Global and structured waves of rs-fMRI signal identified as putative propagation of spontaneous neural activity

Amemiya, Shonan; Takao, Hidemasa; Hanaoka, Shohei; Ohtomo, Kuni

doi:10.1016/j.neuroimage.2016.03.033

Cited by 28 publications

(47 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By contrast, altered neural communication has been implicated in learning, sleep, and ASD, supporting the view that changes in BOLD signal propagation reflect neural activity. Second, a fixed set of varying haemodynamic delays across the brain can only account for one propagation sequence in the data [26,42,53]. As the BOLD signal consists of multiple propagation sequences [42,53], only a minor component of BOLD signal propagation can be attributed to vascular effects.…”

Section: Neural Origin Of Blood Oxygen Leveldependent Signal Propagationmentioning

confidence: 99%

How networks communicate: propagation patterns in spontaneous brain activity

Mitra

Raichle

2016

Phil. Trans. R. Soc. B

111

View full text Add to dashboard Cite

One contribution of 15 to a Theo Murphy meeting issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. Initially regarded as 'noise', spontaneous (intrinsic) activity accounts for a large portion of the brain's metabolic cost. Moreover, it is now widely known that infra-slow (less than 0.1 Hz) spontaneous activity, measured using resting state functional magnetic resonance imaging of the blood oxygen level-dependent (BOLD) signal, is correlated within functionally defined resting state networks (RSNs). However, despite these advances, the temporal organization of spontaneous BOLD fluctuations has remained elusive. By studying temporal lags in the resting state BOLD signal, we have recently shown that spontaneous BOLD fluctuations consist of remarkably reproducible patterns of whole brain propagation. Embedded in these propagation patterns are unidirectional 'motifs' which, in turn, give rise to RSNs. Additionally, propagation patterns are markedly altered as a function of state, whether physiological or pathological. Understanding such propagation patterns will likely yield deeper insights into the role of spontaneous activity in brain function in health and disease.This article is part of the themed issue 'Interpreting blood oxygen level-dependent: a dialogue between cognitive and cellular neuroscience'. Importance of intrinsic activityAs observed by Hans Berger, in reporting on the first measurements of the human electroencephalogram, spontaneous (intrinsic) neural fluctuations are a dominant feature of the brain's electrical activity [1]. In the context of studying task-evoked neural responses, spontaneous activity was long considered merely to be 'noise'. Thus, most early studies of neural activity employed computational strategies to suppress spontaneous activity. More recently, it has been appreciated that investigating spontaneous activity is essential for understanding brain function [2][3][4]. At the systems level, this paradigm shift was prompted by two major findings. First, although the human brain represents only 2% of total body mass, its intrinsic activity consumes 20% of the body's energy, most of which is used to support ongoing neuronal signalling ([5-9], but see also [10]). Task-related increases in neuronal metabolism are generally small (less than 5%) when compared with this large intrinsic energy consumption (for a recent review, see [9]). Thus, to understand how the brain operates, we must take into account the component that consumes most of the brain's energy: spontaneous activity.The second set of findings has been derived from resting state functional magnetic resonance imaging (rs-fMRI) of the blood oxygen level-dependent (BOLD) signal [11]. Biswal et al. used human rs-fMRI to discover that spontaneous infraslow (less than 0.1 Hz) fluctuations of the BOLD signal are highly correlated within the somatomotor system [12]. This basic result has since been extended to multiple functional networks spanning the entire brain ([13-17]; figure 1a,b). Spatial correlat...

show abstract

Section: Neural Origin Of Blood Oxygen Leveldependent Signal Propagationmentioning

confidence: 99%

How networks communicate: propagation patterns in spontaneous brain activity

Mitra

Raichle

2016

Phil. Trans. R. Soc. B

111

View full text Add to dashboard Cite

show abstract

“…Furthermore, cortical slow oscillations (<1 Hz) (20,21) resemble the spatiotemporal characteristics of infraslow coherent activity in brain-wide functional networks detected by rsfMRI. Previous studies suggest that these cortical oscillations may underlie rsfMRI connectivity (22)(23)(24). Further, a recent study indicates the coupling between restingstate hemodynamics and the oscillatory activity of excitatory neurons (25).…”

mentioning

confidence: 93%

Low-frequency hippocampal–cortical activity drives brain-wide resting-state functional MRI connectivity

