1,720,980 research outputs found
Pharmacological basis of the use of the root bark of Zizyphus nummularia Aubrev. (Rhamnaceae) as anti-inflammatory agent
No full textBackground: The root bark of Zizyphus nummularia (Rhamnaceae) is traditionally used as an anti-inflammatory agent. The current study aimed to explore the anti-inflammatory activity (in vivo) of a crude ethanolic extract (EE) and the pure identified octadecahydro-picene-2,3,14,15-tetranone (IC) in the root bark of Z. nummularia. IC was further subjected to suitable in vitro and in silico studies to find out the mechanistic pharmacology.
Methods: EE (100 and 200 mg/kg, p.o.) and (IC) (400 and 600 mu g/kg, p.o.) were subjected to in vivo anti-inflammatory assays to evaluate the anti-inflammatory activity and predict the probable mechanism(s) of action. Suitable acute (carrageenan-induced paw edema, arachidonic acid-induced ear edema, xylene-induced ear edema) and chronic (cotton pellet granuloma) models were employed to investigate in vivo the anti-inflammatory activity. Based on in vivo observation, IC was further subjected to in vitro assays to estimate the inhibition of nitric oxide (NO), prostaglandin-E2 (PGE-2) and tumor necrosis factor-alpha (TNF-alpha) production in PBS stimulated RAW 264.7 cells. Based on the observation of in vitro studies, finally, ADME prediction and molecular docking studies of IC were performed for better understanding of interaction of IC with TNF-alpha.
Results: Oral administration of EE (100 and 200 mg/kg) exhibited significant inhibition of carrageenan (p < 0.05) and arachidonic acid (p < 0.05) induced oedema, and the reduced the granuloma tissue formation (p < 0.05) in experimental mice. IC (400 and 600 mu g/kg, p.o.) exhibited significant (p < 0.01) inhibition of carrageenan, xylene and arachidonic acid-induced edema, and reduced the granuloma tissue formation. In in vitro assays, IC caused a concentration-dependent inhibition of LPS stimulated NO (up to similar to 67.4 % at 50 mu M) and TNF-alpha (similar to 84.5 % at 50 mu M) production. However, the PGE-2 inhibition did not follow dose dependent pattern. Based on in vitro observations, the molecular docking has been performed on the basis of interaction with TNF-alpha. In in silico studies, it was observed that IC showed hydrogen bonding with GLN 47 amino acid residue of TNF-alpha protein.
Conclusions: IC possibly produces anti-inflammatory activity through inhibition of TNF-a and NO production
Characterization of Brain Signals Across Scales Using Temporally Modulated Visual Stimuli
No full textElectrical signals from the brain can be recorded at several different scales, ranging
from spiking activity to local field potential (LFP) in animals to scalp
electroencephalogram (EEG) in humans. Each signal represents a progressively larger
level of neural integration and is thought to reflect different attributes of the underlying
neural population. In this work, we characterized the relationships between these
signals using a common paradigm of temporally modulated visual stimuli, which are
known to engage the underlying neural activity in distinct ways.
In the first part, we asked whether the LFP reflects the input or the output of a cortical
area around the recording microelectrode. Using chronically implanted arrays in the
primary visual cortex (V1) of awake behaving macaque monkeys, we recorded spikes
and LFP in response to drifting sinusoidal gratings of varying temporal frequency.
Previous reports have shown that the primate lateral geniculate nucleus (LGN) which
projects to V1 has a higher temporal frequency cutoff than V1, such that at higher drift
rates, visual input to V1 persists but V1 output ceases, permitting partial dissociation.
Using an adaptive decomposition technique called Matching Pursuit (MP) to generate
the time-frequency spectrum of the LFP at high resolution, we show that distinct
frequency bands in the V1 LFP are tuned differently to temporal frequency, such that
the lower frequencies of the LFP (up to ~50 Hz) likely represent the input, the gamma
band of the LFP (~30-80 Hz) likely represents local cortical processing and the
high-gamma band (above ~80 Hz) represents the output.
