Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task

He, Biyu J.

doi:10.1523/jneurosci.2111-11.2011

Cited by 407 publications

(646 citation statements)

References 74 publications

Supporting

Mentioning

540

Contrasting

Unclassified

Order By: Relevance

“…The oscillation amplitudes are directly correlated with these BOLD fluctuations (16)(17)(18)(19)(20) and exhibit interareal correlations that closely match those of BOLD signals (17,(21)(22)(23). Moreover, BOLD signals also exhibit scale-free temporal (24)(25)(26) and spatiotemporal correlations (27)(28)(29). The scaling laws of LRTCs thus are a unifying fundamental characteristic of spontaneous brain activity (1,30,31).…”

mentioning

confidence: 79%

Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws

Zhigalov

Hirvonen

Korhonen

et al. 2013

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

431

444

View full text Add to dashboard Cite

Scale-free fluctuations are ubiquitous in behavioral performance and neuronal activity. In time scales from seconds to hundreds of seconds, psychophysical dynamics and the amplitude fluctuations of neuronal oscillations are governed by power-law-form longrange temporal correlations (LRTCs). In millisecond time scales, neuronal activity comprises cascade-like neuronal avalanches that exhibit power-law size and lifetime distributions. However, it remains unknown whether these neuronal scaling laws are correlated with those characterizing behavioral performance or whether neuronal LRTCs and avalanches are related. Here, we show that the neuronal scaling laws are strongly correlated both with each other and with behavioral scaling laws. We used source reconstructed magneto-and electroencephalographic recordings to characterize the dynamics of ongoing cortical activity. We found robust power-law scaling in neuronal LRTCs and avalanches in resting-state data and during the performance of audiovisual threshold stimulus detection tasks. The LRTC scaling exponents of the behavioral performance fluctuations were correlated with those of concurrent neuronal avalanches and LRTCs in anatomically identified brain systems. The behavioral exponents also were correlated with neuronal scaling laws derived from a resting-state condition and with a similar anatomical topography. Finally, despite the difference in time scales, the scaling exponents of neuronal LRTCs and avalanches were strongly correlated during both rest and task performance. Thus, long and short time-scale neuronal dynamics are related and functionally significant at the behavioral level. These data suggest that the temporal structures of human cognitive fluctuations and behavioral variability stem from the scaling laws of individual and intrinsic brain dynamics.spontaneous activity | threshold detection | criticality H uman cognitive and behavioral performance is highly variable and exhibits slow fluctuations that are salient in continuous performance tasks (CPTs) (1). Psychophysical time series have been known since the early 1950s to be nonrandomly clustered (2), and later studies have shown that hit-rate and/or reaction-time fluctuations in CPT data are fractal and power-law autocorrelated across hundreds of seconds (3-9). The biological origins and relevance of these dynamic, however, remain unclear (10, 11).Similar to those in behavioral performance, the fluctuations of collective neuronal activity at many levels of the nervous system are scale-free and governed by power-law scaling laws. On short time scales (10 −3 −10 −1 s), negative deflections in local field potentials form spatiotemporal cascades of activity, "neuronal avalanches" (32-34), the size and lifetime distributions of which are power laws akin to those of a critical branching process (33). Neuronal avalanches characterize spontaneous neuronal network activity in organotypic cultures (32), brain slices in vitro (35), and monkey (34) and human cortex (36) in vivo. In monkey cortex, the avalanche...

show abstract

mentioning

confidence: 79%

Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws

Zhigalov

Hirvonen

Korhonen

et al. 2013

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

431

444

View full text Add to dashboard Cite

show abstract

“…This method is based on the theory that the brain resides in a state of “criticality” allowing it to adapt quickly to new situations. A critical state system is characterized by spatial and temporal correlations that show long memory, which theoretically represents the brain network's ability to keep relevant information readily available, allowing it to respond dynamically (Ciuciu et al., 2012; He, 2011). …”

