[Show abstract][Hide abstract] ABSTRACT: Functional magnetic resonance imaging (fMRI) studies utilizing measures of hemodynamic signal, such as the blood-oxygenation-level dependent (BOLD) signal, have discovered that resting-state brain activities are organized into multiple large-scale functional networks, coined as resting state networks (RSNs). However, an important limitation of the available fMRI studies is that hemodynamic signals only provide an indirect measure of neuronal activity. In the contrast, electroencephalography (EEG) directly measures electrophysiological activity of the brain. However, little is known about the brain-wide organization of such spontaneous neuronal population signals at resting state. It is not entirely clear if or how the network structure built upon slowly fluctuating hemodynamic signals is represented in terms of fast, dynamic and spontaneous neuronal activity. In this study, we investigated the electrophysiological representation of RSNs from simultaneously acquired EEG and fMRI data in the resting human brain. We developed a data-driven analysis approach that reconstructed multiple large-scale electrophysiological networks from high-resolution EEG data alone. The networks derived from EEG were then compared with RSNs independently derived from simultaneously acquired fMRI in their spatial structures as well as temporal dynamics. Results reveal spatially and temporally specific electrophysiological correlates for the fMRI-RSNs. Findings suggest that spontaneous activity of various large-scale cortical networks is reflected in macroscopic EEG potentials.
[Show abstract][Hide abstract] ABSTRACT: Background:
While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis.
We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT).
The whole-brain data analysis with more than 100,000 voxels and more than 250 volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small.
Comparison with existing methods:
Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms.
Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.
No preview · Article · Sep 2015 · Journal of Neuroscience Methods
[Show abstract][Hide abstract] ABSTRACT: Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD) and normalize with remission. Real-time fMRI neurofeedback (rtfMRI-nf) training with the goal of upregulating amygdala activity during recall of happy autobiographical memories (AMs) has been suggested, and recently explored, as a novel therapeutic approach which resulted in improvement in self-reported mood in depressed subjects. In the present study we assessed the possibility of sustained brain changes as well as the neuromodulatory effects of rtfMRI-nf training of the amygdala during recall of positive AMs in MDD and matched healthy subjects. MDD and healthy subjects went through one visit of rtfMRI neurofeedback training. Subjects were assigned to receive active neurofeedback from the left amygdala or from a control region putatively not modulated by AM recall or emotion regulation, i.e. the left horizontal segment of the intraparietal sulcus. To assess lasting effects of neurofeedback in MDD, the resting state functional connectivity before and after rtfMRI-nf in 27 depressed subjects, as well as in 27 matched healthy subjects before rtfMRI-nf was measured. Results show that abnormal hypo-connectivity with left amygdala in MDD is reversed after rtfMRI-nf training by recalling positive AMs. Although such neuromodulatory changes are observed in both MDD groups receiving feedback from respective active and control brain regions, only in the active group are larger decreases of depression severity associated with larger increases of amygdala connectivity and a significant, positive correlation is found between the connectivity changes and the days after neurofeedback. Additionally, active neurofeedback training of the amygdala enhances connectivity with temporal cortical regions including the hippocampus. These results demonstrate lasting brain changes induced by amygdala rtfMRI-nf training and suggest the importance of reinforcement learning in rehabilitating emotion regulation in depression.
Full-text · Article · Oct 2014 · Brain Connectivity
[Show abstract][Hide abstract] ABSTRACT: Background: Real-time fMRI neurofeedback (rtfMRI-nf) is a promising approach
for studies and treatment of major depressive disorder (MDD). EEG performed
simultaneously with rtfMRI-nf procedure allows independent evaluation of
rtfMRI-nf effects. Frontal EEG asymmetry in the alpha band is a widely used
measure of emotion and motivation that shows profound changes in depression.
However, it has never been related to simultaneously acquired fMRI data.
Methods: We performed the first study combining rtfMRI-nf with simultaneous
(passive) EEG recordings. MDD patients in the experimental group (n=13) learned
to upregulate BOLD activity of the left amygdala using rtfMRI-nf during a
positive emotion induction task. MDD patients in the control group (n=11) were
provided with sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the
upper-alpha band and BOLD activity across the brain were examined. Results:
Participants in the experimental group showed positive average changes in
frontal EEG asymmetry during the rtfMRI-nf task, which significantly correlated
with depression severity ratings. Temporal correlations between frontal EEG
asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf
task, for the amygdala and many regions of the emotion regulation network.
