FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2949474047> ?p ?o ?g. }

• W2949474047 endingPage "1291.e4" @default.
• W2949474047 startingPage "1283" @default.
• W2949474047 abstract "•Spin-electronics-based probes achieve local magnetic recordings inside the neuropil•Magnetic field recordings were performed in vivo, in anesthetized cat visual cortex•Event-related fields (ERFs) to visual stimuli were up to several nanoteslas in size•ERFs could be detected after averaging less than 20 trials Neuronal activity generates ionic flows and thereby both magnetic fields and electric potential differences, i.e., voltages. Voltage measurements are widely used but suffer from isolating and smearing properties of tissue between source and sensor, are blind to ionic flow direction, and reflect the difference between two electrodes, complicating interpretation. Magnetic field measurements could overcome these limitations but have been essentially limited to magnetoencephalography (MEG), using centimeter-sized, helium-cooled extracranial sensors. Here, we report on in vivo magnetic recordings of neuronal activity from visual cortex of cats with magnetrodes, specially developed needle-shaped probes carrying micron-sized, non-cooled magnetic sensors based on spin electronics. Event-related magnetic fields inside the neuropil were on the order of several nanoteslas, informing MEG source models and efforts for magnetic field measurements through MRI. Though the signal-to-noise ratio is still inferior to electrophysiology, this proof of concept demonstrates the potential to exploit the fundamental advantages of magnetophysiology. Neuronal activity generates ionic flows and thereby both magnetic fields and electric potential differences, i.e., voltages. Voltage measurements are widely used but suffer from isolating and smearing properties of tissue between source and sensor, are blind to ionic flow direction, and reflect the difference between two electrodes, complicating interpretation. Magnetic field measurements could overcome these limitations but have been essentially limited to magnetoencephalography (MEG), using centimeter-sized, helium-cooled extracranial sensors. Here, we report on in vivo magnetic recordings of neuronal activity from visual cortex of cats with magnetrodes, specially developed needle-shaped probes carrying micron-sized, non-cooled magnetic sensors based on spin electronics. Event-related magnetic fields inside the neuropil were on the order of several nanoteslas, informing MEG source models and efforts for magnetic field measurements through MRI. Though the signal-to-noise ratio is still inferior to electrophysiology, this proof of concept demonstrates the potential to exploit the fundamental advantages of magnetophysiology. Neuronal activity entails ionic flows across the cell membrane and along dendrites. This electrical activity can be measured extra-cellularly or intra-cellularly by microelectrodes (Kandel et al., 2000Kandel E.R. Schwartz J.H. Jessell T.M. Principles of Neural Science.Fourth Edition. McGraw-Hill, Health Professions Division, 2000Google Scholar), which are either thin metallic micro-wires, or glass pipettes containing an ionic solution, to realize a conductive interface between the local brain tissue and the recording instrumentation. Intracellular recordings directly reveal the transmembrane voltage or current of an isolated neuron, but intracellular recordings in vivo are difficult in practice and often only brief measurements of single neurons are feasible. Extracellular recordings, on the other hand, measure the aggregate fluctuations in voltage arising from the net neuronal activity around the electrode’s tip, with respect to a reference electrode (Buzsáki et al., 2012Buzsáki G. Anastassiou C.A. Koch C. The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes.Nat. Rev. Neurosci. 2012; 13: 407-420Crossref PubMed Scopus (2221) Google Scholar). Microelectrodes inside the neuropil record action potentials and local field potentials (LFPs), electrocorticographic electrodes provide mesoscopic LFPs, and scalp electrodes deliver the electroencephalographic (EEG) signal. Combining many electrodes into planar (Maynard et al., 1997Maynard E.M. Nordhausen C.T. Normann R.A. The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces.Electroencephalogr. Clin. Neurophysiol. 