Biology subjects

Skibbe, H.

Publications and source records attributed to Skibbe, H..

8 recordsLinked to original sources

bioRxiv2026

Adaptive brain rewiring after brain injury via adult neurogenesis

The adult mammalian brain has limited regenerative capacity yet retains substantial potential for functional reorganization after experience, injury or disease1-3. While local plasticity at injury sites has been described4,5, the brain-wide consequences of a focal injury and the biological processes that drive them remain unknown. To address this, we performed an unbiased, whole brain screen of neuronal activity at single cell resolution following a focal injury to the primary visual cortex (V1) in mice. Olfactory processing areas emerged as loci of remote activation. We further show that V1 injury promotes the recruitment of adult born neurons into the olfactory bulb (OB) and potentiates cortical feedback onto bulbar circuits. Combining high-density multielectrode array recordings with two-photon calcium imaging revealed OB circuit refinement characterized by increased synchrony and sharpened tuning of principal output neurons. These circuit-level functional enhancements coincide with improved behavioral performance in odor-guided tasks. Motor cortex lesions do not elicit similar cellular and behavioral responses, suggesting that such distal adaptation only develops when injury is coupled with increased olfactory demand. Together, these data demonstrate that the adult brain can recruit alternative sensory circuits distant from a focal lesion to undergo adaptive, functionally relevant reorganization, and implicate adult neurogenesis as a contributing mechanism. Our findings expose a previously underappreciated degree of remote plasticity and reveal a novel role of adult neurogenesis in sensory compensation.

neuroscience↗

bioRxiv2024

Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images

How actin filaments (F-actins) are dynamically reorganized in motile cells at the level of individual filaments is an open question. To this end, we developed a high-speed atomic force microscopy (HS-AFM) to live-imagine intracellular dynamics of the individual F-actins. However, noise and low resolution made it difficult to fully recognize individual F-actins in the HS-AFM images. To tackle this problem, we developed a new machine learning method that quantitatively recognizes individual F-actins. The method estimates F-actin orientation from the image while improving the resolution. We found that F-actins were oriented at {+/-}35{degrees} toward the membrane in the lamellipodia, which is consistent with Arp2/3 complex-induced branching. Furthermore, in the cell cortex our results showed non-random orientation at four specific angles, suggesting a new mechanism for F-actin organization demonstrating the potential of our newly developed method to fundamentally improve our understanding of the structural dynamics of F-actin networks.

biophysics↗

bioRxiv2022

Improving the prediction of white matter hyperintensities evolution in brain MRI of patients with small vessel disease using stroke lesions information

Predicting the evolution of white matter hyperintensities (WMH), a common feature in brain magnetic resonance imaging (MRI) scans of older adults (i.e., whether WMH will grow, remain stable, or shrink with time) is important for personalised therapeutic interventions. However, this task is difficult mainly due to the myriad of vascular risk factors and comorbidities that influence it, and the low specificity and sensitivity of the image intensities and textures alone for predicting WMH evolution. Given the predominantly vascular nature of WMH, in this study, we evaluate the impact of incorporating stroke lesion information to a probabilistic deep learning model to predict the evolution of WMH 1-year after the baseline image acquisition, taken soon after a mild stroke event, using T2-FLAIR brain MRI. The Probabilistic U-Net was chosen for this study due to its capability of simulating and quantifying the uncertainties involved in the prediction of WMH evolution. We propose to use an additional loss called volume loss to train our model, and incorporate stroke lesions information, an influential factor in WMH evolution. Our experiments showed that jointly segmenting the disease evolution map (DEM) of WMH and stroke lesions, improved the accuracy of the DEM representing WMH evolution. The combination of introducing the volume loss and joint segmentation of DEM of WMH and stroke lesions outperformed other model configurations with mean volumetric absolute error of 0.0092 ml (down from 1.7739 ml) and 0.47% improvement on average Dice similarity coefficient in shrinking, growing and stable WMH.

