Elsevier

NeuroImage

Volume 54, Issue 4, 14 February 2011, Pages 2950-2959
NeuroImage

Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics

https://doi.org/10.1016/j.neuroimage.2010.10.046Get rights and content

Abstract

The brain's energy economy excessively favors intrinsic, spontaneous neural activity over extrinsic, evoked activity, presumably to maintain its internal organization. Emerging hypotheses capable of explaining such an investment posit that the brain's intrinsic functional architecture encodes a blueprint for its repertoire of responses to the external world. Yet, there is little evidence directly linking intrinsic and extrinsic activity in the brain. Here we relate differences among individuals in the magnitude of task-evoked activity during performance of an Eriksen flanker task, to spontaneous oscillatory phenomena observed during rest. Specifically, we focused on the amplitude of low-frequency oscillations (LFO, 0.01–0.1Hz) present in the BOLD signal. LFO amplitude measures obtained during rest successfully predicted the magnitude of task-evoked activity in a variety of regions that were all activated during performance of the flanker task. In these regions, higher LFO amplitude at rest predicted higher task-evoked activity. LFO amplitude measures obtained during rest were also found to have robust predictive value for behavior. In midline cingulate regions, LFO amplitudes predicted not only the speed and consistency of performance but also the magnitude of the behavioral congruency effect embedded in the flanker task. These results support the emerging hypothesis that the brain's repertoire of responses to the external world are represented and updated in the brain's intrinsic functional architecture.

Research Highlights

►Resting state BOLD amplitude predicts task-evoked activity. ►Resting state BOLD amplitude predicts task performance. ►Intrinsic architecture of the brain represents and updates repertoire of extrinsic responses.

Introduction

As much as 95% of the brain's total energy consumption is devoted to maintaining and updating its internal organization (Raichle, 2010). The magnitude of this investment in intrinsic operations is puzzling given the importance of responding to environmental inputs and external demands. One possibility is that intrinsic brain activity may provide a functional framework for the brain's moment-to-moment responses to the external world (Fox et al., 2006, Raichle, 2010). Support for this hypothesis comes from a recent demonstration of striking correspondence between the functional systems revealed by task-based and task-independent (i.e., resting state) studies (Smith et al., 2009). This led Smith et al. (2009, pg. 13040) to conclude that “the full repertoire of functional networks utilized by the brain in action is continuously and dynamically ‘active’ even when at ‘rest’.”

Building upon this notion, we recently described brain regions in which the magnitude of fMRI activations during Eriksen flanker task performance was predicted by the strength of resting state functional connectivity (RSFC) between those regions and the default mode and task-positive networks (Mennes et al., 2010). Regions exhibiting significant RSFC/task-evoked activity relationships were primarily located in transition zones between task-activated and task-deactivated regions. Those transition zones coincided with the boundaries between the task-positive and default mode resting state networks. Together with the observations of Smith et al. (2009), these findings support the hypothesis that the functional architecture employed by the brain to respond to the external world is effectively represented in patterns of intrinsic activity.