Chan

Leong

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The hippocampus, including the dorsal dentate gyrus (dDG), and cortex engage in bidirectional communication. We propose that low-frequency activity in hippocampal-cortical pathways contributes to brain-wide resting-state connectivity to integrate sensory information. Using optogenetic stimulation and brain-wide fMRI and resting-state fMRI (rsfMRI), we determined the large-scale effects of spatiotemporal-specific downstream propagation of hippocampal activity. Low-frequency (1 Hz), but not high-frequency (40 Hz), stimulation of dDG excitatory neurons evoked robust cortical and subcortical brain-wide fMRI responses. More importantly, it enhanced interhemispheric rsfMRI connectivity in various cortices and hippocampus. Subsequent local field potential recordings revealed an increase in slow oscillations in dorsal hippocampus and visual cortex, interhemispheric visual cortical connectivity, and hippocampal-cortical connectivity. Meanwhile, pharmacological inactivation of dDG neurons decreased interhemispheric rsfMRI connectivity. Functionally, visually evoked fMRI responses in visual regions also increased during and after low-frequency dDG stimulation. Together, our results indicate that low-frequency activity robustly propagates in the dorsal hippocampal-cortical pathway, drives interhemispheric cortical rsfMRI connectivity, and mediates visual processing.hippocampus | resting-state functional connectivity | optogenetic | fMRI | low frequency T he hippocampus (HP) plays a prominent role in central nervous system functions, particularly in episodic memory (1, 2) and spatial navigation (3, 4). The HP, including the dentate gyrus (DG), can evoke large-scale influences on cortical activity, because the HP receives convergent information from sensory and limbic cortices before sending reciprocal divergent projections to create a highly interactive corticohippocampal-cortical network. The HP, including DG, CA3, and CA1, and neocortex, are connected via the entorhinal cortex (EC) (5, 6), such that the dorsolateral-to-ventromedial projection that originates in the EC corresponds to a dorsoventral axis of termination in the HP (5). This anatomical topography suggests that a functional gradient could exist along the HP dorsoventral axis. Previous studies demonstrated that dorsal HP (dHP) and cortex are functionally integrated during sensory processing and memory consolidation (7-9). Specifically, dHP can integrate multimodal sensory information and process memory operations (7) using excitatory longrange projections (10). This process occurs over multiple brain circuits, but the role of dHP in complex networks, particularly its influence on brain-wide functional connectivity, is not well understood.Resting-state functional MRI (rsfMRI) (11-14) provides an invaluable, noninvasive imaging technique to map long-range, brain-wide functional connectivity networks based on the temporal coherence of infraslow (0.005-0.1 Hz) blood oxygen level dependent (BOLD) activity. The functional relevance of specific brain-wide networks in co...

show abstract

“…The Frequency-Phase Analysis is closely related to previous studies that use time-lags4142434445. It was shown that the time-lag propagates in space within conventionally known resting-state networks, depends on neuronal state (e.g., eyes closed or open) and can be used to infer directionality of neural information flow4344.…”

Section: Discussionmentioning

confidence: 94%

“…Indeed, it was suggested that extracting neural propagating information from fMRI data is possible only when the variability of the hemodynamic response function is less than the time scale of information flow49. Efforts were made to characterize the differences between perfusion and neuronal contributions to the BOLD signal45 and to remove any confounds that can bias the time-lag analysis5051. It is generally however accepted that for healthy subjects time-lags are related to neuronal transfer times with some neuro-vascular mapping, not entirely known.…”

Section: Discussionmentioning

confidence: 99%

Frequency-phase analysis of resting-state functional MRI

Goelman

Dan

Růžička

et al. 2017

Sci Rep

View full text Add to dashboard Cite

We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.

show abstract

Global and structured waves of rs-fMRI signal identified as putative propagation of spontaneous neural activity

Cited by 28 publications

References 48 publications

How networks communicate: propagation patterns in spontaneous brain activity

How networks communicate: propagation patterns in spontaneous brain activity

Low-frequency hippocampal–cortical activity drives brain-wide resting-state functional MRI connectivity

Frequency-phase analysis of resting-state functional MRI

Contact Info

Product

Resources

About