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NeuroGrid: recording action potentials from the surface of the brain.

Khodagholy D, Gelinas JN, Thesen T, Doyle W, Devinsky O, Malliaras GG, Buzsáki G - Nat. Neurosci. (2014)

Bottom Line: Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface.We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery.The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

View Article: PubMed Central - PubMed

Affiliation: NYU Neuroscience Institute, School of Medicine, New York University, New York, New York, USA.

ABSTRACT
Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

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Neuron clustering and spike waveform characterization.a) Sample autocorrelograms (in color) of putative single unit spiking from hippocampus (top 2 rows) and cortex (bottom row). Spiking cross-correlations (black) demonstrate excitatory and inhibitory interactions between putative single unit pairs.b) Scatterplot of waveform characteristics of putative single units recorded with the NeuroGrid reveals two broad clusters. Neurons were clustered according to waveform symmetry and mean wideband spike width. Each symbol corresponds to an average spike waveform of a putative isolated neuron. The symmetry of the waveform is defined by the comparison of the peaks of the spike (a and b) and the spike duration is defined by the latency of spike peak to trough (c) as illustrated in the inset figure.c) Nissl-stained coronal sections of cortex (left) and hippocampus (right) deep to NeuroGrid placement. Electrode location on the surface is estimated in yellow (electrodes not to scale; scale = 100 μm; pyr = pyramidal).
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Figure 2: Neuron clustering and spike waveform characterization.a) Sample autocorrelograms (in color) of putative single unit spiking from hippocampus (top 2 rows) and cortex (bottom row). Spiking cross-correlations (black) demonstrate excitatory and inhibitory interactions between putative single unit pairs.b) Scatterplot of waveform characteristics of putative single units recorded with the NeuroGrid reveals two broad clusters. Neurons were clustered according to waveform symmetry and mean wideband spike width. Each symbol corresponds to an average spike waveform of a putative isolated neuron. The symmetry of the waveform is defined by the comparison of the peaks of the spike (a and b) and the spike duration is defined by the latency of spike peak to trough (c) as illustrated in the inset figure.c) Nissl-stained coronal sections of cortex (left) and hippocampus (right) deep to NeuroGrid placement. Electrode location on the surface is estimated in yellow (electrodes not to scale; scale = 100 μm; pyr = pyramidal).

Mentions: Over what spatial distance can the NeuroGrid detect neuronal spikes? To address this question, we analyzed features of spikes acquired from rat neocortex and hippocampus. Auto-correlation of spike trains is routinely used to ensure separation of spikes emanating from neighboring neurons12. Clustered spikes detected by the NeuroGrid generated auto-correlograms with typical neuronal refractory periods (Fig. 2a, in color). Isolated putative single units exhibited a range of firing rates (0.1 – 60 Hz), and some hippocampal units had auto-correlograms with peaks at short interspike intervals (Fig. 2a, in purple), characteristic of burst firing of pyramidal cells20. To directly validate the physiologic nature of spiking activity recorded from the surface with the NeuroGrid, we simultaneously detected some of these spikes using a silicon probe inserted into adjacent brain parenchyma (< 200 μm deep from the surface; Supplementary Fig. 5e). Spikes recorded by the NeuroGrid exhibited a wide distribution of morphologic features (Fig. 2b), suggesting that the recorded units likely reflect activity of both layer 1 interneurons as well as pyramidal cells and fast firing interneurons in deeper layers21,22. Very short duration (< 0.5 ms), ‘triphasic’, symmetric waveforms, consistent with axonal spikes23, were occasionally identified but their amplitude was typically below the unit detection threshold.


NeuroGrid: recording action potentials from the surface of the brain.

Khodagholy D, Gelinas JN, Thesen T, Doyle W, Devinsky O, Malliaras GG, Buzsáki G - Nat. Neurosci. (2014)

Neuron clustering and spike waveform characterization.a) Sample autocorrelograms (in color) of putative single unit spiking from hippocampus (top 2 rows) and cortex (bottom row). Spiking cross-correlations (black) demonstrate excitatory and inhibitory interactions between putative single unit pairs.b) Scatterplot of waveform characteristics of putative single units recorded with the NeuroGrid reveals two broad clusters. Neurons were clustered according to waveform symmetry and mean wideband spike width. Each symbol corresponds to an average spike waveform of a putative isolated neuron. The symmetry of the waveform is defined by the comparison of the peaks of the spike (a and b) and the spike duration is defined by the latency of spike peak to trough (c) as illustrated in the inset figure.c) Nissl-stained coronal sections of cortex (left) and hippocampus (right) deep to NeuroGrid placement. Electrode location on the surface is estimated in yellow (electrodes not to scale; scale = 100 μm; pyr = pyramidal).
© Copyright Policy
Related In: Results  -  Collection

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Figure 2: Neuron clustering and spike waveform characterization.a) Sample autocorrelograms (in color) of putative single unit spiking from hippocampus (top 2 rows) and cortex (bottom row). Spiking cross-correlations (black) demonstrate excitatory and inhibitory interactions between putative single unit pairs.b) Scatterplot of waveform characteristics of putative single units recorded with the NeuroGrid reveals two broad clusters. Neurons were clustered according to waveform symmetry and mean wideband spike width. Each symbol corresponds to an average spike waveform of a putative isolated neuron. The symmetry of the waveform is defined by the comparison of the peaks of the spike (a and b) and the spike duration is defined by the latency of spike peak to trough (c) as illustrated in the inset figure.c) Nissl-stained coronal sections of cortex (left) and hippocampus (right) deep to NeuroGrid placement. Electrode location on the surface is estimated in yellow (electrodes not to scale; scale = 100 μm; pyr = pyramidal).
Mentions: Over what spatial distance can the NeuroGrid detect neuronal spikes? To address this question, we analyzed features of spikes acquired from rat neocortex and hippocampus. Auto-correlation of spike trains is routinely used to ensure separation of spikes emanating from neighboring neurons12. Clustered spikes detected by the NeuroGrid generated auto-correlograms with typical neuronal refractory periods (Fig. 2a, in color). Isolated putative single units exhibited a range of firing rates (0.1 – 60 Hz), and some hippocampal units had auto-correlograms with peaks at short interspike intervals (Fig. 2a, in purple), characteristic of burst firing of pyramidal cells20. To directly validate the physiologic nature of spiking activity recorded from the surface with the NeuroGrid, we simultaneously detected some of these spikes using a silicon probe inserted into adjacent brain parenchyma (< 200 μm deep from the surface; Supplementary Fig. 5e). Spikes recorded by the NeuroGrid exhibited a wide distribution of morphologic features (Fig. 2b), suggesting that the recorded units likely reflect activity of both layer 1 interneurons as well as pyramidal cells and fast firing interneurons in deeper layers21,22. Very short duration (< 0.5 ms), ‘triphasic’, symmetric waveforms, consistent with axonal spikes23, were occasionally identified but their amplitude was typically below the unit detection threshold.

Bottom Line: Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface.We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery.The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

View Article: PubMed Central - PubMed

Affiliation: NYU Neuroscience Institute, School of Medicine, New York University, New York, New York, USA.

ABSTRACT
Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

Show MeSH
Related in: MedlinePlus