Chronic neural probe for simultaneous recording of single-unit, multi-unit, and local field potential activity from multiple brain sites

Pothof, Frederick; Bonini, Luca; Lanzilotto, Marco; Livi, Alessandro; Fogassi, Leonardo; Orban, Guy A.; Oliver, Paul; Ruther, Patrick

doi:10.1088/1741-2560/13/4/046006

Cited by 43 publications

(59 citation statements)

References 42 publications

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“…Similarly, the spatial resolution of intracranial macroelectrodes used for human recordings does not allow assessing specific subnuclei responsible for amygdalar responses. This might become possible in the future with the development of systems allowing simultaneous recording of local field potentials and single- or multi-unit responses 45 . Finally, confining our analysis to the insular–amygdalar relations is necessarily a simplification, and this work should be progressively expanded to incorporate other structures active during the same and subsequent time spans.…”

Section: Discussionmentioning

confidence: 99%

Convergence of sensory and limbic noxious input into the anterior insula and the emergence of pain from nociception

Bastuji

Frot

Perchet

et al. 2018

Sci Rep

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Two parallel di-synaptic routes convey nociceptive input to the telencephalon: the spino-thalamic system projecting principally to the posterior insula, and the spino-parabrachial pathway reaching the amygdalar nucleus. Interplay between the two systems underlies the sensory and emotional aspects of pain, and was explored here in humans with simultaneous recordings from the amygdala, posterior and anterior insulae. Onsets of thermo-nociceptive responses were virtually identical in the posterior insula and the amygdalar complex, but no significant functional connectivity was detected between them using coherence analysis. Anterior insular sectors responded with ~30 ms delay relative to both the posterior insula and the amygdala. While intra-insular functional correlation was significant during the whole analysis period, coherence between the anterior insula and the amygdala became significant after 700 ms of processing. Phase lags indicated information transfer initially directed from the amygdalar complex to the insula. Parallel but independent activation of sensory and limbic nociceptive networks appear to converge in the anterior insula in less than one second. While the anterior insula is often considered as providing input into the limbic system, our results underscore its reverse role, i.e., receiving and integrating very rapidly limbic with sensory input, to initiate a perceptual decision on the stimulus ‘painfulness’.

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Section: Discussionmentioning

confidence: 99%

Convergence of sensory and limbic noxious input into the anterior insula and the emergence of pain from nociception

Bastuji

Frot

Perchet

et al. 2018

Sci Rep

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show abstract

“…For data acquisition, current clinical sEEG implants can be modified in a multitude of ways to improve the spatial resolution and target sampling. By maintaining the same shaft size, the contact size and density can be reduced to be able to record local field potentials along the entire length of the shaft (Pothof et al, 2016). Additionally, microwires can be placed at the tip of the shaft for recording single-units (Pothof et al, 2016).…”

Section: Future Directionsmentioning

confidence: 99%

“…By maintaining the same shaft size, the contact size and density can be reduced to be able to record local field potentials along the entire length of the shaft (Pothof et al, 2016). Additionally, microwires can be placed at the tip of the shaft for recording single-units (Pothof et al, 2016). Such modifications are expected to yield significant improvements in BCI decoding performance as observed when using micro-ECoG in comparison to standard clinical ECoG (Slutzky et al, 2010;Wang et al, 2013;Kellis et al, 2016;Muller et al, 2016).…”

Section: Future Directionsmentioning

confidence: 99%

The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions

2020

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Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.

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“…Although some spike-sorting algorithms have been designed to process neural data online, 26,35,36 most methods remain offline or require an offline preprocessing step, precluding their use in online applications such as brain-computer interfaces (BCIs). Moreover, the current availability of very dense arrays of microelectrodes [37][38][39][40][41][42] creates the need for efficient ways to handle the important data flow generated by these devices and in particular to automate spike-sorting processing. 30,31,34,36,43 Ideally, implantable neural interfaces would strongly benefit from fully automatic spike-sorting algorithms compatible with very-low-power hardware implementation for future embedding at the electrode site.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

Bernert

Yvert

2019

Int. J. Neur. Syst.

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Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.

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Chronic neural probe for simultaneous recording of single-unit, multi-unit, and local field potential activity from multiple brain sites

Cited by 43 publications

References 42 publications

Convergence of sensory and limbic noxious input into the anterior insula and the emergence of pain from nociception

Convergence of sensory and limbic noxious input into the anterior insula and the emergence of pain from nociception

The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

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