Dynamic functional connectivity

Last updated

Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.

Contents

Overview and history

Static connectivity

Functional connectivity refers to the functionally integrated relationship between spatially separated brain regions. Unlike structural connectivity which looks for physical connections in the brain, functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. [1] This type of connectivity was discovered in the mid-1990s and has been seen primarily using fMRI and Positron emission tomography. [2] Functional connectivity is usually measured during resting state fMRI and is typically analyzed in terms of correlation, coherence, and spatial grouping based on temporal similarities. [3] These methods have been used to show that functional connectivity is related to behavior in a variety of different tasks, and that it has a neural basis. These methods assume the functional connections in the brain remain constant in a short time over a task or period of data collection.

The default mode network above is one example of a brain network seen using steady state connectivity. This network is fairly stable in time, but it has been shown to have a variable relationship with other networks, and to vary slightly in its own characteristics in time. Default mode network-WRNMMC.jpg
The default mode network above is one example of a brain network seen using steady state connectivity. This network is fairly stable in time, but it has been shown to have a variable relationship with other networks, and to vary slightly in its own characteristics in time.

The origin of dynamic analysis

Studies that showed brain state dependent changes in functional connectivity were the first indicators that temporal variation in functional connectivity may be significant. Several studies in the mid-2000s examined the changes in FC that were related to a variety of different causes such as mental tasks, [4] sleep, [5] and learning. [6] These changes often occur within the same individual and are clearly relevant to behavior. DFC has now been investigated in a variety of different contexts with many analysis tools. It has been shown to be related to both behavior and neural activity. Some researchers believe that it may be heavily related to high level thought or consciousness. [3]

Significant findings from DFC

Because DFC is such a new field, much of the research related to it is conducted to validate the relevance of these dynamic changes rather than explore their implications; however, many critical findings have been made that help the scientific community better understand the brain. Analysis of dynamic functional connectivity has shown that far from being completely static, the functional networks of the brain fluctuate on the scale of seconds to minutes. These changes are generally seen as movements from one short term state to another, rather than continuous shifts. [3] Many studies have shown reproducible patterns of network activity that move throughout the brain. These patterns have been seen in both animals and humans, and are present at only certain points during a scanner session. [7] In addition to showing transient brain states, DFC analysis has shown a distinct hierarchical organization of the networks of the brain. Connectivity between bilaterally symmetric regions is the most stable form of connectivity in the brain, followed by other regions with direct anatomical connections. Steady state functional connectivity networks exist and have physiological relevance, but have less temporal stability than the anatomical networks. Finally, some functional networks are fleeting enough to only be seen with DFC analysis. These networks also possess physiological relevance but are much less temporally stable than the other networks in the brain. [8]

Methods of analysis

Sliding window

Sliding window analysis is the most common method used in the analysis of functional connectivity, first introduced by Sakoglu and Calhoun in 2009, and applied to schizophrenia. [9] [10] [11] [12] Sliding window analysis is performed by conducting analysis on a set number of scans in an fMRI session. The number of scans is the length of the sliding window. The defined window is then moved a certain number of scans forward in time and additional analysis is performed. The movement of the window is usually referenced in terms of the degree of overlap between adjacent windows. One of the principle benefits of sliding window analysis is that almost any steady state analysis can also be performed using sliding window if the window length is sufficiently large. Sliding window analysis also has a benefit of being easy to understand and in some ways easier to interpret. [3] As the most common method of analysis, sliding window analysis has been used in many different ways to investigate a variety of different characteristics and implications of DFC. In order to be accurately interpreted, data from sliding window analysis generally must be compared between two different groups. Researchers have used this type of analysis to show different DFC characteristics in diseased and healthy patients, high and low performers on cognitive tasks, and between large scale brain states.