Conclusions: Our findings demonstrate an important connection between amygdala
BOLD activity and frontal EEG asymmetry during emotion regulation. The EEG
asymmetry results indicate that the rtfMRI-nf training targeting the amygdala
is beneficial to MDD patients. They further suggest that EEG neurofeedback
based on frontal EEG asymmetry in the alpha band would be compatible with the
amygdala-based rtfMRI-nf, and combination of the two could enhance emotion
regulation training in MDD patients.
[Show abstract][Hide abstract] ABSTRACT: Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD), and normalize with remission. Real-time functional MRI neurofeedback (rtfMRI-nf) offers a non-invasive method to modulate this regional activity. We examined whether depressed participants can use rtfMRI-nf to enhance amygdala responses to positive autobiographical memories, and whether this ability alters symptom severity.
Unmedicated MDD subjects were assigned to receive rtfMRI-nf from either left amygdala (LA; experimental group, n = 14) or the horizontal segment of the intraparietal sulcus (HIPS; control group, n = 7) and instructed to contemplate happy autobiographical memories (AMs) to raise the level of a bar representing the hemodynamic signal from the target region to a target level. This 40s Happy condition alternated with 40s blocks of rest and counting backwards. A final Transfer run without neurofeedback information was included.
Participants in the experimental group upregulated their amygdala responses during positive AM recall. Significant pre-post scan decreases in anxiety ratings and increases in happiness ratings were evident in the experimental versus control group. A whole brain analysis showed that during the transfer run, participants in the experimental group had increased activity compared to the control group in left superior temporal gyrus and temporal polar cortex, and right thalamus.
Using rtfMRI-nf from the left amygdala during recall of positive AMs, depressed subjects were able to self-regulate their amygdala response, resulting in improved mood. Results from this proof-of-concept study suggest that rtfMRI-nf training with positive AM recall holds potential as a novel therapeutic approach in the treatment of depression.
[Show abstract][Hide abstract] ABSTRACT: We observed in a previous study (PLoS ONE 6:e24522) that the self-regulation of amygdala activity via real-time fMRI neurofeedback (rtfMRI-nf) with positive emotion induction was associated, in healthy participants, with an enhancement in the functional connectivity between the left amygdala (LA) and six regions of the prefrontal cortex. These regions included the left rostral anterior cingulate cortex (rACC), bilateral dorsomedial prefrontal cortex (DMPFC), bilateral superior frontal gyrus (SFG), and right medial frontopolar cortex (MFPC). Together with the LA, these six prefrontal regions thus formed the functional neuroanatomical network engaged during the rtfMRI-nf procedure. Here we perform a structural vector autoregression (SVAR) analysis of the effective connectivity for this network. The SVAR analysis demonstrates that the left rACC plays an important role during the rtfMRI-nf training, modulating the LA and the other network regions. According to the analysis, the rtfMRI-nf training leads to a significant enhancement in the time-lagged effect of the left rACC on the LA, potentially consistent with the ipsilateral distribution of the monosynaptic projections between these regions. The training is also accompanied by significant increases in the instantaneous (contemporaneous) effects of the left rACC on four other regions - the bilateral DMPFC, the right MFPC, and the left SFG. The instantaneous effects of the LA on the bilateral DMPFC are also significantly enhanced. Our results are consistent with a broad literature supporting the role of the rACC in emotion processing and regulation. Our exploratory analysis provides, for the first time, insights into the causal relationships within the network of regions engaged during the rtfMRI-nf procedure targeting the amygdala. It suggests that the rACC may constitute a promising target for rtfMRI-nf training along with the amygdala in patients with affective disorders, particularly posttraumatic stress disorder (PTSD).
[Show abstract][Hide abstract] ABSTRACT: Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG-fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI-EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI-EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activity of the human brain. They suggest potential applications of rtfMRI-EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.
