scholarly journals A Predictive Model of Anesthesia Depth Based on SVM in the Primary Visual Cortex

2013 ◽  
Vol 7 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Li Shi ◽  
Xiaoyuan Li ◽  
Hong Wan
Keyword(s):  
Visual Cortex ◽  
Predictive Model ◽  
Primary Visual Cortex ◽  
Complexity Analysis ◽  
Support Vector ◽  
Field Potentials ◽  
Anesthetic Depth ◽  
Precise Analysis ◽  
Novel Model ◽  
Anesthesia Depth

In this paper, a novel model for predicting anesthesia depth is put forward based on local field potentials (LFPs) in the primary visual cortex (V1 area) of rats. The model is constructed using a Support Vector Machine (SVM) to realize anesthesia depth online prediction and classification. The raw LFP signal was first decomposed into some special scaling components. Among these components, those containing higher frequency information were well suited for more precise analysis of the performance of the anesthetic depth by wavelet transform. Secondly, the characteristics of anesthetized states were extracted by complexity analysis. In addition, two frequency domain parameters were selected. The above extracted features were used as the input vector of the predicting model. Finally, we collected the anesthesia samples from the LFP recordings under the visual stimulus experiments of Long Evans rats. Our results indicate that the predictive model is accurate and computationally fast, and that it is also well suited for online predicting.

scholarly journals Visual epidural field potentials possess high functional specificity in single trials

2019 ◽  
Vol 122 (4) ◽  
pp. 1634-1648 ◽  
Author(s):  
Benjamin Fischer ◽  
Andreas Schander ◽  
Andreas K. Kreiter ◽  
Walter Lang ◽  
Detlef Wegener
Keyword(s):  
Visual Cortex ◽  
Minimally Invasive ◽  
Primary Visual Cortex ◽  
Neuronal Activity ◽  
Large Scale ◽  
Field Potentials ◽  
Operating Characteristics ◽  
Invasive Approach ◽  
Spatial Selectivity ◽  
Minimal Invasiveness

Recordings of epidural field potentials (EFPs) allow neuronal activity to be acquired over a large region of cortical tissue with minimal invasiveness. Because electrodes are placed on top of the dura and do not enter the neuronal tissue, EFPs offer intriguing options for both clinical and basic science research. On the other hand, EFPs represent the integrated activity of larger neuronal populations and possess a higher trial-by-trial variability and a reduced signal-to-noise ratio due the additional barrier of the dura. It is thus unclear whether and to what extent EFPs have sufficient spatial selectivity to allow for conclusions about the underlying functional cortical architecture, and whether single EFP trials provide enough information on the short timescales relevant for many clinical and basic neuroscience purposes. We used the high spatial resolution of primary visual cortex to address these issues and investigated the extent to which very short EFP traces allow reliable decoding of spatial information. We briefly presented different visual objects at one of nine closely adjacent locations and recorded neuronal activity with a high-density epidural multielectrode array in three macaque monkeys. With the use of receiver operating characteristics (ROC) to identify the most informative data, machine-learning algorithms provided close-to-perfect classification rates for all 27 stimulus conditions. A binary classifier applying a simple max function on ROC-selected data further showed that single trials might be classified with 100% performance even without advanced offline classifiers. Thus, although highly variable, EFPs constitute an extremely valuable source of information and offer new perspectives for minimally invasive recording of large-scale networks. NEW & NOTEWORTHY Epidural field potential (EFP) recordings provide a minimally invasive approach to investigate large-scale neural networks, but little is known about whether they possess the required specificity for basic and clinical neuroscience. By making use of the spatial selectivity of primary visual cortex, we show that single-trial information can be decoded with close-to-perfect performance, even without using advanced classifiers and based on very few data. This labels EFPs as a highly attractive and widely usable signal.

scholarly journals Dynamic communication of attention signals between the LGN and V1

2018 ◽  
Vol 120 (4) ◽  
pp. 1625-1639 ◽  
Author(s):  
Vanessa L. Mock ◽  
Kimberly L. Luke ◽  
Jacqueline R. Hembrook-Short ◽  
Farran Briggs
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
Visual Information ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Information Communication ◽  
Attentional Modulation ◽  
Visual Thalamus ◽  
The Impact

Correlations and inferred causal interactions among local field potentials (LFPs) simultaneously recorded in distinct visual brain areas can provide insight into how visual and cognitive signals are communicated between neuronal populations. Based on the known anatomical connectivity of hierarchically organized visual cortical areas and electrophysiological measurements of LFP interactions, a framework for interareal frequency-specific communication has emerged. Our goals were to test the predictions of this framework in the context of the early visual pathways and to understand how attention modulates communication between the visual thalamus and primary visual cortex. We recorded LFPs simultaneously in retinotopically aligned regions of the visual thalamus and primary visual cortex in alert and behaving macaque monkeys trained on a contrast-change detection task requiring covert shifts in visual spatial attention. Coherence and Granger-causal interactions among early visual circuits varied dynamically over different trial periods. Attention significantly enhanced alpha-, beta-, and gamma-frequency interactions, often in a manner consistent with the known anatomy of early visual circuits. However, attentional modulation of communication among early visual circuits was not consistent with a simple static framework in which distinct frequency bands convey directed inputs. Instead, neuronal network interactions in early visual circuits were flexible and dynamic, perhaps reflecting task-related shifts in attention. NEW & NOTEWORTHY Attention alters the way we perceive the visual world. For example, attention can modulate how visual information is communicated between the thalamus and cortex. We recorded local field potentials simultaneously in the visual thalamus and cortex to quantify the impact of attention on visual information communication. We found that attentional modulation of visual information communication was not static, but dynamic over the time course of trials.