In the second part, we asked whether steady-state visually evoked potential (SSVEP)
tags used in “frequency tagging” EEG studies of visual attention are independent or not.
In these studies, it is observed that paying attention to one stimulus increases the
amplitude of the SSVEP at the tagging frequency of that stimulus and simultaneously
decreases the SSVEP amplitude at the unattended frequency. This has been explained
using a “push-pull” or “spotlight” mechanism of attention. However, it is unclear whether
changes in SSVEP amplitude could arise due to the presence of competing temporal
frequencies without any top-down cognitive modulation, and whether this depends on
the separation between the tagging frequencies or the features of the stimuli such as
their orientations. To address these questions, we recorded spikes and LFP from V1 as
well as EEG from awake behaving macaque monkeys while they passively fixated plaid
stimuli whose components counterphased at different temporal frequencies. We
observed reliable SSVEP response suppression, but the suppression was much greater
for lower competing temporal frequencies than for higher ones. Further, the strength of
this asymmetry depended on the relative orientation difference between the plaid
components, with similar orientations causing significant suppression and orthogonal
orientations causing little or no suppression.
In the third part, we show that the well-known normalization model, adapted to SSVEP
responses, provides a good account of temporal frequency suppression as a function of
the difference in temporal frequencies and orientation. Our results provide evidence for
interaction between temporal frequencies independent of effects of cognitive modulation and suggest exercising caution in the interpretation of frequency tagging studies
Effect of Stimulus Normalization and Visual Attention at multiple scales of Neural Integration
No full textThe effect of visual attention on neural signals has been extensively studied using various techniques such as macaque neurophysiology and human electro/magneto encephalogram (EEG/MEG). Depending on the technique, different neural measures are typically used for studying attention. For example, in neurophysiology experiments involving macaques, many studies have focused on the modulation in spiking activity or the change in oscillatory power at different frequency bands such as alpha (8-12 Hz) or gamma (30-80 Hz) with attention, or the change in the relationship of spikes with these oscillations. In contrast, human EEG studies, in addition to studying alpha and gamma modulation, often use flickering stimuli that produce a specific neural response called steady-state visually evoked potential (SSVEP), which is also modulated by attention. However, due to the differences in stimuli and task paradigms in such studies, it is difficult to determine the effectiveness of these various neural measures for capturing attentional modulation. To address this, we designed a task paradigm which included both static and counterphase flickering stimuli to generate all the relevant neural measures (alpha/gamma power as well as SSVEPs) under identical recording conditions, which allowed us to compare their effectiveness in studying attention.
Since several reports suggest that attention modulates these neural measures through a canonical neural mechanism called normalization, in the first study of this thesis, we varied the normalization strength parametrically as a proxy for attentional modulation and tested its effect on various neural measures. We manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to two female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex (area V1). We quantified the modulation in MUA, gamma (32-80 Hz), high-gamma (104-248 Hz) power, and SSVEP. Even under similar conditions, normalization strength was different for the four measures; and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model, modified for population responses by varying the tuned normalization parameter and semi-saturation constant.
In the second part of the thesis, we tested the predictions of the gamma phase coding hypothesis in the context of stimulus contrast and visual attention. The gamma phase coding hypothesis posits that the intensity of the incoming stimulus is encoded in the position of the spike relative to the gamma rhythm. Using chronically implanted microelectrode arrays in the primary visual cortex of macaques engaged in an attention task while presenting stimuli of varying contrasts, we tested whether the phase of the gamma rhythm relative to spikes varied as a function of stimulus contrast and attentional state. We analyzed spikes and LFP from different electrodes and found a weak but significant effect of attention, but not stimulus contrast, on the gamma phase relative to spikes. Although we found a significant effect of attention, we argue that a small magnitude of phase shift as well as the dependence of phase angles on gamma power and center frequency, limits the potential role of gamma in phase coding in area V1.
