Biology subjects

Yanik, M. F.

Publications and source records attributed to Yanik, M. F..

5 recordsLinked to original sources

bioRxiv2023

Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI

Blood Oxygen Level-Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows for non-invasive, indirect recordings of neural activity across the whole brain in both humans and animals. However, the relationship between the local neural population activity and the vascular activity is not completely understood. To investigate this relationship, we present a novel MRI compatible single-photon microscope capable of measuring cellular resolution Ca2+ activity of genetically defined neurons during whole-brain BOLD fMRI in awake behaving mice. Using this combined imaging approach, we found a difference in activity patterns between cells which was dependent on their location with respect to the vasculature. Notably, neurons near the vasculature showed pronounced negative activity during contralateral whisker movements at 3 Hz. In a second proof of concept experiment, we demonstrate the potential of recording both local neural activities, like those in the barrel field (SSp-bfd), and BOLD fMRI readings from interlinked brain regions. In sum, the presented technological advancement paves the way for studies examining the interplay between local brain circuits and overarching brain functions. In addition, the new approach enhances our understanding of the vascular BOLD fMRI signal, providing insights into the determinants of local neurovascular functions and the brains organizational framework across various scales.

neuroscience↗

bioRxiv2021

Electrocorticography based monitoring of anaesthetic depth in mice

To improve animal welfare and data quality and reproducibility during research conducted under anaesthesia, anaesthetic depth in laboratory animals must be precisely monitored and controlled. While a variety of methods have been developed to estimate the depth of anaesthesia in humans, such tools for monitoring anaesthetic depth in laboratory animals remain limited. Here we propose an epidural electrocorticogram-based monitoring system that accurately tracks the depth of anesthesia in mice receiving inhalable isoflurane anaesthesia. Several features of the electrocorticogram signals exhibit robust modulation by the concentration of the administered anesthetic, notably, corticocortical coherence serves as an excellent indicator of anaesthetic depth. We developed a gradient boosting regressor framework that utilizes the extracted features to accurately estimate the depth of anaesthesia. Our method for feature extraction and estimation is conducted with a latency of only ten seconds, establishing a system for the real-time tracking of anaesthetic depth in mice.

neuroscience↗

bioRxiv2020

SIPEC: the deep-learning Swiss knife for behavioral data analysis

The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

neuroscience↗

bioRxiv2019

An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data

Large-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS). Complementing this workflow we also present operator guidelines for appropriate ParaVision data input, and a programmatic walk-through detailing how preexisting scans with uninterpretable metadata records can easily be made compliant after the acquisition.

neuroscience↗

bioRxiv2019

An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging

The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We introduce four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 2-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, all of which are indispensable prerequisites for the comparability of scientific results across experiments and centers.

neuroscience↗