Biology subjects

Mathiak, K.

Publications and source records attributed to Mathiak, K..

9 recordsLinked to original sources

bioRxiv2026

Multimodal evidence for impaired glymphatic brain clearance as a mediator of "inflammaging" in schizophrenia - integrating insights from in vivo neuroimaging, omics, and post-mortem single nucleus-RNA-Seq data

Persistent cognitive impairment and reduced life expectancy in schizophrenia may arise from inflammation-driven accelerated aging ("inflammaging"), yet the underlying mechanisms remain unclear. Impaired glymphatic clearance, dependent on polarized aquaporin-4 (AQP4) localization at astrocytic endfeet, offers a plausible mechanistic link between inflammation and brain aging. Here, we integrate in vivo neuroimaging with post-mortem single-nucleus transcriptomics from distinct patient cohorts to demonstrate glymphatic dysfunction in schizophrenia and define its molecular basis. We identify reduced astrocytic expression of components of the dystrophin-associated protein complex (SNTA1, DAG1) required for AQP4 polarization. Both in vivo neuroimaging and post-mortem gene expression signatures of glymphatic dysfunction are restricted to an inflammatory subgroup characterized by peripheral immune activation and pro-inflammatory glial transcriptional profiles. Moreover, imaging signatures of glymphatic impairment are associated with schizophrenia polygenic risk scores enriched for immune - and oxidative stress - related aging pathways as well as cognitive dysfunction. At the cellular level, glymphatic dysfunction coincides with loss of homeostatic programs in microglia and astrocytes, increased inflammatory signaling, and endothelial signatures of blood-brain barrier disruption. Across modalities, glymphatic impairment is linked to accelerated aging, including increased brain and epigenetic age, and aging-related gene expression signatures.

neuroscience↗

bioRxiv2026

Searchlight Optimization Using Representational Similarity Analysis for Subject-Level Voxel Selection in Emotional State Decoding

Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be insufficient for capturing fine-grained representational differences. This challenge becomes particularly relevant in experimental settings and interventions such as neurofeedback training, where voxels are not only measured as neural responses but also used as targets for intervention based on their previously observed activity patterns. In this study, we propose a subject-level searchlight optimization framework that integrates voxel-wise general linear model (GLM)-based univariate analysis with representational similarity analysis (RSA)-based multivariate refinement to identify voxels that are both task-relevant and condition-sensitive. To enhance practical applicability, the framework further incorporates a data-driven hyperparameter tuning step based on Bayesian optimization, enabling efficient identification of high-performing configurations from small pilot datasets, with consistent performance when applied to larger samples. The proposed framework was evaluated using an emotion imagery fMRI dataset with four affective conditions. Results demonstrate that the multivariate refinement improves alignment between empirical and target representational structures compared with univariate selection alone. Compared with a classifier-based voxel selection approach, the RSA-based approach better preserves the representational geometry of emotional states while maintaining discriminative capacity. These findings highlight the effectiveness, efficiency, and robustness of the proposed RSA framework, providing a practical solution for identifying condition-sensitive voxels and supporting more precise multivariate investigation of affective brain states in multi-condition fMRI studies.

neuroscience↗

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↗

bioRxiv2024

Does Unfairness Evoke Anger or Disgust? A Quantitative Neurofunctional Dissection Based on 25 Years of Neuroimaging

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.

neuroscience↗

bioRxiv2024

On the specificity of manual triggers of autonomic nervous system responses using osteopathic cranial manipulative medicine

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.

physiology↗

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

Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions

Real-time fMRI guided neurofeedback training has gained increasing interest as a non-invasive brain regulation technique with the potential to normalize functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI datasets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three datasets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning the finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.

neuroscience↗

bioRxiv2020

Common and separable neural alterations in substance use disorders: evidence from coordinate-based meta-analyses of functional neuroimaging studies in human

Delineating common and separable neural alterations in substance use disorders (SUD) is imperative to understand the neurobiological basis of the addictive process and to inform substance-specific treatment strategies. Given numerous functional MRI (fMRI) studies in different SUDs, meta-analysis could provide an opportunity to determine robust shared and substance-specific alterations. The present study employed a coordinate-based meta-analysis covering fMRI studies in individuals with addictive cocaine, cannabis, alcohol, and nicotine use. The primary meta-analysis demonstrated common alterations in primary dorsal striatal, and frontal circuits engaged in reward/salience processing, habit formation, and executive control across different substances and task-paradigms. Subsequent sub-analyses revealed substance-specific alterations in frontal and limbic regions, with marked frontal and insula-thalamic alterations in alcohol and nicotine use disorders respectively. Finally, examining task-specific alterations across substances revealed pronounced frontal alterations during cognitive processes yet stronger striatal alterations during reward-related processes. Together the findings emphasize the role of dysregulations in striato-frontal circuits and dissociable contributions of these systems in the domains of reward-related and cognitive processes which may contribute to substance-specific behavioral alterations.

neuroscience↗

bioRxiv2019

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

neuroscience↗