Biology subjects

Boehm-Sturm, P.

Publications and source records attributed to Boehm-Sturm, P..

13 recordsLinked to original sources

bioRxiv2026

A harmonized phantom MRI quality control framework identifies sources of longitudinal and multi-site variability

The shift towards open science in preclinical research requires high-quality, comparable data adhering to FAIR principles, yet rigorous quality assurance (QA) and quality control (QC) frameworks remain less established in preclinical magnetic resonance imaging (MRI) than in clinical imaging. To address this limitation, a multicenter study was conducted across 21 international laboratories using standardized commercial liquid phantoms for mouse and rat MRI setups and a harmonized acquisition protocol. Data processing was centralized using AIDAqc, an automated pipeline extracting quantitative metrics including signal-to-noise ratio (SNR), temporal SNR (tSNR), ghost-to-signal ratio (GSR), and motion-equivalent temporal instability, combined with five complementary outlier-detection algorithms. The evaluated datasets encompassed magnetic field strengths from 3.0 to 16.4 T and heterogeneous coil and acquisition configurations. In mouse phantom data, SNR and tSNR were significantly lower at 3 T than at 7 and 9.4 T, whereas artifact-related metrics did not differ significantly between field-strength groups. In rat phantom data, differences between field strengths were less pronounced. At 7 T, substantial dataset-specific differences were observed in anatomical and functional quality metrics, also among datasets using the similar coil configuration. Multicenter reference values for frequently represented 7 T configurations were established, including SNR/tSNR of 48 {+/-} 3/48 {+/-} 1 dB for mouse surface-coil datasets and 49 {+/-} 3/44 {+/-} 4 dB for rat array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting was more variable across time. Automated outlier detection additionally identified transient acquisition instabilities that were not always apparent by visual inspection. Together, these findings show that field strength and nominal coil configuration alone do not adequately characterize MRI performance and support standardized, longitudinal phantom-based QC combined with automated analysis as a practical approach for improving the reliability, transparency, and interoperability of multicenter preclinical MRI data.

bioengineering↗

bioRxiv2026

Probabilistic mouse-human brain correspondence by multimodal optimal transport

The mouse is the principal model for brain mechanism and disease, allowing experiments that cannot be performed in humans. However, findings often translate poorly because homologous regions differ in relative size and some human territories have no clear mouse counterpart. Here we present OTTER, which learns mouse-human brain correspondence as a probabilistic, parcel-resolution coupling using multimodal fused Gromov-Wasserstein optimal transport, integrating functional and structural connectivity with spatial position and curated homologies. OTTER recovers established homologues on a transcriptomic benchmark and preserves broad cross-species organisation along the cortical areal hierarchy. Applying the coupling to human functional connectivity reveals a graded decline in mouse-based reconstruction across evolutionarily expanded association cortex, with the lowest values in lateral prefrontal territory. Finally, the bidirectional map generates testable human predictions from mouse experiments and ranks mouse circuits corresponding to human clinical targets.

neuroscience↗

bioRxiv2026

Multimodal molecular profiling of the metabolic penumbra in hyperacute stroke

BackgroundThe ischemic penumbra, a metabolically compromised yet potentially salvageable region surrounding the ischemic core, is a prime target for acute stroke intervention. Yet an objective molecular definition of the penumbra, particularly during the earliest stages of ischemia, remains lacking. Methods and ResultsWe applied principal component analysis (PCA) followed by k-means clustering to high-resolution mass spectrometry imaging data covering multiple metabolic pathways to identify a metabolically defined penumbra in a mouse model of hyperacute stroke (45 min middle cerebral artery occlusion, MCAO). Targeted spatial metabolomic profiling by matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) reveals a distinct penumbral metabolic profile, marked by relative preservation of high-energy phosphates, comparable lactate accumulation, and reduced succinate accumulation relative to the core. Spatial transcriptomics revealed selective induction of immediate-early genes, including Npas4, Fos and Junb, within the penumbra. Consistently, imaging mass cytometry shows enrichment of phospho-histone H3 (pHH3) within the penumbra, suggesting a chromatin-associated response potentially linked to immediate-early gene activation. ConclusionTogether, these findings provide a multimodal molecular atlas of the hyperacute metabolically defined penumbra and reveal molecular features that facilitates its identification and inform future therapeutic strategies.