1997; 102: 228-239Abstract Full Text PDF PubMed Scopus (426) Google Scholar) or laminar (Lewis et al., 2015Lewis C.M. Bosman C.A. Fries P. Recording of brain activity across spatial scales.Curr. Opin. Neurobiol. 2015; 32: 68-77Crossref PubMed Scopus (45) Google Scholar) arrays allows for the study of whole brain networks and their dynamics in the intact brain (Buzsáki, 2004Buzsáki G. Large-scale recording of neuronal ensembles.Nat. Neurosci. 2004; 7: 446-451Crossref PubMed Scopus (1272) Google Scholar). The electric currents flowing through the active neuropil also give rise to a magnetic signature. Magnetoencephalography (MEG) (Cohen, 1968Cohen D. Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents.Science. 1968; 161: 784-786Crossref PubMed Scopus (449) Google Scholar, Cohen, 1972Cohen D. Magnetoencephalography: detection of the brain’s electrical activity with a superconducting magnetometer.Science. 1972; 175: 664-666Crossref PubMed Scopus (485) Google Scholar, Hari and Salmelin, 2012Hari R. Salmelin R. Magnetoencephalography: From SQUIDs to neuroscience. Neuroimage 20th anniversary special edition.Neuroimage. 2012; 61: 386-396Crossref PubMed Scopus (164) Google Scholar) is a non-invasive method to measure the magnetic fields of active neuronal populations during perceptual or cognitive tasks in the healthy or diseased human brain. This technique uses Superconducting Quantum Interference Devices (SQUIDs) cooled down to the temperature of liquid helium (4.2 K). The apparatus necessary for this cooling imposes a distance to the cortical surface of 3 to 5 cm in in vivo configurations. The spatial resolution is typically better than for EEG recordings, but even under optimal conditions still lies in the order of several mm3, with signal amplitudes in the femtotesla (10−15T) to picotesla (10−12T) range. Local magnetic recordings of neuronal activity could be a complementary technique to electrophysiology, because the magnetic signal provides interesting properties in addition to those realized by the electric signal. Contrary to electric fields, which strongly depend on the dielectric properties of the tissue between neuronal sources and the recording electrode, magnetic fields travel through tissue without distortion, because the respective permeability is essentially the same as free space (Barnes and Greenebaum, 2007Barnes F.S. Greenebaum B. Handbook of Biological Effects of Electromagnetic Fields. Biological and Medical Aspects of Electromagnetic Fields.Third Edition. CRC Press, 2007Google Scholar). Therefore, magnetic fields are only attenuated by the distance to the current source. Ionic flows and the corresponding magnetic fields are likely largest inside neurons. As those magnetic fields pass through the cell membrane without attenuation, extracellular magnetic field measurements might provide functionally intracellular measurements without impaling the neuron. Moreover, while electrophysiological recordings yield scalar values, local magnetic recordings yield information about both amplitude and direction of current sources. Thereby, they might allow the precise localization of the source of neuronal activity at a given moment in time in the 3D volume of the brain. Furthermore, electrodes always measure the electric potential relative to a reference electrode, and the position and type of reference can substantially influence the measured signal. Moreover, in multi-electrode recordings, all channels typically share the same reference, which poses a problem for analyses of functional connectivity, because the resulting signals are not independent. Magnetrodes, presented in this work, provide an elegant solution, because the recorded magnetic signals are reference-free, and therefore allow for an unbiased measure of connectivity and information flow throughout the brain. In addition, these magnetrodes can be used to perform magnetic resonance spectroscopy (Guitard et al., 2016Guitard P.A. Ayde R. Jasmin-Lebras G. Caruso L. Pannetier-Lecoeur M. Fermon C. Local nuclear magnetic resonance spectroscopy with giant magnetic resistance-based sensors.Appl. Phys. Lett. 2016; 108: 212405Crossref Scopus (6) Google Scholar). In order to minimize tissue damages, magnetic probes for insertion into the brain require a needle shape and the miniaturization of the magnetic sensors, while maintaining a very high sensitivity at physiological temperature. Approaches to record the magnetic biological signal closer to the sources than MEG have been successfully realized by using small SQUIDs (Magnelind, 2006Magnelind, P. (2006). High-Tc SQUIDs for magnetophysiology: development of a magnetometer system and measurements of evoked fields from hippocampal neurons in vitro. PhD Thesis: (Chalmers University of Technology, Göteborg, Sweden).Google Scholar), atomic magnetometers (Sander et al., 2012Sander T.H. Preusser J. Mhaskar R. Kitching J. Trahms L. Knappe S. Magnetoencephalography with a chip-scale atomic magnetometer.Biomed. Opt. Express. 