Biology subjects

Vlasov, Y.

Publications and source records attributed to Vlasov, Y..

3 recordsLinked to original sources

bioRxiv2023

Highly localized chemical sampling at sub-second temporal resolution enabled with a silicon nanodialysis platform at exceedingly slow flows.

Microdialysis (MD) is a versatile and powerful technique for chemical profiling of biological tissues and is widely used for quantification of neurotransmitters, neuropeptides, metabolites, biomarkers, and drugs in the central nervous system as well as in dermatology, ophthalmology, and in pain research. However, MD performance is severely limited by fundamental tradeoffs between chemical sensitivity, spatial resolution, and temporal response. Here, by using wafer-scale silicon microfabrication, we develop and demonstrate a nanodialysis (ND) sampling probe that enables highly localized chemical sampling with 100m spatial resolution and sub-second temporal resolution at high recovery rates. These performance metrics, which are 100X-1000X superior to existing MD approaches, are enabled by a 100X reduction of the microfluidic channel cross-section, a corresponding drastic 100X reduction of flow rates to exceedingly slow few nL/min flows, and integration of a nanometer-thin nanoporous membrane with high transport flux into the probe sampling area. Miniaturized ND probes may allow for the minimally invasive and highly localized sampling and chemical profiling in live biological tissues with unprecedented spatio-temporal resolution for clinical, biomedical, and pharmaceutical applications.

bioengineering↗

bioRxiv2023

Discovery of latent dynamic connectivity in brain cortical networks from massive spiking data

Complex cognitive functions in a mammalian brain are distributed across many anatomically and functionally distinct areas and rely on highly dynamic routing of neural activity across the network. While modern electrophysiology methods enable recording of spiking activity from increasingly large neuronal populations at a cellular level, development of probabilistic methods to extract these dynamic inter-area interactions is lagging. Here, we introduce an unsupervised machine learning model that infers dynamic connectivity across the recorded neuronal population from a synchrony of their spiking activity. As opposed to traditional population decoding models that reveal dynamics of the whole population, the model produces cellular-level cell-type specific dynamic functional interactions that are otherwise omitted from analysis. The model is evaluated on ground truth synthetic data and compared to alternative methods to ensure quality and quantification of model predictions. Our strategy incorporates two sequential stages - extraction of static connectivity structure of the network followed by inference of temporal changes of the connection strength. This two-stage architecture enables detailed statistical criteria to be developed to evaluate confidence of the model predictions in comparison with traditional descriptive statistical methods. We applied the model to analyze large-scale in-vivo recordings of spiking activity across mammalian visual cortices. The model enables the discovery of cellular-level dynamic connectivity patterns in local and long-range circuits across the whole visual cortex with temporally varying strength of feedforward and feedback drives during sensory stimulation. Our approach provides a conceptual link between slow brain-wide network dynamics studied with neuroimaging and fast cellular-level dynamics enabled by modern electrophysiology that may help to uncover often overlooked dimensions of the brain code.

neuroscience↗

bioRxiv2022

Pre-neuronal processing of haptic sensory cues via dispersive high-frequency vibrational modes

Sense of touch is one of the major perception channels. Neural coding of object textures conveyed by rodents whiskers has been a model to study early stages of haptic information uptake. While high-precision spike timing has been observed during whisker sweeping across textured surfaces, the exact nature of whisker micromotions that spikes encode remains elusive. Here, we discovered that a single micro-collision of a whisker with surface features generates vibrational eigenmodes spanning frequencies up to 10KHz. While propagating along the whisker, these high-frequency modes can carry up to 80% of shockwave energy, exhibit 100X smaller damping ratio, and arrive at the follicle 10X faster than low frequency components. The mechano-transduction of these energy bursts into a time-sequenced population spike trains may generate temporally unique "bar code" with ultra-high information capacity. This hypothesis of pre-neuronal processing of haptic signals based on dispersive temporal separation of the vibrational modal frequencies can shed light on neural coding of haptic signals in many whisker-like sensory organs across the animal world as well as in texture perception in primates glabrous skin. Significance StatementUnderstanding how the outside world is encoded in neurons spikes in sensory organs and how these neural codes contribute to perception remains elusive. Using a model system - a whisker of a mouse - we discovered that tiny whisker vibrations induced at the whisker tip by collisions with external objects generate a time series of energy bursts. This creates a temporally unique "bar code" of a time-sequenced population spike trains with ultra-high information capacity. We hypothesize that such a "pre-neuronal processing" of touch events into time-coded spikes can provide a conceptual link to understand neural coding in many whisker-like sensory organs across the animal world as well as in texture perception in primates glabrous skin.

neuroscience↗