neuroscience↗

bioRxiv2025

Network Adaptability Governs Resilience and Susceptibility to Social Defeat

Stress resilience is defined as the ability to maintain mental health after adversity. In humans, resilience is marked by threat-safety discrimination and extinction of fear, whereas susceptibility involves fear generalization and extinction resistance. Using a translational mouse model of chronic social defeat stress combined with preregistered multimodal brain imaging, we investigated structural and network-level markers of resilience, susceptibility, and threat learning. Resilience was characterized by preserved dentate gyrus integrity, greater microstructural complexity in prefrontal regions, and signatures consistent with enhanced inhibitory gating within amygdalar circuits. Susceptibility, in contrast, involved reduced dentate gyrus complexity and diminished microstructural flexibility, with a weaker but parallel pattern in the basolateral amygdala. Impaired threat learning was linked to compromised CA1/CA2 integrity and reduced pons connectivity, highlighting hippocampal-brainstem interactions in memory consolidation. Overall, these findings show that resilience emerges from adaptive network reorganization, whereas susceptibility and impaired learning reflect distinct dysfunctions, underscoring individual coping differences even among genetically identical animals.

neuroscience↗

bioRxiv2025

Temporal dynamics of neuroplasticity and neurodegeneration in the central auditory system following noise-induced hearing loss: A multimodal imaging and histological study

Noise-induced hearing loss (NIHL) is a sensorineural disorder that triggers profound neuroplastic and neurodegenerative consequences in the central auditory nervous system (CNS). This study examined how neuronal density, axonal integrity and glutamatergic and GABAergic neurotransmission are affected after acute overstimulation in the central inferior colliculus (CIC) and the ventral medial geniculate body of the thalamus (MGV). To achieve this, a correlative multimodal approach combining audiometric, magnetic resonance imaging (MRI) and histological biomarkers was performed. Adult mice were noise-exposed to broadband white noise (5-20 kHz) for 3 hours at either high (115 dB SPL) or moderate (90 dB SPL) intensity, while unexposed mice were used as controls. Separate cohorts of mice were investigated 1-, 7-, 56- and 84-days post-exposure using in vivo magnetic resonance imaging (MRI) techniques: Voxel-based morphometry (VBM) of gray matter density (GMD), diffusion MRI (dMRI) of microstructure and connectivity, and single-voxel proton magnetic resonance spectroscopy (1H-MRS) for glutamate and GABA quantification. Frequency-specific auditory brainstem responses (ABR) were recorded at 4, 8, 16 and 32 kHz before and after exposure to examine hearing threshold (HT) shifts. Following imaging, brains were processed for fluorescence immunohistochemistry (FIHC) targeting Neuronal Nuclear Protein (NeuN), 4,6-diamidino-2-fenilindo (DAPI), Neurofilament (SMI312), vesicular GABA transporter (VGAT), and vesicular glutamate transporters 1 (VGLUT1) and 2 (VGLUT2). HTs were significantly elevated 7d, 56d and 84d after 115 dB noise exposure, suggesting a NIHL phenotype. Neurofilament density significantly increased in the CIC and MGV 1d after 115 dB noise exposure, and returned to baseline at later time points. dMRI transient microstructural alterations were observed 7d after 90 dB noise exposure. Glutamate and GABA decreases 84d after 90 dB exposure were also detected. Moreover, correlation analysis between audiometric, histological and MRI datasets revealed scarce relationships between the investigated parameters. These findings advance our understanding of the CNS adaptations of NIHL and emphasizes the need for more sensitive and integrative approaches to identify robust biomarkers of central auditory dysfunction.

neuroscience↗

bioRxiv2025

2-Deoxy-D-glucose chemical exchange-sensitive spin-lock MRI of cerebral glucose metabolism after stroke in the rat

Rapid breakdown of cerebral glucose metabolism is a hallmark in stroke pathology. Metabolic activity delineates the penumbra from the infarct core, representing tissue that is potentially salvageable by therapeutic interventions. Tools to image dynamics of glucose and its spatial distribution could provide biomarkers of disease severity and of the success of therapeutic interventions. Here, we developed a new protocol to measure glucose metabolism in a rat model of stroke using chemical exchange-sensitive spin-lock (CESL) MRI of the glucose analog 2-deoxy-D-glucose (2DG). We further implemented a protocol that combines 2DG-CESL-MRI with perfusion and diffusion MRI to relate this new signal to established definitions of hypoperfused tissue, cytotoxic edema and the penumbra. We found that 2DG-CESL-MRI provides a biomarker of disturbed glucose metabolism after stroke with high effect size. This is the first study to investigate CESL MRI of 2DG in the context of metabolism imaging in rodent stroke.

