Biology subjects

Luehrs, M.

Publications and source records attributed to Luehrs, M..

9 recordsLinked to original sources

bioRxiv2025

Toward navigating emotional states using real-time representational similarity analysis fMRI neurofeedback - a feasibility study

Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a promising non-invasive brain-computer-interface (BCI) technique for enhancing self-regulation of affective states in the brain. However, conventional univariate rt-fMRI-NF approaches are limited in their ability to distinguish neural patterns of distinct emotions that involve overlapping brain regions. In this study, we applied an rt-fMRI semantic neurofeedback (rt-fMRI-sNF) paradigm, incorporating real-time representational similarity analysis (rt-RSA) to enable navigation between emotional states. Four emotional patterns were first derived from functional localizer runs, each designed to evoke a specific emotion, and then applied as target patterns during neurofeedback. Using an RSA-informed circular semantic map (CSM), participants received real-time visual feedback indicating both the similarity and intensity of their current brain activity relative to target emotional patterns. Participants were instructed to use mental imagery to shift their brain activity toward the specific target pattern and increase its intensity. Twenty-four healthy participants completed the localizer runs, and two consecutive neurofeedback runs in the same session. Ten participants successfully engaged with both the similarity and intensity components of the CSM, showing effective modulations of their mental states. Analyses of the localizer runs revealed overlapping regional activations across emotions and demonstrated that RSA outperformed univariate analysis in distinguishing between them. For the neurofeedback runs, linear mixed-effects model (LMM) analyses across multiple performance metrics indicated consistent within-run improvements and higher initial performance in the second run, while significant between-run learning effects emerged only in exploratory models with quadratic time terms. A block-wise comparison also showed significantly higher performance at the end of each run compared to the beginning based on the intensity metric. These findings support the usability of RSA in differentiating multiple emotional states and demonstrate the feasibility of the rt-fMRI-sNF paradigm for emotion regulation.

neuroscience↗

bioRxiv2022

Retrieving fMRI data in real-time: difficulties and pitfalls

One of the significant challenges in real-time fMRI environments is to ensure that the functional images are exported in real-time. The prerequired ability to reconstruct these images immediately after the acquisition has already been resolved in 2004. Nowadays, more sophisticated sequences allow for higher resolution and faster repetition times and thereby challenging the ability to export this data in real-time. In this article, we tackle the potentially arising problem of sending the reconstructed data from the MRI to an external PC to perform the real-time fMRI analysis. We show that depending on the implementation of the data transfer, long delays can occur that can differ drastically in time and how often they occur. In addition, we propose a solution for SIEMENS MRI devices which was tested and applied already on multiple MRI devices including 3T and 7T machines on different vendor software versions. This new technique can be used as a blueprint that can be directly applied to other manufacturers. We also provide the source code of the described solution and show that the delay in the data transfer can be significantly reduced to a tolerable level using our proposed procedure. Finally, we integrate measurement options for the data transfer times to improve quality measures in real-time fMRI environments (e.g., clinical) that can implement the proposed solution. Efforts should be taken by the real-time community and MRI manufacturers to employ a standardized real-time export e.g., similar to the lab streaming layer which is used as a standard export method in EEG environments.

neuroscience↗

bioRxiv2022

Performance comparison of systemic activity correction in fNIRS for methods with and without short distance channels

SignificanceFunctional near-infrared spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain-computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts, and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short-distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. AimThis study aimed at comparing the performance of SA correction methods with and without SDCs. ApproachSemisimulated and real motor task data of healthy older adults were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared with data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. ResultsAll correction methods were found to improve signal quality and enhance spatial specificity as compared with the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semisimulated and real data, and also between quality measures. ConclusionsOverall, results confirmed that both {Delta}[HbO] and {Delta}[HbR] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.

neuroscience↗

bioRxiv2021

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience. HighlightsO_LIAcute stress affects the allocation of neural resources between large-scale brain networks C_LIO_LIWe provide a first successful demonstration of neurofeedback training based on stress-related large-scale brain networks C_LIO_LINovel approach has the potential to train control over central response to stressors in real-life C_LIO_LICould build foundation for future clinical interventions to increase resilience C_LI

neuroscience↗

bioRxiv2020

Towards Semantic fMRI Neurofeedback: Navigating among Mental States using Real-time Representational Similarity Analysis

