Biology subjects

Hellrung, L.

Publications and source records attributed to Hellrung, L..

6 recordsLinked to original sources

bioRxiv2024

Neural Mechanisms of Feedback Processing and Behavioral Adaptation during Neurofeedback Training

The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity. Understanding how feedback-driven learning occurs requires examining how feedback is evaluated and how regulation adjusts in response to feedback signals. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (N = 153 individuals) to investigate brain regions where activity and connectivity are linked to feedback processing and regulation recalibration (i.e., regulation after feedback) during training. We harmonized feedback scores presented during training in these studies and computed their linear associations with brain activity and connectivity using parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in the reward system, dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity within the salience network. During regulation recalibration, no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results suggest that neurofeedback is processed in the reward system, supporting the theory that reinforcement learning shapes this form of brain training. In addition, the involvement of large-scale networks in feedback processing, continuously transitioning between evaluating external feedback and internally assessing the adopted cognitive state, suggests that higher-level processing is integral to this type of learning. Our findings highlight the pivotal role of performance-related feedback as a driving force in learning, potentially extending beyond neurofeedback training to other feedback-based processes. Key PointsWe conducted a pre-registered mega-analysis integrating data from eight fMRI neurofeedback studies to examine feedback processing and regulation recalibration during neurofeedback training. During feedback processing, feedback was associated with activity in the reward system, dorsal attention network, default mode network, and cerebellum; as well as with reward system-related connectivity within the salience network. We found no positive results during regulation blocks; however, additional analyses suggest that recalibration may have already occurred during feedback presentation.

neuroscience↗

bioRxiv2020

The effects of multi-echo fMRI combination and rapid T2*-mapping on offline and real-time BOLD sensitivity

A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real-time use cases: a single-echo time series (based on Echo 2), the real-time T2*-mapped time series (T2*FIT) and four combined time series (T2*-weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T2*FIT-weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset (N=28) with four task-based fMRI runs and two resting state runs. We show that the T2*FIT-weighted combination yields the largest increase in temporal signal-to-noise ratio across task and resting state runs. We demonstrate additionally for all tasks that the T2*FIT time series consistently yields the largest offline effect size measures and real-time region-of-interest based functional contrasts. These improvements show the possible utility of multi-echo fMRI for studies employing real-time paradigms, while caution is still advised due to decreased tSNR of the T2*FIT time series. We recommend the use and continued exploration of T2*FIT for offline task-based and real-time fMRI analysis. Supporting information includes: a data repository (https://dataverse.nl/dataverse/rt-me-fmri), an interactive web-based application to explore the data (https://rt-me-fmri.herokuapp.com/), and further materials and code for reproducibility (https://github.com/jsheunis/rt-me-fMRI).

neuroscience↗

bioRxiv2020

rt-me-fMRI: A task and resting state dataset for real-time, multi-echo fMRI methods development and validation

A multi-echo fMRI dataset (N=28 healthy participants) with four task-based and two resting state runs was collected, curated and made available to the community. Its main purpose is to advance the development of methods for real-time multi-echo functional magnetic resonance imaging (rt-me-fMRI) analysis with applications in neurofeedback, real-time quality control, and adaptive paradigms, although the variety of experimental task paradigms supports a multitude of use cases. Tasks include finger tapping, emotional face and shape matching, imagined finger tapping and imagined emotion processing. This work provides a detailed description of the full dataset; methods to collect, prepare, standardize and preprocess it; quality control measures; and data validation measures. A web-based application is provided as a supplementary tool with which to interactively explore, visualize and understand the data and its derivative measures: https://rt-me-fmri.herokuapp.com/. The dataset itself can be accessed via a data use agreement on DataverseNL at https://dataverse.nl/dataverse/rt-me-fmri. Supporting information and code for reproducibility can be accessed at https://github.com/jsheunis/rt-me-fMRI.

neuroscience↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2019

Individual differences in the mechanistic control of the dopaminergic midbrain

The dopaminergic midbrain is associated with brain functions, such as reinforcement learning, motivation and decision-making that are often disturbed in neuropsychiatric disease. Previous research has shown that activity in the dopaminergic midbrain can be endogenously modulated via neurofeedback, suggesting potential for non-pharmacological interventions. However, the robustness of endogenous modulation, a requirement for clinical translation, is unclear. Here, we examined how self-modulation capability relates to regulation transfer. Moreover, to elucidate potential mechanisms underlying successful self-regulation, we studied individual prediction error coding, and, during an independent monetary incentive delay (MID) task, individual reward sensitivity. Fifty-nine participants underwent neurofeedback training either in a veridical or inverted feedback group. Successful self-regulation was associated with post-training activity within the cognitive control network and accompanied by decreasing prefrontal prediction error signals and increased prefrontal reward sensitivity in the MID task. The correlative link of dopaminergic self-regulation with individual differences in prefrontal prediction error and reward sensitivity suggests that reinforcement learning contributes to successful self-regulation. Our findings therefore provide new insights in the control of dopaminergic midbrain activity and pave the way to improve neurofeedback training in neuropsychiatric patients.

neuroscience↗

bioRxiv2018

Motion and physiological noise effects on amygdala real-time fMRI neurofeedback learning

Real-time fMRI neurofeedback allows to learn control over activity in a localized brain region. However, with fMRI, physiological factors such as the cardiac cycle and respiration interfere with the measurement of brain activation. In conventional fMRI studies this is usually mitigated by inclusion of motion parameters and/or physiological parameters as nuisance regressors at the analysis stage, allowing to correct for and filter out such confounders. In real-time fMRI, however, such an approach is not routinely feasible due to the necessity to process all signals during the runtime of an experiment. The absence of on-line correction can therefore compromise real-time fMRI study outcomes reporting volitional self-regulation capability as BOLD signal changes. This is especially true for BOLD signal changes in subcortical regions situated close to blood vessels or air cavities, such as the amygdala. We therefore aimed to establish the effects of motion, heart rate, heart rate variability, and respiratory volume on learning effects, which means here an increase in BOLD signal change over NF training, in an amygdala neurofeedback experiment. Specifically, we investigate motion parameters from two emotion regulation studies - performed at 3T and 7T scanners - and additionally acquired physiological variance for the latter one. Our results revealed differences in these parameters between groups and especially between regulation and resting periods within each participant. However, strictly considering these parameters as nuisance regressors in data analysis revealed that the learning of volitional self-regulation of the amygdala is not driven by motion and physiological changes. As validation of our real-time findings, we compare them to the gold standard of assessment of motion and physiology from the Human Connectome Project. Based on this, we recommend to carefully report neurofeedback study results including physiological nuisance regression. To our knowledge, this is the first study investigating the effects of motion and physiological noise correction on neurofeedback BOLD effects.

neuroscience↗