Real‐time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm

Abstract

Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n=15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n=14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for non-invasive neuromodulation studies of human brain function.

Self‐regulation of ventromedial prefrontal cortex activation using real‐time fMRI neurofeedback—Influence of default mode network

“…The sham feedback signal, for each condition block, was generated using a linear combination of seven Legendre polynomials with randomly selected coefficients, projected from −1 to +1 onto the 40 s time interval and initialized to a random seed value at the start of each experiment. As reported in our previous study (Zotev et al, ), this random feedback appeared to provide meaningful real‐time information, although the waveform's shape was random, and varied randomly across condition blocks and subjects. Subjects were assigned to either EG or CG indiscriminately.…”

“…We adopted this mental strategy because vmPFC is related to subjective value (Brosch & Sander, ; Clithero & Rangel, ) and value‐based decision‐making (Bechara et al, ; Bechara et al, ; Bechara et al, ; Damasio, ), and this strategy was estimated to enhance vmPFC BOLD signal. We assigned the same task to CG participants; however, they received computer‐generated sham feedback, calculated using a linear combination of seven Legendre polynomials with randomly selected coefficients (Zotev et al, ). We employed this strategy for the sham signal because the vmPFC is part of the DMN and has extensive anatomical and functional connections with subcortical and cortical brain regions, which makes providing sham feedback from another brain region problematic.…”

“…We employed this strategy for the sham signal because the vmPFC is part of the DMN and has extensive anatomical and functional connections with subcortical and cortical brain regions, which makes providing sham feedback from another brain region problematic. The computer‐generated sham feedback has been previously successfully deployed (Zotev et al, ) and helps avoid interpretation ambiguities related to vmPFC functional connections between additional brain regions.…”

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

“…For example, Yao et al (2016) used a signal from the average of a whole-brain slice distal from the target insula ROI as sham feedback for the control neurofeedback condition. Other studies used non-brain activity as the sham feedback signal (Zotev et al, 2018a) or used signal from another subject's active neurofeedback session (yoked neurofeedback; e.g., Hamilton et al, 2011). There were also no feedback control conditions, where subjects completed the scans and were presented the same stimuli but were not given any feedback signal (e.g., Johnston et al, 2011) or where subjects did not go through any version of the neurofeedback scan (e.g., Linden et al, 2012).…”

Neurofeedback: Principles, appraisal, and outstanding issues

“…This is perfectly justified in regard to the fact that neuronal network rhythmic activity at specific frequency bands is thought to contribute to information transfer and processing in the brain (Engel, Fries, & Singer, 2001;Fries, 2005). In fMRI-guided NF protocols, the target function is often the BOLD signal amplitude in well-defined brain regions (Caria & de Falco, 2015;Hellrung et al, 2018;Young et al, 2014;Zotev, Misaki, Phillips, Wong, & Bodurka, 2018;Zotev et al, 2011). However, a target could in principle be any function of possibly spatially (Koush et al, 2015;Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014) and temporally (Diaz Hernandez, Rieger, Baenninger, Brandeis, & Koenig, 2016;Ros et al, 2017) non-local dynamics.…”