Michael Lührs

The paper examines how signal quality affects inter-individual variability in fNIRS data and introduces an open dataset with multi-task recordings and peripheral physiology measures.

This study compares resting-state functional connectivity and networks derived from fNIRS and fMRI, showing fNIRS captures key features of fMRI-based networks with partial correlations improving edge-level agreement.

This paper introduces a subject-level searchlight optimization framework combining univariate and multivariate analyses to identify condition-sensitive voxels for emotional state decoding in fMRI.

This paper compares methods for detecting resting-state functional connectivity using fNIRS, finding that independent component analysis (ICA) outperforms seed-based approaches in accuracy and cross-chromophore consistency.

This study explores using real-time representational similarity analysis in fMRI neurofeedback to navigate emotional states, showing feasibility and improved performance with repeated sessions.

This study identifies classification inaccuracies in fMRI-based attention state decoding due to design choices and confounders like motion artifacts and task timing.

This study examines how participants evaluate their performance in fMRI neurofeedback tasks, finding that prediction accuracy improves with learning but confidence remains stable.

This study shows that fNIRS-based neurofeedback can modulate rTPJ activity and affect attention reorienting, with learning differences between up- and downregulation groups.

The paper addresses real-time fMRI data export challenges, showing delays in data transfer and proposing a solution for Siemens MRI devices that reduces delays and improves quality measures.

This paper compares SA correction methods with and without short-distance channels in fNIRS, finding that methods with SDCs generally perform better but methods without SDCs can still improve signal quality.

This paper introduces a fast pRF mapping method suitable for real-time applications, validated on simulated and empirical fMRI data.

This study shows participants can learn to self-regulate stress-related brain network balance using real-time fMRI neurofeedback and transfer this skill to a stressor scenario.

The paper compares optode-layout design approaches for fNIRS using individual fMRI data, finding that incorporating more individual MRI data improves signal quality and sensitivity.

This paper introduces a real-time fMRI neurofeedback method using semantic representations to allow participants to navigate mental states by controlling neural activity towards target brain states.

This study shows that fMRI neurofeedback can modulate ipsilateral motor cortex activity and induce opposite changes in corpus callosum white matter microstructure in healthy adults.

This study shows that fMRI-based neurofeedback modulates emotion regulation networks in adolescent females, with effects influenced by neurotransmitter concentrations.

The paper investigates the role of paracingulate cortex in reality monitoring, finding reduced involvement in schizophrenia patients and demonstrating neurofeedback-induced improvements in healthy individuals.

This study shows that tactile feedback via medical tape reduces head motion during MRI, improving image quality, with greater reduction for subjects with more motion without feedback.