Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks

Abstract

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Resting state connectivity differences in eyes open versus eyes closed conditions

“…Resting state functional magnetic resonance imaging (rs-fMRI) is a data collection method that does not require participants to engage in a specific task. This allows rs-fMRI to be acquired from a wide range of populations including patient groups, participants with intellectual disabilities, pediatric groups, and even unconscious patients (Smitha et al, 2017). Since Biswal, Yetkin, Haughton, and Hyde (1995) initially observed that temporal correlation of low frequency fluctuations in rs-fMRI (<0.1 Hz) can provide an estimate of functional connectivity (FC), research on rs-fMRI has greatly increased (Agcaoglu et al, 2018;Agcaoglu, Miller, Mayer, Hugdahl, & Calhoun, 2016;Allen et al, 2011;Cetin et al, 2016;Haak, Marquand, & Beckmann, 2018;Hart et al, 2018;Park, Friston, Pae, Park, & Razi, 2018;Rashid et al, 2018;Smitha et al, 2017).…”

Short-term test-retest reliability of the human intrinsic functional connectome

“…In most studies, FC is measured by computing a Pearson correlation of the fMRI derived Blood-Oxygen-Level-Dependent (BOLD) timeseries of a set of regions while the participant is awake and not performing any task [7]. This is what we will intend as FC in the present work, but it is important to note that FC can also be computed while participants are not completely idle and using a wide array of methods besides correlations, such as independent component analysis, analyses in the frequency domain, Bayesian models and dynamic approaches (for review, [7,8,9,10]).…”

Detecting Perfusion Pattern Based on the Background Low-Frequency Fluctuation in Resting-State Functional Magnetic Resonance Imaging Data and Its Influence on Resting-State Networks: An Iterative Postprocessing Approach

“…RS-fMRI has been improving for the last 20 years and its applications extend from research to clinics (Barkhof et al, 2014;Fox and Greicius, 2010). The spotlight here is on the intrinsic brain activity in the absence of any sensory or cognitive stimulus (Smitha et al, 2017). Application of this technique has allowed the identification of various Resting State Networks (RSNs) (Lee et al, 2013).…”