Biology subjects

Wenk, P.

Publications and source records attributed to Wenk, P..

3 recordsLinked to original sources

bioRxiv2026

Digital-twin optimized transcranial temporal interference electrical stimulation alters rat brain activity patterns and metabolism including deep structures

Non-invasive deep-brain stimulation (DBS) using transcranial alternating current stimulation (tACS) with temporal interference (TI) has emerged as a promising approach for therapeutic applications in motor and neuropsychiatric disorders. Here, we present a preclinical TI tACS protocol for adult rats that follows a systems approach: an anatomically realistic finite-element digital twin of the rat head is used to optimize electrode placement and injected currents for a deep target, and the model predictions are then tested in vivo. We combine, minimal invasive TI tACS with functional MRI (fMRI) to characterize stimulation-related brain-wide neural responses, alterations in functional connectivity, and changes in neurotransmitter levels. The optimized montage targeted in particular the ventral (motor-related) thalamus. Stimulation-evoked fMRI revealed robust bilateral activation across both subcortical and cortical regions, such as the thalamus, hippocampus, amygdala, retrosplenial and anterior cingulate cortex, which, in turn, reflects direct and indirect activations of brain circuitries by TI tACS. Changes in the connectivity patterns during resting-state fMRI were limited (e.g., fimbria of the hippocampus), but partially overlapped with regional stimulation-evoked fMRI. Therefore, we assume only a short-term influence of TI on neural networks following a single stimulation. MR spectroscopy indicated a trend towards an increased glutamate/GABA ratio in the thalamus. Overall, we demonstrate that TI is a feasible and effective approach for non-invasive DBS in rodents, and that digital twin-based optimization combined with in vivo validation provides a workflow towards individualized stimulation and a foundation for future mechanistic and translational studies in disease models.

neuroscience↗

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↗

bioRxiv2024

Neural networks representing temporal expectation in mice

The ability to attend to specific moments in time is crucial for survival across species facilitating perception and motor performance by leveraging prior temporal knowledge for predictive processing. Despite its importance, the neural mechanisms underlying the utilization of macro-scale and meso-scale neural resources during temporal processing and their relationship to behavioural strategies and motor responses remain largely unexplored. To investigate the capacity for predictive temporal structure of multisensory stimuli to optimize motor behaviour, we established a behavioural paradigm, in which mice were trained to an auditory-cue and visual-target presented at expected or unexpected temporal delays. Using a combination of stimulus-evoked and resting-state functional magnetic resonance imaging, we examined task-related evoked activity in brain-wide networks and found that that the formation of temporal expectations relying on accumulated sensory information and combined multisensory input involves plasticity across large macro-scale cortical networks comprised of primary sensory systems, sensory association areas including posterior parietal cortex, retrosplenial cortex, prefrontal top-down executive control centres of the brain, as well as hippocampal networks. Additionally, employing in vivo two-photon calcium imaging, we explored local single-cell dynamics within the posterior parietal cortex during this task and found that temporal expectation could be decoded directly from neuronal activity within this brain region. Overall, our study provides insights into the neural correlates underlying the formation of multisensory temporal expectations in the mouse brain and highlights the recruitment of neural resources across temporally-driven statistical learning processes.

neuroscience↗