Activation patterns

One of the first methods ever used to analyze DFC was pattern analysis of fMRI images to show that there are patterns of activation in spatially separated brain regions that tend to have synchronous activity. It has become clear that there is a spatial and temporal periodicity in the brain that probably reflects some of the constant processes of the brain. Repeating patterns of network information have been suggested to account for 25–50% of the variance in fMRI BOLD data. [7] [13] These patterns of activity have primarily been seen in rats as a propagating wave of synchronized activity along the cortex. These waves have also been shown to be related to underlying neural activity, and has been shown to be present in humans as well as rats. [7]

Point process analysis

Departing from the traditional approaches, recently an efficient method was introduced to analyze rapidly changing functional activations patterns which transforms the fMRI BOLD data into a point process. [14] [15] This is achieved by selecting for each voxel the points of inflection of the BOLD signal (i.e., the peaks). These few points contain a great portion of the information pertaining functional connectivity, because it has been demonstrated, that despite the tremendous reduction on the data size (> 95%), it compares very well with inferences of functional connectivity [16] [17] obtained with standard methods which uses the full signal.

The large information content of these few points is consistent with the results of Petridou et al. [18] who demonstrated he contribution of these "spontaneous events" to the correlation strength and power spectra of the slow spontaneous fluctuations by deconvolving the task hemodynamic response function from the rest data. Subsequently, similar principles were successfully applied under the name of co-activation patterns (CAP). [19] [20] [21]

Other methods

Time-frequency analysis has been proposed as an analysis method that is capable of overcoming many of the challenges associated with sliding windows. Unlike sliding window analysis, time frequency analysis allows the researcher to investigate both frequency and amplitude information simultaneously. The wavelet transform has been used to conduct DFC analysis that has validated the existence of DFC by showing its significant changes in time. This same method has recently been used to investigate some of the dynamic characteristics of accepted networks. For example, time frequency analysis has shown that the anticorrelation between the default mode network and the task-positive network is not constant in time but rather is a temporary state. [22] Independent component analysis has become one of the most common methods of network generation in steady state functional connectivity. ICA divides fMRI signal into several spatial components that have similar temporal patterns. More recently, ICA has been used to divide fMRI data into different temporal components. This has been termed temporal ICA and it has been used to plot network behavior that accounts for 25% of variability in the correlation of anatomical nodes in fMRI. [23]

Controversy and limitations

Several researchers have argued that DFC may be a simple reflection of analysis, scanner, or physiological noise. Noise in fMRI can arise from a variety of different factors including heart beat, changes in the blood brain barrier, characteristics of the acquiring scanner, or unintended effects of analysis. Some researchers have proposed that the variability in functional connectivity in fMRI studies is consistent with the variability that one would expect from simply analyzing random data. This complaint that DFC may reflect only noise has been recently lessened by the observation of electrical basis to fMRI DFC data and behavioral relevance of DFC characteristics. [3]

In addition to complaints that DFC may be a product of scanner noise, observed DFC could be criticized based on the indirect nature of fMRI which is used to observe it. fMRI data is collected by quickly acquiring a sequence of MRI images in time using echo planar imaging. The contrast in these images is heavily influenced by the ratio of oxygenated and deoxygenated blood. Since active neurons require more energy than resting neurons, changes in this contrast is traditionally interpreted an indirect measure of neural activity. Because of its indirect nature, fMRI data in DFC studies could be criticized as potentially being a reflection of non neural information. This concern has been alleviated recently by the observed correlation between fMRI DFC and simultaneously acquired electrophysiology data. [24] Battaglia and colleagues have tried to address those controversies, linking dynamic functional connectivity to causality or effective connectivity. The scientists claim indeed that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. [25]

Physiological evidence

fMRI is the primary means of investigating DFC. This presents unique challenges because fMRI has fairly low temporal resolution, typically 0.5 Hz, and is only an indirect measure of neural activity. The indirect nature of fMRI analysis suggests that validation is needed to show that findings from fMRI are actually relevant and reflective of neural activity.

