Dataset Information

Multichannel Silicon Probes for Awake Hippocampal Recordings in Large Animals.

ABSTRACT: Decoding laminar information across deep brain structures and cortical regions is necessary in order to understand the neuronal ensembles that represent cognition and memory. Large animal models are essential for translational research due to their gyrencephalic neuroanatomy and significant white matter composition. A lack of long-length probes with appropriate stiffness allowing penetration to deeper structures with minimal damage to the neural interface is one of the major technical limitations to applying the approaches currently utilized in lower order animals to large animals. We therefore tested the performance of multichannel silicon probes of various solutions and designs that were developed specifically for large animal electrophysiology. Neurophysiological signals from dorsal hippocampus were recorded in chronically implanted awake behaving Yucatan pigs. Single units and local field potentials were analyzed to evaluate performance of given silicon probes over time. EDGE-style probes had the highest yields during intra-hippocampal recordings in pigs, making them the most suitable for chronic implantations and awake behavioral experimentation. In addition, the cross-sectional area of silicon probes was found to be a crucial determinant of silicon probe performance over time, potentially due to reduction of damage to the neural interface. Novel 64-channel EDGE-style probes tested acutely produced an optimal single unit separation and a denser sampling of the laminar structure, identifying these research silicon probes as potential candidates for chronic implantations. This study provides an analysis of multichannel silicon probes designed for large animal electrophysiology of deep laminar brain structures, and suggests that current designs are reaching the physical thresholds necessary for long-term (?1 month) recordings with single-unit resolution.

SUBMITTER: Ulyanova AV

PROVIDER: S-EPMC6497800 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Multichannel Silicon Probes for Awake Hippocampal Recordings in Large Animals.

Ulyanova Alexandra V AV Cottone Carlo C Adam Christopher D CD Gagnon Kimberly G KG Cullen D Kacy DK Holtzman Tahl T Jamieson Brian G BG Koch Paul F PF Chen H Isaac HI Johnson Victoria E VE Wolf John A JA

Frontiers in neuroscience 20190426

Decoding laminar information across deep brain structures and cortical regions is necessary in order to understand the neuronal ensembles that represent cognition and memory. Large animal models are essential for translational research due to their gyrencephalic neuroanatomy and significant white matter composition. A lack of long-length probes with appropriate stiffness allowing penetration to deeper structures with minimal damage to the neural interface is one of the major technical limitation ...[more]

PMID: 31080400

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Project description:Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.Significance statementNeuroscience studies often involve simultaneous recordings in a large number of sensors in which a smaller number of dynamic components generate the complex spatio-temporal patterns observed in the data. Current blind source separation techniques produce sub-optimal results and are difficult to interpret because these methods lack an appropriate generative model that can guide both statistical inference and interpretation. Here we describe a novel component analysis method employing a dynamic generative model that can decompose high-dimensional multivariate data into a smaller set of oscillatory components are learned in a data-driven way, with parameters that are immediately interpretable. We show how this method can be applied to neurophysiological recordings with millisecond precision that exhibit oscillatory activity such as electroencephalography and magnetoencephalography.

Dataset Information

Multichannel Silicon Probes for Awake Hippocampal Recordings in Large Animals.

Publications

Multichannel Silicon Probes for Awake Hippocampal Recordings in Large Animals.

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