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

ABSTRACT 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.

Download full-text


Available from: Dylan G. Gee, Aug 15, 2015
1 Follower
  • Source
    • "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). "
    [Show abstract] [Hide abstract]
    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; DOI:10.1016/j.neuroimage.2015.07.036 · 6.36 Impact Factor
  • Source
    • "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. "
  • Source
    • "Although the DMN is the best characterized of these RSNs, model-free connectivity analysis methods have identified a diverse array of such networks (Allen et al., 2011; Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; Kiviniemi et al., 2009; Pendse et al., 2011; Zuo et al., 2010) that have also been shown to interact with each other (Allen et al., 2012a; Doucet et al., 2011), revealing the brain to be a complex, highly interconnected neural system. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Functional magnetic resonance imaging (fMRI) studies have revealed the existence of robust, interconnected brain networks exhibiting correlated low frequency fluctuations during rest, which can be derived by examining inherent spatio-temporal patterns in functional scans independent of any a priori model. In order to explore the electrophysiological underpinnings of these networks, analogous techniques have recently been applied to magnetoencephalography (MEG) data, revealing similar networks that exhibit correlated low frequency fluctuations in the power envelope of beta band (14-30Hz) power. However, studies to date using this technique have concentrated on healthy subjects, and no method has yet been presented for group comparisons. We extended the ICA resting state MEG method to enable group comparisons, and demonstrate the technique in a sample of subjects with major depressive disorder (MDD). We found that the intrinsic resting state networks evident in fMRI appeared to be disrupted in individuals with MDD compared to healthy participants, particularly in the subgenual cingulate, although the electrophysiological correlates of this are unknown. Networks extracted from a combined group of healthy and MDD participants were examined for differences between groups. Individuals with MDD showed reduced correlations between the subgenual anterior cingulate (sgACC) and hippocampus in a network with primary nodes in the precentral and middle frontal gyri. Individuals with MDD also showed increased correlations between insulo-temporal nodes and amygdala compared to healthy controls. To further support our methods and findings, we present test/re-test reliability on independent recordings acquired within the same session. Our results demonstrate that group analyses are possible with the resting state MEG-independent components analysis (ICA) technique, highlighting a new pathway for analysis and discovery. This study also provides the first evidence of altered sgACC connectivity with a motor network. This finding, reliable across multiple sessions, suggests that the sgACC may partially mediate the psychomotor symptoms of MDD via synchronized changes in beta-band power, and expands the idea of the sgACC as a hub region mediating cognitive and emotional symptomatic domains in MDD. Findings of increased connectivity between the amygdala and cortical nodes further supports the role of amygdalar networks in mediated depressive symptomatology. NCT00024635 (ZIA-MH002927-04). Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 05/2015; 118. DOI:10.1016/j.neuroimage.2015.05.051 · 6.36 Impact Factor
Show more