2012; 3: 981-990Crossref PubMed Scopus (218) Google Scholar) or winded coils (Roth and Wikswo, 1985Roth B.J. Wikswo Jr., J.P. The magnetic field of a single axon. A comparison of theory and experiment.Biophys. J. 1985; 48: 93-109Abstract Full Text PDF PubMed Scopus (84) Google Scholar), and very recently with nitrogen-vacancy centers in diamond on a living invertebrate (Barry et al., 2016Barry J.F. Turner M.J. Schloss J.M. Glenn D.R. Song Y. Lukin M.D. Park H. Walsworth R.L. Optical magnetic detection of single-neuron action potentials using quantum defects in diamond.Proc. Natl. Acad. Sci. USA. 2016; 113: 14133-14138Crossref PubMed Scopus (300) Google Scholar). However, limitations due to the millimeter size of the sensors or to its operating conditions never allowed penetration into the neuropil or recording at distances of merely tens of microns from active cells. Spin electronics (Baibich et al., 1988Baibich M.N. Broto J.M. Fert A. Nguyen Van Dau F. Petroff F. Etienne P. Creuzet G. Friederich A. Chazelas J. Giant magnetoresistance of (001)Fe/(001)Cr magnetic superlattices.Phys. Rev. Lett. 1988; 61: 2472-2475Crossref PubMed Scopus (7902) Google Scholar) offers the capability to reduce magnetic sensors to micron size and to reach sensitivity in the sub-nanotesla range while working at body temperature and thereby avoiding bulky vacuum isolation (Reig, 2013Reig C. Giant Magnetoresistance (GMR) Sensors: From Basis to State-of-the-Art Applications.First Edition. Springer, 2013Crossref Google Scholar). We have designed spin valve (Dieny et al., 1991Dieny B. Speriosu V.S. Parkin S.S. Gurney B.A. Wilhoit D.R. Mauri D. Giant magnetoresistive in soft ferromagnetic multilayers.Phys. Rev. B Condens. Matter. 1991; 43: 1297-1300Crossref PubMed Scopus (1647) Google Scholar) giant magneto-resistance (GMR) sensors consisting of five segments of 4 x 30 μm2 arranged in a meandering configuration on silicon substrate that was ground to a thickness of 200 μm and etched to form a needle shape for tissue penetration (Figure 1A). The sensors have been electrically insulated by a dielectric bilayer of Si3N4/Al2O3. We refer to these probes as “magnetrodes,” for a magnetic equivalent of electrodes (see STAR Methods for details of manufacturing and characterization). GMR sensors are magnetic-field-dependent resistors. To measure magnetic field strength, we applied an input voltage to the GMR and recorded the output voltage (Figure S1). The GMR output voltage varies sigmoidally as a function of the in-plane component of the magnetic field (Figure 1B). The sensor is configured such that very weak magnetic fields, around zero, result in outputs constrained to the steep linear part of the curve, thereby maximizing the dynamic range in the region of interest. In the linear part, the slope is 1.8%/mT, corresponding to a sensitivity of 10 to 25 Voltout/(VoltinxTesla). The noise spectrum at a typical input voltage of 0.5 V leads to sensitivities of 7 nT/√Hz at 10 Hz, 2 nT/√Hz at 100 Hz and 370 pT/√Hz in the thermal noise regime above 1 kHz (Figure 1C). We performed in vivo recordings in primary visual cortex of anesthetized cats (see STAR Methods). Figure 2 shows a schematic representation of the experimental setup. A magnetrode was inserted into the tissue to a depth of less than 1 mm from the cortical surface using micromanipulators under microscope inspection. The magnetrode was sensitive to fields orthogonal to the tip, that is, parallel to the cortical surface. A tungsten electrode was targeted to be less than 1 mm from the magnetrode, to simultaneously obtain an independent electric recording. Recordings were performed without shielding. To physiologically activate the recorded brain area, we presented a flash of light directly to one eye of the cat. The duration of light stimulation was either 100 ms or 500 ms, with a variable inter-stimulus interval