Resting state fMRI
|Resting state fMRI|
Movie of the in vivo BOLD signal from the cortical surface of a human subject from HCP, acquired using resting state fMRI, pre-processed to suppress the noise in data and played back at a real-time rate. The BOLD signal intensities are visualized on a smoothed cortical surface. At each point on the cortex, white color represents the average BOLD signal, while blue and red colors represents lower and higher signal than average BOLD signal respectively.
|Purpose||evaluate regional interactions that occur in resting state(brain mapping)|
Resting state fMRI (rsfMRI or R-fMRI) is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed. A number of resting-state conditions are identified in the brain, one of which is the default mode network. These resting brain state conditions are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI. Because brain activity is intrinsic, present even 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 mental disorders. Resting-state functional connectivity research has revealed a number of networks which are consistently found in healthy subjects, different stages of consciousness and across species, and represent specific patterns of synchronous activity.
- 1 Basics of fMRI
- 2 Physiological basis
- 3 History
- 4 Connectivity
- 5 Networks
- 6 Analyzing data
- 7 Combining imaging techniques
- 8 Potential pitfalls
- 9 Current and future applications
- 10 See also
- 11 References
Basics of fMRI
Functional magnetic resonance imaging (functional MRI or fMRI) is a specific magnetic resonance imaging (MRI) procedure that measures brain activity by detecting associated changes in blood flow. More specifically, brain activity is measured through low frequency BOLD 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 research, and to a lesser extent, in clinical settings. It can also be combined and complemented with other measures of brain physiology such as EEG and NIRS. Arterial spin labeling fMRI can be used as a complementary approach for assessing resting brain functions.
The physiological blood-flow response largely decides the temporal sensitivity, how well neurons that are active can be measured in BOLD fMRI. The basic time resolution parameter is the sampling rate, or TR, which dictates how often a particular brain slice is excited and allowed to lose its magnetization. TRs could vary from the very short (500 ms) to the very long (3 seconds). For fMRI specifically, the haemodynamic response is assumed to last over 10 seconds, rising multiplicatively (that is, as a proportion of current value), peaking at 4 to 6 seconds, and then falling multiplicatively. Changes in the blood-flow system, the vascular system, integrate responses to neuronal activity over time. Because this response is a smooth continuous function, sampling with faster TRs helps only to map faster fluctuations like respiratory and heart rate signals.
While fMRI strives to measure the neuronal activity in the brain, the BOLD signal can be influenced by many other physiological factors other than neuronal activity. For example, respiratory fluctuations and cardiovascular cycles affect the BOLD signal being measured in the brain and therefore are usually tried to be removed during processing of the raw fMRI data. Due to these sources of noise, there have been many experts who have approached the idea of resting state fMRI very skeptically during the early uses of fMRI. It has only been very recently that researchers have become confident that the signal being measured is not an artifact caused by other physiological function.
Resting state functional connectivity between spatially distinct brain regions reflects the repeated history of co-activation patterns within these regions, thereby serving as a measure of plasticity.
- In 1992, Bharat Biswal started his work as a graduate student at The Medical College of Wisconsin under the direction of his advisor, James S. Hyde, and discovered that the brain, even during rest, contains information about its functional organization. He had used fMRI to study how different regions of the brain communicate while the brain is at rest and not performing any active task. Though at the time, Biswal’s research was mostly disregarded and attributed to another signal source, his resting neuroimaging technique has now been widely replicated and considered a valid method of mapping functional brain networks. Mapping the brain’s activity while it is at rest holds many potentials for brain research and even helps doctors diagnose various diseases of the brain.
- Experiments by neurologist Marcus 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 (the resting state). His lab has been primarily focused on finding the basis of this resting activity and is credited with many groundbreaking discoveries. These include the relative independence of blood flow and oxygen consumption during changes in brain activity, which provided the physiological basis of fMRI, as well the discovery of the well known Default Mode Network.