scholarly journals Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information

2008 ◽  
Vol 28 (22) ◽  
pp. 5696-5709 ◽  
Author(s):  
A. Belitski ◽  
A. Gretton ◽  
C. Magri ◽  
Y. Murayama ◽  
M. A. Montemurro ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
Visual Information ◽  
Local Field Potentials ◽  
Low Frequency ◽  
Field Potentials

scholarly journals Response Properties of Local Field Potentials and Neighboring Single Neurons in Awake Primary Visual Cortex

2012 ◽  
Vol 32 (33) ◽  
pp. 11396-11413 ◽  
Author(s):  
R. Lashgari ◽  
X. Li ◽  
Y. Chen ◽  
J. Kremkow ◽  
Y. Bereshpolova ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Single Neurons ◽  
Response Properties

Components of field potentials evoked by white matter stimulation in isolated slices of primary visual cortex: spatial distributions and synaptic order

1990 ◽  
Vol 64 (5) ◽  
pp. 1484-1501 ◽  
Author(s):  
R. B. Langdon ◽  
M. Sur
Keyword(s):  
Visual Cortex ◽  
White Matter ◽  
Synaptic Transmission ◽  
Primary Visual Cortex ◽  
Field Potential ◽  
Population Spike ◽  
Spatial Distributions ◽  
Field Potentials ◽  
Layer I ◽  
Cell Somata

1. We have recorded profiles of the spatial distributions of extracellular field potentials in transverse slices of rat primary visual cortex. Responses were evoked by electrical stimulation near the white matter/layer VI border and sampled from layers I to V along the radial axis orthogonal to the laminae and intersecting the stimulation site ("on-beam" recording). To assess the activity of "horizontal" connections, we also recorded profiles along axes parallel to the cortical lamination ("off-beam" recording), usually in layer III. Overall, our goal was to extend understanding of the physiology and organization of neocortical circuitry and to provide a basis for comparisons of data from different experiments and experimenters when neocortical field potentials are used in studies of plasticity and pharmacology. 2. Responses were highly specific with respect to the cortical layers. We distinguish four major components: two kinds of population spike ("S1" and "S2") and two slower waveforms ("W1" and "W2"). The latter appear to represent flow of current in apical dendrites of the supragranular layers. Component W1, the earliest slow component, is a synaptically driven field potential dipole that is positive in layer I and negative in layer II. Based on estimates of current source densities (CSDs), we attribute this to entry of depolarizing current into dendrites and/or cell somata in layer II, ascending intradendritic current, and passive depolarization of inactive dendritic membrane in layer I. Component W1 rises during the 20 ms after stimulation and falls during the 50-100 ms thereafter. Component W2 is also positive in layer I but maximally negative in layer III. It rises for approximately 100 ms after stimulation and decays during the following 400-800 ms. 3. Component S1 does not depend on synaptic transmission because it persists during the application of glutamate receptor antagonists or medium that is low in Ca2+. This component is largest in layer III, radial to the site of stimulation. There, it is a negative deflection, typically 1-2 mV in amplitude and lasting roughly 2 ms, with a latency to peak between 2 and 4.5 ms. Component S1 is most likely a population spike due to synchronized firing of cell somata activated antidromically via unmyelinated efferent axons. 4. Component S2 is a short (less than 20 ms) burst of population spikes specifically in layer III. Individual S2 spikes closely resemble S1 spikes, and we propose that the same neuronal population generates both. However, S2 spikes require glutamatergic synaptic transmission.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Effect of Cross-Orientation Normalization on Different Neural Measures in Macaque Primary Visual Cortex

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Aritra Das ◽  
Supratim Ray
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Evoked Potential ◽  
Population Level ◽  
Field Potentials ◽  
Similar Stimulus ◽  
Brain Areas ◽  
Population Responses ◽  
Divisive Normalization ◽  
High Gamma

Abstract Divisive normalization is a canonical mechanism that can explain a variety of sensory phenomena. While normalization models have been used to explain spiking activity in response to different stimulus/behavioral conditions in multiple brain areas, it is unclear whether similar models can also explain modulation in population-level neural measures such as power at various frequencies in local field potentials (LFPs) or steady-state visually evoked potential (SSVEP) that is produced by flickering stimuli and popular in electroencephalogram studies. To address this, we manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to 2 female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex and quantified the modulation in MUA, gamma (32–80 Hz), high-gamma (104–248 Hz) power, as well as SSVEP. Even under similar stimulus conditions, normalization strength was different for the 4 measures and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model that was modified for population responses, by varying the tuned normalization parameter and semisaturation constant. Our results show that different neural measures can reflect the effect of stimulus normalization in different ways, which can be modeled by a simple normalization model.

Sign in / Sign up

Export Citation Format

Share Document