In the third part of the thesis, we recorded EEG signals from 26 human participants while they were engaged in an attention task and analyzed alpha and gamma band powers for both static and flickering stimuli and SSVEP power for flickering stimuli. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, we found that non-foveal stimuli produced weak gamma despite various stimulus optimizations and therefore showed a negligible effect of attention although the same participants showed robust gamma activity for full-screen gratings. Thus, alpha and SSVEP won over gamma in capturing attentional modulation in human EEG. This result was in contrast to the findings of a comparable study in monkeys, where gamma and alpha won over SSVEPs. This study highlights the effectiveness of various neural measures in studying visual spatial attention and further implicates their usefulness in decoding behavior and attentional state in humans.DBT-Wellcome Trust India Alliance (Grant IA/S/18/2/504003), Tata Trusts, DBT-IISc Partnership Programm
Estimation of the spatial spread of brain signals at multiple scales
No full textSpatial spread of a particular brain signal can be defined as the area of the cortical tissue around
the recording electrode that contributes to the electrical activity recorded by the electrode. More
specifically, assuming brain signals to be a weighted sum of electrical activity of a pool of
neurons, spatial spread represents the spatial weighting function that primarily depends on the
properties of the recording electrode such as its size, impedance and location as well as some
properties of the brain tissue such as its conductance and filtering characteristics. Different
signals, depending on the frequency content, represent different types of neuronal activity. For
example, multi-unit activity (MUA), obtained by band-pass filtering the signal recorded from
a microelelctrode (tip diameter of a few microns) primarily represents the weighted sum of
action potentials, while the local field potential (LFP), obtained by low-pass filtering the same
signal, primarily represents summed synaptic activity. Another signal is Electrocorticogram
(ECoG), obtained by low-pass filtering the signal obtained from a macro-electrode (diameter
of 2.3 mm) placed subdurally on the surface of the cortex of epileptic patients for localization
of the seizure focus. These ECoG signals are used to determine the brain area that is responsible
for seizures, which is subsequently surgically removed. Accurate estimation of the spatial
spread of ECoG is therefore extremely important from a clinical perspective. Similarly,
accurate estimation of the spatial spread of LFP is important from a basic science perspective,
since these signals are now routinely used to study cognition and behavior, and also in braincomputer
interfacing applications. However, the spatial spread of ECoG is unknown, and that
of LFP is highly controversial. In the first two studies in this thesis, we investigate the spatial
spreads of LFP and ECoG.
Brain signals are often analyzed in the spectral domain where the slope of the power spectral
density (PSD), as well as oscillations that are observed as peaks in the spectra, can reveal
important information about the neural network. For example, gamma oscillations observed in
the 30-70 Hz frequency range has been associated with several high-level cognitive functions
such as attention, memory, perception etc. Further, the high-gamma activity observed as a
broadband in 60-250 Hz frequency range has shown to be correlated with the spiking activity.
These different signatures provide a robust measure to understand the brain dynamics at
different recording levels. In the third study, we compare the tuning properties of gamma
oscillations and high-gamma activity for different stimulus properties in LFP and ECoG.
In the first study, we examined whether different frequencies of LFP spread differently.
Recording from a microelectrode array implanted in the primary visual cortex (V1) of two
macaques, we estimated the LFP spread as a function of frequency. We found that LFP spread
is neither “low-pass” nor ‘all-pass” as suggested by previous studies but “band-pass” with
frequencies in the high-gamma (60-150 Hz) range spreading more than both lower (20-40 Hz)
and higher (>250 Hz) frequencies. Further, we found that this increase in high-gamma range is
mirrored by an increase in the phase coherency across neighboring sites in the same frequency
range.