Multi modal approach

Electrophysiology

Correlation between DFC and electrophysiology has led some scientists to suggest that DFC could reflect hemodynamic results of dynamic network behavior that has been seen in single cell analysis of neuron populations. Although hemodynamic response is too slow to reflect a one-to-one correspondence with neural network dynamics, it is plausible that DFC is a reflection of the power of some frequencies of electrophysiology data. [3]

Full EEG caps like the one above are often used simultaneously with fMRI in order to capture information about the electrical signals underlying the BOLD signal. EEG recording.jpg
Full EEG caps like the one above are often used simultaneously with fMRI in order to capture information about the electrical signals underlying the BOLD signal.

Electroencephalography (EEG) has also been used in humans to both validate and interpret observations made in DFC. EEG has poor spatial resolution because it is only able to acquire data on the surface of the scalp, but it is reflective of broad electrical activity from many neurons. EEG has been used simultaneously with fMRI to account for some of the inter scan variance in FC. EEG has also been used to show that changes in FC are related to broad brain states observed in EEG. [26] [27] [28] [29]

MEG

Magnetoencephalography (MEG) can be used to measure the magnetic fields produced by electrical activity in the brain. MEG has high temporal resolution and has generally higher spatial resolution than EEG. Resting state studies with MEG are still limited by spatial resolution, but the modality has been used to show that resting state networks move through periods of low and high levels of correlation. This observation is consistent with the results seen in other DFC studies such as DFC activation pattern analysis. [3]

Neuronal mechanisms

Single-unit recording were used in order to explore the extent, strength and plasticity of functional connectivity between individual cortical neurons in cats and monkeys. Such studies revealed correlated activity at various time scales. At the fastest time scale, that of 1 – 20 ms, correlation coefficients were typically < 0.05. [30] [31] These functional connections were found to be plastic – changing the correlation for a conditioning period of Ts (typically a few minutes), by means of spike-triggered sensory stimulations, induced short-term (typically < Ts) lasting changes of the connections. The pre-post conditioning strengthening of a functional connection was typically equal to the square root of its pre-during conditioning strengthening. [32]

Dynamic Functional Connectivity studied using fMRI may be related to a phenomenon previously discovered in macaque prefrontal cortex termed Dynamic Network Connectivity, whereby arousal mechanisms rapidly alter the strength of glutamate synaptic connections onto dendritic spines by opening or closing potassium channels on spines, thus weakening or strengthening connectivity, respectively. [33] [34] For example, dopamine D1 receptor and/or noradrenergic beta-1 receptor stimulation on spines can increase cAMP-PKA-calcium signaling to open HCN, KCNQ2, and/or SK channels to rapidly weaken a connection, e.g. as occurs during stress. [35]

Behavioral basis

DFC has been shown to be significantly related to human performance, including vigilance and aspects of attention. It has been proposed and supported that the network behavior immediately prior to a task onset is a strong predictor of performance on that task. Traditionally, fMRI studies have focused on the magnitude of activation in brain regions as a predictor of performance, but recent research has shown that correlation between networks as measured with sliding window analysis is an even stronger predictor of performance. [24] Individual differences in functional connectivity variability (FCV) across sliding windows within fMRI scans have been shown to correlate with the tendency to attend to pain. [36] The degree to which a subject is mind wandering away from a sensory stimulus has also been related to FCV. [37]

Clinical relevance

One of the principal motivations of DFC analysis is to better understand, detect and treat neurological diseases. Static functional connectivity has been shown to be significantly related to a variety of diseases such as depression, schizophrenia, and Alzheimer's disease. Because of the newness of the field, DFC has only recently been used to investigate disease states, but since 2012 each of these three diseases has been shown to be correlated to dynamic temporal characteristics in functional connectivity. Most of these differences are related to the amount of time that is spent in different transient states. Patients with Schizophrenia have less frequent state changes than healthy patients, and this result has led to the suggestion that the disease is related to patients being stuck in certain brain states where the brain is unable to respond quickly to different queues. [38] Also, a study in the visual sensory network showed that schizophrenia subjects spent more time than the healthy subjects in a state in which the connectivity between the middle temporal gyrus and other regions of the visual sensory network is highly negative. [39] Studies with Alzheimer's disease have shown that patients with this ailment have altered network connectivity as well as altered time spent in the networks that are present. [40] The observed correlation between DFC and disease does not imply that the changes in DFC are the cause of any of these diseases, but information from DFC analysis may be used to better understand the effects of the disease and to more quickly and accurately diagnose them.

