특정 과제를 기반으로 하는 과제 기반 fMRI(functional magnetic resonance imaging)와 달리, 휴지 상태 fMRI(resting-state fMRI, rs-fMRI)는 참가자들이 쉬는 동안 자료를 수집하며 영상 획득 시간(약 10분)이 상대적으로 짧다. 이러한 이점을 기반으로 rs-fMRI는 과제와 관련 없는 개인차를 연구할 때나 다양한 집단(임상집단 혹은 정상인)을 대상으로 자료를 수집하는데 유용하다. 본 개괄에서, rs-fMRI 자료들을 분석하는 몇몇 기법들을 소개할 것이다. 이 기법은 휴지기 두뇌에서 나타나는 특정 두뇌 영역과 기능적 연결성을 보이는 두뇌 영역들을 규명하거나(seed-기반 기능적 연결성 분석, 독립 성분 분석), 다수의 두뇌 영역, 즉 노드들로 구성된 네트워크의 특징을 이해하거나(그래프 기반 네트워크 분석), 또는 자발적 활동 패턴을 이해하는 것(국소적 동질성, 저-주파수 진동 분석, Hurst 지수 분석)을 가능하게 만들었다. 본 논문은 또한 이 방법을 응용하여 휴지기 동안 두뇌 활동 양상의 차이가 정상인의 특정 개인차(예, 성격 특질) 또는 우울증, 알츠하이머, 자폐증과 같은 임상적 장애를 연구한 예들을 소개하고자 하였다. 분석 방법론의 충분한 이해를 통하여 인간의 두뇌와 마음의 관계를 조사하는 경험적 연구의 연구 물음에 가장 적절한 rs-fMRI 분석법을 선택하는 것이 가능해질 수 있을 것이다.
In contrast to the task-based fMRI, the resting-state fMRI (rs-fMRI) doesn’t require a specific task, since data are obtained during rest for a relatively short scan time (about 10 min). Therefore, rs-fMRI provides advantages in studying individual differences not associated with the task, and in obtaining data from a large population of various groups (clinical or normal healthy). In the current review, we introduced several analyzing techniques for rs-fMRI. These techniques allow us to identify the functional connectivity among specific regions (seed-based functional connectivity analysis, independent component analysis), a network pattern composed of nodes (graph-based network analysis), or the spontaneous activity pattern (regional homogeneity, analysis of low-frequency fluctuation) during rest. The individual differences found during rest with these techniques have been shown to be related to individual differences (e.g., personality traits) or clinical diseases, such as depression, Alzheimer’s diseases, and autism. Choosing an optimal research technique for a specific study question would be possible only with a deep understanding of these analysis techniques.
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