle JV, Johnston S, et al. Realtime functional magnetic resonance imaging neurofeedback for treatment of Parkinson's disease. J Neuroscf. 2011:31:16309-16317. Copyright © Society for Neuroscience 2011
Figure 3.. Cognitive-affective brain systems that could…
Figure 3.. Cognitive-affective brain systems that could become targets for neuromoduiation in depression. DLPFC, dorsolateral prefronta! cortex; VLPFC, ventrolateral prefrontal cortex; ACC, anterior cinguiate cortex; Amy, amygdala Adapted from ref 38: Esmail S, Linden D. Cogn Sci. 2011 ;6. Copyright © Nova Science Publishers, Inc.

References

    1. Dierks T., Linden DE., Jandl M., et al Activation of Heschl's gyrus during auditory hallucinations. Neuron. 1999;22:615–621.
    1. Linden DE. The challenges and promise of neuroimaging in psychiatry. Neuron. 2012;73:8–22.
    1. Uttal WR. Reliability in Cognitive Neuroscience: a Meta-Meta Analysis. Cambridge, MA: MIT Press; 2013
    1. Weiskopf N., Mathiak K., Bock S., et al Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng. 2004;51:966–970.
    1. Gevensleben H., Holl B., Albrecht B., et al Neurofeedback training in children with ADHD: 6-month follow-up of a randomised controlled trial. Eur Child Adolesc Psychiatry. 2010;19:715–724.
    1. Gevensleben H., Rothenberger A., Moll GH., Heinrich H. Neurofeedback in children with ADHD: validation and challenges. Exp Rev Neurother. 2012;12:447–460.
    1. Sonuga-Barke EJ., Brandeis D., Cortese S., et al Nonpharmacological interventions for ADHD: systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments. Am J Psychiatry. 2013;170:275–289.
    1. Lozano AM., Lipsman N. Probing and regulating dysfunctional circuits using deep brain stimulation. Neuron. 2013;77:406–424.
    1. Goodman WK., Alterman RL. Deep brain stimulation for intractable psychiatric disorders. Annu Rev Med. 2012;63:511–524.
    1. Bandura A. Self-efficacy: the Exercise of Control. New York, NY: WH Freeman; 1997
    1. Mayberg HS., Liotti M., Brannan SK., et al Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry. 1999;156:675–682.
    1. Phillips M., Ladouceur C., Drevets W. A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry. 2008;13:829,833–857.
    1. Beck A. The evolution of the cognitive model of depression and its neurobiological correlates. Am J Psychiatry. 2008;165:969–977.
    1. Clark D., Beck A. Cognitive theory and therapy of anxiety and depression: convergence with neurobiological findings. Trends Cogn Sci. 2010;14:418–424.
    1. Linden D. Biological psychiatry: time for new paradigms. Br 1 Psychiatry. 2013;202:166–167.
    1. Healy D. The structure of psychopharmacological revolutions. Psychiatr Dev. 1987;5:349–376.
    1. Hyman SE. Revolution stalled. SciTransI Med. 2012;4:155cm11.
    1. Schoene-Bake JC., Parpaley Y., Weber B., Panksepp J., Hurwitz TA., Coenen VA. Tractographic analysis of historical lesion surgery for depression. Neuropsychopharmacology. 2010;35:2553–2563.
    1. Linden D. The Biology of Psychological Disorders. Basingstoke, UK: Palgrave Macmillan; 2011
    1. Mayberg HS. Depression: a neuropsychiatric perspective. In: Panksepp J, ed. Biological Psychiatry. Hoboken, NJ: Wiley-Liss; 2004:197–228.
    1. Economics A-PPGoW. Cost of Depression in England, 2010. All-party parliamentary group on wellbeing economics web site. . Accessed December 13, 2013
    1. Henderson M., Harvey SB., Overland S., Mykletun A., Hotopf M. Work and common psychiatric disorders. J R Soc Med. 2011;104:198–207.
    1. Rush A., Trivedi M., Wisniewski S., et al Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163:1905–1917.
    1. Kim KH., Lee SM., Paik JW., Kim NS. The effects of continuous antidepressant treatment during the first 6 months on relapse or recurrence of depression. J Affect Disord. 2011;132:121–129.
    1. Pine DS., Helfinstein SM., Bar-Haim Y., Nelson E., Fox NA. Challenges in developing novel treatments for childhood disorders: lessons from research on anxiety. Neuropsychopharmacology. 2009;34:213–228.
    1. Beutel ME., Stern E., Silbersweig DA. The emerging dialogue between psychoanalysis and neuroscience: neuroimaging perspectives. J Am Psychoanal Assoc. 2003;51:773–801.
    1. Linden DE. How psychotherapy changes the brain—the contribution of functional neuroimaging. Moi Psychiatry. 2006;11:528–538.
    1. Linden DE. Brain imaging and psychotherapy: methodological considerations and practical implications. Eur Arch Psychiatry Clin Neurosci. 2008;258 (suppl 5):71–75.
