Klaus Mathiak’s research while affiliated with RWTH Aachen University and other places

One of the fundamental questions in real‐time functional magnetic resonance imaging neurofeedback (rt‐fMRI NF) investigations is the definition of a suitable neural target for training. Previously, we applied a meta‐analytical approach to define a network‐level target for connectivity‐based rt‐fMRI NF in substance use disorders. The analysis yielded consistent connectivity alterations between the insula and anterior cingulate cortex (ACC) as well as the dorsal striatum and the ACC. In the current investigation, we addressed the feasibility of regulating this network and its functional relevance using connectivity‐based neurofeedback. In a double‐blind, sham‐controlled design, 60 nicotine users were randomly assigned to the experimental or sham control group for one NF training session. The preregistered primary outcome was defined as improved inhibitory control performance after regulation of the target network compared to sham control. Secondary outcomes were (1) neurofeedback‐specific changes in functional connectivity of the target network; (2) changes in smoking behavior and impulsivity measures; and (3) changes in resting‐state connectivity profiles. Our results indicated no differences in behavioral measures after receiving feedback from the target network compared to the sham feedback. Target network connectivity was increased during regulation blocks compared to rest blocks, however, the experimental and sham groups could regulate to a similar degree. Accordingly, the observed activation patterns may be related to the mental strategies used during regulation attempts irrespective of the group assignment. We discuss several crucial factors regarding the efficacy of a single‐session connectivity‐based neurofeedback for the target network. This includes high fluctuation in the connectivity values of the target network that may impact controllability of the signal. To our knowledge, this investigation is the first randomized, double‐blind controlled real‐time fMRI study in nicotine users. This raises the question of whether previously observed effects in nicotine users are specific to the neurofeedback signal or reflect more general self‐regulation attempts.

Over the last decades, the traditional 'Homo economicus' model has been increasingly challenged by convergent evidence underscoring the impact of emotions on decision-making. A classic example is the perception of unfairness operationalized in the Ultimatum Game where humans readily sacrifice personal gains to punish those who violate fairness norms. While the emotional mechanism underlying costly punishments has been widely acknowledged, the distinct contributions of moral emotions (anger or disgust) remain debated, partly due to methodological limitations of the conventional experiments. Here, we capitalize on a quantitative neurofunctional dissection approach by combining recent developments in neuroimaging meta-analyses, behavioral-level, network-level, and neurochemical-level decoding and data from 3,266 participants from functional neuroimaging studies to determine the common and distinct neural representations between unfairness and the two moral emotions. Experience of unfairness engaged a widespread bilateral network encompassing insular, cingulate, and frontal regions, with dorsal striatal regions mediating the decision to reject unfair offers. Disgust engaged a defensive-avoidance circuit encompassing amygdalar, occipital, and frontal regions, while anger engaged non-overlapping systems including mid-cingulate, thalamic, and frontal regions. Unfairness and anger or disgust respectively commonly engaged the anterior and mid-insula, while the latter additionally showed common recruitment of ventrolateral prefrontal and orbitofrontal cortices. Multimodal network, behavioral, and serotonergic decoding provided a more granular and convincing dissection of these results. Findings indicate a shared neuroaffective basis underlying the impact of emotions on unfairness-induced punishment behavior and suggest a common brain circuit has been evolutionarily shaped to protect individuals from personal harm and enforce societal norms.

Significant autonomic nervous system responses to a specific osteopathic intervention, the cranial vault hold (CVH), have recently been demonstrated in forehead skin blood volume changes, heart rate, and respiration frequencies. The specificity of the CVH-intervention-related autonomic responses yet requires differentiation. Thus, we compared autonomic responses to CVH with responses to compression of the fourth ventricle (CV4) and to two corresponding SHAM conditions. Analysis of frequencies and amplitudes for changes in skin blood volume and respiration in low (LF; 0.05 - 0.12 Hz), intermediate (IM; 0.12 - 0.18 Hz), and high (HF; 0.18 - 0.4 Hz) frequency bands, and metrics of heartrate variability revealed significant decreases in LF range (from 0.12 to 0.10 Hz), increased LF and decreased IM durations, and increased skin blood volume amplitudes in response to CVH, but no significant skin blood volume responses to any of the control interventions. Ratio changes for respiration and skin blood volume frequencies approximately at 3:1 during CVH, remained unchanged in all other interventions. Heart rate decreased across conditions, indicating an increase in parasympathetic tone. This was also indicated by a significant increase in root mean of squared successive difference following CV4. We incurred that rhythmic response patterns in the LF and IM bands only appeared in CVH. This suggests specific physiological responses to CVH warranting further investigation by studying e.g., responses to CVH in physical or mental health disorders with autonomic involvement.