neuroscience↗

bioRxiv2025

Nurr1 orchestrates claustrum development and functionality

Claustrum is the part of the forebrain known to be most widely interconnected tissue with almost all global regions. It is believed to be involved in coordinating multiple cognitive behaviors including consciousness formation. However, little is known about the molecular mechanisms underlying its development and involvement in behavioral control. Here we show that Nurr1 (Nr4a2) is the key transcription factor orchestrating claustral morphogenesis, functional connectivity and thus behaviors. Nurr1 deficient claustral cells aberrantly migrate into insular cortex, shaping claustrum into an abnormal wing-like structure, and ectopically turn on insular corticex genetic program. Accordingly, functional connectivity of the claustrum is not properly formed and relevant behaviors are dysregulated in Nurr1 deficient mice. We show that Nurr1 regulates claustral neuron positioning by suppressing G-protein signaling.

neuroscience↗

bioRxiv2023

Refined movement analysis in the Staircase test reveals differential motor deficits in mouse models of stroke

Accurate assessment of post-stroke deficits is vital in translational research. Recent advances in machine learning provide unprecedented precision in quantifying rodent motor behavior post-stroke. However, the extent to which these tools can detect lesion-specific upper extremity deficits remains unclear. Using proximal middle cerebral artery occlusion (MCAO) and cortical photothrombosis (PT), we assessed post-stroke impairments in mice through the Staircase test. Lesion locations were identified using 7T-MRI. Machine learning was applied to reconstruct kinematic trajectories using MouseReach, a data-processing toolbox. This yielded 30 refined outcome parameters effectively capturing motor deficits. Lesion reconstructions located ischemic centers in the striatum (MCAO) and sensorimotor cortex (PT). Pellet retrieval was altered in both cases but did not correlate with stroke volume or ischemia extent. Instead, cortical ischemia was characterized by increased hand slips and modified reaching success. Striatal ischemia led to progressively prolonged reach durations, mirroring delayed symptom onset in basal ganglia strokes. In summary, refined machine learning-based movement analysis revealed specific deficits in mice after cortical and striatal ischemia. These findings emphasize the importance of thorough behavioral profiling in preclinical stroke research to increase translational validity of behavioral assessments.

neuroscience↗

bioRxiv2022

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation.

neuroscience↗

bioRxiv2022

Prediction of Stroke Outcome in Mice Based on Non-Invasive MRI and Behavioral Testing

BackgroundPrediction of post-stroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are frequently used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. MethodsWe introduced heterogeneity into our study to broaden the applicability of our prediction tools. We analyzed the effect of 30, 45 and 60 minutes of arterial occlusion on the variance of stroke volumes. Next, we built a heterogeneous cohort of 215 mice using data from 15 studies that included 45 minutes of middle cerebral artery occlusion and various genotypes. Motor function was measured using the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were co-registered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits. ResultsVariance of stroke volumes was increased by 45 minutes of arterial occlusion compared to 60 minutes and including various genotypes. We detected both a subacute and residual motor-functional deficit after stroke and different recovery trajectories. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit was most accurately predicted by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomical regions within the ischemic lesion had particular impact on the prediction of long-term outcome. ConclusionsWe developed and validated a robust tool for the prediction of functional outcome after stroke in mice using a large heterogeneous cohort. Study design and imaging limitations are discussed. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.

neuroscience↗

bioRxiv2022

Impaired functional connectivity of the hippocampus in murine models of NMDA-receptor antibody associated pathology