of 0.9 to 1.5 s to avoid adaptation or entrainment. The stimulus was presented 1,000 times. The output signals from the tungsten electrode and the magnetrode were preprocessed and averaged with respect to stimulus onset, to calculate the event-related potential (ERP) for the electrode and the event-related field (ERF) for the magnetrode (see STAR Methods). We estimated the field strength that we could expect, when recording magnetic fields inside the neuropil. As a starting point, we used the well-established magnetic fields recorded with MEG. When MEG signals are recorded from human subjects presented with visual stimuli, ERFs can be obtained with typical amplitudes in the range of 50 fT (Salmelin et al., 1994Salmelin R. Hari R. Lounasmaa O.V. Sams M. Dynamics of brain activation during picture naming.Nature. 1994; 368: 463-465Crossref PubMed Scopus (279) Google Scholar). For these MEG sensor-level field strengths, detailed models of the underlying sources estimate dipole strengths in the range of 10 nA∗m (Hämäläinen et al., 1993Hämäläinen M. Hari R. Ilmoniemi R.J. Knuutila J. Lounasmaa O.V. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain.Rev. Mod. Phys. 1993; 65: 413-497Crossref Scopus (3553) Google Scholar, Murakami and Okada, 2006Murakami S. Okada Y. Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals.J. Physiol. 2006; 575: 925-936Crossref PubMed Scopus (231) Google Scholar). We constructed a model of an ensemble of neurons, which can produce such dipole strength, to then calculate the field strengths expected for magnetrode measurements very near or inside this neuronal ensemble. We simulated a square array of 10,000 aligned neurons with a mean center-to-center separation of 5 μm. In each neuron, a current was simulated, such that the ensemble of neurons appeared as a dipole of 10 nA∗m, when recorded from a large distance. The corresponding difference in electric potentials was simulated to occur over a distance of 200 μm. This distance was estimated from current-source density measurements in cat visual cortex in response to visual stimuli (Mitzdorf, 1985Mitzdorf U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena.Physiol. Rev. 1985; 65: 37-100Crossref PubMed Scopus (1215) Google Scholar). The currents in the neuronal ensemble gave rise to a magnetic signal of 50 fT at a distance of 6 cm, 800 fT at 1.5 cm, 126 pT at 1 mm, 1.3 nT at 100 μm, and 2.3 nT inside the neuronal ensemble. Thus, these simulations predict that magnetic field measurements within or in close proximity to the activated neurons will give ERFs in the range of a few nano-Tesla. When magnetrodes are introduced into the neuropil, they might face direct capacitive coupling to electric currents flowing in the neuropil. Therefore, we developed a measurement scheme that suppressed this capacitive coupling. In this scheme, the GMR sensors were fed with alternating current (AC, Figure S2) with frequencies in the range of 20–80 kHz, and the sensor output was demodulated separately for components that were in-phase with the AC modulation and those that were out-of-phase. The currents fed to the GMR during the AC measurement scheme are not expected to directly influence neurons in the vicinity of the magnetrode. We estimated, for a typical AC current, the resulting magnetic and electric field intensities induced in the neuropil, and they were several orders of magnitude below thresholds for neuronal stimulation (see STAR Methods). We used two phantoms, one to generate purely magnetic fields, and another one to generate purely electric fields. When the input to the GMR was a time-varying magnetic field, the GMR output reflected this almost purely on the in-phase component (Figure 3A). By contrast, when the input to the GMR was a time-varying electric field, the GMR output reflected this primarily at higher frequencies and then primarily in the out-of-phase component (Figure 3B). Electric fields also induced a small in-phase component, presumably due to a mixing in the silicon substrate. The phantom measurements provided GMR in-phase and out-of-phase outputs for all physiologically relevant frequencies of electric or magnetic field input. Thereby, they provided a transfer function for electric fields and a transfer function for magnetic fields. In order to estimate contamination from electric field in vivo, we used the ERP recorded in one session (cat 2B) and convolved