Properties of the Evoked Potential Generators: Current Source-Density Analysis of Visually Evoked Potentials in the Cat Cortex

Mitzdorf, U.

doi:10.3109/00207458708985928

Cited by 261 publications

(186 citation statements)

References 68 publications

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“…During every trial we simultaneously recorded field potentials, which reflect integrated synaptic activity (Mitzdorf, 1987). The response location was identified by 569 nm optical imaging, and a burr hole drilled in the thinned skull near the activation center.…”

Section: Electrophysiologymentioning

confidence: 99%

“…By recording field potentials simultaneously with hemodynamic signals, we obtained a measure of synaptic activity evoked by the stimulation, since field potentials are thought to reflect the weighted sum of postsynaptic currents (Mitzdorf, 1987). The degree to which hemodynamic responses propagate both retrograde and anterograde increases with increasing synaptic activity.…”

Section: Neurovascular Couplingmentioning

confidence: 99%

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Spatiotemporal Evolution of Functional Hemodynamic Changes and Their Relationship to Neuronal Activity

Sheth

Nemoto

Guiou

et al. 2005

J Cereb Blood Flow Metab

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Brain imaging techniques such as functional magnetic resonance imaging (fMRI) have provided a wealth of information about brain organization, but their ability to investigate fine-scale functional architecture is limited by the spatial specificity of the hemodynamic responses upon which they are based. We investigated the spatiotemporal evolution of hemodynamic responses in rat somatosensory cortex to electrical hindpaw stimulation. We combined the advantages of optical intrinsic signal imaging and spectroscopy to produce high-resolution two-dimensional maps of functional changes in tissue oxygenation and blood volume. Cerebral blood flow changes were measured with laser-Doppler flowmetry, and simultaneously recorded field potentials allowed comparison between hemodynamic changes and underlying neuronal activity. For the first 2 to 3 secs of activation, hemodynamic responses overlapped in a central parenchymal focus. Over the next several seconds, cerebral blood volume changes propagated retrograde into feeding arterioles, and oxygenation changes anterograde into draining veins. By 5 to 6 secs, responses localized primarily in vascular structures distant from the central focus. The peak spatial extent of the hemodynamic response increased linearly with synaptic activity. This spatial spread might be because of lateral subthreshold activation or passive vascular overspill. These results imply early microvascular changes in volume and oxygenation localize to activated neural columns, and that spatial specificity will be optimal within a 2-to 3-sec window after neuronal activation.

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Section: Electrophysiologymentioning

confidence: 99%

Section: Neurovascular Couplingmentioning

confidence: 99%

Spatiotemporal Evolution of Functional Hemodynamic Changes and Their Relationship to Neuronal Activity

Sheth

Nemoto

Guiou

et al. 2005

J Cereb Blood Flow Metab

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“…Evidence for their origin was often gathered from current-source density (CSD) analysis and combined field potential and intracellular recordings (Mitzdorf 1985;Nadasdy et al 1998). Mitzdorf suggested that LFPs actually reflect a weighted average of synchronized dendrosomatic components of the synaptic signals of a neural population within 0.5-3 mm of the electrode tip (Juergens et al 1999;Mitzdorf 1987). Later studies, however, provided evidence of the existence of other types of slow activity unrelated to synaptic events, including voltage-dependent membrane oscillations (e.g., Kamondi et al 1998) and spike afterpotentials.…”

Section: Introductionmentioning

confidence: 99%

Inferring Spike Trains From Local Field Potentials

Rasch¹,

Gretton²,

Murayama³

et al. 2008

Journal of Neurophysiology

206

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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“…Instead of viewing the cortex as a single network, we now conceive of the cortical laminae as a stack of loosely interconnected but distinct neuronal networks (1)(2)(3)(4)(5). Each lamina has different specific inputs, projection targets, and feedback connections.…”

mentioning

confidence: 99%

Laminar analysis of visually evoked activity in the primary visual cortex

Xing

Yeh

Burns

et al. 2012

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

189

166

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Studying the laminar pattern of neural activity is crucial for understanding the processing of neural signals in the cerebral cortex. We measured neural population activity [multiunit spike activity (MUA) and local field potential, LFP] in Macaque primary visual cortex (V1) in response to drifting grating stimuli. Sustained visually driven MUA was at an approximately constant level across cortical depth in V1. However, sustained, visually driven, local field potential power, which was concentrated in the γ-band (20-60 Hz), was greatest at the cortical depth corresponding to corticocortical output layers 2, 3, and 4B. γ-band power also tends to be more sustained in the output layers. Overall, cortico-cortical output layers accounted for 67% of total γ-band activity in V1, whereas 56% of total spikes evoked by drifting gratings were from layers 2, 3, and 4B. The high-resolution layer specificity of γ-band power, the laminar distribution of MUA and γ-band activity, and their dynamics imply that neural activity in V1 is generated by laminar-specific mechanisms. In particular, visual responses of MUA and γ-band activity in cortico-cortical output layers 2, 3, and 4B seem to be strongly influenced by laminar-specific recurrent circuitry and/or feedback.B ased on anatomy and neurophysiology, our view of the cerebral cortex has changed. Instead of viewing the cortex as a single network, we now conceive of the cortical laminae as a stack of loosely interconnected but distinct neuronal networks (1-5). Each lamina has different specific inputs, projection targets, and feedback connections.The goal of this study is the determination of the spatial distribution of stimulus-driven neural activity throughout the depth of the cortex and across cortical laminae. Pursuing this goal, we chose to study Macaque primary visual cortex (V1), because V1 is a cortical area where the experimenter has control of the neuronal input by controlling visual stimulation. V1 laminar inputs, outputs, and local connections are well-known (1, 3, 6). Signals come from the thalamus [lateral geniculate nucleus (LGN)] into V1 layers 4C and 6. After intracortical processing, neuronal signals are routed to other cortical areas from cells in superficial layers 2, 3, and 4B of V1; subcortical targets receive cortical outputs from cells in layers 5 and 6. Extrastriate cortical feedback targets layers 2, 3, and 6 (1, 3, 7). The visual functional properties of cells in different layers are markedly different (2, 8-13), reflecting local circuitry that is layer-specific (1, 3, 6). Because of the similarities of laminar cortical circuitry throughout the cerebral cortex (14-16), we used V1 as a test bed to study laminar patterns of stimulus-driven responses.To sample from many neurons in each layer, we measured multiunit spike activity (MUA) and the local field potential (LFP) with multiple microelectrodes (Methods details the operational definitions of MUA and LFP). We measured the power in the LFP at frequencies < 100 Hz, because the largest changes in LFP power...

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Properties of the Evoked Potential Generators: Current Source-Density Analysis of Visually Evoked Potentials in the Cat Cortex

Cited by 261 publications

References 68 publications

Spatiotemporal Evolution of Functional Hemodynamic Changes and Their Relationship to Neuronal Activity

Spatiotemporal Evolution of Functional Hemodynamic Changes and Their Relationship to Neuronal Activity

Inferring Spike Trains From Local Field Potentials

Laminar analysis of visually evoked activity in the primary visual cortex

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