Functional connectivity is the connectivity between brain regions that share functional properties. More specifically, it can be defined as the temporal correlation between spatially remote neurophysiological events, expressed as deviation from statistical independence 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. Functional connectivity has also been evaluated using the perfusion time series sampled with arterial spin labeled perfusion fMRI. Functional connectivity MRI (fcMRI), which can include resting state fMRI and task-based MRI, might someday help provide more definitive diagnoses for mental health disorders such as bipolar disorder and may also aid in understanding the development and progression of post-traumatic stress disorder as well as evaluate the effect of treatment. Functional connectivity has been suggested to be an expression of the network behavior underlying high level cognitive function partially because unlike structural connectivity, functional connectivity often changes on the order of seconds as in the case of dynamic functional connectivity.
The connectivity of brain regions that are physically or anatomically attached to one another is referred to as structural connectivity. Regions of the brain do not have to be structurally connected to have functional connectivity, and a structural connection does not necessarily signify a functional link. Several initiatives such as the Human Connectome Project try to build a connectome or a neural connectivity map that will shed light on the anatomical as well as functional connectivity within the healthy human brain.
Default mode network
- The default mode network (DMN) is a network of brain regions that are active when an individual is awake and at rest. The default mode network is an interconnected and anatomically defined brain system that preferentially activates when individuals focus on internal tasks such as daydreaming, envisioning the future, retrieving memories, and gauging others' perspectives. It is negatively correlated with brain systems that focus on external visual signals. It is one of the most studied networks present during resting state and is one of the most easily visualized networks.
Other resting state networks
- Depending on the method of resting state analysis, functional connectivity studies have reported a number of neural networks that result to be strongly functionally connected during rest. The key networks, also referred as components, which are more frequently reported include: the DMN, the sensory/motor component, the executive control component, up to three different visual components, two lateralized frontal/parietal components, the auditory component and the temporal/parietal component. As already reported, these resting-state networks consist of anatomically separated, but functionally connected regions displaying a high level of correlated BOLD signal activity. These networks are found to be quite consistent across studies, despite differences in the data acquisition and analysis techniques. Importantly, most of these resting-state components represent known functional networks, that is, regions that are known to share and support cognitive functions.
Many programs exist for the processing and analyzing of resting state fMRI data. Some of the most commonly used programs include SPM, AFNI, FSL (esp. Melodic for ICA), CONN, C-PAC, and Connectome Computation System (CCS).
Methods of analysis
There are many methods of both acquiring and processing rsfMRI data. The most popular methods of analysis focus either on independent components or on regions of correlation.
Independent component analysis
- Independent component analysis (ICA) is a 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 reliably extract default mode network as well as many other networks with very high consistency. ICA remains in the forefront of the research methods.
- Other methods of observing networks and connectivity in the brain include the seed-based d mapping and region of interest (ROI) methods of analysis. In these cases, 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 connectivity in brain areas of interest. Overall connectivity between an ROI (such as the prefrontal cortex) and all other voxels of the brain can also be averaged, providing a measure of global brain connectivity (GBC) specific to that ROI.
Other methods for characterizing resting-state networks include partial correlation, coherence and partial coherence, phase relationships, dynamic time warping distance, clustering, and graph theory.
Reliability and reproducibility
Resting-state functional magnetic resonance imaging (rfMRI) can image low-frequency fluctuations in the spontaneous brain activities, representing a popular tool for macro-scale functional connectomics to characterize inter-individual differences in normal brain function, mind-brain associations, and the various disorders. This suggests reliability and reproducibility for commonly used rfMRI-derived measures of the human brain functional connectomics. These metrics hold great potentials of accelerating biomarker identification for various brain diseases, which call the need of addressing reliability and reproducibility at first place.
Combining imaging techniques
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. This dual technique combines the EEG’s well documented ability to characterize certain brain states with high temporal resolution and to reveal pathological patterns, with fMRI’s (more recently discovered and less well understood) ability to image blood dynamics through the entire brain with high spatial resolution. Up to now, EEG-fMRI has been mainly seen as an fMRI technique in which the synchronously acquired EEG is used to characterize brain activity (‘brain state’) across time allowing to map (through statistical parametric mapping, for example) the associated haemodynamic changes.