[Show abstract][Hide abstract] ABSTRACT: Low-frequency temporal fluctuations of physiological signals (<0.1 Hz), such as the respiration and cardiac pulse rate, occur naturally during rest and have been shown to be correlated with blood-oxygenation-level-dependent (BOLD) signal fluctuation. Such physiological signal modulations have been considered as sources of noise and their effects on BOLD signal are commonly removed in functional magnetic resonance imaging (fMRI) studies. However, possible neural correlates of the physiological fluctuations have not been considered nor examined in detail. In the present study we investigated this possibility by simultaneously acquiring electroencephalogram (EEG) with BOLD fMRI data, respiratory and cardiac waveforms in healthy human subjects at eyes-closed and eyes-open resting. We quantified the concurrent changes of the EEG power in the alpha frequency band, the respiration volume, and the cardiac pulse rate, then assessed the temporal correlations between alpha EEG power and physiological signal fluctuations. In addition, time-shifted time courses of alpha EEG power or physiological data were included as regressors to examine their correlations with the whole-brain BOLD fMRI signals. We observed a significant correlation between alpha EEG global field power and respiration, particularly at eyes-closed resting condition. Similar spatial patterns were observed between the correlation maps of BOLD with alpha EEG power and respiration, with negative correlations coinciding in the visual cortex, superior/middle temporal gyrus, inferior frontal gyrus, and inferior parietal lobule and positive correlations in the thalamus and caudate. Regressing out the physiological variations in the BOLD signal resulted in reduced correlation between BOLD and alpha EEG power. These results suggest a mutual link of neuronal origin between alpha EEG power, respiration, and BOLD signals.
[Show abstract][Hide abstract] ABSTRACT: We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are utilized for slice-specific motion correction of unprocessed fMRI data. Performance of the method is demonstrated by correction of fMRI data from five patients with major depressive disorder, who exhibited head movements by 1-3mm during a resting EEG-fMRI run. The fMRI datasets, corrected using eight to ten EEG-based motion regressors, show significant improvements in temporal SNR (TSNR) of fMRI time series, particularly in the frontal brain regions and near the surface of the brain. The TSNR improvements are as high as 50% for large brain areas in single-subject analysis and as high as 25% when the results are averaged across the subjects. Simultaneous application of the EEG-based motion correction and physiological noise correction by means of RETROICOR leads to average TSNR enhancements as high as 35% for extended brain regions. These TSNR improvements are largely preserved after the subsequent fMRI volume registration and regression of fMRI motion parameters. The proposed EEG-assisted method of retrospective fMRI motion correction (referred to as E-REMCOR) can be applied to improve quality of fMRI data with severe motion artifacts and to reduce spurious correlations between the EEG and fMRI data caused by head movements. It does not require any specialized equipment beyond the standard EEG-fMRI instrumentation and can be applied retrospectively to any existing EEG-fMRI data set.
[Show abstract][Hide abstract] ABSTRACT: Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN. We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.
[Show abstract][Hide abstract] ABSTRACT: Real-time functional magnetic resonance imaging (rtfMRI) with neurofeedback allows investigation of human brain neuroplastic changes that arise as subjects learn to modulate neurophysiological function using real-time feedback regarding their own hemodynamic responses to stimuli. We investigated the feasibility of training healthy humans to self-regulate the hemodynamic activity of the amygdala, which plays major roles in emotional processing. Participants in the experimental group were provided with ongoing information about the blood oxygen level dependent (BOLD) activity in the left amygdala (LA) and were instructed to raise the BOLD rtfMRI signal by contemplating positive autobiographical memories. A control group was assigned the same task but was instead provided with sham feedback from the left horizontal segment of the intraparietal sulcus (HIPS) region. In the LA, we found a significant BOLD signal increase due to rtfMRI neurofeedback training in the experimental group versus the control group. This effect persisted during the Transfer run without neurofeedback. For the individual subjects in the experimental group the training effect on the LA BOLD activity correlated inversely with scores on the Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale. The whole brain data analysis revealed significant differences for Happy Memories versus Rest condition between the experimental and control groups. Functional connectivity analysis of the amygdala network revealed significant widespread correlations in a fronto-temporo-limbic network. Additionally, we identified six regions--right medial frontal polar cortex, bilateral dorsomedial prefrontal cortex, left anterior cingulate cortex, and bilateral superior frontal gyrus--where the functional connectivity with the LA increased significantly across the rtfMRI neurofeedback runs and the Transfer run. The findings demonstrate that healthy subjects can learn to regulate their amygdala activation using rtfMRI neurofeedback, suggesting possible applications of rtfMRI neurofeedback training in the treatment of patients with neuropsychiatric disorders.