Related Research Articles

<span class="mw-page-title-main">Magnetoencephalography</span> Mapping brain activity by recording magnetic fields produced by currents in the brain

Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs are currently the most common magnetometer, while the SERF magnetometer is being investigated for future machines. Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and neurofeedback. This can be applied in a clinical setting to find locations of abnormalities as well as in an experimental setting to simply measure brain activity.

<span class="mw-page-title-main">Functional magnetic resonance imaging</span> MRI procedure that measures brain activity by detecting associated changes in blood flow

Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.

<span class="mw-page-title-main">Functional neuroimaging</span>

Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.

Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.

<span class="mw-page-title-main">Neural oscillation</span> Brainwaves, repetitive patterns of neural activity in the central nervous system

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.

<span class="mw-page-title-main">Functional near-infrared spectroscopy</span> Optical technique for monitoring brain activity

Functional near-infrared spectroscopy (fNIRS) is an optical brain monitoring technique which uses near-infrared spectroscopy for the purpose of functional neuroimaging. Using fNIRS, brain activity is measured by using near-infrared light to estimate cortical hemodynamic activity which occur in response to neural activity. Alongside EEG, fNIRS is one of the most common non-invasive neuroimaging techniques which can be used in portable contexts. The signal is often compared with the BOLD signal measured by fMRI and is capable of measuring changes both in oxy- and deoxyhemoglobin concentration, but can only measure from regions near the cortical surface. fNIRS may also be referred to as Optical Topography (OT) and is sometimes referred to simply as NIRS.

<span class="mw-page-title-main">Posterior cingulate cortex</span> Caudal part of the cingulate cortex of the brain

The posterior cingulate cortex (PCC) is the caudal part of the cingulate cortex, located posterior to the anterior cingulate cortex. This is the upper part of the "limbic lobe". The cingulate cortex is made up of an area around the midline of the brain. Surrounding areas include the retrosplenial cortex and the precuneus.

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.

<span class="mw-page-title-main">Default mode network</span> Large-scale brain network active when not focusing on an external task

In neuroscience, the default mode network (DMN), also known as the default network, default state network, or anatomically the medial frontoparietal network (M-FPN), is a large-scale brain network primarily composed of the dorsal medial prefrontal cortex, posterior cingulate cortex, precuneus and angular gyrus. It is best known for being active when a person is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming and mind-wandering. It can also be active during detailed thoughts related to external task performance. Other times that the DMN is active include when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future. The DMN creates a coherent "internal narrative" control to the construction of a sense of self.

EEG microstates are transient, patterned, quasi-stable states or patterns of an electroencephalogram. These tend to last anywhere from milliseconds to seconds and are hypothesized to be the most basic instantiations of human neurological tasks, and are thus nicknamed "the atoms of thought". Microstate estimation and analysis was originally done using alpha band activity, though broader bandwidth EEG bands are now typically used. The quasi-stability of microstates means that the "global [EEG] topography is fixed, but strength might vary and polarity invert."

Brain connectivity estimators represent patterns of links in the brain. Connectivity can be considered at different levels of the brain's organisation: from neurons, to neural assemblies and brain structures. Brain connectivity involves different concepts such as: neuroanatomical or structural connectivity, functional connectivity and effective connectivity.

<span class="mw-page-title-main">Resting state fMRI</span> Type of functional magnetic resonance imaging

Resting state 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 brain networks have been identified, one of which is the default mode network. These brain networks 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.

<span class="mw-page-title-main">Large-scale brain network</span> Collections of brain regions

Large-scale brain networks are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.

Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods.