The clinical value of these findings is the subject of ongoing investigations, but recent researches suggest an acceptable reliability for EEG-fMRI studies and better sensitivity in higher field scanner. Outside the field of epilepsy, EEG-fMRI has been used to study event-related (triggered by external stimuli) brain responses and provided important new insights into baseline brain activity in during resting wakefulness and sleep.
fMRI with TMS
Transcranial magnetic stimulation (TMS) uses small and relatively precise magnetic fields to stimulate regions of the cortex without dangerous invasive procedures. When these magnetic fields stimulate an area of the cortex, focal blood flow increases at the site of stimulation as well as at distant sites anatomically connected to the stimulated location. Positron emission tomography (PET) can then be used to image the brain and changes in blood flow and results show very similar regions of connectivity confirming networks found in fMRI studies and TMS can also be used to support and provide more detailed information on the connected regions.
Potential pitfalls when using rsfMRI to determine functional network integrity are contamination of the BOLD signal by sources of physiological noise such as heart rate, respiration , and head motion. These confounding factors can often bias results in studies where patients are compared to healthy controls in the direction of hypothesized effects, for example a lower coherence might be found in the default network in the patient group, while the patient groups also moved more during the scan. Also, it has been shown that the use of global signal regression can produce artificial correlations between a small number of signals (e.g., two or three). Fortunately, the brain has many signals.
Current and future applications
Disease Condition and Changes in Resting State Functional Connectivity
- Alzheimer's disease: decreased connectivity
- Mild cognitive impairment: abnormal connectivity 
- Autism: altered connectivity
- Depression and effects of antidepressant treatment: abnormal connectivity
- Bipolar Disorder and effects of mood stabilizers: abnormal connectivity and network properties
- Schizophrenia: disrupted networks
- Attention deficit hyperactivity disorder (ADHD): Altered "small networks" and Thalamus changes
- Aging brain: disruption of brain systems and motor network
- Epilepsy: disruption and decrease/increase in connectivity
- Parkinson's disease: altered connectivity
- Obsessive Compulsive Disorder:increase/decrease in connectivity
- Pain Disorder: altered connectivity
- Anorexia nervosa: connectivity alterations within corticolimbic circuitry and of insular cortex
Other types of current and future 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. As resting state measurements have no cognitive demands (instead of psychological experiments including tasks), cognitively impaired persons can also be measured easily.
- Smith, Stephen M; Andersson, Jesper; Auerbach, Edward J.; Beckmann, Christian F; Bijsterbosch, Janine; Douaud, Gwenaëlle; Duff, Eugene; Feinberg, David A; Griffanti, Ludovica (2013-10-15). "Resting-state fMRI in the Human Connectome Project". NeuroImage. 80: 144–168. doi:10.1016/j.neuroimage.2013.05.039. ISSN 1053-8119. PMC 3720828. PMID 23702415.
- Bhushan, Chitresh; Chong, Minqi; Choi, Soyoung; Joshi, Anand A.; Haldar, Justin P.; Damasio, Hanna; Leahy, Richard M. (2016-07-08). "Temporal Non-Local Means Filtering Reveals Real-Time Whole-Brain Cortical Interactions in Resting fMRI". PLOS ONE. 11 (7): e0158504. Bibcode:2016PLoSO..1158504B. doi:10.1371/journal.pone.0158504. ISSN 1932-6203. PMC 4938391. PMID 27391481.
- Biswal, B. B. (2012). "Resting state fMRI: A personal history. [Review]". NeuroImage. 62 (2): 938–944. doi:10.1016/j.neuroimage.2012.01.090. PMID 22326802.
- Buckner, RL; Krienen, FM; Yeo, BT (2013). "Opportunities and limitations of intrinsic functional connectivity MRI". Nature Neuroscience. 16 (7): 832–837. doi:10.1038/nn.3423. PMID 23799476.
- Sharaev, Maksim G.; Zavyalova, Viktoria V.; Ushakov, Vadim L.; Kartashov, Sergey I.; Velichkovsky, Boris M. (2016). "Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data". Frontiers in Human Neuroscience. 10: 14. doi:10.3389/fnhum.2016.00014. PMC 4740785. PMID 26869900.
