Frequency-Dependent Attentional Modulation of Local Field Potential Signals in Macaque Area MT

Khayat, Paul S.; Niebergall, Robert; Martinez‐Trujillo, Julio

doi:10.1523/jneurosci.0404-10.2010

Cited by 50 publications

(55 citation statements)

References 54 publications

(89 reference statements)

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“…A recent study (Khayat et al 2010) showed that LFP spectral power in the beta band is attenuated when attention is directed to MT (see also Harris and Thiele 2011;Lee et al 2013). It could account for several DP trends if, after detecting the motion pulse, the monkey focused more attention on the visual stimulus.…”

Section: Discussionmentioning

confidence: 99%

“…A change in attention could also explain why high-gamma LFP-DP increased late in the trial, as this band is known to respond to changes in attention (Fries et al 2001;Khayat et al 2010;Rotermund et al 2009;Womelsdorf et al 2006; but see Ray and Maunsell 2011a) and possibly perceptual state (Panagiotaropoulos et al 2012). Since the beta and gamma bands respond differently to attention signals in MT (Khayat et al 2010) and because late beta LFP-DP had no pairwise correlation with high-gamma LFP-DP (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Gamma-band LFPs are thought to carry stimulus information (Belitski et al 2010) and reflect the synaptic inputs from earlier stages (Khawaja et al 2009) and the activity of local neurons (Ray and Maunsell 2011a), while beta-band LFPs may reflect intercortical feedback (Saalmann et al 2007). Correspondingly, attention increases gamma LFP power in a manner similar to that of local spiking activity, while beta power is attenuated (Khayat et al 2010). Although LFPs contain some components due to spike activity, they also allow us to observe nonspike components (e.g., postsynaptic potentials).…”

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confidence: 96%

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Dynamics of the functional link between area MT LFPs and motion detection

Smith

Beliveau

Schoen

et al. 2015

Journal of Neurophysiology

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-The evolution of a visually guided perceptual decision results from multiple neural processes, and recent work suggests that signals with different neural origins are reflected in separate frequency bands of the cortical local field potential (LFP). Spike activity and LFPs in the middle temporal area (MT) have a functional link with the perception of motion stimuli (referred to as neural-behavioral correlation). To cast light on the different neural origins that underlie this functional link, we compared the temporal dynamics of the neural-behavioral correlations of MT spikes and LFPs. Wide-band activity was simultaneously recorded from two locations of MT from monkeys performing a threshold, two-stimuli, motion pulse detection task. Shortly after the motion pulse occurred, we found that high-gamma (100 -200 Hz) LFPs had a fast, positive correlation with detection performance that was similar to that of the spike response. Beta (10 -30 Hz) LFPs were negatively correlated with detection performance, but their dynamics were much slower, peaked late, and did not depend on stimulus configuration or reaction time. A late change in the correlation of all LFPs across the two recording electrodes suggests that a common input arrived at both MT locations prior to the behavioral response. Our results support a framework in which early high-gamma LFPs likely reflected fast, bottom-up, sensory processing that was causally linked to perception of the motion pulse. In comparison, late-arriving beta and highgamma LFPs likely reflected slower, top-down, sources of neuralbehavioral correlation that originated after the perception of the motion pulse.behavior; cortex; local field potential; MT; vision VISUALLY GUIDED DECISIONS involve multiple neural components, such as sensory and evaluative stages (Gold and Shadlen 2007). But when and how do different neural components make their contribution? This question is typically studied by measuring the trial-by-trial correlation between cortical spiking and perceptual behavior (Parker and Newsome 1998). Interpreting these neural-behavioral correlations, however, is difficult because they do not tell us whether fluctuations in neural activity directly produced fluctuations in perception. Thus additional data are needed to better understand and model the causality behind observed neural-behavioral correlations.Recent efforts have focused on the local field potential (LFP) and suggest that distinct neurophysiological processes can be measured from different LFP frequency bands (Bastos et al. 2015;Katzner et al. 2009;Kopell et al. 2010;Siegel et al. 2012). Gamma-band LFPs are thought to carry stimulus information (Belitski et al. 2010) and reflect the synaptic inputs from earlier stages (Khawaja et al. 2009) and the activity of local neurons (Ray and Maunsell 2011a), while beta-band LFPs may reflect intercortical feedback (Saalmann et al. 2007). Correspondingly, attention increases gamma LFP power in a manner similar to that of local spiking activity, while beta power is attenuated (Khaya...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 96%