IntroductionWhile decreased hippocampal connectivity and disruption of functional networks are established MRI features in human anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, the underlying pathophysiology for brain network alterations remains poorly understood. Application of patient-derived monoclonal antibodies against the NR1 subunit of the NMDAR allows for the investigation of potential functional connectivity alterations in experimental murine NMDAR antibody disease models. ObjectiveTo explore functional connectivity changes in NR1 antibody mouse models using resting-state functional MRI (rs-fMRI). MethodsAdult C57BL/6J mice (n=10) were intrathecally injected with a recombinant human NR1 antibody over 14 days and then studied using rs-fMRI at 7 Tesla. In addition, a newly established mouse model with in utero exposure to a human recombinant NR1 antibody characterized by a neurodevelopmental disorder (NR1-offspring) was investigated with rs-fMRI at the age of 8 weeks (n=15) and 10 months (n=14). Mice exposed to isotype-matched control antibodies served as controls. Independent component analysis (ICA) and dual regression analysis were performed to compare functional connectivity between NMDAR antibody mouse models and control mice. ResultsAdult NR1-antibody injected mice showed significantly impaired functional connectivity within the dentate gyrus of the left hippocampus in comparison to controls, resembling impaired hippocampal functional connectivity patterns observed in human patients with NMDAR encephalitis. Similarly, analyses showed significantly reduced functional connectivity in the dentate gyrus in NR1-offspring compared after 8 weeks, and impaired connectivity in the dentate gyrus and CA3 hippocampal subregion in NR1-offspring at the age of 10 months. ConclusionFunctional connectivity changes within the hippocampus resulting from both direct application and in utero exposure to NMDAR antibodies can be modeled in experimental murine systems. With this translational approach, we successfully reproduced functional MRI alterations previously observed in human NMDAR encephalitis patients. Future experimental studies will identify the detailed mechanisms that cause functional network alterations and may eventually allow for non-invasive monitoring of disease activity and therapeutic effects in autoimmune encephalitis.

neuroscience↗

bioRxiv2020

Vascular cognitive impairment in the mouse reshapes visual, spatial network functional connectivity.

Connectome analysis of neuroimaging data is a rapidly expanding field to identify disease specific biomarkers. Structural diffusion MRI connectivity has been useful in individuals with radiological features of small vessel disease, such as white matter hyperintensities. Global efficiency, a network metric calculated from the structural connectome, is an excellent predictor of cognitive decline. To dissect the biological underpinning of these changes, animal models are required. We tested whether the structural connectome is altered in a mouse model of vascular cognitive impairment. White matter damage was more pronounced by 6 compared to 3 months. Global efficiency remained intact, but the visual association cortex exhibited increased structural connectivity with other brain regions. Exploratory resting state functional MRI connectivity analysis revealed diminished default mode network activity in the model compared to shams. Further perturbations were observed in a primarily cortical hub and the retrosplenial and visual cortices, and the hippocampus were the most affected nodes. Behavioural deficits were observed in the cued water maze, supporting the suggestion that the visual and spatial memory networks are affected. We demonstrate specific circuitry is rendered vulnerable to vascular stress in the mouse, and the model will be useful to examine pathophysiological mechanisms of small vessel disease. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

neuroscience↗

bioRxiv2020

Selective inhibition of histone deacetylase 1 and 3 improves motor phenotype and alleviates transcriptional dysregulation in Huntington's disease mice

Huntingtons disease (HD) is an autosomal dominant neurodegenerative disease characterized by a late clinical onset of psychiatric, cognitive, and motor symptoms. Transcriptional dysregulation is an early and central disease mechanism which is accompanied by epigenetic alterations in HD. Previous studies demonstrated that targeting transcriptional changes by inhibition of histone deacetylases (HDACs), especially the class I HDACs, provides therapeutic effects. Yet, their exact mechanisms of action and the features of HD pathology, on which these inhibitors act remain to be elucidated. Here, using transcriptional profiling, we found that selective inhibition of HDAC1 and HDAC3 by RGFP109 repaired the expression of a number of genes, including the transcription factor genes Neurod2 and Nr4a2, and 43% of the gene sets that were dysregulated by mutant Huntingtin expression in the striatum and improved motor skill learning deficit in the R6/1 mouse model of HD. RGFP109-treated R6/1 mice showed improved coordination on the RotaRod over four consecutive trials, while vehicle-treated R6/1 animals displayed no improvement in coordination skills and fell 50 seconds earlier off the rod in the fourth trial. We also found, by volumetric MRI, a widespread brain atrophy in the R6/1 mice at the symptomatic disease stage, on which RGFP109 showed a modest effect. Collectively, our combined work presents new evidence for specific HDAC1 and HDAC3 inhibition as a therapeutic strategy for alleviating the phenotypic and molecular features of HD.

neuroscience↗