- Biswal, B. B. (2011). "Resting State Functional Connectivity". Biological Psychiatry. 69 (9): 200S. doi:10.1016/j.biopsych.2011.03.032.
- Rosazza, C.; Minati, L. (2011). "Resting-state brain networks: literature review and clinical applications". Neurol Sci. 32 (5): 773–785. doi:10.1007/s10072-011-0636-y. PMID 21667095.
- Cole, David M.; Smith, Stephen M.; Beckmann, Christian F. (2010). "Advances and pitfalls in the analysis and interpretation of resting-state FMRI data". Frontiers in Systems Neuroscience. 4: 8. doi:10.3389/fnsys.2010.00008. PMC 2854531. PMID 20407579.
- DeYoe, E. A.; Bandettini, P.; Neitz, J.; Miller, D.; Winans, P. (1994). "Functional magnetic resonance imaging (FMRI) of the human brain". J Neurosci Methods. 54 (2): 171–187. doi:10.1016/0165-0270(94)90191-0.
- Bandettini, P. A. (2009). "Seven topics in functional magnetic resonance imaging". J Integr Neurosci. 8 (3): 371–403. doi:10.1142/s0219635209002186. PMC 3143579. PMID 19938211.
- Bandettini, P (2007). "Functional MRI today". Int J Psychophysiology. 63 (2): 138–145. doi:10.1016/j.ijpsycho.2006.03.016. PMID 16842871.
- Korhonen, V; et al. (2014). "Synchronous multiscale neuroimaging environment for critically sampled physiological analysis of brain function: hepta-scan concept". Brain Connect. 4(9) (9): 677–689. doi:10.1089/brain.2014.0258. PMC 4238249. PMID 25131996.
- Chuang, KH.; et al. (2008). "Mapping resting-state functional connectivity using perfusion MRI". NeuroImage. 40 (4): 1595–605. doi:10.1016/j.neuroimage.2008.01.006. PMC 2435272. PMID 18314354.
- Huettel, S. A.; Song, A. W.; McCarthy, G. (2009), Functional Magnetic Resonance Imaging (2 ed.), Massachusetts: Sinauer, ISBN 978-0-87893-286-3
- Damoiseaux, J. S.; Rombouts, S.; Barkhof, F.; Scheltens, P.; Stam, C. J.; Smith, S. M.; et al. (2006). "Consistent resting-state networks across healthy subjects". Proceedings of the National Academy of Sciences of the United States of America. 103 (37): 13848–13853. Bibcode:2006PNAS..10313848D. doi:10.1073/pnas.0601417103. PMC 1564249. PMID 16945915.
- Guerra-Carrillo, B.; Mackey, A. P.; Bunge, S. A. (21 February 2014). "Resting-State fMRI: A Window into Human Brain Plasticity". The Neuroscientist. 20 (5): 522–533. doi:10.1177/1073858414524442. PMID 24561514.
- Fox, M. D.; Snyder, A. Z.; Vincent, J. L.; Corbetta, M.; Van Essen, D. C.; Raichle, M. E. (2005). "The human brain is intrinsically organized into dynamic, anticorrelated functional networks". Proceedings of the National Academy of Sciences of the United States of America. 102 (27): 9673–9678. Bibcode:2005PNAS..102.9673F. doi:10.1073/pnas.0504136102. PMC 1157105. PMID 15976020.
- Biswal, B. B.; VanKylen, J.; Hyde, J. S. (1997). "Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps". NMR in Biomedicine. 10 (4–5): 165–170. Bibcode:2000NMRBm..13..229M. doi:10.1002/(sici)1099-1492(199706/08)10:4/5<165::aid-nbm454>3.0.co;2-7.
- Friston, K (2009). "Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging. [Editorial Material]". PLOS Biology. 7 (2): 220–225. doi:10.1371/journal.pbio.1000033. PMC 2642881. PMID 19226186.
