Biology subjects

Kreitz, S.

Publications and source records attributed to Kreitz, S..

5 recordsLinked to original sources

bioRxiv2025

Gene expression patterns decompose fMRI activation in a sub-region-specific manner in mice after nociceptive stimulation

The results of functional magnetic resonance imaging (fMRI) cannot be interpreted directly at the molecular or genetic level. Our integrative workflow aims to add such a new dimension to the functional interpretation using a publicly available mouse brain gene expression database from the Allen Institute for Brain Science (ABA). From an average of 164 in-house measured mouse thermal pain fMRI datasets, we identified the top and bottom 5% of voxels according to their activation probability (AP) in response to warm and hot hind paw stimulation. Here, we investigated whether high AP voxels differ from low AP voxels in terms of gene expression: Analyzing nine core brain regions of the pain/saliency system, the top (high AP) and bottom (low AP) 5% of voxels showed distinct gene expression profiles. In nearly all regions, only high AP voxels were significantly enriched for gene ontology (GO) terms related to neurotransmitter activity, synaptic structure and neuronal function, while only the dorsal striatum showed GO term enrichment in low AP voxels. Notably, randomly selected voxels showed no significant enrichment, demonstrating the reliability of this approach. These results highlight the potential of gaining knowledge by integrating gene expression and fMRI data. Despite the limitations of using fixed gene expression data from the ABA cohort, this approach may provide new insights into physiological processes and improve the parcellation and interpretation of imaging data.

neuroscience↗

bioRxiv2023

Distinct Functional cerebral Hypersensitivity networks during incisional and inflammatory pain in rats

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions, especially with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal pain neuroimaging studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. Yet, the challenges of identifying neuroimaging signatures at a pain entity- and modality-specific level are manifold. Therefore, we developed a comprehensive framework for brain network analysis in disease-specific pain models combining functional MRI with graph-theory and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus (mechanical vs. electrical) modality processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. In short, graph-theoretical analyses revealed distinct Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA, resulting in impaired discrimination of stimulus modalities in both pain models compared to control conditions (CTR). Such specific neuroimaging signatures are an important step toward identifying novel pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic options, which ultimately can be translated to the clinic.

neuroscience↗

bioRxiv2022

Capturing early human memory consolidation utilizing the higher functional specificity of 7T compared to 3T-fMRI

Functional magnetic resonance imaging (fMRI) visualizes brain structures at increasingly higher resolution and better signal-to-noise ratio (SNR) as field strength increases. Yet, mapping the BOLD response to distinct neuronal processes continues to be challenging. Here, we performed 3T and 7T-fMRI analysis of motor-task activation and resting-state connectivity with adjusted SNR. We then applied graph theory to analyze resting-state neuronal networks detected by fMRI after a simple motor task. Despite adjusted SNR, 7T achieved a higher functional specificity of the BOLD response than 3T-fMRI. Following the motor task, 7T-fMRI therefore enabled the detection of an offline replay that was directly linked to brain regions associated with memory consolidation. These findings reveal how memory processing is initiated even after simple motor tasks and begins earlier than previously shown. Thus, the superior capability of 7T-fMRI to detect subtle functional dynamics promises to improve diagnostics and therapeutic assessment of neurological diseases.

neuroscience↗

bioRxiv2019

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

neuroscience↗

bioRxiv2017

A new analysis of resting state connectivity and graph theory reveals distinctive short-term modulations due to whisker stimulation in rats.

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.

neuroscience↗