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Orientation selectivity is a fundamental property of visual cortical neurons and plays a crucial role in pattern perception. Although many studies have dedicated to explain how the orientation selectivity emerged, the mechanism underlying orientation selectivity is still not clear. In this work, we investigated the synchronization between spikes and local field potentials (LFP) in gamma band, with the aim of providing a new avenue to analyze the orientation selectivity. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, where each array consisted of 48 electrodes and was placed over V1 and V4, respectively, in two macaques performing a selective visual attention task. An unbiased and robust measure for quantifying the synchronization between spikes and LFP was employed in the analysis process, which is termed as spike-triggered correlation matrix synchronization (SCMS) and performs reliably for limited samples of data. We observed the spike-LFP synchronization in three cases, i.e., spikes and LFP in V1, spikes and LFP in V4, spikes in V4 and LFP in V1. From the orientation tuning curves based on the spike-LFP synchronization, it is found that there is a strong correlation between the synchronization and grating orientation. The neurons in both V1 and V4 exhibit orientation selectivity, but V1 is stronger. In addition, the spike-LFP synchronization strength between V1 and V4 also shows orientation selectivity to drifting gratings. It means that the synchronization not only reflects the basic features of visual stimulation, but also describes the orientation tuning characteristics of neurons in different regions. Our results suggest that the spike-LFP synchronization can be used as an alternative and effective method to study the mechanism for generating orientation selectivity of visual neurons.
Pubmed ID: 31354464
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Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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