- Fernandez-Seara, MA (2011). "Effects on resting cerebral blood flow and functional connectivity induced by metoclopramide: a perfusion MRI study in healthy volunteers". Br J Pharmacol. 163 (8): 1639–52. doi:10.1111/j.1476-5381.2010.01161.x. PMC 3166692. PMID 21175574.
- Smith, S. M. (2012). "The future of FMRI connectivity". NeuroImage. 62 (2): 1257–1266. doi:10.1016/j.neuroimage.2012.01.022. PMID 22248579.
- Biswal, B. B.; Mennes, M.; Zuo, X. N.; Gohel, S.; Kelly, C.; Smith, S. M.; et al. (2010). "Toward discovery science of human brain function". Proc Natl Acad Sci U S A. 107 (10): 4734–4739. Bibcode:2010PNAS..107.4734B. doi:10.1073/pnas.0911855107. PMC 2842060. PMID 20176931.
- Van Essen, D. C.; Ugurbil, K. (2012). "The future of the human connectome. [Review]". NeuroImage. 62 (2): 1299–1310. doi:10.1016/j.neuroimage.2012.01.032. PMC 3350760. PMID 22245355.
- Raichle, M. E. (2015). "The brain's default mode network". Annual Review of Neuroscience. 38 (1): 433–447. doi:10.1146/annurev-neuro-071013-014030. PMID 25938726.
- Greicius, M. D.; Krasnow, B.; Reiss, A. L.; Menon, V. (2003). "Functional connectivity in the resting brain: A network analysis of the default mode hypothesis". Proceedings of the National Academy of Sciences of the United States of America. 100 (1): 253–258. Bibcode:2003PNAS..100..253G. doi:10.1073/pnas.0135058100. PMC 140943. PMID 12506194.
- Buckner, R. L. (2012). "The serendipitous discovery of the brain's default network". NeuroImage. 62 (2): 1137–1145. doi:10.1016/j.neuroimage.2011.10.035. PMID 22037421.
- Moussa, M. N.; Steen, M. R.; Laurienti, P. J.; Hayasaka, S. (2012). "Consistency of network modules in resting-state FMRI connectome data". PLOS ONE. 7 (8): e44428. Bibcode:2012PLoSO...744428M. doi:10.1371/journal.pone.0044428. PMC 3432126. PMID 22952978.
- Lee, M. H.; Hacker, C. D.; Snyder, A. Z.; Corbetta, M.; Zhang, D. Y.; Leuthardt, E. C.; et al. (2012). "Clustering of Resting State Networks". PLOS ONE. 7 (7): 7. Bibcode:2012PLoSO...740370L. doi:10.1371/journal.pone.0040370. PMC 3392237. PMID 22792291.
- Kiviniemi, Vesa J.; Kantola, Juha-Heikki; Jauhiainen, Jukka; Hyvärinen, Aapo; Tervonen, Osmo (2003). "Independent component analysis of nondeterministic fMRI signal sources". NeuroImage. 19 (2 Pt 1): 253–260. doi:10.1016/S1053-8119(03)00097-1. PMID 12814576.
- Beckmann, C. F.; DeLuca, M.; Devlin, J. T.; Smith, S. M. (2005). "Investigations into resting-state connectivity using independent component analysis". Philosophical Transactions of the Royal Society B: Biological Sciences. 360 (1457): 1001–1013. doi:10.1098/rstb.2005.1634. PMC 1854918. PMID 16087444.
- Calhoun, VD; de Lacy, N (November 2017). "Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis". Neuroimaging Clinics of North America. 27 (4): 561–579. doi:10.1016/j.nic.2017.06.012. PMID 28985929.
- Margulies, D. S.; Kelly, A. M. C.; Uddin, L. Q.; Biswal, B. B.; Castellanos, F. X.; Milham, M. P. (2007). "Mapping the functional connectivity of anterior cingulate cortex". NeuroImage. 37 (2): 579–588. doi:10.1016/j.neuroimage.2007.05.019. PMID 17604651.
- Van Dijk, KRA; Hedden, T; Venkataraman, A; Evans, KC; Lazar, SW; Buckner, RL (2010). "Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization". Journal of Neurophysiology. 103 (1): 297–321. doi:10.1152/jn.00783.2009. PMC 2807224. PMID 19889849.
- Cole, Michael W.; Yarkoni, Tal; Repovs, Grega; Anticevic, Alan; Braver, Todd S. (2012-06-27). "Global connectivity of prefrontal cortex predicts cognitive control and intelligence". The Journal of Neuroscience. 32 (26): 8988–8999. doi:10.1523/JNEUROSCI.0536-12.2012. ISSN 0270-6474. PMC 3392686. PMID 22745498.
- Chang, C.; Glover, G. H. (2010). "Time-frequency dynamics of resting-state brain connectivity measured with fMRI". NeuroImage. 50 (1): 81–98. doi:10.1016/j.neuroimage.2009.12.011. PMC 2827259. PMID 20006716.
- Faria, A. V.; Joel, S. E.; Zhang, Y. J.; Oishi, K.; van Zjil, P. C. M.; Miller, M. I.; et al. (2012). "Atlas-based analysis of resting-state functional connectivity: Evaluation for reproducibility and multi-modal anatomy-function correlation studies". NeuroImage. 61 (3): 613–621. doi:10.1016/j.neuroimage.2012.03.078. PMC 3358461. PMID 22498656.
- Meszlényi, Regina J.; Hermann, Petra; Buza, Krisztian; Gál, Viktor; Vidnyánszky, Zoltán (2017-01-01). "Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping". Brain Imaging Methods. 11: 75. doi:10.3389/fnins.2017.00075. PMC 5313507. PMID 28261052.
- Zuo, XN; Xing, XX (2014). "Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective". Neuroscience & Biobehavioral Reviews. 45: 100–118. doi:10.1016/j.neubiorev.2014.05.009. PMID 24875392.
- Majeed, W.; Magnuson, M.; Keilholz, S. D. (2009). "Spatiotemporal Dynamics of Low Frequency Fluctuations in BOLD fMRI of the Rat". Journal of Magnetic Resonance Imaging. 30 (2): 384–393. doi:10.1002/jmri.21848. PMC 2758521. PMID 19629982.
- Keilholz, S. D.; Magnuson, M.; Thompson, G. (2010). "Evaluation of data-driven network analysis approaches for functional connectivity MRI". Brain Structure & Function. 215 (2): 129–140. doi:10.1007/s00429-010-0276-7. PMID 20853181.
- Fox, M. D.; Halko, M. A.; Eldaief, M. C.; Pascual-Leone, A. (2012). "Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS)". NeuroImage. 62 (4): 2232–2243. doi:10.1016/j.neuroimage.2012.03.035. PMC 3518426. PMID 22465297.
- Birn, R.M.; Diamond, J.B.; Smith, M.A.; Bandettini, P.A. (2006). "Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI". NeuroImage. 31 (4): 1536–1548. doi:10.1016/j.neuroimage.2006.02.048. PMID 16632379.
- Chang, C., Glover, G.H., 2009. Relationship between respiration, end-tidal CO(2), and BOLD signals in resting-state fMRI. NeuroImage.
- Ing, A; Schwarzbauer, C (2012). "A dual echo approach to motion correction for functional connectivity studies". NeuroImage. 63 (3): 1487–1497. doi:10.1016/j.neuroimage.2012.07.042. PMID 22846657.
- Van Dijk, K.R.A.; Sabuncu, M.R.; Buckner, R.L. (2012). "The influence of head motion on intrinsic functional connectivity MRI". NeuroImage. 59 (1): 431–438. doi:10.1016/j.neuroimage.2011.07.044. PMC 3683830. PMID 21810475.
- Power, J.D.; Barnes, K.A.; Snyder, A.Z.; Schlaggar, B.L.; Petersen, S.E. (2012). "Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion". NeuroImage. 59 (3): 2142–2154. doi:10.1016/j.neuroimage.2011.10.018. PMC 3254728. PMID 22019881.
- Satterthwaite, T.D.; Wolf, D.H.; Loughead, J.; Ruparel, K.; Elliott, M.A.; Hakonarson, H.; Gur, R.C.; Gur, R.E. (2012). "Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth". NeuroImage. 60 (1): 623–632. doi:10.1016/j.neuroimage.2011.12.063. PMC 3746318. PMID 22233733.