it with the transfer function estimated for electric fields in the phantom measurements. This provided an estimate of the GMR output that would be expected if the input were purely an electric field with the waveform of an ERP (Figure 4A). In this case, the GMR out-of-phase component (green line) was larger than the in-phase component (red line). Subsequently, we convolved the same ERP waveform with the transfer function for magnetic fields. This provided an estimate of the GMR output that would be expected if the input were purely a magnetic field with the waveform of an ERP (Figure 4B). In this case, the in-phase component (red line) was substantially larger than the out-of-phase component (green line). We used the ERP waveform for both simulations, to aid direct comparison and to avoid circularity, when we compare, in the next step, the simulated GMR outputs to the experimentally observed GMR output. The observed GMR output (Figure 4C) showed a substantially larger in-phase component (red line) than out-of-phase component (green line). This pattern corresponds to the pattern estimated for magnetic field input (Figure 4B), which suggests that the GMR output is mainly determined by the neuronally generated magnetic fields. The magnetic field input is primarily reflected by the in-phase component of the GMR output. Therefore, in the following, we refer to the in-phase component of the GMR output as ERFs, and we compare them to the ERPs recorded simultaneously through the tungsten electrode. Figure 5A shows the ERF and Figure 5B the simultaneously recorded ERP for the recording in the first animal (cat 1) with a visual stimulus duration of 100 ms. Figure 5C shows a magnification of the data with the ERF (red) and ERP (green) scaled and superimposed to facilitate comparison. The ERF showed a magnetic response starting 20 ms after stimulus onset, corresponding to the conduction delay between the retina and the primary visual cortex. The ERF was characterized by a strong negative component at 36 ms and a positive peak around 61 ms. The peak-to-peak amplitude was 2.5 nT. The onset of the ERP was comparable to the magnetic one, with a trough at slightly shorter latency and a peak at similar latency as the magnetic signal. Figure 5D shows the Pearson correlation coefficient between ERF and ERP as a function of time lag, with positive lag values indicating that the ERF lagged the ERP. The correlation function peaked at a value of approximately 0.55, for a lag of approximately 2 ms. The side peaks and troughs are due to the partially rhythmic nature of the ERP and ERF. Similar results were obtained in two separate recordings from another animal (cat 2A and cat 2B). Figures 5E–5H shows the results for one recording site and a visual stimulus duration of 100 ms. Figures 5I–5L present the data from another recording site later in the experiment, with a visual stimulus duration of 500 ms. With the longer stimulus duration, the on and off responses were clearly separated, as evident in the magnetic and electric recordings. The signal amplitude of the magnetic (and of the electric) recordings was larger in cat 2, with peak-to-peak amplitudes of approximately 10 nT. Similar to cat 1, the electric signal had a shorter latency than the magnetic signal, but in cat 2 the difference was only a few milliseconds. The cross-correlation functions between the ERFs and ERPs of cat 2 showed peak values around 0.85 at a lag of 2–3 ms. To further characterize the magnetic responses, we determined two metrics of signal quality. In a first approach, we calculated a simple metric of signal-to-noise ratio (SNR), based on the mean squared ERF or ERP (see STAR Methods for details). When this SNR was determined for ERFs based on averaging all 1,000 trials, it reached values between 12 and 17 (Figure 6A). When ERFs were based on averaging increasing numbers of trials, they reached significance at 229, 103, and 95 trials, for recording sessions cat 1, cat 2A, and cat 2B, respectively (Figure 6B; bootstrap test, see STAR Methods). For ERPs, it reached maximal values between 30 and 36 and was significant for single trials (Figure 6C). While the SNR metric is simple, it is not very sensitive. Therefore, in a second approach, we quantified how many trials had to be averaged for the ERF or ERP to assume its final shape. We first selected a random half of all trials to calculate a template shape. We