Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) depicts changes in deoxyhemoglobin concentration consequent to task-induced or spontaneous modulation of neural ...metabolism. Since its inception in 1990, this method has been widely employed in thousands of studies of cognition for clinical applications such as surgical planning, for monitoring treatment outcomes, and as a biomarker in pharmacologic and training programs. More recently, attention is turning to the use of pattern classification and other statistical methods to draw increasingly complex inferences about cognitive brain states from fMRI data. This article reviews the methods, challenges, and future of fMRI.
Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of ...the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time–frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the “anticorrelated” (“task-positive”) network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks.
Previous studies have reported that the spontaneous, resting-state time course of the default-mode network is negatively correlated with that of the “task-positive network”, a collection of regions ...commonly recruited in demanding cognitive tasks. However, all studies of negative correlations between the default-mode and task-positive networks have employed some form of normalization or regression of the whole-brain average signal (“global signal”); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the task-positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and positive correlations with the default-mode network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode network and regions of the task-positive network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within task-positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in positive correlations within the default-mode network, reducing apparent false positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.
The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced ...our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.
•Imaging studies have recently begun to examine dynamic properties of FC.•Dynamic FC may yield novel insights into brain function and dysfunction.•We review results, methods, interpretations, and limitations in this emerging field.
Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies ≤ 0.1 Hz, and studies of these ...low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8 Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5 Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at a higher frequency range; (2) conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1 Hz (non-BOLD/BOLD contrast at 0.4-0.5 Hz is ~4 times that at <0.1 Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1 Hz are discussed.
A significant component of BOLD fMRI physiological noise is caused by variations in the depth and rate of respiration. It has previously been demonstrated that a breath-to-breath metric of ...respiratory variation (respiratory volume per time; RVT), computed from pneumatic belt measurements of chest expansion, has a strong linear relationship with resting-state BOLD signals across the brain. RVT is believed to capture breathing-induced changes in arterial CO2, which is a cerebral vasodilator; indeed, separate studies have found that spontaneous fluctuations in end-tidal CO2 (PETCO2) are correlated with BOLD signal time series. The present study quantifies the degree to which RVT and PETCO2 measurements relate to one another and explain common aspects of the resting-state BOLD signal. It is found that RVT (particularly when convolved with a particular impulse response, the “respiration response function”) is highly correlated with PETCO2, and that both explain remarkably similar spatial and temporal BOLD signal variance across the brain. In addition, end-tidal O2 is shown to be largely redundant with PETCO2. Finally, the latency at which PETCO2 and respiration belt measures are correlated with the time series of individual voxels is found to vary across the brain and may reveal properties of intrinsic vascular response delays.
Functional connectivity has been observed to fluctuate across the course of a resting state scan, though the origins and functional relevance of this phenomenon remain to be shown. The present study ...explores the link between endogenous dynamics of functional connectivity and autonomic state in an eyes-closed resting condition. Using a sliding window analysis on resting state fMRI data from 35 young, healthy male subjects, we examined how heart rate variability (HRV) covaries with temporal changes in whole-brain functional connectivity with seed regions previously described to mediate effects of vigilance and arousal (amygdala and dorsal anterior cingulate cortex; dACC). We identified a set of regions, including brainstem, thalamus, putamen, and dorsolateral prefrontal cortex, that became more strongly coupled with the dACC and amygdala seeds during states of elevated HRV. Effects differed between high and low frequency components of HRV, suggesting specific contributions of parasympathetic and sympathetic tone on individual connections. Furthermore, dynamics of functional connectivity could be separated from those primarily related to BOLD signal fluctuations. The present results contribute novel information about the neural basis of transient changes of autonomic nervous system states, and suggest physiological and psychological components of the recently observed non-stationarity in resting state functional connectivity.
► HRV covaried with fluctuations in resting connectivity seeded from dACC and amygdala. ► Distinct effects were found between high-frequency and low-frequency HRV. ► Autonomic fluctuations may contribute to nonstationarity in resting connectivity.
Near infrared spectroscopy (NIRS) is an increasingly popular technology for studying brain function. NIRS presents several advantages relative to functional magnetic resonance imaging (fMRI), such as ...measurement of concentration changes in both oxygenated and deoxygenated hemoglobin, finer temporal resolution, and ease of administration, as well as disadvantages, most prominently inferior spatial resolution and decreased signal-to-noise ratio (SNR). While fMRI has become the gold standard for in vivo imaging of the human brain, in practice NIRS is a more convenient and less expensive technology than fMRI. It is therefore of interest to many researchers how NIRS compares to fMRI in studies of brain function. In the present study we scanned participants with simultaneous NIRS and fMRI on a battery of cognitive tasks, placing NIRS probes over both frontal and parietal brain regions. We performed detailed comparisons of the signals in both temporal and spatial domains. We found that NIRS signals have significantly weaker SNR, but are nonetheless often highly correlated with fMRI measurements. Both SNR and the distance between the scalp and the brain contributed to variability in the NIRS/fMRI correlations. In the spatial domain, we found that a photon path forming an ellipse between the NIRS emitter and detector correlated most strongly with the BOLD response. Taken together these findings suggest that, while NIRS can be an appropriate substitute for fMRI for studying brain activity related to cognitive tasks, care should be taken when designing studies with NIRS to ensure that: 1) the spatial resolution is adequate for answering the question of interest and 2) the design accounts for weaker SNR, especially in brain regions more distal from the scalp.
► We performed simultaneous NIRS/fMRI recording during motor and cognitive tasks. ► NIRS signals have weaker signal-to-noise (SNR) ratio, but correlate with fMRI. ► SNR and scalp-brain distance contributed to variability in NIRS/fMRI correlations. ► NIRS correlated with BOLD from an ellipse between emitter and detector (~14mm depth).
Spiral imaging in fMRI Glover, Gary H.
NeuroImage,
08/2012, Volume:
62, Issue:
2
Journal Article
Peer reviewed
Open access
T2*-weighted Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) requires efficient acquisition methods in order to fully sample the brain in a several second time ...period. The most widely used approach is Echo Planar Imaging (EPI), which utilizes a Cartesian trajectory to cover k-space. This trajectory is subject to ghosts from off-resonance and gradient imperfections and is intrinsically sensitive to cardiac-induced pulsatile motion from substantial first- and higher order moments of the gradient waveform near the k-space origin. In addition, only the readout direction gradient contributes significant energy to the trajectory. By contrast, the spiral method samples k-space with an Archimedean or similar trajectory that begins at the k-space center and spirals to the edge (spiral-out), or its reverse, ending at the origin (spiral-in). Spiral methods have reduced sensitivity to motion, shorter readout times, improved signal recovery in most frontal and parietal brain regions, and exhibit blurring artifacts instead of ghosts or geometric distortion. Methods combining spiral-in and spiral-out trajectories have further advantages in terms of diminished susceptibility-induced signal dropout and increased BOLD signal. In measurements of temporal signal to noise ratio measured in 8 subjects, spiral-in/out exhibited significant increases over EPI in voxel volumes recovered in frontal and whole brain regions (18% and 10%, respectively).
Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We ...sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.
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