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      In silico assessment of electrophysiological neuronal recordings mediated by magnetoelectric nanoparticles

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          Abstract

          Magnetoelectric materials hold untapped potential to revolutionize biomedical technologies. Sensing of biophysical processes in the brain is a particularly attractive application, with the prospect of using magnetoelectric nanoparticles (MENPs) as injectable agents for rapid brain-wide modulation and recording. Recent studies have demonstrated wireless brain stimulation in vivo using MENPs synthesized from cobalt ferrite (CFO) cores coated with piezoelectric barium titanate (BTO) shells. CFO–BTO core–shell MENPs have a relatively high magnetoelectric coefficient and have been proposed for direct magnetic particle imaging (MPI) of brain electrophysiology. However, the feasibility of acquiring such readouts has not been demonstrated or methodically quantified. Here we present the results of implementing a strain-based finite element magnetoelectric model of CFO–BTO core–shell MENPs and apply the model to quantify magnetization in response to neural electric fields. We use the model to determine optimal MENPs-mediated electrophysiological readouts both at the single neuron level and for MENPs diffusing in bulk neural tissue for in vivo scenarios. Our results lay the groundwork for MENP recording of electrophysiological signals and provide a broad analytical infrastructure to validate MENPs for biomedical applications.

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          Reconstruction and Simulation of Neocortical Microcircuitry.

          We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies.
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            Tomographic imaging using the nonlinear response of magnetic particles.

            The use of contrast agents and tracers in medical imaging has a long history. They provide important information for diagnosis and therapy, but for some desired applications, a higher resolution is required than can be obtained using the currently available medical imaging techniques. Consider, for example, the use of magnetic tracers in magnetic resonance imaging: detection thresholds for in vitro and in vivo imaging are such that the background signal from the host tissue is a crucial limiting factor. A sensitive method for detecting the magnetic particles directly is to measure their magnetic fields using relaxometry; but this approach has the drawback that the inverse problem (associated with transforming the data into a spatial image) is ill posed and therefore yields low spatial resolution. Here we present a method for obtaining a high-resolution image of such tracers that takes advantage of the nonlinear magnetization curve of small magnetic particles. Initial 'phantom' experiments are reported that demonstrate the feasibility of the imaging method. The resolution that we achieve is already well below 1 mm. We evaluate the prospects for further improvement, and show that the method has the potential to be developed into an imaging method characterized by both high spatial resolution as well as high sensitivity.
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              What we can do and what we cannot do with fMRI.

              Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and analysis methods promise to push forward fMRI from mere cartography to the true study of brain organization. However, fundamental questions concerning the interpretation of fMRI data abound, as the conclusions drawn often ignore the actual limitations of the methodology. Here I give an overview of the current state of fMRI, and draw on neuroimaging and physiological data to present the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation.
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                Author and article information

                Contributors
                ahai@wisc.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 May 2022
                19 May 2022
                2022
                : 12
                : 8386
                Affiliations
                [1 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Biomedical Engineering, , University of Wisconsin-Madison, ; Madison, WI USA
                [2 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Electrical and Computer Engineering, , University of Wisconsin-Madison, ; Madison, WI USA
                [3 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Integrative Biology, , University of Wisconsin-Madison, ; Madison, WI USA
                [4 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Grainger Institute for Engineering, , University of Wisconsin-Madison, ; Madison, WI USA
                [5 ]Wisconsin Institute for Translational Neuroengineering (WITNe), Madison, WI USA
                Article
                12303
                10.1038/s41598-022-12303-4
                9120189
                35589877
                4927680a-6d08-4837-b09d-f1f934fdf49c
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 March 2022
                : 9 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: DP2NS122605
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                biomedical engineering,nanoparticles
                Uncategorized
                biomedical engineering, nanoparticles

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