No abstract

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner.

The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data.Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromodulation.Recent applications of this technique, known as decoded neurofeedback, have manipulated fear conditioning, visual perception, confidence judgements and facial preference. However, there has yet to be an empirical justification of the timing and data processing parameters of these experiments. Suboptimal parameter settings could impact the efficacy of neurofeedback learning and contribute to the 'non-responder' effect. The goal of this study was to investigate how design parameters of decoded neurofeedback experiments affect decoding accuracy and neurofeedback performance. Subjects participated in three fMRI sessions: two 'finger localizer' sessions to identify the fMRI patterns associated with each of the four fingers of the right hand, and one 'finger finding' neurofeedback session to assess neurofeedback performance. Using only the localizer data, we show that real-time decoding can be degraded by poor experiment timing or ROI selection. To set key parameters for the neurofeedback session, we used offline simulations of decoded neurofeedback using data from the localizer sessions to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Overall, this work demonstrates the usefulness of offline simulation to improve the success of real-time decoded neurofeedback experiments.

Previous work has shown that fMRI activity patterns associated with individual fingers can be 1 shifted by temporary impairment of the hand (e.g. by gluing two fingers together for 24 hours). 2 Here, we investigated whether these neural activity patterns could be modulated endogenously 3 and whether any behavioral changes result from modulation of these patterns. We used decoded 4 neurofeedback in healthy individuals to encourage participants to shift the neural activity pattern in 5 sensorimotor cortex of the middle finger towards the index finger, and the ring finger towards the 6 little finger. We first mapped out the neural activity patterns for all fingers of the right hand in an 7 fMRI pattern localizer session. Then, in three subsequent neurofeedback sessions, participants 8 were rewarded after middle/ring finger presses according to their activity pattern overlap during 9 each trial. A force-sensitive keyboard was used to ensure that participants were not altering their 10 physical finger coordination patterns. We found evidence that participants could learn to shift 11 the activity pattern of the 4th digit (ring finger) but not of the 3rd digit (middle finger). Increased 12 variability of these activity patterns during the localizer session was associated with the ability 13 of participants to modulate them using neurofeedback. Participants also showed an increased 14 preference for the ring finger but not for the middle finger in a post-neurofeedback motor task. Our 15 results show that neural activity and behaviors associated with the ring finger are more readily 16 modulated than those associated with the middle finger. These results have broader implications 17 for rehabilitation of individual finger movements, which may be limited or enhanced by individual 18 finger plasticity after neurological injury. 19 1

An acknowledgement to Mohit Rana's funding source was also missing. The following sentence should have been included in the acknowledgments section: M.R. is supported by a Fondecyt postdoctoral fellowship (project no. 3100648).