    1. DeRubeis R., Siegle G., Hollon S. Cognitive therapy versus medication for depression: treatment outcomes and neural mechanisms. Nat Rev Neurosci. 2008;9:788–796.
    1. Mayberg H., Lozano A., Voon V., et al Deep brain stimulation for treatment-resistant depression. Neuron. 2005;45:651–660.
    1. Davidson RJ., Ekman P., Saron CD., Senulis JA., Friesen WV. Approachwithdrawal and cerebral asymmetry: emotional expression and brain physiology. I. J Pers Soc Psychol. 1990;58:330–341 .
    1. Harmon-Jones E., Gable PA., Peterson CK. The role of asymmetric frontal cortical activity in emotion-related phenomena: a review and update. Biol Psychol. 2010;84:451–462.
    1. Henriques JB., Davidson RJ. Regional brain electrical asymmetries discriminate between previously depressed and healthy control subjects. J Abnorm Psychol. 1990;99:22–31.
    1. Henriques JB., Davidson RJ. Left frontal hypoactivation in depression. J Abnorm Psychol. 1991;100:535–545.
    1. Bhogal SK., Teasell R., Foley N., Speechley M. Lesion location and poststroke depression: systematic review of the methodological limitations in the literature. Stroke. 2004;35:794–802.
    1. Reid SA., Duke LM., Allen JJ. Resting frontal electroencephalographs asymmetry in depression: inconsistencies suggest the need to identify mediating factors. Psychophysiology. 1998;35:389–404.
    1. Thibodeau R., Jorgensen RS., Kim S. Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. J Abnorm Psychol. 2006;115:715–729.
    1. Esrnail S., Linden D. Emotion regulation networks and neurofeedback in depression. Cogn Sci. 2011;6(2)
    1. Rosenfeld JP., Baehr E., Baehr R., Gotlib IH., Ranganath C. Preliminary evidence that daily changes in frontal alpha asymmetry correlate with changes in affect in therapy sessions. Int J Psychophysiol. 1996;23:137–141.
    1. Choi SW., Chi SE., Chung SY., Kim JW., Ahn CY., Kim HT. Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study. Neuropsychobiology. 2011;63:43–51.
    1. Bledowski C., Linden DE.., Wibral M. Combining electrophysiology and functional imaging - different methods for different questions. Trends Cogn. So. 2007;11:500–502.
    1. Sulzer J., Haller S., Scharnowski F., et al Real-time fMRI neurofeedback: progress and challenges. Neuroimage. 2013;76:396–399.
    1. Subramanian L., Hindle JV., Johnston S., et al Real-time functional magnetic resonance imaging neurofeedback for treatment of Parkinson's disease. J Neurosci. 2011;31:16309–16317.
    1. Johnston SJ., Boehm SG., Healy D., Goebel R., Linden DE. Neurofeedback: a promising tool for the self-regulation of emotion networks. Neuroimage. 2010;49:1066–1072.
    1. Johnston S., Linden DE.., Healy D., Goebel R., Habes I., Boehm SG. Upregulation of emotion areas through neurofeedback with a focus on positive mood. Cogn Affect Behav Neurosci. 2011;11:44–51.
    1. Linden DE.., Habes I., Johnston SJ., et al Real-time self-regulation of emotion networks in patients with depression. PLoS One. 2012;7:e38115.
    1. Hawkinson JE., Ross AJ., Parthasarathy S., et al Quantification of adverse events associated with functional MRI scanning and with real-time fMRIbased training. Int J Behav Med. 2012;19:372–381.
    1. Bandura A. Social cognitive theory: an agentic perspective. Asian J Soc Psychol. 1999;2:21–41.
    1. Goncalves R., Pedrozo AL., Coutinho ES., Figueira I., Ventura P. Efficacy of virtual reality exposure therapy in the treatment of PTSD: a systematic review. PLoS One. 2012;7:e48469.
    1. Sitaram R., Lee S., Ruiz S., Rana M., Veit R., Birbaumer N. Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage. 2011;56:753–765.
    1. LaConte SM. Decoding fMRI brain states in real-time. Neuroimage. 2011;56:440–454.
    1. Habes I., Krall S., Johnston S., et al Pattern classification of valence in depression. Neuroimage Clin. 2013;2:675–683.
    1. Wells A., Davies M. The Thought Control Questionnaire: a measure of individual differences in the control of unwanted thoughts. Behav Res Ther. 1994;32:871–878.
    1. Luciano JV., Algarabel S., Tomas JM., Martinez JL. Development and validation of the thought control ability questionnaire. Pers Individ Dif. 2005;38:997–1008.
    1. Carver CS., White TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the BIS/BAS scales. J Pers Soc Psychol. 1994;67:319–333.

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj