Author: Stuti Chakraborty
Research by: Ruchi Sharma
Developments in technology enabling the recording and visualisation of the internal activities of the brain, have thus far been one of the greatest feats in neurology and neuroscience. One of the major advancements in neuroimaging over the last few decades has undoubtedly been the use of functional magnetic resonance imaging or fMRI, which has aided in guiding crucial aspects of understanding the brain since its inception in 1992.
What is fMRI?
fMRI is a tool used for neuroimaging which essentially employs MRI to image changes occurring dynamically within the brain tissue caused due to evolving neural metabolism. The MR contrast mechanism that is used for nearly all fMRI relies on blood oxygenation level dependent (BOLD) changes in brain tissue. This is exhibited when a region of the brain undergoes altered levels of oxygen consumption as a consequence of up-or-down regulation of metabolic activity, that can be caused by the performance of a simple cognitive task. In simple words, the neurons inside our brains undergo multiple fluctuations as we undertake such as simple tasks (grabbing a bottle of water) or more complex, dynamic tasks such as interpretation of language.
What is task-based fMRI?
In task-based fMRI, time series data are compared against a hypothetical neural functioning model, which is dependent on the cognitive task being performed. An fMRI dataset essentially consists of cuboid elements known as voxels that vary in dimension and consist of associated time-series (time points equivalent to as many volumes acquired per person). In a typical fMRI experiment, sensory stimuli is utilized to cue the participant to perform a behavioral task while BOLD contrast images are acquired for a fixed duration of minutes. Such stimuli can be either visual, auditory or of other forms depending on the desired behavioral manipulation.
What is rs (resting state)-fMRI?
In contrast to paradigm - or task-based functional MR imaging, resting-state fMRI (rs-fMRI) is acquired in the absence of a stimulus or a task, or simple words - at rest. However, the principle of rs-fMRI is also based on the variations in BOLD signals, similar to task-based fMRI. rs-fMRI focuses on spontaneous alterations of BOLD signals and data can be acquired by either asking individuals to simply be at a state of rest or inferring resting state data from in-between rest periods that are integrated with a set of tasks.
Why is using rs-fMRI more advantageous than task-based fMRI?
This is due multiple reasons.
Task-based fMRI cannot be performed on non-responsive clients with neurological disorders (for e.g., people in a coma or vegetative state), among vulnerable populations such as children and on non-cooperative clients.
It needs specific hardware and software for delivery of the task-related stimuli.
There is a requirement of different scans to be performed for each network within the brain, rendering a large amount of data to be analyzed.
A dedicated and trained personnel for evaluating the client’s cognitive status, selecting carefully curated cognitive tasks, and assessing task performance is required.
Recent research has also shown that the specificity and sensitivity of pre-surgical language mapping by task-based fMRI is highly variable because of different language tasks, different MRI machines and analysis paradigms.
Considering all the above factors, the utilization of resting state fMRI has gained rapid traction.
A 2019 survey reported that 82% of neuroradiology professionals found rs-fMRI data is easy to collect. Additionally, using rs-fMRI, multiple brain networks can be derived using only one rs-fMRI scan as opposed to requiring multiple task-based fMRI for each eloquent cortical network. Another major advantage of using rs-fMRI is that it reduces or nullifies the involvement of parameters that may cause problems in interpretation of the task.
How can rs-fMRI change the landscape of research and clinical practice?
There are multiple ways this can happen and some are currently already in place. In clinical practice, rs-fMRI has been applied to various resting state networks (RSNs) in order to prepare for pre-surgical planning in clients with brain tumor or epilepsy. Studies have also shown that the spatial resolution offered by rs-fMRI is also higher when compared with electroencephalography (EEG) and thus can be advantageous in mapping specific epileptic foci. One major future direction for rs-fMRI is its use in the selection, identification and evaluation of post-surgical outcomes in epilepsy. Undoubtedly, another realm of research and clinical practice that can be spearheaded by the use of rs-fMRI is in understanding functional network organization and how it is impacted in Alzheimer’s disease as well as other neurocognitive disorders.
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