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Resting State fMRI (rsfMRI or R-fMRI) is a novel, powerful method of brain imaging (fMRI) that can be used to evaluate regional interactions that occur when a subject is not performing an explicit task ^[1]. Because brain activity activity continues in the absence of an externally prompted task, any brain region will have spontaneous fluctuations in BOLD signal. The resting state approach is useful to explore the brain’s functional organization and to examine if it is altered in neurological or psychiatric diseases. Resting-state functional connectivity has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity^[2].

Basics of fMRI edit

Functional Magnetic Resonance Imaging or functional MRI (fMRI) is an MRI procedure that measures brain activity by detecting associated changes in blood flow. More specifically, brain activity is measured through low frequency BOLD (Blood Oxygen Level Dependent) signal in the brain.

The procedure is similar to MRI but uses the change in magnetization between oxygen-rich and oxygen-poor blood as its basic measure. This measure is frequently corrupted by noise from various sources and hence statistical procedures are used to extract the underlying signal. The resulting brain activation can be presented graphically by color-coding the strength of activation across the brain or the specific region studied. The technique can localize activity to within millimeters but, using standard techniques, no better than within a window of a few seconds.

FMRI is used both in the research world, and to a lesser extent, in the clinical world. It can also be combined and complemented with other measures of brain physiology such as EEG and NIRS. Newer methods which improve both spatial and time resolution are being researched, and these largely use biomarkers other than the BOLD signal. Some companies have developed commercial products such as lie detectors based on fMRI techniques, but the research is not believed to be ripe enough for widespread commercialization.

Physiological Basis edit

While fMRI strives to measure the neuronal activity in the brain, the BOLD signal can be influenced by many other physiological factors other that neuronal activity. For example, respiratory fluctuations and cardiovascular cycles can effect the BOLD signal being measured in the brain and therefore should be regressed out during preprocessing of the raw fMRI data.

History of Resting State fMRI edit

-Bharat Biswal

-Marcus Raichle

Experiments by neurologist Marcus E. Raichle's lab at Washington University School of Medicine and other groups showed that the brain's energy consumption is increased by less than 5% of its baseline energy consumption while performing a focused mental task. These experiments showed that the brain is constantly active with a high level of activity even when the person is not engaged in focused mental work. Research thereafter focused on finding the regions responsible for this constant background activity level.

Functional Connectivity edit

Functional connectivity is defined as the temporal correlation between spatially remote neurophysiological events ^[3], expressed as deviation from statistical independence (temporal correlation) across these events in distributed neuronal groups and areas. This applies to both resting state and task-state studies. While functional connectivity can refer to correlations across subjects, runs, blocks, trials, or individual time points, resting state functional connectivity focuses on connectivity assessed across individual BOLD time points during resting conditions.

Resting State Networks edit

-Default Mode Network

The Default Network is a network of brain regions that are active when an individual is awake and at rest.

-Other Resting State Networks...

Depending on the method of analysis, many different resting state networks can be separated based on the similarity of the fluctuating signals in the regions of the brain. Some of the most common networks include the motor network and visual network among others found in a study by researchers at Washington University in St. Louis. (Include possible image of resting state networks)

Analyzing Data edit

-Processing data

Many programs exist for the processing of resting state fMRI data. This includes the many common programs for processing and analyzing fMRI data such as SPM^[4] and AFNI^[5].

-Acquiring Resting State Data

Sequences for Acquisition

Common Methods edit

There are many methods of both acquiring and processing rsfMRI data...

- ICA/PCA Independent Component Analysis (ICA) is a very useful statistical approach in the detection of resting state networks. ICA separates a signal into non-overlapping spatial and time components. It is highly data-driven and allows for better removal of noisy components of the signal (motion, scanner drift, etc). It also has been shown to relably extract default mode network as well as many others with very high consistency.

- Seed-Based/ROI Another method of observing networks and connectivity in the brain is the Seed-Based or Region of Interest (ROI) method of analysis. In this case, signal from only a certain voxel or cluster of voxels known as the seed or ROI are used to calculate correlations with other voxels of the brain. This provides a much more precise and detailed look at specific areas of interest in the brain.

- Dynamics

Combining Imaging Techniques edit

-fMRI with EEG

Many imaging experts feel that in order to obtain the best combination of spatial and temporal information from brain activity, both fMRI as well as Electroencephalography (EEG) should be used simultaneously.

-fMRI with TMS

-Task-Based and Resting State Analysis

These fMRI images are from a study showing parts of the brain lighting up on seeing houses and other parts on seeing faces. The 'r' values are correlations, with higher positive or negative values indicating a better match.

Current and Future Applications edit

Resting state data holds a very large potential not only for research, but also for clinical applications as well.

Research Applications edit

Clinical Applications edit

Resting State fMRI has already proven to have many useful clinical applications, including use in the assessment of many different diseases and movement disorders ^[6].

Disease Condition and Changes in Resting State Functional Connectivity

Alzheimer's Disease: decreased connectivity
Depression: increased connectivity
Schizophrenia: disrupted networks
ADHD: Altered "small networks" and Thalamus changes
Aging Brain: disruption of brain systems and motor network
Epilepsy: Disruption and decrease/increase in connectivity

Other types of clinical applications for resting state fMRI include identifying group differences in brain disease, obtaining diagnostic and prognostic information, longitudinal studies and treatment effects, clustering in heterogeneous disease states, and pre-operative mapping and targeting intervention ^[7].

References edit

^ Biswal, B. B. (2012). Resting state fMRI: A personal history. [Review]. Neuroimage, 62(2), p. 938-944.
^ Rosazza, C., & Minati, L. (2011). Resting-state brain networks: literature review and clinical applications. Neurol Sci, 32(5), 773-785.
^ Friston, K. (2009). Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging. [Editorial Material]. Plos Biology, 7(2), 220-225.
^ http://www.fil.ion.ucl.ac.uk/spm/
^ afni.nimh.nih.gov/afni/
^ Holtbernd, F., & Eidelberg, D. (2012). Functional brain networks in movement disorders: recent advances. Current Opinion in Neurology, 25(4), p. 392-401.
^ Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Front Syst Neurosci, 4, 19.

[1] Biswal, B. B. (2012). Resting state fMRI: A personal history. [Review]. Neuroimage, 62(2), p. 938-944.

[2] Rosazza, C., & Minati, L. (2011). Resting-state brain networks: literature review and clinical applications. Neurol Sci, 32(5), 773-785.

[3] Friston, K. (2009). Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging. [Editorial Material]. Plos Biology, 7(2), 220-225.

[4] ttp://www.fil.ion.ucl.ac.uk/spm/

[5] .nimh.nih.gov/afni/

[6] Holtbernd, F., & Eidelberg, D. (2012). Functional brain networks in movement disorders: recent advances. Current Opinion in Neurology, 25(4), p. 392-401.

[7] Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Front Syst Neurosci, 4, 19.

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