Topography and intracranial sources of somatosensory evoked potentials in the monkey. I. early components

Arezzo, Joseph C.; Vaughan, Herbert G.; Legatt, Alan D.

doi:10.1016/0013-4694(79)90065-8

Cited by 218 publications

(39 citation statements)

References 17 publications

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“…These predictions are consistent with previous studies. Studies focused specifically on investigating the neural origin of primary somatosensory evoked surface potentials (SEPs) (Towe, 1966;Arezzo et al, 1979Arezzo et al, , 1981Allison et al, 1980;Gardner et al, 1984;Desmedt, 1988;Peterson et al, 1995) and fields (SEFs) (Hari and Forss, 1999;Ikeda et al, 2005) have primarily analyzed peaks emerging Ͻ50 ms after stimulus. A consensus from studies in which electrophysiological activity was recorded from multiple cortical layers is that the net laminar current flow that produces these early evoked responses derives from the postsynaptic current flow in PNs in the supragranular and infragranular layers (Towe, 1966;Peterson et al, 1995;Ikeda et al, 2005).…”

Section: Origin Of Evoked Response and Modulation With Detectionmentioning

confidence: 99%

Neural Correlates of Tactile Detection: A Combined Magnetoencephalography and Biophysically Based Computational Modeling Study

Jones¹,

Pritchett²,

Stufflebeam³

et al. 2007

J. Neurosci.

150

373

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Previous reports conflict as to the role of primary somatosensory neocortex (SI) in tactile detection. We addressed this question in normal human subjects using whole-head magnetoencephalography (MEG) recording. We found that the evoked signal (0 -175 ms) showed a prominent equivalent current dipole that localized to the anterior bank of the postcentral gyrus, area 3b of SI. The magnitude and timing of peaks in the SI waveform were stimulus amplitude dependent and predicted perception beginning at ϳ70 ms after stimulus. To make a direct and principled connection between the SI waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. The SI evoked response was successfully reproduced from the intracellular currents in pyramidal neurons driven by a sequence of lamina-specific excitatory input, consisting of output from the granular layer (ϳ25 ms), exogenous input to the supragranular layers (ϳ70 ms), and a second wave of granular output (ϳ135 ms). The model also predicted that SI correlates of perception reflect stronger and shorter-latency supragranular and late granular drive during perceived trials. These findings strongly support the view that signatures of tactile detection are present in human SI and are mediated by local neural dynamics induced by lamina-specific synaptic drive. Furthermore, our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception.

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Section: Origin Of Evoked Response and Modulation With Detectionmentioning

confidence: 99%

Neural Correlates of Tactile Detection: A Combined Magnetoencephalography and Biophysically Based Computational Modeling Study

Jones¹,

Pritchett²,

Stufflebeam³

et al. 2007

J. Neurosci.

150

373

View full text Add to dashboard Cite

show abstract

“…200-300 m radius, with the electrode at its centre. Spikes produced by the synchronous firings of many cells can, in principle, be enhanced by summation and thus detected at a larger distance (Huang & Buchwald 1977;Arezzo et al 1979).…”

Section: (Iv) Origin Of Lfp and Mua: Experimental Evidencementioning

confidence: 99%

The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

Logothetis

2002

Phil. Trans. R. Soc. Lond. B

817

516

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Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygenlevel-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single-and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.

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“…Spikes produced by the synchronous firings of many cells can, in principle, be enhanced by summation and thus detected over a larger distance (Arezzo et al 1979;Huang and Buchwald 1977). In general, experiments have shown that large-amplitude signal variations in the MUA range reflect large-amplitude extracellular potentials and that small-amplitude fast activity is correlated with small ones (Buchwald and Grover 1970;Gasser and Grundfest 1939;Grover and Buchwald 1970;Hunt 1951;Nelson 1966).…”

Section: Introductionmentioning

confidence: 99%

Inferring Spike Trains From Local Field Potentials

Rasch¹,

Gretton²,

Murayama³

et al. 2008

Journal of Neurophysiology

205

200

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We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted. Two kinds of features of the LFP are exploited for prediction: the frequency power of bands in the high ␥-range (40 -90 Hz) and information contained in lowfrequency oscillations (Ͻ10 Hz), where both phase and power modulations are informative. Information analysis revealed that both features code (mainly) independent aspects of the spike-to-LFP relationship, with the low-frequency LFP phase coding for temporally clustered spiking activity. Although both features and prediction quality are similar during seminatural movie stimuli and spontaneous activity, prediction performance during spontaneous activity degrades much more slowly with increasing electrode distance. The general trend of data obtained with anesthetized animals is qualitatively mirrored in that of a more limited data set recorded in V1 of non-anesthetized monkeys. In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity.

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Topography and intracranial sources of somatosensory evoked potentials in the monkey. I. early components

Cited by 218 publications

References 17 publications

Neural Correlates of Tactile Detection: A Combined Magnetoencephalography and Biophysically Based Computational Modeling Study

Neural Correlates of Tactile Detection: A Combined Magnetoencephalography and Biophysically Based Computational Modeling Study

The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

Inferring Spike Trains From Local Field Potentials

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