Method Article

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

DOI:

10.3791/58187

October 30th, 2018

* These authors contributed equally

In This Article

Summary

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We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Abstract

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Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

Introduction

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Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is one of the primary goals of cognitive neuroscience. However, accomplishing this goal is not easy. One popular method used to record neural activity is functional magnetic resonance imaging (MRI). Although functional MRI offers several advantages, such as a high spatial resolution at the millimeter level and non-invasive recording, a clear disadvantage of functional MRI is its low temporal resolution. In addition, functional MRI measures blood-oxygen-level-dependent signals, which only indirectly reflect electric neural activity. Popular electrophy....

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Protocol

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Our study was approved by the local institutional ethics committee.

1. Basic Information

NOTE: The analytical protocols can be applied to various types of data without any restrictions as to specific participants, electrodes, reference methods, or electrode locations. In our example, we tested six patients suffering from pharmacologically intractable focal epilepsy. We tested patients who had no epileptic foci in the regions of interest.

  1. Record intracranial EEG data during the cognitive experiment at the target electrodes.
    1. Implant depth electrodes using the stereotactic method

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Results

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Using the protocol presented herein, we analyzed intracranial EEG data in response to faces18,19. We recorded data from six patients during the passive viewing of faces, mosaics, and houses in upright and inverted orientations. The contrasts of upright faces versus upright mosaics and upright faces versus upright houses revealed the face effect (i.e., face-specific brain activity relative to other object.......

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Discussion

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The analytical protocols for intracranial EEG data using the SPM software introduced herein have several advantages compared with functional MRI. First, the protocols can depict neural activation at a high temporal resolution. Therefore, the results indicate whether the cognitive correlates of neural activation are implemented at early or late stages of processing. In our example, the face effect was identified during the very early stages (i.e., 110 ms) of visual processing. In addition, the comparison of the t.......

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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This study was supported by funds from the Benesse Corporation, Japan Society for the Promotion of Science (JSPS) Funding Program for Next Generation World-Leading Researchers (LZ008), the Organization for Promoting Research in Neurodevelopmental Disorders, and the JSPS KAKENHI (15K04185; 18K03174).

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References

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  1. Lachaux, J. P., Rudrauf, D., Kahane, P. Intracranial EEG and human brain mapping. Journal of Physiology - Paris. 97 (4-6), 613-628 (2003).
  2. Kilner, J. M., Kiebel, S. J., Friston, K. J. Applications of random field theory to electrophysiology. Neuroscience Letters. 374

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Tags

Intracranial EEGSPM SoftwareTime Frequency AnalysisDynamic Causal ModelingNeural ActivityConnectivity AnalysisInferior Occipital GyrusAmygdala ConnectivityWavelet DecompositionBayesian Model Selection

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