Comparison of Recordings from Microelectrode Arrays and Single Electrodes in the Visual Cortex

Kelly, Ryan C.; Ma, Smith; Samonds, Jason M.; Kohn, Adam; Bonds, A. B.; Movshon, J. Anthony; Lee, Tai Sing

doi:10.1523/jneurosci.4906-06.2007

Cited by 184 publications

(142 citation statements)

References 15 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…In particular, if ictogenesis is a rather spatially local mechanism in the seizure onset zone, electrode contacts need to be abundant enough to cover a large volume (covering the seizure onset zone) and able to record from small enough volume in order to pick up any preictal change. Such electrode contacts do exist in the form of micro-contacts (Kelly et al, 2007;Stead et al, 2010) and studies on seizure prediction analysing data recorded with these electrodes are expected to provide answers to important questions on seizure predictability.…”

Section: Discussionmentioning

confidence: 99%

Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity

Gadhoumi

Lina

Gotman

2013

Clinical Neurophysiology

View full text Add to dashboard Cite

Objectives-In patients with intractable epilepsy, predicting seizures above chance and with clinically acceptable performance has yet to be demonstrated. In this study, an intracranial EEGbased seizure prediction method using measures of similarity with a reference state is proposed.Methods-1565 h of continuous intracranial EEG data from 17 patients with mesial temporal lobe epilepsy were investigated. The recordings included 175 seizures. In each patient the data was split into a training set and a testing set. EEG segments were analyzed using continuous wavelet transform. During training, a reference state was defined in the immediate preictal data and used to derive three features quantifying the discrimination between preictal and interictal states. A classifier was then trained in the feature space. Its performance was assessed using testing set and compared with a random predictor for statistical validation.Results-Better than random prediction performance was achieved in 7 patients. The sensitivity was higher than 85%, the warning rate was less than 0.35/h and the proportion of time under warning was less than 30%.Conclusion-Seizures are predicted above chance in 41% of patients using measures of state similarity.Significance-Sensitivity and specificity levels are potentially interesting for closed-loop seizure control applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity

Gadhoumi

Lina

Gotman

2013

Clinical Neurophysiology

View full text Add to dashboard Cite

show abstract

“…Data analysis was carried out in MATLAB (Mathworks). Spikes were sorted offline (56)(57)(58)(59). All recorded single units (n = 823) were classified as putative interneurons (pINs) or putative principal cells (pPCs) based on action potential width ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Coding principles of the canonical cortical microcircuit in the avian brain

Calabrese

Woolley

2015

Proc. Natl. Acad. Sci. U.S.A.

113

View full text Add to dashboard Cite

Mammalian neocortex is characterized by a layered architecture and a common or "canonical" microcircuit governing information flow among layers. This microcircuit is thought to underlie the computations required for complex behavior. Despite the absence of a six-layered cortex, birds are capable of complex cognition and behavior. In addition, the avian auditory pallium is composed of adjacent information-processing regions with genetically identified neuron types and projections among regions comparable with those found in the neocortex. Here, we show that the avian auditory pallium exhibits the same information-processing principles that define the canonical cortical microcircuit, long thought to have evolved only in mammals. These results suggest that the canonical cortical microcircuit evolved in a common ancestor of mammals and birds and provide a physiological explanation for the evolution of neural processes that give rise to complex behavior in the absence of cortical lamination.functional connectivity | cortex evolution | songbird | sensory coding T he cognitive abilities of birds suggest that the avian brain contains sophisticated information-processing circuitry. Recent studies have demonstrated that corvids, such as crows, rooks, and jays, exhibit innovative tool manufacture (1), referential gesturing (2), causal reasoning (3, 4), mirror self-recognition (5), and planning for future needs using recent experience (6). Other birds can perform complex pattern recognition (7), long-term recollection (8), and numerical discrimination (9), paralleling the performance of primates. Songbirds, such as zebra finches (Taeniopygia guttata, the species studied here), learn to produce and recognize complex vocalizations for social communication, with numerous parallels to speech learning and perception (10).Although cognitive skills in birds and nonhuman mammals are often comparable, avian and mammalian palliums exhibit very different anatomical organization. Although the organization of ascending sensory pathways and subpallial structures are similar in avian and mammalian brains (11), the avian pallium (referred to hereafter as the cortex) lacks the distinctive six-layered structure of the mammalian neocortex (12). Instead of a laminar structure, the avian cortex is composed of interconnected nuclei and bands of neurons that form distinct processing regions. The avian primary auditory cortex (avian A1) is organized into adjacent processing regions, collectively called the field L/CM (caudal mesopallium) complex, that are stacked in a dorsorostral to ventrocaudal orientation (13, 14) ( Fig. 1 and Fig. S1). Regions of avian A1 are delineated by differences in cytoarchitecture and connectivity (13, 15) (SI Materials and Methods) and are organized into superficial (field L1 and lateral caudal mesopallium, CML), intermediate (field L2a and -b), and deep (field L3) regions, similar to neocortical layers. A posterior region, the caudal nidopallium (NC) is referred to as "secondary" cortex here. Fig. 2A shows a schemati...