then averaged increasing numbers of the remaining trials and calculated the correlation between the resulting shapes and the template shape. When the correlation was determined for ERFs based on averaging all remaining 500 trials, it reached values between 0.92 and 0.97. The correlation reached significance for 31, 18, and 16 trials, for recording sessions cat 1, cat 2A, and cat 2B, respectively (Figure 6E; bootstrap test, see STAR Methods). For ERPs, it reached maximal values of 0.99 and was significant for single trials (Figure 6F). In summary, we have shown that magnetrodes based on spin electronics can be used to record in vivo magnetic signals originating from neuronal activity. This was possible, because GMR sensors combine a small size of a few tens of microns with sufficient magnetic field sensitivity. Magnetic field recordings inside the tissue offer unique opportunities, because they are reference free, they measure the direction of magnetic fields and thereby of the underlying (intracellular) current flows, and because these magnetic fields are not smeared by intervening neuropil. In vivo magnetic field measurements might contribute to a better understanding of the commonly recorded extracranial MEG signal. There are also efforts to record neuronally generated magnetic fields by means of magnetic resonance imaging (MRI) (Bandettini et al., 2005Bandettini P.A. Petridou N. Bodurka J. Direct detection of neuronal activity with MRI: Fantasy, possibility, or reality?.Appl. Magn. Reson. 2005; 29: 65-88Crossref Scopus (74) Google Scholar, Körber et al., 2013Körber R. Nieminen J.O. Höfner N. Jazbinšek V. Scheer H.J. Kim K. Burghoff M. An advanced phantom study assessing the feasibility of neuronal current imaging by ultra-low-field NMR.J. Magn. Reson. 2013; 237: 182-190Crossref PubMed Scopus (16) Google Scholar), and our magnetrode recordings provide ground-truth measurements for this. We would like to highlight the potential utility of GMR-based sensing of neuronal activity for recordings from untethered implanted devices. Implanted recording probes play an important role in many neurotechnological scenarios. Untethered probes are particularly intriguing, as they avoid connection wires and corresponding limitations (Seo et al., 2016Seo D. Neely R.M. Shen K. Singhal U. Alon E. Rabaey J.M. Carmena J.M. Maharbiz M.M. Wireless recording in the peripheral nervous system with ultrasonic neural dust.Neuron. 2016; 91: 529-539Abstract Full Text Full Text PDF PubMed Scopus (299) Google Scholar). Yet, for untethered probes to be maximally useful, they need to be tiny, and this results in a fundamental problem for electrical recordings. Electrical recordings require two electrochemical interfaces with sufficient distance, such that the electrical potential difference does not become vanishingly small. The necessary distance restricts the size to which untethered devices based on electric recordings can be reduced. Magnetic field recordings do not suffer from this problem, because they require merely a singular GMR. Thus, magnetrode-based untethered recordings, while challenging, might provide a unique combination of recording and transmitting modalities for future neurotechnology. We revealed visually evoked magnetic fields by averaging over multiple stimulus repetitions. This was possible, because the underlying postsynaptic potentials (PSPs) are long lasting compared to their temporal jitter across trials. Thereby, PSPs temporally superimpose in the cross-trial average. This holds not only for PSPs of one postsynaptic neuron, but for PSPs of many neurons in the vicinity of the magnetrode. Thus, the ERF became detectable due to effective summation of the PSP-related magnetic fields across neurons and across trials. ERFs in the different recording sites reached significance after averaging 16–31 trials (Figure 6E). Thus, moderate improvements in sensitivity and shielding will likely enable detection of ERFs on single trials. If the detection of single-trial ERFs will succeed, also the detection of magnetic fields corresponding to single action potentials (APs) appears realistic. AP amplitudes, when recorded with electrodes close to the cell body, substantially exceed ERP amplitudes. This is likely due to the fact that each AP reflects massive transmembrane currents that move the transmembrane voltage across the cell body from −60 mV to +30 mV. Whether these strong currents generate detectable magnetic fields crucially depends on their spatial symmetry and temporal simultaneity. If all involved currents flew simultaneously and with spherical symmetry, they would generate no detectable magnetic field. However, it is known that APs emerge in the axon hillock and retrogradely invade the cell body and sometimes the dendrites (McCormick et al., 2007McCormick D.A. Shu Y. Yu Y. Neurophysiology: Hodgkin and Huxley model–still standing?.Nature. 