- Saad, Z.S.; Gotts, S.J.; Murpy, K.; Chen, G.; Jo, H.J.; Martin, A.; Cox, R.W. (2012). "Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression". Brain Connectivity. 2 (1): 25–32. doi:10.1089/brain.2012.0080. PMC 3484684. PMID 22432927.
- Cordes, D.; Nandy, R.R. (2006). "Estimation of the intrinsic dimensionality of fMRI data". NeuroImage. 29 (1): 145–154. doi:10.1016/j.neuroimage.2005.07.054. PMID 16202626.
- Holtbernd, F.; Eidelberg, D. (2012). "Functional brain networks in movement disorders: recent advances". Current Opinion in Neurology. 25 (4): 392–401. doi:10.1097/wco.0b013e328355aa94. PMC 4554600. PMID 22710361.
- Li, R., Wu, X., Chen, K., Fleisher, A. S., Reiman, E. M., & Yao, L. (2012). Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease. AJNR Am J Neuroradiol.
- Liang, P.; Wang, Z.; Yang, Y.; Li, K. (2012). "Three subsystems of the inferior parietal cortex are differently affected in mild cognitive impairment". Journal of Alzheimer's Disease. 30 (3): 475–487. doi:10.3233/JAD-2012-111721. PMID 22451310.
- Müller, R-A.; Shih, P.; Keehn, B.; Deyoe, J.; Leyden, K.; Shukla, D. (2011). "Underconnected but how? A survey of functional connectivity MRI studies in autism spectrum disorders". Cerebral Cortex. 21 (10): 2233–2243. doi:10.1093/cercor/bhq296. PMC 3169656. PMID 21378114.
- Vigneshwaran S, Mahanand B. S., Suresh S, Sundararajan N. Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging: A spatial filtering approach. Medical image analysis. 2017;35:375–389. doi:10.1016/j.media.2016.08.003.
- Anand, A; Li, Y; Wang, Y; Wu, J; Gao, S; Kalnin, A; et al. (2005). "Activity and connectivity of mood regulating circuit in depression: a functional magnetic resonance study". Biological Psychiatry. 57 (10): 1079–88. doi:10.1016/j.biopsych.2005.02.021. PMID 15866546.
- Greicius, MD; Flores, BH; Menon, V; Glover, GH; Solvason, HB; Kenna, H; et al. (2007). "Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus". Biological Psychiatry. 62 (5): 429–37. doi:10.1016/j.biopsych.2006.09.020. PMC 2001244. PMID 17210143.
- Anand, Amit; Li, Yu; Wang, Yang; Wu, Jingwei; Gao, Sujuan; Bukhari, Lubna; Mathews, Vincent P; Kalnin, Andrew; Lowe, Mark J (July 2005). "Antidepressant Effect on Connectivity of the Mood-Regulating Circuit: An fMRI Study". Neuropsychopharmacology. 30 (7): 1334–1344. doi:10.1038/sj.npp.1300725. ISSN 1740-634X. PMID 15856081.
- Anand, Amit; Li, Yu; Wang, Yang; Gardner, Kathryn; Lowe, Mark J. (2007-07-01). "Reciprocal Effects of Antidepressant Treatment on Activity and Connectivity of the Mood Regulating Circuit: An fMRI Study". The Journal of Neuropsychiatry and Clinical Neurosciences. 19 (3): 274–282. doi:10.1176/jnp.2007.19.3.274. ISSN 0895-0172. PMC 3465666. PMID 17827412.
- Anand, A; Li, Y; Wang, Y; Lowe, MJ; Dzemidzic, M (2009). "Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression". Psychiatry Research-Neuroimaging. 171 (3): 189–98. doi:10.1016/j.pscychresns.2008.03.012. PMC 3001251. PMID 19230623.
- Spielberg, Jeffrey M; Beall, Erik B; Hulvershorn, Leslie A; Altinay, Murat; Karne, Harish; Anand, Amit (December 2016). "Resting State Brain Network Disturbances Related to Hypomania and Depression in Medication-Free Bipolar Disorder". Neuropsychopharmacology. 41 (13): 3016–3024. doi:10.1038/npp.2016.112. ISSN 1740-634X. PMC 5101549. PMID 27356764.
- Altinay, Murat I.; Hulvershorn, Leslie A.; Karne, Harish; Beall, Erik B.; Anand, Amit (2015-12-20). "Differential Resting-State Functional Connectivity of Striatal Subregions in Bipolar Depression and Hypomania". Brain Connectivity. 6 (3): 255–265. doi:10.1089/brain.2015.0396. ISSN 2158-0014. PMC 4827275. PMID 26824737.
- Altinay, Murat; Karne, Harish; Anand, Amit (2018). "Lithium monotherapy associated clinical improvement effects on amygdala-ventromedial prefrontal cortex resting state connectivity in bipolar disorder". Journal of Affective Disorders. 225: 4–12. doi:10.1016/j.jad.2017.06.047. PMC 5844774. PMID 28772145.
- Venkataraman, A.; Whitford, T. J.; Westin, C. F.; Golland, P.; Kubicki, M. (2012). "Whole brain resting state functional connectivity abnormalities in schizophrenia". Schizophrenia Research. 139 (1–3): 7–12. doi:10.1016/j.schres.2012.04.021. hdl:1721.1/100215. PMC 3393792. PMID 22633528.
- Uddin, L. Q.; Kelly, A. M. C.; Biswal, B. B.; Margulies, D. S.; Shehzad, Z.; Shaw, D.; et al. (2008). "Network homogeneity reveals decreased integrity of default-mode network in ADHD". Journal of Neuroscience Methods. 169 (1): 249–254. doi:10.1016/j.jneumeth.2007.11.031. PMID 18190970.
- Wurina, Zang; Zhao, S. G. (2012). "Resting-state fMRI studies in epilepsy". Neuroscience Bulletin. 28 (4): 449–455. doi:10.1007/s12264-012-1255-1. PMC 5561896. PMID 22833042.
- Tessitore, A.; Amboni, M.; Esposito, F.; Russo, A.; Picillo, M.; Marcuccio, L.; et al. (2012). "Resting-state brain connectivity in patients with Parkinson's disease and freezing of gait". Parkinsonism & Related Disorders. 18 (6): 781–787. doi:10.1016/j.parkreldis.2012.03.018. PMID 22510204.
- Li, P.; Li, S. F.; Dong, Z. Y.; Luo, J.; Han, H. Y.; Xiong, H. F.; et al. (2012). "Altered resting state functional connectivity patterns of the anterior prefrontal cortex in obsessive-compulsive disorder". NeuroReport. 23 (11): 681–686. doi:10.1097/wnr.0b013e328355a5fe. PMID 22692554.
- Otti, A; Guendel, H; Wohlschlager, A; Zimmer, C; Noll-Hussong, M (2013). "Frequency shifts in the anterior default mode network and the salience network in chronic pain disorder". BMC Psychiatry. 13: 84. doi:10.1186/1471-244x-13-84. PMC 3616999. PMID 23497482.
- Otti, A; Guendel, H; Henningsen, P; Zimmer, C; Wohlschlaeger, AM; Noll-Hussong, M (Jan 2013). "Functional network connectivity of pain-related resting state networks in somatoform pain disorder: an exploratory fMRI study". J Psychiatry Neurosci. 38 (1): 57–65. doi:10.1503/jpn.110187. PMC 3529220. PMID 22894821.
- Gaudio S, Wiemerslage L, Brooks SJ, Schiöth HB (2016). "A systematic review of resting-state functional-MRI studies in anorexia nervosa: Evidence for functional connectivity impairment in cognitive control and visuospatial and body-signal integration". Neurosci Biobehav Rev. 71: 578–589. doi:10.1016/j.neubiorev.2016.09.032. PMID 27725172.
- Fox, M. D.; Greicius, M. (2010). "Clinical applications of resting state functional connectivity". Front Syst Neurosci. 4: 19.