Biology subjects

Avants, B.

Publications and source records attributed to Avants, B..

7 recordsLinked to original sources

bioRxiv2025

Magnetic activation of electrically active cells

Magnetic control of cell activity has applications ranging from non-invasive neurostimulation to remote activation of cell-based therapies. Unlike other methods of regulating cell activity like heat and light, which are based on known receptors or proteins, no magnetically gated channel has been identified to date. As a result, effective approaches for magnetic control of cell activity are based on strong alternating magnetic fields able to induce electric fields or materials that convert magnetic energy into electrical, thermal, or mechanical energy to stimulate cells. In our investigations of magnetic cell responses, we found that a spiking HEK cell line with no other co-factors responds to a magnetic field that reaches a maximum of 500 mT within 200 ms using a permanent magnet. The response is rare, approximately 1 in 50 cells, but is fast and reproducible, generating an action potential within 200 ms of magnetic field stimulation. The magnetic field stimulation is over 10,000 times slower than the magnetic fields used in transcranial magnetic stimulation (TMS) and the induced electric field is more than an order of magnitude lower than necessary for neuromodulation, suggesting that induced electric currents do not drive the cell response. Instead, our calculation suggests that this response depends on mechanoreception pathways activated by the magnetic torque of TRP-associated lipid rafts. Despite the relatively rare response to magnetic stimulation, when cells form gap junctions, the magnetic stimulation can propagate to nearby cells, causing tissue-level responses. As an example, we co-cultured spiking HEK cells with beta-pancreatic MIN6 cells and found that this co-culture responds to magnetic fields by increasing insulin production. Together, these results point toward a method for the magnetic control of biological activity without the need for a material co-factor such as synthetic nanoparticles. By better understanding this mechanism and enriching for magneto-sensitivity it may be possible to adapt this approach to the rapidly expanding tool kit for wireless cell activity regulation.

biophysics↗

bioRxiv2022

Application of Aligned-UMAP to longitudinal biomedical studies

Longitudinal multi-dimensional biological datasets are ubiquitous and highly abundant. These datasets are essential to understanding disease progression, identifying subtypes, and drug discovery. Discovering meaningful patterns or disease pathophysiologies in these datasets is challenging due to their high dimensionality, making it difficult to visualize hidden patterns. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. Recently proposed Aligned-UMAP, an extension of the UMAP algorithm, can visualize high-dimensional longitudinal datasets. In this work, we applied Aligned-UMAP on a broad spectrum of clinical, imaging, proteomics, and single-cell datasets. Aligned-UMAP reveals time-dependent hidden patterns when color-coded with the metadata. We found that the algorithm parameters also play a crucial role and must be tuned carefully to utilize the algorithms potential fully. Altogether, based on its ease of use and our evaluation of its performance on different modalities, we anticipate that Aligned-UMAP will be a valuable tool for the biomedical community. We also believe our benchmarking study becomes more important as more and more high-dimensional longitudinal data in biomedical research becomes available. Highlights- explored the utility of Aligned-UMAP in longitudinal biomedical datasets - offer insights on optimal uses for the technique - provide recommendations for best practices In BriefHigh-dimensional longitudinal data is prevalent yet understudied in biological literature. High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Though few methods are available for visualizing high dimensional longitudinal data, they are not studied extensively in real-world biological datasets. A recently developed nonlinear dimensionality reduction technique, Aligned-UMAP, analyzes sequential data. Here, we give an overview of applications of Aligned-UMAP on various biomedical datasets. We further provide recommendations for best practices and offer insights on optimal uses for the technique.

bioinformatics↗

bioRxiv2021

Wireless endovascular nerve stimulation with a millimeter-sized magnetoelectric implant

