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

Wataru Sato; Takanori Kochiyama; Shota Uono; Naotaka Usui; Akihiko Kondo; Kazumi Matsuda; Keiko Usui; Motomi Toichi; Yushi Inoue

doi:10.3791/58187

신경 활동과 연결 Intracranial 뇌 파 데이터를 사용 하 여 SPM 소프트웨어 분석

JoVE Journal
Neuroscience

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JoVE Journal Neuroscience

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

신경 활동과 연결 Intracranial 뇌 파 데이터를 사용 하 여 SPM 소프트웨어 분석

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06:50 min

October 30, 2018

DOI: 10.3791/58187-v

Wataru Sato*¹, Takanori Kochiyama*², Shota Uono³, Naotaka Usui⁴, Akihiko Kondo⁵, Kazumi Matsuda⁵, Keiko Usui⁶, Motomi Toichi⁷, Yushi Inoue⁵

¹Kokoro Research Center,Kyoto University, ²Brain Activity Imaging Center,Advanced Telecommunications Research Institute International, ³Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine,Kyoto University, ⁴National Epilepsy Center, ⁵Shizuoka Institute of Epilepsy and Neurological Disorders, ⁶Department of System Neuroscience,Sapporo Medical University, ⁷Faculty of Human Health Science, Graduate School of Medicine,Kyoto University

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Overview

This study presents analytical protocols for analyzing intracranial electroencephalography (iEEG) data using Statistical Parametric Mapping (SPM) software. The two main approaches include time-frequency statistical parametric mapping analysis for assessing neural activity and dynamic causal modeling (DCM) for evaluating intra- and inter-regional connectivity.

Key Study Components

Area of Science

Neuroscience
Electrophysiology
Cognitive neuroscience

Background

Intracranial EEG data provides insights into neural activity and connectivity.
Time-frequency analysis reveals the dynamics of spectral components.
Dynamic causal modeling identifies connectivity patterns related to sensory inputs.

Purpose of Study

To develop methods for detailed analysis of iEEG data.
To enable exploration of how neural connectivity changes during cognitive processes.
To validate the approaches with neural activity during specific tasks.

Methods Used

SPM12 software is utilized for data analysis.
The key model is based on intracranial EEG from human subjects.
Time-frequency analysis involves continuous wavelet decomposition with Morlet wavelets.
DCM is applied to assess intrinsic connectivity and modulatory effects of different conditions.

Main Results

Time-frequency maps show neural activity associated with different cognitive phases.
DCM reveals excitatory and inhibitory connectivity patterns between brain regions.
The analysis demonstrates significant temporal and frequency profiles relevant to cognitive processing.

Conclusions

The study illustrates effective methodologies for iEEG data analysis.
These approaches enhance the understanding of neural oscillations and connectivity in cognitive neuroscience.
Findings have implications for modeling neural mechanisms underlying cognitive functions.

Frequently Asked Questions

What are the advantages of using SPM software for iEEG analysis?

SPM software provides robust statistical tools for analyzing spatiotemporal data, allowing for sophisticated models of neural activity and connectivity.

How is time-frequency analysis implemented in this method?

Time-frequency analysis involves preprocessing iEEG data, applying continuous wavelet transformation with specific parameters, and visualizing results as time-frequency maps.

What types of connectivity patterns can be identified using dynamic causal modeling?

DCM can identify both intrinsic neural connections and the modulatory effects of sensory inputs on neural states, helping to elucidate the dynamics of connectivity during cognitive tasks.

How does this study enhance understanding of cognitive processes?

By analyzing neural activity and connectivity, the study provides insights into how different brain regions interact during cognitive processing, contributing to the understanding of brain function.

What limitations should be considered when using these analytical approaches?

Considerations include the quality of the iEEG recordings, the assumptions made in statistical models, and the complexity of interpreting connectivity results across different cognitive states.

선물이 intracranial electroencephalography 데이터 통계 파라미터 매핑 (SPM) 소프트웨어를 사용 하 여 분석 하는 데 사용할 수 있는 두 개의 분석 프로토콜: 신경 활동, 및 동적 인과 관계에 대 한 시간-주파수 통계적 파라미터 매핑 분석 내부 및 남북 regional 연결에 대 한 유발된 반응의 모델링.

이 방법은 인지 신경 과학 분야의 주요 질문에 대답하는 데 도움이 될 수 있습니다. 이 기술의 주요 장점은 신경 활동과 고공간 측두성 진화에 대한 연결을 분석할 수 있다는 것입니다. 두개 내 EEG 데이터 분석을 시작하려면 SPM12를 설정하고 MEG EEG 분석 메뉴를 선택합니다.

먼저 미리 정의된 매개 변수를 기반으로 모레 웨이블릿을 사용하여 연속 웨이블릿 분해를 사용하여 각 시험의 전처리된 두개내 EEG 데이터에 대한 시간 주파수 분석을 수행합니다. 스펙트럼 성분의 시간적 진화를 밝히기 위해, 4~300 헤르츠의 주파수 범위를 사용하여 전체 1, 000 ~ 2, 000 밀리초의 전체 시대에 7사이클 모렛 웨이블릿을 사용하여 웨이블릿 분해를 실시한다. 다음으로, 마더 웨이블릿과 사이클 수를 결정하고 사이클 수가 시간 주파 해상도를 제어하고 추정 안정성을 보장하기 위해 5 개 이상이어야한다는 점에 유의하십시오.

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