<span class="mw-page-title-main">Functional ultrasound imaging</span> Ultrasound technique

Functional ultrasound imaging (fUS) is a medical ultrasound imaging technique of detecting or measuring changes in neural activities or metabolism, for example, the loci of brain activity, typically through measuring blood flow or hemodynamic changes. The method can be seen as an extension of Doppler imaging.

Network neuroscience is an approach to understanding the structure and function of the human brain through an approach of network science, through the paradigm of graph theory. A network is a connection of many brain regions that interact with each other to give rise to a particular function. Network Neuroscience is a broad field that studies the brain in an integrative way by recording, analyzing, and mapping the brain in various ways. The field studies the brain at multiple scales of analysis to ultimately explain brain systems, behavior, and dysfunction of behavior in psychiatric and neurological diseases. Network neuroscience provides an important theoretical base for understanding neurobiological systems at multiple scales of analysis.

<span class="mw-page-title-main">Dimitri Van De Ville</span> Swiss-Belgian computer scientist and neuroscientist specialized in brain activity networks

Dimitri Van De Ville is a Swiss and Belgian computer scientist and neuroscientist specialized in dynamical and network aspects of brain activity. He is a professor of bioengineering at EPFL and the head of the Medical Image Processing Laboratory at EPFL's School of Engineering.

Neural synchrony is the correlation of brain activity across two or more people over time. In social and affective neuroscience, neural synchrony specifically refers to the degree of similarity between the spatio-temporal neural fluctuations of multiple people. This phenomenon represents the convergence and coupling of different people's neurocognitive systems, and it is thought to be the neural substrate for many forms of interpersonal dynamics and shared experiences. Some research also refers to neural synchrony as inter-brain synchrony, brain-to-brain coupling, inter-subject correlation, between-brain connectivity, or neural coupling. In the current literature, neural synchrony is notably distinct from intra-brain synchrony—sometimes also called neural synchrony—which denotes the coupling of activity across regions of a single individual's brain.

Functional MRI imaging methods have allowed researchers to combine neurocognitive testing with structural neuroanatomical measures, consider cognitive and affective paradigms, and create computer-aided diagnosis techniques and algorithms. Functional MRI has several benefits, such as its non-invasive quality, relatively high spatial resolution, and decent temporal resolution. One particular method used in recent research is resting-state functional magnetic resonance imaging, rs-fMRI. fMRI imaging has been applied to numerous behavioral studies for schizophrenia, the findings of which have hinted toward potential brain regions that govern key characteristics in cognition and affect.

Susan Whitfield-Gabrieli is an American scientist, psychologist/neuroscientist, academic and researcher. She is a professor of psychology, the Founding Director of the Biomedical Imaging Center at Northeastern University, Researcher in the Department of Psychiatry at Massachusetts General Hospital, Harvard Medical School and a Research Affiliate of McGovern Institute for Brain Research at Massachusetts Institute of Technology.

References

  1. ↑ Friston, Karl (2011). "Functional and Effective Connectivity: a review". Brain Connectivity. 1 (1): 13–36. CiteSeerX   10.1.1.222.9471 . doi:10.1089/brain.2011.0008. PMID   22432952. S2CID   6116761.
  2. ↑ Biswal, B.; Zerrin Yetkin, F. Z.; Haughton, V. M.; Hyde, J. S. (1995). "Functional connectivity in the motor cortex of resting human brain using echo-planar MRI". Magnetic Resonance in Medicine. 34 (4): 537–541. doi:10.1002/mrm.1910340409. PMID   8524021. S2CID   775793.
  3. 1 2 3 4 5 6 7 Hutchison, R. M.; Womelsdorf, T.; Allen, E. A.; Bandettini, P. A.; Calhoun, V. D.; Corbetta, M.; Della Penna, S.; Duyn, J. H.; Glover, G. H.; Gonzalez-Castillo, J.; Handwerker, D. A.; Keilholz, S.; Kiviniemi, V.; Leopold, D. A.; De Pasquale, F.; Sporns, O.; Walter, M.; Chang, C. (2013). "Dynamic functional connectivity: Promise, issues, and interpretations". NeuroImage. 80: 360–378. doi:10.1016/j.neuroimage.2013.05.079. PMC   3807588 . PMID   23707587.
  4. ↑ Esposito, F.; Bertolino, A.; Scarabino, T.; Latorre, V.; Blasi, G.; Popolizio, T.; Tedeschi, G.; Cirillo, S.; Goebel, R.; Di Salle, F. (2006). "Independent component model of the default-mode brain function: Assessing the impact of active thinking". Brain Research Bulletin. 70 (4–6): 263–269. doi:10.1016/j.brainresbull.2006.06.012. PMID   17027761. S2CID   23195652.
  5. ↑ Horovitz, S. G.; Fukunaga, M.; De Zwart, J. A.; Van Gelderen, P.; Fulton, S. C.; Balkin, T. J.; Duyn, J. H. (2008). "Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG-fMRI study". Human Brain Mapping. 29 (6): 671–682. doi:10.1002/hbm.20428. PMC   6871022 . PMID   17598166.
  6. ↑ Bassett, D. S.; Wymbs, N. F.; Porter, M. A.; Mucha, P. J.; Carlson, J. M.; Grafton, S. T. (2011). "Dynamic reconfiguration of human brain networks during learning". Proceedings of the National Academy of Sciences. 108 (18): 7641–7646. arXiv: 1010.3775 . Bibcode:2011PNAS..108.7641B. doi: 10.1073/pnas.1018985108 . PMC   3088578 . PMID   21502525.
  7. 1 2 3 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.
  8. ↑ Gonzalez, Castillo; J. Wu; P. Robinson; M. Handwerker; D. Inati; S. Bandettini (2012). Detection of task transitions on 45mins long continuous muli task runs using whole brain connectivity. Biennial Resting state Conference. Magdeburg, Germany.
  9. ↑ Sakoglu, U.; Calhoun, V. D. (2009). "Dynamic windowing reveals task-modulation of functional connectivity in schizophrenia patients vs healthy controls" (PDF). Proc. ISMRM. 17: 3675.
  10. ↑ Sakoglu, U.; Calhoun, V. D. (2009). "Temporal Dynamics of Functional Network Connectivity at Rest: A Comparison of Schizophrenia Patients and Healthy Controls". NeuroImage. 47 (Suppl. 1): S169. doi:10.1016/S1053-8119(09)71811-7. S2CID   54291742.
  11. ↑ Sakoglu, U.; Michael, A. M.; Calhoun, V. D. (2009). "Classification of schizophrenia patients vs healthy controls with dynamic functional network connectivity". NeuroImage. 47 (Suppl. 1): S57. doi:10.1016/S1053-8119(09)70216-2. S2CID   54432053.
  12. ↑ Sakoglu, U.; Pearlson, G. D.; Kiehl, K. A.; Wang, Y. M.; Michael, A. M.; Calhoun, V. D. (2010). "A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia". Magnetic Resonance Materials in Physics, Biology and Medicine. 23 (6): 351–366. doi:10.1007/s10334-010-0197-8. PMC   2891285 . PMID   20162320.