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Dynamics of the functional link between area MT LFPs and motion detection

Smith

Beliveau

Schoen

et al. 2015

Journal of Neurophysiology

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“…The LFP response to a given stimulus condition was taken as ⌬R averaged over the gamma band (25-90 Hz), which has been widely studied (Pesaran et al, 2002;Henrie and Shapley, 2005;Viswanathan and Freeman, 2007;Katzner et al, 2009) and found to be highly tuned to visual stimuli (Frien et al, 2000;Henrie and Shapley, 2005;Liu and Newsome, 2006;Berens et al, 2008;Khayat et al, 2010). Data analyses.…”

Section: Methodsmentioning

confidence: 99%

Functional Organization of Envelope-Responsive Neurons in Early Visual Cortex: Organization of Carrier Tuning Properties

Baker

2012

J. Neurosci.

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It is well established that visual cortex neurons having similar selectivity for orientation, direction of motion, ocular dominance, and other properties of first-order (luminance-defined) stimuli are clustered into a columnar organization. However, the cortical architecture of neuronal responses to second-order (contrast/texture-defined) stimuli is poorly understood. A useful second-order stimulus is a contrast envelope, consisting of a finely detailed pattern (carrier) whose contrast varies on a coarse spatial scale (envelope). In this study, we analyzed the cortical organization of carrier tuning properties of neurons, which responded to contrast-modulated stimuli. We examined whether neurons tuned to similar carrier properties are clustered spatially and whether such spatial clusters are arranged in columns. To address these questions, we recorded single-unit activity, multiunit activity, and local field potentials simultaneously from area 18 of anesthetized cats, using single-channel microelectrodes and multielectrode arrays. Our data showed that neurons tuned to similar carrier spatial frequency are distributed in a highly clustered manner; neurons tuned to similar carrier orientation are also significantly clustered. Neurons along linear arrays perpendicular to the brain surface always exhibited similar optimal carrier spatial frequency, indicating a columnar organization. Multi-pronged tetrode recordings indicated that the diameter of these columns is Ն450 m. Optimal carrier orientation was also significantly clustered but with finer-grain organization and greater scatter. These results indicate a fine anatomical structure of cortical organization of second-order information processing and suggest that there are probably more maps in cat area 18 than previously believed.

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“…These analyses provide valuable information to study brain structures and functions in a more precise manner. As cases in point, it has been employed in the following studies: (1) decoding spoken words using LFP in cortical surface (Kellis et al 2010), (2) modulation of LFP by contrast, and the direction of motion in medial temporal (MT) (Khayat et al 2010), (3) tuning LFP for speed, direction and observation of high correlation in certain frequencies, especially the gamma band in the MT area (Liu and Newsome 2006), (4) decoding dexterous finger movements using LFP in M1 (Mollazadeh et al 2008), (5) decoding hand movement using LFP in motor cortex (Mehring et al 2003), and (6) application of LFP in decoding of stimulus-reward pairing in V4 of monkeys (Manyakov et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of local field potential signals in decoding of visual attention

Seif

Daliri

2015

Cogn Neurodyn

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In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time-frequency domain.

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Frequency-Dependent Attentional Modulation of Local Field Potential Signals in Macaque Area MT

Cited by 50 publications

References 54 publications

Dynamics of the functional link between area MT LFPs and motion detection

Dynamics of the functional link between area MT LFPs and motion detection

Functional Organization of Envelope-Responsive Neurons in Early Visual Cortex: Organization of Carrier Tuning Properties

Evaluation of local field potential signals in decoding of visual attention

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