Magnetic Detection of Neural Activity by Nanocoil Transducers.

Electrophysiological recordings from brain cells are performed routinely using implanted electrodes, but they traditionally require a wired connection to the outside of the brain. A completely passive, wireless device that does not require on-board power for active transmission but that still facilitates remote detection could open the door for mass-scale direct recording of action potentials and transform the way we acquire brain signals. We present a nanofabricated coil that forms a neuroelectromagnetic junction, yielding a highly enhanced magnetic field transduction of electrophysiology. We show that this micrometer-scale device enables remote magnetic detection of neuronal fields from the center of the coil using room temperature superconducting quantum interference device (SQUID) microscopy. Further, time-locked stimulation in conjunction with magnetometry demonstrates thresholding behavior that affirms the viability of the technology for detection with no requirement for wires or on-board power. This strategy may permit unprecedented detection of electrophysiology using magnetoencephalography and magnetic resonance imaging.

Stimulation-mediated reverse engineering of silent neural networks.

Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.

Wireless in vivo Recording of Cortical Activity by an Ion-Sensitive Field Effect Transistor.

Wireless brain technologies are empowering basic neuroscience and clinical neurology by offering new platforms that minimize invasiveness and refine possibilities during electrophysiological recording and stimulation. Despite their advantages, most systems require on-board power supply and sizeable transmission circuitry, enforcing a lower bound for miniaturization. Designing new minimalistic architectures that can efficiently sense neurophysiological events will open the door to standalone microscale sensors and minimally invasive delivery of multiple sensors. Here we present a circuit for sensing ionic fluctuations in the brain by an ion-sensitive field effect transistor that detunes a single radiofrequency resonator in parallel. We establish sensitivity of the sensor by electromagnetic analysis and quantify response to ionic fluctuations in vitro. We validate this new architecture in vivo during hindpaw stimulation in rodents and verify correlation with local field potential recordings. This new approach can be implemented as an integrated circuit for wireless in situ recording of brain electrophysiology.

Inference of network connectivity from temporally binned spike trains.

BACKGROUND: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. NEW METHOD: We infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. RESULTS: Results reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. COMPARISON WITH EXISTING METHOD(S): Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. CONCLUSIONS: We provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.

Nanofabricated high turn-density spiral coils for on-chip electromagneto-optical conversion.

Circuit-integrated electromagnets are fundamental building blocks for on-chip signal transduction, modulation, and tunability, with specific applications in environmental and biomedical micromagnetometry. A primary challenge for improving performance is pushing quality limitations while minimizing size and fabrication complexity and retaining spatial capabilities. Recent efforts have exploited highly involved three-dimensional synthesis, advanced insulation, and exotic material compositions. Here, we present a rapid nanofabrication process that employs electron beam dose control for high-turn-density diamond-embedded flat spiral coils; these coils achieve efficient on-chip electromagnetic-to-optical signal conversion. Our fabrication process relies on fast 12.3 s direct writing on standard poly(methyl methacrylate) as a basis for the metal lift-off process. Prototypes with 70 micrometer overall diameters and 49-470 nm interturn spacings with corresponding inductances of 12.3-12.8 nH are developed. We utilize optical micromagnetometry to demonstrate that magnetic field generation at the center of the structure effectively correlates with finite element modeling predictions. Further designs based on our process can be integrated with photolithography to broadly enable optical magnetic sensing and spin-based computation.

Unraveling the Gene Regulatory Networks of the Global Regulators VeA and LaeA in Aspergillus nidulans.