  13. ↑ Majeed, W.; Magnuson, M.; Hasenkamp, W.; Schwarb, H.; Schumacher, E. H.; Barsalou, L.; Keilholz, S. D. (2011). "Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans". NeuroImage. 54 (2): 1140–1150. doi:10.1016/j.neuroimage.2010.08.030. PMC   2997178 . PMID   20728554.
  14. ↑ Tagliazucchi, Enzo; Balenzuela, Pablo; Fraiman, Daniel; Montoya, Pedro; Chialvo, Dante R. (2011-01-20). "Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state". Neuroscience Letters. 488 (2): 158–163. doi:10.1016/j.neulet.2010.11.020. PMC   3014405 . PMID   21078369.
  15. ↑ Tagliazucchi, Enzo; Balenzuela, Pablo; Fraiman, Daniel; Chialvo, Dante R. (2012-01-01). "Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis". Frontiers in Physiology. 3: 15. doi: 10.3389/fphys.2012.00015 . PMC   3274757 . PMID   22347863.
  16. ↑ Tagliazucchi, Enzo; Carhart-Harris, Robin; Leech, Robert; Nutt, David; Chialvo, Dante R. (2014-11-01). "Enhanced repertoire of brain dynamical states during the psychedelic experience". Human Brain Mapping. 35 (11): 5442–5456. arXiv: 1405.6466 . Bibcode:2014arXiv1405.6466T. doi:10.1002/hbm.22562. ISSN   1097-0193. PMC   6869695 . PMID   24989126.
  17. ↑ Tagliazucchi, Enzo; Siniatchkin, Michael; Laufs, Helmut; Chialvo, Dante R. (2016-01-01). "The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process". Frontiers in Neuroscience. 10: 381. doi: 10.3389/fnins.2016.00381 . PMC   4994538 . PMID   27601975.
  18. ↑ Petridou, Natalia; Gaudes, César Caballero; Dryden, Ian L.; Francis, Susan T.; Gowland, Penny A. (2013-06-01). "Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity". Human Brain Mapping. 34 (6): 1319–1329. doi:10.1002/hbm.21513. ISSN   1097-0193. PMC   6869909 . PMID   22331588.
  19. ↑ Liu, Xiao; Duyn, Jeff H. (2013-03-12). "Time-varying functional network information extracted from brief instances of spontaneous brain activity". Proceedings of the National Academy of Sciences. 110 (11): 4392–4397. Bibcode:2013PNAS..110.4392L. doi: 10.1073/pnas.1216856110 . ISSN   0027-8424. PMC   3600481 . PMID   23440216.
  20. ↑ Liu, Xiao; Chang, Catie; Duyn, Jeff H. (2013-01-01). "Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns". Frontiers in Systems Neuroscience. 7: 101. doi: 10.3389/fnsys.2013.00101 . PMC   3913885 . PMID   24550788.
  21. ↑ Chen, Jingyuan E.; Chang, Catie; Greicius, Michael D.; Glover, Gary H. (2015-05-01). "Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics". NeuroImage. 111: 476–488. doi:10.1016/j.neuroimage.2015.01.057. PMC   4386757 . PMID   25662866.
  22. ↑ 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.
  23. ↑ Weissman-Fogel, I.; Moayedi, M.; Taylor, K. S.; Pope, G.; Davis, K. D. (2010). "Cognitive and default-mode resting state networks: Do male and female brains "rest" differently?". Human Brain Mapping. 31 (11): 1713–1726. doi:10.1002/hbm.20968. PMC   6870948 . PMID   20725910.
  24. 1 2 Thompson, G. J.; Magnuson, M. E.; Merritt, M. D.; Schwarb, H.; Pan, W. J.; McKinley, A.; Tripp, L. D.; Schumacher, E. H.; Keilholz, S. D. (2013). "Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually". Human Brain Mapping. 34 (12): 3280–3298. doi:10.1002/hbm.22140. PMC   6870033 . PMID   22736565.
  25. ↑ Battaglia, Demian; Witt, Annette; Wolf, Fred; Geisel, Theo (2012). "Dynamic effective connectivity of inter-areal brain circuits". PLoS Computational Biology. 8 (3): e1002438.
