Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles

Abstract

Background: Affective neurofeedback constitutes a suitable approach to control abnormal neural activities associated with psychiatric disorders and might consequently relief symptom severity. However, different aspects of neurofeedback remain unclear, such as its neural basis, the performance variation, the feedback effect, among others. Aim: First, we aimed to propose a functional near-infrared spectroscopy (fNIRS)-based affective neurofeedback based on the self-regulation of frontal and occipital networks. Second, we evaluated three different feedback approaches on performance: real, fixed, and random feedback. Third, we investigated different demographic, psychological, and physiological predictors of performance. Approach: Thirty-three healthy participants performed a task whereby an amorphous figure changed its shape according to the elicited affect (positive or neutral). During the task, the participants randomly received three different feedback approaches: real feedback, with no change of the classifier output; fixed feedback, keeping the feedback figure unmodified; and random feedback, where the classifier output was multiplied by an arbitrary value, causing a feedback different than expected by the subject. Then, we applied a multivariate comparison of the whole-connectivity profiles according to the affective states and feedback approaches, as well as during a pretask resting-state block, to predict performance. Results: Participants were able to control this feedback system with 70.00% AE 24.43% (p < 0.01) of performance during the real feedback trials. No significant differences were found when comparing the average performances of the feedback approaches. However, the whole functional connectivity profiles presented significant Mahalanobis distances (p ≪ 0.001) when comparing both affective states and all feedback approaches. Finally, task performance was positively correlated to the pretask resting-state whole functional connectivity (r ¼ 0.512, p ¼ 0.009). Conclusions: Our results suggest that fNIRS might be a feasible tool to develop a neurofeedback system based on the self-regulation of affective networks. This finding enables future investigations using an fNIRS-based affective neurofeedback in psychiatric populations. Furthermore, functional connectivity profiles proved to be a good predictor of performance and suggested an increased effort to maintain task control in the presence of feedback distractors.

Subject-independent decoding of affective states using functional near-infrared spectroscopy

“…Neutral" discrimination in the active condition was slightly over the 70% threshold suggested by the brain-computer interface and neurofeedback communities as sufficient to perform device control and communication (Tai andChau, 2009, McFarland et al, 2006). This is a crucial finding since the differentiation between the active elicitation of positive affect and a neutral (resting-state) condition is the state of the art of hemodynamic-based neurofeedback protocols applied to both health and psychiatric populations (Johnston et al, 2011, Zotev et al, 2011, Zotev et al, 2013, Moll et al, 2014, Young et al, 2014, Trambaiolli et al, 2018a. It is also important to emphasize that this result was reached using only three channels.…”

“…Negative" combinations, for blocks of passive and active elicitation separately. Here we followed this binary approach since many current neurofeedback protocols are mainly based on two-class designs (Hamilton et al, 2011, Johnston et al, 2011, Brühl et al, 2014, Zotev et al, 2013, Young et al, 2014, Hamilton et al, 2016, Trambaiolli et al, 2018a.…”

“…For potential neurofeedback applications, self-regulation of neural activity related to a specific affective task are desirable (Birbaumer et al, 2013, Kim andBirbaumer, 2014). In cases where potential users present increased levels of negative emotions and decreased levels of positive emotions (Gross and Muñoz, 1995), improving the neural and cognitive discrimination between Positive and Neutral states might be clinically relevant (Trambaiolli et al, 2018a).…”

“…fNIRS-based affective neurofeedback protocols were recently applied to healthy (Sakatani et al, 2013, Trambaiolli et al, 2018a and psychiatric populations (Ehlis et al, 2018), including patients with schizophrenia (Storchak et al, 2019), autism disorder (Liu et al, 2016), and attention-deficit/hyperactivity disorder (ADHD) (Marx et al, 2015, Hudak et al, 2017. However, all these protocols have subject-specific designs which require training blocks or calibration trials for every experimental session.…”

“…Using a fronto-occipital fNIRS setup, our research group achieved 80 to 95% of within-subject affective classification accuracy (Trambaiolli et al, 2018b), and developed a functional subject-specific neurofeedback protocol (Trambaiolli et al, 2018a). Here, considering the above-mentioned technical challenges for subject-independent affective decoding, we aimed to evaluate whether these fronto-occipital fNIRS signals provide enough information to the subject-independent offline classification of affective states.…”