show abstract

“…Note that variation in electrode impedance across sessions is not likely to meaningfully affect our results, as intracranial signals (Nelson and Pouget 2010) and scalp EEG (Kappenman and Luck 2010) have been shown to be impacted negligibly by electrode impedance if the input impedance to the amplifier is much greater, as it was in our case. These waveform segments were sorted with an automated clustering algorithm (Shoham et al 2003) followed by manual refinement with custom MATLAB software (Kelly et al 2007; available at http://www.smithlab.net/spikesort.html), taking into account the waveform shapes and interspike interval distributions. After sorting, we calculated the signal-to-noise ratio (SNR) of each candidate unit as the ratio of the average waveform amplitude to the standard deviation of the waveform noise (Kelly et al 2007).…”

Section: Methodsmentioning

confidence: 99%

“…These waveform segments were sorted with an automated clustering algorithm (Shoham et al 2003) followed by manual refinement with custom MATLAB software (Kelly et al 2007; available at http://www.smithlab.net/spikesort.html), taking into account the waveform shapes and interspike interval distributions. After sorting, we calculated the signal-to-noise ratio (SNR) of each candidate unit as the ratio of the average waveform amplitude to the standard deviation of the waveform noise (Kelly et al 2007). Candidates with an SNR below 2.5 were discarded.…”

Section: Methodsmentioning

confidence: 99%

Stimulus-dependent spiking relationships with the EEG

Snyder

2015

Journal of Neurophysiology

View full text Add to dashboard Cite

Snyder AC, Smith MA. Stimulus-dependent spiking relationships with the EEG. J Neurophysiol 114: 1468 -1482. First published June 24, 2015 doi:10.1152/jn.00427.2015The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. EEG; local field potential; spiking activity ELECTROENCEPHALOGRAPHY (EEG) has been a boon to human neuroscience. Because EEG is noninvasive, it is one of the few methods that can be used to measure human brain activity in both health and disease. Moreover, EEG has advantages over other neuroimaging methods. The cost is relatively low, compared with approaches such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG); there is no ionizing radiation, as with positron emission tomography (PET); the materials are portable (even usable with actively moving subjects; De Sanctis et al. 2012;Makeig et al. 2009), unlike nearly all alternatives; and the temporal resolution is exquisitely fine, unlike functional near-infrared spectroscopy (fNIRS) and fMRI. EEG covers a wide range of neurophysiological applications, from diagnosis of brain stem lesions to basic research on complex cognition.The principal limitation of EEG is spatial resolution. Typically, EEG is useful for implicating activity in parts of the brain on the order of a cubic centimeter (Sejnowski et al. 2014). The computational machinations of the neurons within ...

show abstract

Comparison of Recordings from Microelectrode Arrays and Single Electrodes in the Visual Cortex

Cited by 184 publications

References 15 publications

Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity

Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity

Coding principles of the canonical cortical microcircuit in the avian brain

Stimulus-dependent spiking relationships with the EEG

Contact Info

Product

Resources

About