Brain Networks Underlying Strategy Execution and Feedback Processing in an Efficient Functional Magnetic Resonance Imaging Neurofeedback Training Performed in a Parallel or a Serial Paradigm.

Wan Ilma Dewiputri,Renate Schweizer,Tibor Auer

doi:10.3389/fnhum.2021.645048

Abstract

Neurofeedback (NF) is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from strategy execution. Twenty participants attempted to control their anterior midcingulate cortex (aMCC) using functional magnetic resonance imaging (fMRI) NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps (BMs). Brain activation in the parallel condition correlates with the basal ganglia (BG) network, the cingulo-opercular network (CON), and the frontoparietal control network (FPCN); brain activation in the serial strategy execution condition with the default mode network (DMN), the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus (SMG) and superior temporal gyrus (STG) activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task.

Highlights

Neurofeedback (NF) is a psychophysiological technique that enables individuals to learn how to influence the activation of specific brain areas through executing mental strategies
The present analyses identify and compare the brain areas associated with a NF task targeting the anterior midcingulate cortex (aMCC) in two distinct paradigms that differed in the timing of feedback presentation: the parallel paradigm, which entails processing the delayed feedback concurrently with executing a mental strategy, and the serial paradigm, which temporally separates the strategy execution and feedback processing
We performed whole-brain functional magnetic resonance imaging (fMRI) analysis of the most efficient NF training, i.e., the training run with the largest increase of aMCC activation and positive feedback, separately for the three different conditions to obtain the associated blood oxygen leveldependent (BOLD)-activation maps: (1) parallel paradigm: concurrent strategy execution and feedback processing; (2) serial paradigm: only strategy execution; and (3) serial paradigm: only feedback processing

Summary

Introduction

Neurofeedback (NF) is a psychophysiological technique that enables individuals to learn how to influence the activation of specific brain areas through executing mental strategies. NF was originally implemented using electroencephalography (EEG), which measures cortical neural activation at high temporal resolution. This provides the participants with immediate feedback on their efficiency in regulating the targeted brain area, as the two subcomponents of the NF task—execution of a strategy and processing of the resulting feedback—occur essentially simultaneously. The close temporal relationship between executing a strategy and receiving feedback, the high temporal resolution, and the relative convenience in practice made EEG NF a favorite tool for clinical therapeutic applications, i.e., stroke (Kober et al, 2017) or attention-deficit/hyperactivity disorder (Strehl et al, 2017), and in the fast-growing field of brain computer interfaces (BCI) where a continuous signal in real time is essential, i.e., for the control of a robotic arm (Edelman et al, 2019)

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