Spatial spreads can be estimated by measuring the receptive field (RF) and multiplying it with
the cortical magnification factor, but this method overestimates the spatial spread because RF
size gets inflated due to several factors such as eye jitter, stimulus size and RF scatter. This
issue can be partially addressed by comparing the RFs of two measures (such as LFP and multiunit
activity). Therefore, in the second study, we estimated the spatial spread of ECoG by
simultaneously recording LFP and ECoG from the primary visual cortex (V1) of three
behaving monkeys using a specialized hybrid grid which consists of both ECoG electrodes and
a microelectrode array. We simultaneously mapped the RF responses of MUA, LFP, and ECoG
at several cortical sites and found that spatial spread of ECoG is surprisingly local (standard
deviation of ~1.5 millimeters, or a diameter of ~3 mm), only ~3 times the spread of the LFP,
even though the size of the electrode is several hundred times larger than the microelectrode.
Further, using a completely different approach, we estimated the spatial spread of ECoG by
comparing the slope of the PSD of LFP and ECoG for spontaneous activity (no stimulus
condition). We found that the slope of the ECoG was much steeper than LFP in the 20-100 Hz
frequency range. Next, using a simple model based on linear superposition, we simulated the
ECoG signal by averaging LFP signals over a progressively larger set of electrodes. We found
that around ~50 LFP electrodes that correspond to a 7x7 grid when averaged had the similar
slope as ECoG. The estimated spread in millimeters- 7 x 400 μm (inter-microelectrode
distance) = 2.8 mm was remarkably similar to the first approach.
Finally, in the last part, as an indirect measure, we investigated the spatial extent of ECoG by
comparing the high-gamma activity observed in LFP and ECoG signals. We simultaneously
recorded the LFP and ECoG signals for six different stimulus radii and computed the change
in high-gamma power as a function of stimulus size. We found that tuning curve for LFP and
ECoG were similar, with a maximum ECoG high-gamma power for the stimulus of 0.3° radii,
suggesting local origins of ECoG. Further, we compared the orientation preference of LFP and
ECoG for gamma oscillations. The preferred orientation for gamma oscillations in ECoG was
similar to the gamma oscillation in LFP even though the ECoG electrodes were widely
distributed in the cortex (center- to-center distance of 10 mm). This suggests that orientation
tuning of gamma is not location specific but monkey specific.
Overall, our results suggest that ECoG is a local signal which can provide a useful tool for
clinical purposes, cognitive neuroscience and brain-machine-interface application
Role of gamma oscillations in processing of natural stimuli
No full textGamma oscillations (~30-80 Hz) are a prominent signature of electrophysiological signals recorded from the brain with proposed roles in various cognitive functions such as attention and visual perception. Although gamma is induced strongly in the visual cortex by visual stimuli such as achromatic gratings, bars and gabors, whether natural stimuli also generate strong gamma is a debated issue. Unlike small achromatic gratings, natural stimuli cover a large visual area, are colorful, and have discontinuities along many dimensions. In this thesis I have studied the effect of these three important aspects of natural stimuli - size, chromaticity and stimulus discontinuities - on gamma oscillations in the local field potential (LFP) recorded from monkey primary visual cortex (area V1), while the monkeys were shown experimentally designed visual stimuli.
In the first part, we show that large grating stimuli induce a second gamma oscillation (~25-45 Hz, termed as slow gamma) in addition to the traditionally known gamma oscillation (~45-70 Hz, fast gamma) in the LFP with distinct tuning to stimulus size, orientation and contrast. Fast gamma have a shorter latency, stronger spike-entrainment and are coherent over shorter distances than the slow gamma, suggesting that the two gamma oscillations may be involved in processing across different spatial ranges.
In the second part, we show that colored stimuli generate gamma oscillations of extremely high magnitude in V1 LFP, far exceeding the gamma generated by optimally tuned achromatic gratings. Further, they are the strongest for reddish (long-wavelength) hues, depend critically on the purity (saturation) of the hue and are highly correlated with only the positive L-M cone contrast generated by the color stimuli, suggesting that gamma could be a marker of specific mechanisms underlying this computation in V1.