ObjectiveReal-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high level (semantic) representations of experimental stimuli via real-time representational similarity analysis of multi-voxel patterns of brain activity. ApproachIn a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 Tesla using imagery of concrete objects. The dependence on noise is more systematically assessed on artificial fMRI data with similar (simulated) spatio-temporal structure and variable (injected) signal and noise. A series of brain activity patterns from the ventral visual cortex is evaluated via on-line and off-line analyses and the performances of the method are reported under different noise conditions. Main resultsThe participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target, in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent. SignificanceOur proof-of-concept study demonstrates the potential of semantic NF designs where the participant navigates among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.

neuroscience↗

bioRxiv2020

Neurofeedback fMRI in the motor system elicits bi-directional changes in activity and white-matter structure in the healthy adult human brain

Neurofeedback can be used to alter brain activity and is therefore an attractive tool for neuromodulation in clinical contexts. Different contexts might call for different patterns of activity modulation. For example, following stroke, alternative therapeutic strategies could involve up or down-regulation of activity in the ipsilateral motor cortex. However, effects of such strategies on activity and brain structure are unknown. In a proof of concept study in healthy individuals, we showed that fMRI neurofeedback can be used to drive activity up or down in ipsilateral motor cortex during hand movement. Given evidence for activity-dependent white matter plasticity, we also tested effects of activity modulation on white matter microstructure using diffusion tensor imaging (DTI). We show rapid opposing changes in corpus callosum microstructure that depend on the direction of activity modulation. Bidirectional modulation of ipsilateral motor cortex activity is therefore possible, and results not only in online changes in activity patterns, but also in changes in microstructure detectable 24 hours later.

neuroscience↗

bioRxiv2019

Active head motion reduction in Magnetic Resonance Imaging using tactile feedback

Head motion is a common problem in clinical as well as empirical (functional) Magnetic Resonance Imaging applications, as it can lead to severe artefacts that reduce image quality. The scanned individuals themselves, however, are often not aware of their head motion. The current study explored whether providing subjects with this information using tactile feedback would reduce their head motion and consequently improve image quality. In a single session that included six runs, 24 participants performed three different cognitive tasks: (1) passive viewing, (2) mental imagery, and (3) speeded responses. These tasks occurred in two different conditions: (a) with a strip of medical tape applied from one side of the MR head-coil, via the participants forehead, to the other side, and (b) without the medical tape being applied. Results revealed that application of medical tape to the forehead of subjects to provide tactile feedback significantly reduced both translational as well as rotational head motion. While this effect did not differ between the three cognitive tasks, there was a negative quadratic relationship between head motion with and without feedback. That is, the more head motion a subject produced without feedback, the stronger the motion reduction given the feedback. In conclusion, the here tested method provides a simple and cost-efficient way to reduce subjects head motion, and might be especially beneficial when extensive head motion is expected a priori.

neuroscience↗

bioRxiv2018

Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks during adolescence

Research has shown that difficulties with emotion regulation abilities in childhood and adolescence increase the risk for developing symptoms of mental disorders, e.g anxiety. We investigated whether functional magnetic resonance imaging (fMRI)-based neurofeedback (NF) can modulate brain networks supporting emotion regulation abilities in adolescent females. We performed three studies (total N=63). We first compared different NF implementations regarding their effectiveness of modulating prefrontal cortex (PFC)-amygdala functional connectivity (fc). Further we assessed the effects of fc-NF on neural measures, emotional/metacognitive measures and their associations. Finally, we probed the mechanism underlying fc-NF by examining concentrations of inhibitory and excitatory neurotransmitters. Results showed that NF implementations differentially modulate PFC-amygdala fc. Using the most effective NF implementation we observed important relationships between neural and emotional/metacognitive measures, such as practice-related change in fc was related with change in thought control ability. Further, we found that the relationship between state anxiety prior to the MRI session and the effect of fc-NF was moderated by GABA concentrations in the PFC and anterior cingulate cortex. To conclude, we were able to show that fc-NF can be used in adolescent females to shape neural and emotional/metacognitive measures underlying emotion regulation. We further show that neurotransmitter concentrations moderate fc-NF-effects.

neuroscience↗

bioRxiv2018

Real-time functional connectivity-based neurofeedback of amygdala-frontal pathways reduces anxiety

Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising strategy to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training. In a randomized within-subject sham-controlled design high anxious subjects (n = 26) underwent real-time fMRI-guided training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after three days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes. Training of the target, yet not the sham-control, pathway significantly increased amygdala-vlPFC connectivity and decreased subjective anxiety levels. On the individual level stronger connectivity increases were significantly associated with anxiety reduction. At follow-up, volitional control over the target pathway and decreased anxiety level were maintained in the absence of feedback. The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel strategy to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.

clinical trials↗