Resting-state fMRI (RS-fMRI) measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. This technique has enabled the identification of resting-state networks (RSNs), which are spatially distinct brain areas that show synchronized BOLD fluctuations at rest. Various methods are used to analyze RS-fMRI data, including seed-based approaches, independent component analysis (ICA), graph methods, clustering algorithms, neural networks, and pattern classifiers. While clinical applications of RS-fMRI are still in early stages, it shows promise in presurgical planning for patients with brain tumors and epilepsy, and may have future roles in providing diagnostic and prognostic information for neurological and psychiatric diseases. RS-fMRI investigates synchronous activations between spatially distinct brain regions without a task or stimulus, helping to identify RSNs. The default mode network (DMN) is one of the most fundamental RSNs, identified through PET and later confirmed with fMRI. Other RSNs include the somatosensory, visual, language, dorsal attention, ventral attention, frontoparietal control, and cinguloopercular networks. These networks are involved in various cognitive and behavioral functions. Analysis methods for RS-fMRI include preprocessing steps such as correction for time shifts, motion, and noise, followed by spatial smoothing and filtering. Statistical and mathematical approaches like seed-based analysis, ICA, graph methods, and clustering algorithms are used to analyze data. These methods have shown that the brain exhibits a small-world topology, characterized by high clustering coefficients and short path lengths. RS-fMRI has been applied in clinical settings for presurgical planning, particularly in identifying eloquent cortex areas in patients with brain tumors. It is less demanding than task-based methods and can be used in patients who cannot perform tasks, such as children or those with altered mental status. RS-fMRI has also been used in epilepsy surgery to identify epileptogenic areas and has shown promise in distinguishing Alzheimer's disease from other conditions through network analysis. In pediatric populations, RS-fMRI has been used to study the development of RSNs, revealing differences between term and preterm infants. It has also been applied to investigate differences in RSN development across various pediatric disease states, including Tourette syndrome, ADHD, and autism. Future directions include further research to compare analysis methods and their efficacy in detecting disease states, with the Human Connectome Project aiming to enhance understanding of functional and structural connectivity. RS-fMRI is non-invasive and may be particularly useful for patients who cannot undergo traditional lesion localization methods. It holds potential for identifying patients with Alzheimer's disease and other neurological and psychiatric conditions.Resting-state fMRI (RS-fMRI) measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. This technique has enabled the identification of resting-state networks (RSNs), which are spatially distinct brain areas that show synchronized BOLD fluctuations at rest. Various methods are used to analyze RS-fMRI data, including seed-based approaches, independent component analysis (ICA), graph methods, clustering algorithms, neural networks, and pattern classifiers. While clinical applications of RS-fMRI are still in early stages, it shows promise in presurgical planning for patients with brain tumors and epilepsy, and may have future roles in providing diagnostic and prognostic information for neurological and psychiatric diseases. RS-fMRI investigates synchronous activations between spatially distinct brain regions without a task or stimulus, helping to identify RSNs. The default mode network (DMN) is one of the most fundamental RSNs, identified through PET and later confirmed with fMRI. Other RSNs include the somatosensory, visual, language, dorsal attention, ventral attention, frontoparietal control, and cinguloopercular networks. These networks are involved in various cognitive and behavioral functions. Analysis methods for RS-fMRI include preprocessing steps such as correction for time shifts, motion, and noise, followed by spatial smoothing and filtering. Statistical and mathematical approaches like seed-based analysis, ICA, graph methods, and clustering algorithms are used to analyze data. These methods have shown that the brain exhibits a small-world topology, characterized by high clustering coefficients and short path lengths. RS-fMRI has been applied in clinical settings for presurgical planning, particularly in identifying eloquent cortex areas in patients with brain tumors. It is less demanding than task-based methods and can be used in patients who cannot perform tasks, such as children or those with altered mental status. RS-fMRI has also been used in epilepsy surgery to identify epileptogenic areas and has shown promise in distinguishing Alzheimer's disease from other conditions through network analysis. In pediatric populations, RS-fMRI has been used to study the development of RSNs, revealing differences between term and preterm infants. It has also been applied to investigate differences in RSN development across various pediatric disease states, including Tourette syndrome, ADHD, and autism. Future directions include further research to compare analysis methods and their efficacy in detecting disease states, with the Human Connectome Project aiming to enhance understanding of functional and structural connectivity. RS-fMRI is non-invasive and may be particularly useful for patients who cannot undergo traditional lesion localization methods. It holds potential for identifying patients with Alzheimer's disease and other neurological and psychiatric conditions.