The oscillating brain: Complex and reliable

Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience at the New York University Child Study Center, New York, NY, USA.
NeuroImage (Impact Factor: 6.36). 09/2009; 49(2):1432-45. DOI: 10.1016/j.neuroimage.2009.09.037
Source: PubMed


The human brain is a complex dynamic system capable of generating a multitude of oscillatory waves in support of brain function. Using fMRI, we examined the amplitude of spontaneous low-frequency oscillations (LFO) observed in the human resting brain and the test-retest reliability of relevant amplitude measures. We confirmed prior reports that gray matter exhibits higher LFO amplitude than white matter. Within gray matter, the largest amplitudes appeared along mid-brain structures associated with the "default-mode" network. Additionally, we found that high-amplitude LFO activity in specific brain regions was reliable across time. Furthermore, parcellation-based results revealed significant and highly reliable ranking orders of LFO amplitudes among anatomical parcellation units. Detailed examination of individual low frequency bands showed distinct spatial profiles. Intriguingly, LFO amplitudes in the slow-4 (0.027-0.073 Hz) band, as defined by Buzsáki et al., were most robust in the basal ganglia, as has been found in spontaneous electrophysiological recordings in the awake rat. These results suggest that amplitude measures of LFO can contribute to further between-group characterization of existing and future "resting-state" fMRI datasets.

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Available from: Dylan G. Gee, Sep 30, 2015
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    • "The interest in the spectral properties of the BOLD signal was also extended to functional connectivity studies. Specifically, there have been reports analyzing the spectral components of the BOLD signal in the defaultmode (DMN) and other networks using typical EPI with TR = 2 s (Baria et al., 2011; Chang and Glover, 2010; Salvador et al., 2008; Zuo et al., 2010) and fast MRI method (Lee et al., 2013), which can estimate frequency components up to 0.5 Hz and 5 Hz, respectively. Frequencydependent subcomponents were identified in the DMN network (Barbaresi et al., 1995). "
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    ABSTRACT: Granger causality analysis has been suggested as a method of estimating causal modulation without specifying the direction of information flow a priori. Using BOLD-contrast functional MRI (fMRI) data, such analysis has been typically implemented in the time domain. In this study, we used magnetic resonance inverse imaging, a method of fast fMRI enabled by massively parallel detection allowing up to 10Hz sampling rate, to investigate the causal modulation at different frequencies up to 5Hz. Using a visuomotor two-choice reaction-time task, both the spectral decomposition of Granger causality and isolated effective coherence revealed that the BOLD signal at frequency up to 3Hz can still be used to estimate significant dominant directions of information flow consistent with results from the time-domain Granger causality analysis. We showed the specificity of estimated dominant directions of information flow at high frequencies by contrasting causality estimates using data collected during the visuomotor task and resting state. Our data suggest that hemodynamic responses carry physiological information related to inter-regional modulation at frequency higher than what has been commonly considered. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 07/2015; 121. DOI:10.1016/j.neuroimage.2015.07.036 · 6.36 Impact Factor
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    • "The ALFF measures the power or intensity of low frequency (b0.08 Hz) oscillations of the BOLD time courses, which is considered to be physiologically meaningful and reflective of regional spontaneous neural activity (Yang et al., 2007; Zang et al., 2007; Zou et al., 2008; Duff et al., 2008; Jiang et al., 2011; Xu et al., 2014). Previous studies have shown that ALFF has high test-retest reliability and is closed related to CBF (Zuo et al., 2010; Li et al., 2012a, 2012b), suggesting that ALFF may be a useful index to examine state-dependent resting brain function changes associated with TOT effects. We hypothesized that TOT would alter ALFF in the DMN. "
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    ABSTRACT: After continuous and prolonged cognitive workload, people typically show reduced behavioral performance and increased feelings of fatigue, which are known as "time-on-task (TOT) effects". Although TOT effects are pervasive in modern life, their underlying neural mechanisms remain elusive. In this study, we induced TOT effects by administering a 20-minute continuous psychomotor vigilance test (PVT) to a group of 16 healthy adults and used resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine spontaneous brain activity changes associated with fatigue and performance. Behaviorally, subjects displayed robust TOT effects, as reflected by increasingly slower reaction times as the test progressed and higher self-reported mental fatigue ratings after the 20-minute PVT. Compared to pre-test measurements, subjects exhibited reduced amplitudes of low-frequency fluctuation (ALFF) in the default mode network (DMN) and increased ALFF in the thalamus after the test. Subjects also exhibited reduced anti-correlations between the posterior cingulate cortex (PCC) and right middle prefrontal cortex after the test. Moreover, pre-test resting ALFF in the PCC and medial prefrontal cortex (MePFC) predicted subjects' subsequent performance decline; individuals with higher ALFF in these regions exhibited more stable reaction times throughout the 20-minute PVT. These results support the important role of both task-positive and task-negative networks in mediating TOT effects and suggest that spontaneous activity measured by resting-state BOLD fMRI may be a marker of mental fatigue. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 07/2015; 120. DOI:10.1016/j.neuroimage.2015.07.030 · 6.36 Impact Factor
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    • "This phenomenon is not unique to the fMRI signal (Fox et al., 2007; Zarahn et al., 1997) but rather is a common feature observed in electrophysiological studies (Leopold et al., 2003; Linkenkaer-Hansen et al., 2001), implying a common biological feature of neural oscillations (Buzsáki and Draguhn, 2004). Previous studies have shown the spectral behavior in resting-state fMRI signal over a relatively wide frequency range (0–1.25 Hz), demonstrating the effects of both slow fluctuations and physiological noise on a variety of networks (Cordes et al., 2001; Salvador et al., 2008; Wu et al., 2008; Zuo et al., 2010). However, due to the lack of spectral resolution, uncertainty of the frequency range specific to spontaneous fluctuations makes it difficult to relate fMRI fluctuations with underlying electrophysiology signals. "
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