2007; 445 (E1-2; discussion E2-3)Crossref PubMed Scopus (96) Google Scholar, Stuart et al., 1997Stuart G. Spruston N. Sakmann B. Häusser M. Action potential initiation and backpropagation in neurons of the mammalian CNS.Trends Neurosci. 1997; 20: 125-131Abstract Full Text Full Text PDF PubMed Scopus (595) Google Scholar). Thus, APs are likely magnetically visible. Single extracellular metal microelectrodes typically record spikes from merely a handful of neurons, because insulating cell membranes isolate them from the hundreds of neurons in their immediate vicinity (Buzsáki, 2004Buzsáki G. Large-scale recording of neuronal ensembles.Nat. Neurosci. 2004; 7: 446-451Crossref PubMed Scopus (1272) Google Scholar). Magnetic fields corresponding to action potentials, that is “action fields” or AFs, should travel from neurons to the magnetrode without attenuation. This might enable AF recordings from tens or even hundreds of neurons from the vicinity of the magnetrode. The separation of spikes originating from these many neurons would be a challenge. Electrophysiological recordings allow separation of a handful of spikes, based on the relatively stereotypical spike waveform of a given neuron and the fact that millisecond-precise spike coincidences of neighboring neurons occur with a very low probability of 0.01–0.001 (Jia et al., 2013Jia X. Tanabe S. Kohn A. γ and the coordination of spiking activity in early visual cortex.Neuron. 2013; 77: 762-774Abstract Full Text Full Text PDF PubMed Scopus (112) Google Scholar, Kohn and Smith, 2005Kohn A. Smith M.A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque.J. Neurosci. 2005; 25: 3661-3673Crossref PubMed Scopus (401) Google Scholar). Magnetic recordings would be able to benefit from the same factors, and in addition from the vectorial nature of magnetic sources and the corresponding vectorial sensitivity of the sensors. Sensors specific for the three spatial dimensions could be combined on a single magnetrode to estimate the 3D position of each neuronal source relative to the magnetrode. Importantly, the magnetrode as presented here, without further modifications or improvements, can provide useful ERF measurements even in an unshielded environment after averaging over merely 16–31 stimulus repetitions. These values are similar to the number of 30 stimulus repetitions, which has been estimated as the minimum to obtain a consistent visually evoked ERP from human EEG recordings (Thigpen et al., 2017Thigpen N.N. Kappenman E.S. Keil A. Assessing the internal consistency of the event-related potential: An example analysis.Psychophysiology. 2017; 54: 123-138Crossref PubMed Scopus (70) Google Scholar). Thus, for event-related experimental designs, the fundamental benefits of magnet" @default.
• W2949474047 created "2019-06-27" @default.
• W2949474047 creator A5001333487 @default.
• W2949474047 creator A5015429589 @default.
• W2949474047 creator A5017109964 @default.
• W2949474047 creator A5019200647 @default.
• W2949474047 creator A5027187758 @default.
• W2949474047 creator A5029116571 @default.
• W2949474047 creator A5033011073 @default.
• W2949474047 creator A5042178089 @default.
• W2949474047 creator A5054808541 @default.
• W2949474047 creator A5054984405 @default.
• W2949474047 creator A5073937337 @default.
• W2949474047 creator A5082941446 @default.
• W2949474047 creator A5083802365 @default.
• W2949474047 date "2017-09-01" @default.
• W2949474047 modified "2023-10-16" @default.
• W2949474047 title "In Vivo Magnetic Recording of Neuronal Activity" @default.
• W2949474047 cites W123176792 @default.
• W2949474047 cites W1551715170 @default.
• W2949474047 cites W1965245884 @default.
• W2949474047 cites W1968495411 @default.
• W2949474047 cites W1973275441 @default.
• W2949474047 cites W1982321167 @default.
• W2949474047 cites W1989843341 @default.
• W2949474047 cites W2007495909 @default.
• W2949474047 cites W2016544342 @default.
