63 research outputs found
Editorial: Perspectives in neuroscience: mechanical forces for the modulation of axonal mechanics and nerve regeneration
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Can repetitive mechanical motion cause structural damage to axons?
No full textBiological structures have evolved to very efficiently generate, transmit, and withstand mechanical forces. These biological examples have inspired mechanical engineers for centuries and led to the development of critical insights and concepts. However, progress in mechanical engineering also raises new questions about biological structures. The past decades have seen the increasing study of failure of engineered structures due to repetitive loading, and its origin in processes such as materials fatigue. Repetitive loading is also experienced by some neurons, for example in the peripheral nervous system. This perspective, after briefly introducing the engineering concept of mechanical fatigue, aims to discuss the potential effects based on our knowledge of cellular responses to mechanical stresses. A particular focus of our discussion are the effects of mechanical stress on axons and their cytoskeletal structures. Furthermore, we highlight the difficulty of imaging these structures and the promise of new microscopy techniques. The identification of repair mechanisms and paradigms underlying long-term stability is an exciting and emerging topic in biology as well as a potential source of inspiration for engineers
Neuronal morphology as an instrument for information coding: studying the influence of axonal radius and branching points
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Remote Magnetic Orientation of 3D Collagen Hydrogels for Directed Neuronal Regeneration
No full textHydrogel matrices are valuable platforms
for neuronal tissue engineering. Orienting gel fibers to achieve a
directed scaffold is important for effective functional neuronal regeneration.
However, current methods are limited and require treatment of gels
prior to implantation, ex-vivo, without taking into consideration
the pathology in the injured site. We have developed a method to control
gel orientation dynamically and remotely in situ. We have mixed into
collagen hydrogels magnetic nanoparticles then applied an external
magnetic field. During the gelation period the magnetic particles
aggregated into magnetic particle strings, leading to the alignment
of the collagen fibers. We have shown that neurons within the 3D magnetically
induced gels exhibited normal electrical activity and viability. Importantly,
neurons formed elongated cooriented morphology, relying on the particle
strings and fibers as supportive cues for growth. The ability to inject
the mixed gel directly into the injured site as a solution then to
control scaffold orientation remotely opens future possibilities for
therapeutic engineered scaffolds
Editorial: Perspectives in neuroscience: mechanical forces for the modulation of axonal mechanics and nerve regeneration
No full textBranching morphology determines signal propagation dynamics in neurons
No full textAbstractComputational modeling of signal propagation in neurons is critical to our understanding of basic principles underlying brain organization and activity. Exploring these models is used to address basic neuroscience questions as well as to gain insights for clinical applications. The seminal Hodgkin Huxley model is a common theoretical framework to study brain activity. It was mainly used to investigate the electrochemical and physical properties of neurons. The influence of neuronal structure on activity patterns was explored, however, the rich dynamics observed in neurons with different morphologies is not yet fully understood. Here, we study signal propagation in fundamental building blocks of neuronal branching trees, unbranched and branched axons. We show how these simple axonal elements can code information on spike trains, and how asymmetric responses can emerge in axonal branching points. This asymmetric phenomenon has been observed experimentally but until now lacked theoretical characterization. Together, our results suggest that axonal morphological parameters are instrumental in activity modulation and information coding. The insights gained from this work lay the ground for better understanding the interplay between function and form in real-world complex systems. It may also supply theoretical basis for the development of novel therapeutic approaches to damaged nervous systems.</jats:p
A leech brain in the dish: a method for detailed analysis of specifically labeled single cells
No full textNeuronal Cell Type Classification using Deep Learning
No full textThe brain is likely the most complex organ, given the variety of functions it
controls, the number of cells it comprises, and their corresponding diversity.
Studying and identifying neurons, the brain's primary building blocks, is a
crucial milestone and essential for understanding brain function in health and
disease. Recent developments in machine learning have provided advanced
abilities for classifying neurons. However, these methods remain black boxes
with no explainability and reasoning. This paper aims to provide a robust and
explainable deep-learning framework to classify neurons based on their
electrophysiological activity. Our analysis is performed on data provided by
the Allen Cell Types database containing a survey of biological features
derived from single-cell recordings of mice and humans. First, we classify
neuronal cell types of mice data to identify excitatory and inhibitory neurons.
Then, neurons are categorized to their broad types in humans using domain
adaptation from mice data. Lastly, neurons are classified into sub-types based
on transgenic mouse lines using deep neural networks in an explainable fashion.
We show state-of-the-art results in a dendrite-type classification of
excitatory vs. inhibitory neurons and transgenic mouse lines classification.
The model is also inherently interpretable, revealing the correlations between
neuronal types and their electrophysiological properties
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