Implanted bioelectronic devices have the potential to treat disorders that are resistant to traditional pharmacological therapies; however, reaching many therapeutic nerve targets requires invasive surgeries and implantation of centimeter-sized devices. Here we show that it is possible to stimulate peripheral nerves from within blood vessels using a millimeter-sized wireless implant. By directing the stimulating leads through the blood vessels we can target specific nerves that are difficult to reach with traditional surgeries. Furthermore, we demonstrate this endovascular nerve stimulation (EVNS) with a millimeter sized wireless stimulator that can be delivered minimally invasively through a percutaneous catheter which would significantly lower the barrier to entry for neuromodulatory treatment approaches because of the reduced risk. This miniaturization is achieved by using magnetoelectric materials to efficiently deliver data and power through tissue to a digitally-programmable 0.8 mm2 CMOS system-on-a-chip. As a proof-of-principle we show wireless stimulation of peripheral nerve targets both directly and from within the blood vessels in rodent and porcine models. The wireless EVNS concept described here provides a path toward minimally invasive bioelectronics where mm-sized implants combined with endovascular stimulation enable access to a number of nerve targets without open surgery or implantation of battery-powered pulse generators.

bioengineering↗

bioRxiv2021

Hierarchy-guided Neural Networks for Species Classification

O_LISpecies classification is an important task that is the foundation of industrial, commercial, ecological, and scientific applications involving the study of species distributions, dynamics, and evolution. C_LIO_LIWhile conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes. C_LIO_LIIn this work, we propose a new approach for species classification termed Hierarchy-Guided Neural Network (HGNN), which infuses hierarchical taxonomic information into the neural networks training to guide the structure and relationships among the extracted features. We perform extensive experiments on an illustrative use-case of classifying fish species to demonstrate that HGNN outperforms conventional ConvNet models in terms of classification accuracy, especially under scarce training data conditions. C_LIO_LIWe also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied. C_LI

evolutionary biology↗

bioRxiv2018

Magnetoelectric materials for miniature, wireless neural stimulation at therapeutic frequencies

A fundamental challenge for bioelectronics is to deliver power to miniature devices inside the body. Wires are common failure points and limit device placement. On the other hand, wireless power by electromagnetic or ultrasound waves must overcome absorption by the body and impedance mismatches between air, bone, and tissue. In contrast, magnetic fields suffer little absorption by the body or differences in impedance at interfaces between air, bone, and tissue. These advantages have led to magnetically-powered stimulators based on induction or magnetothermal effects. However, fundamental limitations in these power transfer technologies have prevented miniature magnetically-powered stimulators from applications in many therapies and disease models because they do not operate in clinical "high-frequency" ranges above 50 Hz. Here we show that magnetoelectric materials - applied in bioelectronic devices - enable miniature magnetically-powered neural stimulators that can operate up to clinically-relevant high-frequencies. As an example, we show that ME neural stimulators can effectively treat the symptoms of a hemi-Parkinsons disease model in freely behaving rodents. We further demonstrate that ME-powered devices can be miniaturized to mm-sized devices, fully implanted, and wirelessly powered in freely behaving rodents. These results suggest that ME materials are an excellent candidate for wireless power delivery that will enable miniature bioelectronics for both clinical and research applications.

bioengineering↗

bioRxiv2018

Microfluidics for Electrophysiology, Imaging, and Behavioral Analysis of Hydra

The cnidarian Hydra vulgaris provides an exciting opportunity to discover the relationship between animal behavior and the activity of every neuron in highly plastic, diffuse network of spiking cells. However, Hydras deformable and contractile body makes it difficult to manipulate the local environment while recording neural activity. Here, we present a suite of microfluidic technologies capable of simultaneous electrical, chemical, and optical interrogation of these soft, deformable organisms. Specifically, we demonstrate devices that can immobilize Hydra for hours-long simultaneous electrical and optical recording, and chemical stimulation of behaviors revealing neural activity during muscle contraction. We further demonstrate quantitative locomotive and behavioral tracking made possible by confining the animal to quasi-two-dimensional micro-arenas. Together, these proof-of-concept devices show that microfluidics provide a platform for scalable, quantitative cnidarian neurobiology. The experiments enabled by this technology may help reveal how highly plastic networks of neurons provide robust control of animal behavior.

neuroscience↗

bioRxiv2017

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N=20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.

neuroscience↗