Self-Regulation of Brain Activity in Patients with Postherpetic Neuralgia: A Double-Blind Randomized Study Using Real-Time fMRI Neurofeedback

Abstract

BackgroundA pilot study has shown that real-time fMRI (rtfMRI) neurofeedback could be an alternative approach for chronic pain treatment. Considering the relative small sample of patients recruited and not strictly controlled condition, it is desirable to perform a replication as well as a double-blinded randomized study with a different control condition in chronic pain patients. Here we conducted a rtfMRI neurofeedback study in a subgroup of pain patients – patients with postherpetic neuralgia (PHN) and used a different sham neurofeedback control. We explored the feasibility of self-regulation of the rostral anterior cingulate cortex (rACC) activation in patients with PHN through rtfMRI neurofeedback and regulation of pain perception.MethodsSixteen patients (46–71 years) with PHN were randomly allocated to a experimental group (n = 8) or a control group (n = 8). 2 patients in the control group were excluded for large head motion. The experimental group was given true feedback information from their rACC whereas the control group was given sham feedback information from their posterior cingulate cortex (PCC). All subjects were instructed to perform an imagery task to increase and decrease activation within the target region using rtfMRI neurofeedback.ResultsOnline analysis showed 6/8 patients in the experimental group were able to increase and decrease the blood oxygen level dependent (BOLD) fMRI signal magnitude during intermittent feedback training. However, this modulation effect was not observed in the control group. Offline analysis showed that the percentage of BOLD signal change of the target region between the last and first training in the experimental group was significantly different from the control group’s and was also significantly different than 0. The changes of pain perception reflected by numerical rating scale (NRS) in the experimental group were significantly different from the control group. However, there existed no significant correlations between BOLD signal change and NRS change.ConclusionPatients with PHN could learn to voluntarily control over activation in rACC through rtfMRI neurofeedback and alter their pain perception level. The present study may provide new evidence that rtfMRI neurofeedback training may be a supplemental approach for chronic clinical pain management.

Maintenance of Voluntary Self-regulation Learned through Real-Time fMRI Neurofeedback

“…To date, several studies show that neurofeedback training causes behavioral effects that are specific to the functional role of the targeted cortical area (Weiskopf et al, 2004; Bray et al, 2007; Caria et al, 2007; Rota et al, 2009; Shibata et al, 2011; Scharnowski et al, 2012, 2015; Robineau et al, 2014; Koush et al, 2015; Scharnowski and Weiskopf, 2015). Even more importantly, real-time fMRI neurofeedback training has also been shown to have therapeutic effects in chronic pain patients (deCharms et al, 2005; Guan et al, 2015), Parkinson’s disease (Subramanian et al, 2011), tinnitus (Haller et al, 2010), depression (Linden et al, 2012; Young et al, 2014), obsessive-compulsive disorder (Scheinost et al, 2013, 2014), spider phobia (Zilverstand et al, 2015), and addiction (Li et al, 2013; Karch et al, 2015; Kirsch et al, 2015; Hartwell et al, 2016). Especially for clinical applications of neurofeedback it is crucial that the learning effects persist beyond the initial training period and that voluntary control transfers to situations without neurofeedback information.…”

The self-regulating brain and neurofeedback: Experimental science and clinical promise

“…Likewise, multiple experiments train down-regulation of the ACC to mitigate pain (deCharms et al, 2005;Guan et al, 2015) or inhibit cigarette cravings (Canterberry et al, 2013;Li et al, 2012), while others encourage ACC activity to heighten valence ratings (Gröne et al, 2015) or limit the effects of cognitive interference (Mathiak et al, 2010). Thus, researchers are already training regulation in opposite directions, but shy away from directly comparing the behavioral effects of up-versus down-regulation using a specific pattern of brain activity.…”

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

“…Real-time fMRI-based neurofeedback enables subjects to learn control over brain activity in localized regions of interest (ROIs). Brain areas that have been investigated in fMRI-based neurofeedback studies include the anterior cingulate cortex (deCharms et al, 2005;Emmert et al, 2014;Gröne et al, 2015;Guan et al, 2014;Li et al, 2013), anterior insula (Yao et al, 2016), amygdala (Brühl et al, 2014;Gerin et al, 2016;Keynan et al, 2016;Nicholson et al, 2017;Paret et al, 2014;Young et al, 2014), auditory cortex Haller, Birbaumer, & Veit, 2010), default mode network (DMN;McDonald et al, 2017), dorsolateral prefrontal cortex (Sherwood, Kane, Weisend, & Parker, 2016), hippocampus , insula (Korhan Buyukturkoglu et al, 2015;Caria et al, 2007;Emmert et al, 2014;Frank et al, 2012;Zilverstand, Sorger, Sarkheil, & Goebel, 2015), motor cortex (Auer, Schweizer, & Frahm, 2015;Blefari, Sulzer, Hepp-Reymond, Kollias, & Gassert, 2015;Buyukturkoglu et al, 2013;Marins et al, 2015;Scharnowski et al, 2015;Yoo, Lee, O'Leary, Panych, & Jolesz, 2008), nucleus accumbens (Greer, Trujillo, Glover, & Knutson, 2014), parahippocampal gyrus , ventral tegmental area (MacInnes, Dickerson, Chen, & Adcock, 2016;Sulzer et al, 2013), and the visual cortex (Scharnowski, Hutton, Josephs, Weiskopf, & Rees, 2012;Shibata, Watanabe, Sasaki, & Kawato, 2011). More recently, functional brain networks have also been successfully trained employing connectivity-informed neurofeedback in networks sub-serving emotion...…”

“…Real-time fMRI neurofeedback has been shown to improve behavioral and cognitive functions in healthy participants (e.g. Rota et al, 2009;Scharnowski et al, 2015Scharnowski et al, , 2012Sherwood et al, 2016;Shibata et al, 2011), and to reduce clinical symptoms in neurological and psychiatric patient populations, such as patients suffering from adipositas (Frank et al, 2012), alcohol and nicotine addiction Hanlon et al, 2013;Hartwell et al, 2016;Karch et al, 2015;Kim et al, 2015;Li et al, 2013), borderline personality disorder (Paret et al, 2016), chronic pain (deCharms et al, 2005;Guan et al, 2014), depression (Linden et al, 2012;Young et al, 2017Young et al, , 2014, Huntington's disease (Papoutsi et al, 2018), obsessive compulsory disorder (Buyukturkoglu et al, 2015), Parkinson's disease (Buyukturkoglu et al, 2013;Subramanian et al, 2011), phobia (Zilverstand et al, 2015), post-traumatic stress disorder (Gerin et al, 2016;Nicholson et al, 2017), and tinnitus Haller et al, 2010).…”