• W2949474047 cites W2020769260 @default.
• W2949474047 cites W2036662626 @default.
• W2949474047 cites W2038735815 @default.
• W2949474047 cites W2054256672 @default.
• W2949474047 cites W2057426891 @default.
• W2949474047 cites W2066804398 @default.
• W2949474047 cites W2072307422 @default.
• W2949474047 cites W2078357680 @default.
• W2949474047 cites W2080680776 @default.
• W2949474047 cites W2081517547 @default.
• W2949474047 cites W2084333685 @default.
• W2949474047 cites W2111736228 @default.
• W2949474047 cites W2119837022 @default.
• W2949474047 cites W2134028972 @default.
• W2949474047 cites W2149900207 @default.
• W2949474047 cites W2165642563 @default.
• W2949474047 cites W2166073443 @default.
• W2949474047 cites W2266469747 @default.
• W2949474047 cites W2286562184 @default.
• W2949474047 cites W2403801331 @default.
• W2949474047 cites W2495352079 @default.
• W2949474047 cites W2518694010 @default.
• W2949474047 cites W2565387215 @default.
• W2949474047 cites W2609275912 @default.
• W2949474047 cites W39438812 @default.
• W2949474047 doi "https://doi.org/10.1016/j.neuron.2017.08.012" @default.
• W2949474047 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5744593" @default.
• W2949474047 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28844526" @default.
• W2949474047 hasPublicationYear "2017" @default.
• W2949474047 type Work @default.
• W2949474047 sameAs 2949474047 @default.
• W2949474047 citedByCount "42" @default.
• W2949474047 countsByYear W29494740472017 @default.
• W2949474047 countsByYear W29494740472018 @default.
• W2949474047 countsByYear W29494740472019 @default.
• W2949474047 countsByYear W29494740472020 @default.
• W2949474047 countsByYear W29494740472021 @default.
• W2949474047 countsByYear W29494740472022 @default.
• W2949474047 countsByYear W29494740472023 @default.
• W2949474047 crossrefType "journal-article" @default.
• W2949474047 hasAuthorship W2949474047A5001333487 @default.
• W2949474047 hasAuthorship W2949474047A5015429589 @default.
• W2949474047 hasAuthorship W2949474047A5017109964 @default.
• W2949474047 hasAuthorship W2949474047A5019200647 @default.
• W2949474047 hasAuthorship W2949474047A5027187758 @default.
• W2949474047 hasAuthorship W2949474047A5029116571 @default.
• W2949474047 hasAuthorship W2949474047A5033011073 @default.
• W2949474047 hasAuthorship W2949474047A5042178089 @default.
• W2949474047 hasAuthorship W2949474047A5054808541 @default.
• W2949474047 hasAuthorship W2949474047A5054984405 @default.
• W2949474047 hasAuthorship W2949474047A5073937337 @default.
• W2949474047 hasAuthorship W2949474047A5082941446 @default.
• W2949474047 hasAuthorship W2949474047A5083802365 @default.
• W2949474047 hasBestOaLocation W29494740471 @default.
• W2949474047 hasConcept C150903083 @default.
• W2949474047 hasConcept C15744967 @default.
• W2949474047 hasConcept C169760540 @default.
• W2949474047 hasConcept C207001950 @default.
• W2949474047 hasConcept C74254510 @default.
• W2949474047 hasConcept C86803240 @default.
• W2949474047 hasConceptScore W2949474047C150903083 @default.
• W2949474047 hasConceptScore W2949474047C15744967 @default.
• W2949474047 hasConceptScore W2949474047C169760540 @default.
• W2949474047 hasConceptScore W2949474047C207001950 @default.
• W2949474047 hasConceptScore W2949474047C74254510 @default.
• W2949474047 hasConceptScore W2949474047C86803240 @default.
• W2949474047 hasIssue "6" @default.
• W2949474047 hasLocation W29494740471 @default.
• W2949474047 hasLocation W29494740472 @default.
• W2949474047 hasLocation W29494740473 @default.
• W2949474047 hasLocation W29494740474 @default.

• next