Section: Discussionmentioning

confidence: 99%

“…However, their study involved a task‐fMRI paradigm, whereas ours involved resting‐state fMRI. Hurst exponent is highest during resting state and tends to decrease with increased task load (He, 2011; He, Zempel, Snyder, & Raichle, 2010). Therefore, the Churchill et al.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Disrupted brain network functional dynamics and hyper‐correlation of structural and functional connectome topology in patients with breast cancer prior to treatment

et al. 2017

View full text Add to dashboard Cite

IntroductionSeveral previous studies have demonstrated that cancer chemotherapy is associated with brain injury and cognitive dysfunction. However, evidence suggests that cancer pathogenesis alone may play a role, even in non‐CNS cancers.MethodsUsing a multimodal neuroimaging approach, we measured structural and functional connectome topology as well as functional network dynamics in newly diagnosed patients with breast cancer. Our study involved a novel, pretreatment assessment that occurred prior to the initiation of any cancer therapies, including surgery with anesthesia. We enrolled 74 patients with breast cancer age 29–65 and 50 frequency‐matched healthy female controls who underwent anatomic and resting‐state functional MRI as well as cognitive testing.ResultsCompared to controls, patients with breast cancer demonstrated significantly lower functional network dynamics (p = .046) and cognitive functioning (p < .02, corrected). The breast cancer group also showed subtle alterations in structural local clustering and functional local clustering (p < .05, uncorrected) as well as significantly increased correlation between structural global clustering and functional global clustering compared to controls (p = .03). This hyper‐correlation between structural and functional topologies was significantly associated with cognitive dysfunction (p = .005).ConclusionsOur findings could not be accounted for by psychological distress and suggest that non‐CNS cancer may directly and/or indirectly affect the brain via mechanisms such as tumor‐induced neurogenesis, inflammation, and/or vascular changes, for example. Our results also have broader implications concerning the importance of the balance between structural and functional connectome properties as a potential biomarker of general neurologic deficit.

show abstract

“…fMRI BOLD signals display rich temporal organization, including scale-free 1/f power spectra and long-range temporal autocorrelations (16)(17)(18), with activity at any given time being influenced by the previous history of the system up to several minutes into the past. These landmarks of complex information processing and rapid adaptability are shared by many systems found in nature (19,20).…”

mentioning

confidence: 99%

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Tagliazucchi

Wegner

Morzelewski

et al. 2013

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

220

197

View full text Add to dashboard Cite

The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as longterm memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest.T he human brain displays complex spatiotemporal patterns of energy-consuming activity, even in the absence of an explicit task or stimulation (1). Large efforts have been devoted to the study of spontaneous neural activity encoded in the slow (∼0.1 Hz) fluctuations of the blood oxygen level-dependent (BOLD) signal, which are measured with functional MRI (fMRI). Nontrivial spatial organization of functional brain activity in resting state networks (RSNs) was consistently shown (2-4), comprising brain regions with high BOLD signal coherence and anatomical consistency with systems activated during task performance or stimulation (5).Remarkably, although human nonrapid eye movement (NREM) sleep is characterized by impaired awareness and reduced conscious mentation, organization into RSNs is preserved in light sleep (6) and to a large extent, deeper sleep stages (7, 8) (SI Appendix, Fig. S8.1). In particular, the default mode network (DMN; a set of task-deactivated regions implied with internal conscious cognitive processes) (9, 10) was repeatedly observed during deep sleep, albeit with reduced frontal connectivity (11,12). Although brain modules are preserved, even in the absence of conscious awareness, their functional integration is greatly impaired (8,13,14), which was predicted by an information integration account of consciousness (15). These results suggest that ongoing conscious mentation is not the only origin of RSN activity, whereas the level of consciousness is reflected in the interaction of functional networks.However, brain activity is not completely characterized in the spatial domain only. fMRI BOLD signals display rich temporal organization, including scale-free 1/f power spectra and long-range temporal autocorrelations (16)(17)(18), with activity at any given time being influenced by the previous history of the system up to several minutes into the past. These landmarks of complex information processing and rapid adaptability are shared by many systems found in nature (19,20). Evidence for such properties is also manifest...

show abstract

Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task

Cited by 407 publications

References 74 publications

Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws

Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws

Disrupted brain network functional dynamics and hyper‐correlation of structural and functional connectome topology in patients with breast cancer prior to treatment

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Contact Info

Product

Resources

About