Trambaiolli Lucas R, Biazoli Claudinei E, Cravo André M, Falk Tiago H, Sato João R
Universidade Federal do ABC, Mathematics, Computation and Cognition Center, Santo André, São Paulo, Brazil.
University of Quebec, Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, Montreal, Quebec, Canada.
Neurophotonics. 2018 Jul;5(3):035009. doi: 10.1117/1.NPh.5.3.035009. Epub 2018 Sep 18.
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. 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. 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. Participants were able to control this feedback system with ( ) 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 ( ) when comparing both affective states and all feedback approaches. Finally, task performance was positively correlated to the pretask resting-state whole functional connectivity ( , ). 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.
情感神经反馈是一种控制与精神疾病相关的异常神经活动的合适方法，因此可能减轻症状严重程度。然而，神经反馈的不同方面仍不清楚，例如其神经基础、性能变化、反馈效果等。首先，我们旨在基于额叶和枕叶网络的自我调节提出一种基于功能近红外光谱（fNIRS）的情感神经反馈。其次，我们评估了三种不同的反馈方式对性能的影响：真实反馈、固定反馈和随机反馈。第三，我们研究了性能的不同人口统计学、心理和生理预测因素。33名健康参与者执行了一项任务，即一个无定形图形根据引发的情感（积极或中性）改变其形状。在任务过程中，参与者随机接受三种不同的反馈方式：真实反馈，分类器输出不变；固定反馈，反馈图形保持不变；随机反馈，分类器输出乘以一个任意值，导致反馈与受试者预期不同。然后，我们根据情感状态和反馈方式以及任务前静息状态块期间的全连接性概况进行多变量比较，以预测性能。在真实反馈试验中，参与者能够以（ ）的性能控制这个反馈系统。比较反馈方式的平均性能时未发现显著差异。然而，在比较情感状态和所有反馈方式时，全功能连接性概况呈现出显著的马氏距离（ ）。最后，任务性能与任务前静息状态的全功能连接性呈正相关（ ， ）。我们的结果表明，fNIRS可能是一种基于情感网络自我调节开发神经反馈系统的可行工具。这一发现使得未来能够在精神疾病人群中使用基于fNIRS的情感神经反馈进行研究。此外，功能连接性概况被证明是性能的良好预测指标，并表明在存在反馈干扰的情况下，为维持任务控制需要付出更多努力。