bioinformatics↗

bioRxiv2022

MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy. MiniVess consists of 70 3D image volumes with segmented ground truths. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. Code for image preprocessing steps and the U-Net are provided. Supervised machine learning methods have been widely used for automated image processing of biomedical images. While much emphasis has been placed on the development of new network architectures and loss functions, there has been an increased emphasis on the need for publicly available annotated, or segmented, datasets. Annotated datasets are necessary during model training and validation. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.

neuroscience↗

bioRxiv2022

The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space

We report on the implementation and features of the Brain/MINDS Marmoset Connectivity Atlas, BMCA, a new resource that provides access to anterograde neuronal tracer data in the prefrontal cortex of a marmoset brain. Neuronal tracers combined with fluorescence microscopy are a key technology for the systematic mapping of structural brain connectivity. We selected the prefrontal cortex for mapping due to its important role in higher brain functions. This work introduces the BMCA standard image preprocessing pipeline and tools for exploring and reviewing the data. We developed the BMCA-Explorer, which is an online image viewer designed for data exploration. Unlike other existing image explorers, it visualizes the data of different individuals in a common reference space at an unprecedented high resolution, facilitating comparative studies. To foster the integration with other marmoset brain image databases and cross-species comparisons, we added fiber tractography data from diffusion MRI, retrograde neural tracer data from the Marmoset Brain Connectivity Atlas project, and tools to map image data between marmoset and the human brain image space. This version of BMCA allows direct comparison between the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset.

neuroscience↗

bioRxiv2022

Distinctiveness and continuity in transcriptome and connectivity in the anterior-posterior axis of the paraventricular nucleus of thalamus

The paraventricular nucleus of the thalamus (PVT) projects axons to multiple areas and mediates a wide range of behaviors. Heterogeneity of functions and axonal projections in PVT have been reported, but what cell types exist in PVT and how different they are have not been addressed. We applied single-cell RNA sequencing to depict transcriptomic characteristics of mouse PVT neurons. The transcriptome of PVT neurons had a continuous distribution with the largest variance corresponding to the anterior-posterior axis. Although the single-cell transcriptome classified PVT neurons into four types, transcriptomic and histological analyses showed their continuity. Similarly, anterior and posterior subpopulations had nearly non-overlapping axon projection patterns, while another population showed intermediate patterns. In addition, they responded differently to appetite-related neuropeptides, and their chemogenetic activation showed opposing effects in food consumption. Our studies showed contrasts and continuity of PVT neurons underlying their function as a behavior-modulating hub.

neuroscience↗

bioRxiv2022

Connectional architecture of the prefrontal cortex in the marmoset brain

Prefrontal cortex (PFC) has dramatically expanded in primates, but its organization and interactions with other brain regions are only partially understood. We performed high-resolution connectomic mapping of marmoset PFC and found two contrasting corticocortical and corticostriatal projection patterns: "patchy" projections that formed many columns of submillimeter scale in nearby and distant regions and "diffuse" projections that spread widely across the cortex and striatum. Parcellation-free analyses revealed representations of PFC gradients in these projections local and global distribution patterns. We also demonstrated column-scale precision of reciprocal cortico-cortical connectivity, suggesting that PFC contains a mosaic of discrete columns. Diffuse projections showed considerable diversity in the laminar patterns of axonal spread. In mice, columnar projections were much less conspicuous, underscoring the importance of the primate model. Altogether, these fine-grained analyses reveal important principles of local and long-distance PFC circuits in marmosets and provide insights into the functional organization of the primate brain.

neuroscience↗

bioRxiv2019

The NanoZoomer Connectomics Pipeline for Tracer Injection Studies of the Marmoset Brain

We describe our connectomics pipeline for processing tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the anterograde tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data was mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, each brain was cytoarchitectonically annotated and used for making an individual 3D atlas. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze tracer segmentation and brain registration accuracy. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.

neuroscience↗