In the concluding part, we show that gamma oscillations are very sensitive to stimulus discontinuities, reducing drastically in the presence of small discontinuities in grating orientation, spatial phase, contrast or an annular cut. Our results suggest that gamma may behave like a resonant phenomenon that depends critically on the excitation-inhibition balance in the neuronal network.
These results may give useful insights into mechanisms of gamma oscillations, their role in natural vision and chromatic and achromatic visual processing in the primate primary visual cortex
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Representation of Natural Stimuli in Neural Signals across Scales and Frequencies
No full textNeural activity from the brain can be recorded at different scales using a variety of electrodes, which vary in their resolution, cortical spread and invasiveness. The electroencephalogram (EEG) is recorded from the scalp, electrocorticogram (ECoG) from the cortical surface, while microelectrodes are inserted into the cortex which record local field potentials (LFP) and spiking activity in animals. These signals have been used to drive Brain Machine Interfaces with varying degrees of success, but an objective comparison of their efficacy has not been performed. A sensory system such as the visual cortex can be used as a model to compare the information available across these scales.
In this work, using a customized hybrid array containing both micro and ECoG electrodes, we recorded simultaneous signals from up to four scales (spikes, LFP, ECoG and EEG) from the visual cortex of two monkeys while they viewed a large array of natural images as well as parametric stimuli such as gratings. Complementary information theoretic and decoding approaches were used to quantify the information content about naturalistic and parametric stimuli at each of the scales. We found that the information content in ECoG exceeded all other measures, including spiking activity. Further, the maximum information content was found in the gamma (30-80 Hz) frequency range of the signals.
Several theories have been proposed to explain a potential role of gamma oscillations in the coding and visual information and its communication across brain areas. We instead tested whether gamma oscillations elicited by natural images could be explained simply based on the local image properties. To do this, first the gamma response for multiple visual features (such as orientation, spatial frequency, size, contrast, hue, saturation etc.) needs to be determined. Though the dependence of gamma on such features has been well studied when presented alone, how these features jointly affect gamma has not been investigated in detail. We found that gamma responses to a pair of features were largely separable in both LFP and ECoG. Based on this, we developed a multiplicative model in which the response to multiple features is simply a scaled product of individual features, and used it to predict the gamma responses to parametric gratings and chromatic patches.
Finally, we built an image computable model to predict gamma responses to complex natural images by extracting simple features from them and incorporating the previously learnt dependencies of gamma response. Our model was able to estimate the gamma responses to both chromatic and grayscale images.
Overall, the comparative study of information across scales can help in designing more accurate and reliable BMIs, while the predictability of responses can be used to increase the precision of BMIs. The prediction of gamma responses based on low level features also offers a simple “null” model based on local image properties, against which more advanced theories of gamma based on predictive coding or selective communication can be tested
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Analysis of Local Field Potential and Gamma Rhythm Using Matching Pursuit Algorithm
Full text linkSignals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. These signals also have transient structures related to spiking or sudden onset of a stimulus, which have a duration not exceeding tens of milliseconds. Further, brain signals are highly non-stationary because both behavioral state and external stimuli can change over a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal. In Chapter 2, we describe a multi-scale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both sharp stimulus-onset transient and sustained gamma rhythm in local field potential recorded from the primary visual cortex.
Gamma rhythm (30 to 80 Hz), often associated with high-level cortical functions, has been proposed to provide a temporal reference frame (“clock”) for spiking activity, for which it should have least center frequency variation and consistent phase for extended durations. However, recent studies have proposed that gamma occurs in short bursts and it cannot act as a reference. In Chapter 3, we propose another gamma duration estimator based on matching pursuit (MP) algorithm, which is tested with synthetic brain signals and found to be estimating the gamma duration efficiently. Applying this algorithm to real data from awake monkeys, we show that the median gamma duration is more than 330 ms, which could be long enough to support some cortical computations
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