In the filamentous fungus Aspergillus nidulans, the velvet family protein VeA and the global regulator of secondary metabolism LaeA govern development and secondary metabolism mostly by acting as the VelB/VeA/LaeA heterotrimeric complex. While functions of these highly conserved controllers have been well studied, the genome-wide regulatory networks governing cellular and chemical development remain to be uncovered. Here, by integrating transcriptomic analyses, protein-DNA interactions, and the known A. nidulans gene/protein interaction data, we have unraveled the gene regulatory networks governed by VeA and LaeA. Within the networks, VeA and LaeA directly control the expression of numerous genes involved in asexual/sexual development and primary/secondary metabolism in A. nidulans. Totals of 3,190 and 1,834 potential direct target genes of VeA and LaeA were identified, respectively, including several important developmental and metabolic regulators such as flbA·B·C, velB·C, areA, mpkB, and hogA. Moreover, by analyzing over 8,800 ChIP-seq peaks, we have revealed the predicted common consensus sequences 5'-TGATTGGCTG-3' and 5'-TCACGTGAC-3' that VeA and LaeA might bind to interchangeably. These findings further expand the biochemical and genomic studies of the VelB/VeA/LaeA complex functionality in the gene regulation. In summary, this study unveils genes that are under the regulation of VeA and LaeA, proposes the VeA- and LaeA-mediated gene regulatory networks, and demonstrates their genome-wide developmental and metabolic regulations in A. nidulans. IMPORTANCE Fungal development and metabolism are genetically programmed events involving specialized cellular differentiation, cellular communication, and temporal and spatial regulation of gene expression. In genus Aspergillus, the global regulators VeA and LaeA govern developmental and metabolic processes by affecting the expression of downstream genes, including multiple transcription factors and signaling elements. Due to their vital roles in overall biology, functions of VeA and LaeA have been extensively studied, but there still has been a lack of knowledge about their genome-wide regulatory networks. In this study, employing the model fungus A. nidulans, we have identified direct targets of VeA and LaeA and their gene regulatory networks by integrating transcriptome, protein-DNA interaction, and protein-protein interaction analyses. Our results demonstrate the genome-wide regulatory mechanisms of these global regulators, thereby advancing the knowledge of fungal biology and genetics.

Wireless agents for brain recording and stimulation modalities.

New sensors and modulators that interact wirelessly with medical modalities unlock uncharted avenues for in situ brain recording and stimulation. Ongoing miniaturization, material refinement, and sensitization to specific neurophysiological and neurochemical processes are spurring new capabilities that begin to transcend the constraints of traditional bulky and invasive wired probes. Here we survey current state-of-the-art agents across diverse realms of operation and evaluate possibilities depending on size, delivery, specificity and spatiotemporal resolution. We begin by describing implantable and injectable micro- and nano-scale electronic devices operating at or below the radio frequency (RF) regime with simple near field transmission, and continue with more sophisticated devices, nanoparticles and biochemical molecular conjugates acting as dynamic contrast agents in magnetic resonance imaging (MRI), ultrasound (US) transduction and other functional tomographic modalities. We assess the ability of some of these technologies to deliver stimulation and neuromodulation with emerging probes and materials that provide minimally invasive magnetic, electrical, thermal and optogenetic stimulation. These methodologies are transforming the repertoire of readily available technologies paired with compatible imaging systems and hold promise toward broadening the expanse of neurological and neuroscientific diagnostics and therapeutics.

Wireless in vivo Recording of Cortical Activity by an Ion-Sensitive Field Effect Transistor.

Wireless brain technologies are empowering basic neuroscience and clinical neurology by offering new platforms that minimize invasiveness and refine possibilities during electrophysiological recording and stimulation. Despite their advantages, most systems require on-board power supply and sizeable transmission circuitry, enforcing a lower bound for miniaturization. Designing new minimalistic architectures that can efficiently sense neurophysiological events will open the door to standalone microscale sensors and minimally invasive delivery of multiple sensors. Here we present a circuit for sensing ionic fluctuations in the brain by an ion-sensitive field effect transistor that detunes a single radiofrequency resonator in parallel. We establish sensitivity of the sensor by electromagnetic analysis and quantify response to ionic fluctuations in vitro . We validate this new architecture in vivo during hindpaw stimulation in rodents and verify correlation with local field potential recordings. This new approach can be implemented as an integrated circuit for wireless in situ recording of brain electrophysiology.

In silico assessment of electrophysiological neuronal recordings mediated by magnetoelectric nanoparticles.

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.