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 Points We 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.

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

Introduction Deficits in emotion recognition and processing are characteristic for patients with schizophrenia [SCZ]. Methods We targeted both emotion recognition and affective sharing, one in static and one in dynamic facial stimuli, during functional magnetic resonance imaging [fMRI] in 22 SCZ patients and 22 matched healthy controls [HC]. Current symptomatology and cognitive deficits were assessed as potential influencing factors. Results Behaviorally, patients only showed a prolonged response time in age-discrimination trials. For emotion-processing trials, patients showed a difference in neural response, without an observable behavioral correlate. During emotion and age recognition in static stimuli, a reduced activation of the bilateral anterior cingulate cortex [ACC] and the right anterior insula [AI] emerged. In the affective sharing task, patients showed a reduced activation in the left and right caudate nucleus, right AI and inferior frontal gyrus [IFG], right cerebellum, and left thalamus, key areas of empathy. Discussion We conclude that patients have deficits in complex visual information processing regardless of emotional content on a behavioral level and that these deficits coincide with aberrant neural activation patterns in emotion processing networks. The right AI as an integrator of these networks plays a key role in these aberrant neural activation patterns and, thus, is a promising candidate area for neurofeedback approaches.

Cardiovascular rhythms representing functional states of the autonomic nervous system (ANS) are insufficiently reflected by the current physiological model based on low and high frequency bands (LF, HF, resp.). An intermediate (IM) frequency band generated by a brainstem pacemaker was included in systemic physiological ANS analyses of forehead skin perfusion (SP), ECG, and respiration. Data of 38 healthy participants at T0 and T1 (+1 week) before, during, and following osteopathic cranial vault hold (CVH) stimulation were analyzed including momentary frequencies of highest amplitude, amplitudes in low (0.05–0.12 Hz), IM (0.12–0.18 Hz), and high (0.18–0.4 Hz) frequency bands, and established heart rate variability (HRV) metrics. During CVH, LF interval durations increased, whereas IM/HF band durations decreased significantly. Amplitudes increased significantly in all frequency bands. A cluster analysis found one response pattern dominated by IM activity (47% of participants) with highly stable 0.08 Hz oscillation to CVH, and one dominated by LF activity (0.10 Hz) at T0, increasing to IM activity at T1. Showing frequency ratios at ≈3:1, respiration was not responsible for oscillations in PPG during CVH. HRV revealed no significant responses. Rhythmic patterns in SP and respiration matched previous findings on a reticular “0.15 Hz rhythm”. Involvement of baroreflex pathways is discussed as alternative explanation.

Objective.Driver assistance systems play an increasingly important role in modern vehicles. In the current level of technology, the driver must continuously supervise the driving and intervene whenever necessary when using driving assistance systems. The driver's attentiveness plays an important role in this human-machine interaction. Our aim was to design a simplistic technical framework for studying neural correlates of driving situations in a functional magnetic resonance imaging (fMRI) setting. In this work we assessed the feasibility of our proposed platform.Methods.We proposed a virtual environment (VE) simulation of driver assistance as a framework to investigate brain states related to partially automated driving. We focused on the processing of auditory signals during different driving scenarios as they have been shown to be advantageous as warning stimuli in driving situations. This provided the necessary groundwork to study brain auditory attentional networks under varying environmental demands in an fMRI setting. To this end, we conducted a study with 20 healthy participants to assess the feasibility of the VE simulation.Results.We demonstrated that the proposed VE can elicit driving related brain activation patterns. Relevant driving events evoked, in particular, responses in the bilateral auditory, sensory-motor, visual and insular cortices, which are related to perceptual and behavioral processes during driving assistance. Conceivably, attentional mechanisms increased somatosensory integration and reduced interoception, which are relevant for requesting interactions during partially automated driving.Significance.In modern vehicles, driver assistance technologies are playing an increasingly prevalent role. It is important to study the interaction between these systems and drivers' attentional responses to aid in future optimizations of the assistance systems. The proposed VE provides a foundational first step in this endeavor. Such simulated VEs provide a safe setting for experimentation with driving behaviors in a semi-naturalistic environment.