  26. ↑ Thompson, G. J.; Merritt, M. D.; Pan, W. J.; Magnuson, M. E.; Grooms, J. K.; Jaeger, D.; Keilholz, S. D. (2013). "Neural correlates of time-varying functional connectivity in the rat". NeuroImage. 83: 826–836. doi:10.1016/j.neuroimage.2013.07.036. PMC   3815981 . PMID   23876248.
  27. ↑ Tagliazucchi, E; von Wegner, F; Morzelewski, A; Brodbeck, V; Laufs, H (2012). "Dynamic BOLD functional connectivity in humans and its electrophysiological correlates". Frontiers in Human Neuroscience. 6: 339. doi: 10.3389/fnhum.2012.00339 . PMC   3531919 . PMID   23293596.
  28. ↑ Chang, C; Liu, Z; Chen, M. C.; Liu, X; Duyn, J. H. (2013). "EEG correlates of time-varying BOLD functional connectivity". NeuroImage. 72: 227–36. doi:10.1016/j.neuroimage.2013.01.049. PMC   3602157 . PMID   23376790.
  29. ↑ Mehrkanoon, S; Breakspear, M; Boonstra, T. W. (2014). "Low-dimensional dynamics of resting-state cortical activity". Brain Topography. 27 (3): 338–52. doi:10.1007/s10548-013-0319-5. PMID   24104726. S2CID   16494240.
  30. ↑ Frostig, R.D., Gottlieb, Y., Vaadia, E. & Abeles, M. The effects of stimuli on the activity and functional connectivity of local neuronal groups in the cat auditory cortex. Brain Res. 272, 211-221 (1983)
  31. ↑ Aertsen, A., et al. Neural interactions in the frontal cortex of a behaving monkey: signs of dependence on stimulus context and behavioral state. J.Hirnforsch. 32, 735-743 (1991)
  32. ↑ Ahissar, E., et al. Dependence of cortical plasticity on correlated activity of single neurons and on behavioral context. Science 257, 1412-1415 (1992)
  33. ↑ Arnsten, A.F.T., Wang, M., Paspalas, C.D. Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron. 76, 223-39 (2012)
  34. ↑ Arnsten, A.F.T., Wang, M., D'Esposito. Dynamic Network Connectivity: from monkeys to humans. Front Hum Neurosci. 18, 1353043 (2024)
  35. ↑ Datta, D., Yang, S. et al., Key Roles of CACNA1C/Cav1.2 and CALB1/Calbindin in Prefrontal Neurons Altered in Cognitive Disorders. JAMA Psychiatry. epub May 22 (2024)
  36. ↑ Kucyi, A; Salomons, T. V.; Davis, K. D. (2013). "Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks". Proceedings of the National Academy of Sciences. 110 (46): 18692–7. Bibcode:2013PNAS..11018692K. doi: 10.1073/pnas.1312902110 . PMC   3832014 . PMID   24167282.
  37. ↑ Kucyi, A; Davis, K. D. (2014). "Dynamic functional connectivity of the default mode network tracks daydreaming". NeuroImage. 100: 471–80. doi:10.1016/j.neuroimage.2014.06.044. PMID   24973603. S2CID   13082197.
  38. ↑ Damaraju, E.; J. Turner; A. Preda; T. Erp Van; D. Mathalon; J.M. Ford; S. Potkin; V.D. Calhoun (2012). "Static and dynamic functional network connectivity during resting state in schizophrenia". American College of Neuropsychopharmacology.
  39. ↑ Sendi, M.S.E.; Godfrey D. Pearlson; Daniel H. Mathalon; J.M. Ford; A. Preda; T. Erp Van; V.D. Calhoun (2021). "Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia" (PDF). Schizophrenia Research. 228: 103–111. doi:10.1016/j.schres.2020.11.055. PMID   33434723. S2CID   231391460.
  40. ↑ Jones, D. T.; Vemuri, P.; Murphy, M. C.; Gunter, J. L.; Senjem, M. L.; Machulda, M. M.; Przybelski, S. A.; Gregg, B. E.; Kantarci, K.; Knopman, D. S.; Boeve, B. F.; Petersen, R. C.; Jack Jr, C. R. (2012). He, Yong (ed.). "Non-Stationarity in the "Resting Brain's" Modular Architecture". PLOS ONE. 7 (6): e39731. Bibcode:2012PLoSO...739731J. doi: 10.1371/journal.pone.0039731 . PMC   3386248 . PMID   22761880.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.