Resting-state fMRI: a review of methods and clinical applications

Abstract

Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Fig 1.

Surface plots of RSNs. A, Default mode network. B, Somatomotor network. C, Visual network. D, Language network. E, Dorsal attention network. F, ventral attention network. G, Frontoparietal control network.

Fig 2.

Identification of motor and language areas by using RS-fMRI in 2 patients with glioblastoma multiforme. A, The somatosensory area is displaced anterior to the tumor. B, Broca area is displaced anterior to the tumor.

Fig 3.

Selected seed-based correlation maps. Columns show the seeds (left), preoperative maps (middle), and postoperative maps (right). The map quantity illustrated is the Fisher z-transformed correlation coefficient thresholded at ± 0.2. A, Left somatomotor cortex seed (−39 −26 51). Postoperatively, the somatomotor RSN is improved and involves primary somatosensory areas (arrows). B, Visual cortex seed (−20 −75 12). Postoperatively, the visual RSN is improved and involves the occipital lobe (arrow).