Here, we shift our focus from measures of functional connectivity during rest, which index synchronization of low-frequency BOLD oscillations (LFO; 0.01–0.1Hz) between spatially distinct brain regions, to non-relational, regional properties of the brain's intrinsic functional dynamics. The value of focusing on the temporal dynamics of the BOLD signal at a given voxel is increasingly appreciated in studies using a variety of approaches, including standard deviation-based measures (Biswal et al., 1995, Garrett et al., 2010), Fourier-based frequency domain measures (Zang et al., 2007, Zou et al., 2008, Zou et al., 2009), and wavelet and fractal analyses (Barnes et al., 2009, Maxim et al., 2005). Frequency-based approaches have the advantage of providing frequency-specific indices of oscillatory phenomena, thus allowing the investigation of BOLD signal variability in specific frequencies or frequency bands. Accordingly, we employed two increasingly popular voxel-wise, frequency-based measures of low-frequency BOLD oscillations: amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) (Zang et al., 2007, Zou et al., 2008, Zou et al., 2009). ALFF is defined as the total power in the low-frequency range (0.01–0.1 Hz). By definition, ALFF is equivalent to the standard deviation within that specific low-frequency band. In contrast, fractional ALFF (fALFF) is defined as the total power in the low-frequency range (0.01–0.1 Hz) relative to the total power across all measurable frequencies. As such, fALFF is a normalized version of ALFF and has been shown to be less susceptible to artifactual signals in regions located within the vicinity of vessels and/or significant pulsatile motion (e.g., ventricles, brainstem; Zuo et al., 2010, Zou et al., 2008). Accordingly, we focused on fALFF in the present analyses. Both ALFF and fALFF are test–retest reliable across time (Zuo et al., 2010) and are promising potential biomarkers of psychiatric disorders (Hoptman et al., 2010, Huang et al., 2010, Lui et al., 2010, Zang et al., 2007, Zhang et al., 2010). Importantly, ALFF/fALFF measures can be used to study the dynamics of the BOLD signal at the local, voxel-wise level, without assessing the relationship between regions. In addition, like independent component analysis (Beckmann et al., 2005, Calhoun et al., 2001, Damoiseaux et al., 2006, McKeown et al., 1998, Smith et al., 2009) and clustering approaches (Bellec et al., 2010, Cohen et al., 2008, Cordes et al., 2002, Kelly et al., 2010, van den Heuvel et al., 2008), local amplitude measures do not require the a priori selection of regions of interest. Using fALFF as a local index of intrinsic brain activity, we tested whether we could predict inter-individual differences in the magnitude of BOLD activity evoked by an Eriksen flanker task based on inter-individual differences in intrinsic brain activity. By employing the same dataset used in our previous study of the relationship between RSFC and task-evoked activity (Mennes et al., 2010), we can contrast findings obtained with regional resting state measures (ALFF/fALFF) to findings obtained with relational resting-state measures (RSFC).

Given our hypothesis that common neural mechanisms underlie intrinsic and extrinsic BOLD activity, we predicted that for a given region, participants exhibiting higher LFO amplitudes during rest would also exhibit greater task-evoked BOLD responses during task performance. This hypothesis is a corollary of the idea that extrinsic activity builds on underlying intrinsic activity (Fox et al., 2006, Smith et al., 2009). Alternatively, extrinsic and intrinsic activity may be in competition with one another. This would lead to the prediction that participants with higher LFO amplitudes will show lower task-evoked BOLD activity, as the LFO may act as a source of noise in the measurement of extrinsic activity (i.e., decrease the signal-to-noise ratio), and may even interfere with extrinsic phenomena more directly (e.g., due to competition for resources). Additionally, we took the opportunity to explore possible relationships between resting state LFO amplitude measures and behavioral performance on the Eriksen flanker task.

Section snippets

Participants and experimental paradigm

We used a dataset comprising 26 participants (mean age 20.5 ± 4.8 years, 11 males), previously included in other studies by our laboratory (Kelly et al., 2008, Mennes et al., 2010). All participants were without a history of psychiatric or neurological illness as confirmed by psychiatric assessment. Written informed consent was obtained prior to participation as approved by the institutional review boards of New York University (NYU) and the NYU School of Medicine.

Two 5-min fMRI scans were

LFO amplitude during rest predicts magnitude of task-evoked BOLD response

Voxel-matched regression analyses identified several brain regions where fALFF obtained for a participant during rest predicted the magnitude of event-related BOLD responses during flanker task performance, regardless of trial type (congruent, incongruent, overall; Fig. 1, Table 1 and Supplementary Fig. S1). Highly similar results for ALFF are described in the Supplementary Material accompanying this article. Moreover, regions exhibiting significant relationships were centered within clusters

Discussion

Our findings substantiate the notion that the intrinsic functional architecture of the brain provides a framework for its repertoire of extrinsic responses (Braun and Mattia, 2010, Fox et al., 2006, Raichle, 2010, Smith et al., 2009, Steyn-Ross et al., 2009). Voxel-matched regression analyses revealed an array of regions in which an individual's LFO amplitude measures obtained during rest predicted the magnitude of task-evoked BOLD activations. For all regions exhibiting significant

Conclusions

The amplitude of low-frequency oscillations measured during rest can predict the magnitude of task-evoked, extrinsic BOLD activations observed during an Eriksen flanker task. Intrinsic activity, represented by low-frequency oscillations detected during a separate resting state fMRI scan, was also robustly associated with behavioral performance in medial cingulate regions. These results support the notion that intrinsic brain activity serves as a framework for extrinsic, evoked brain activity

Acknowledgments

The authors thank all participants for their cooperation. This research was partially supported by grants from NIMH (R01MH083246 and K23MH087770), Autism Speaks, the Stavros Niarchos Foundation, the Leon Levy Foundation, and gifts from Joseph P. Healy, Linda and Richard Schaps, Jill and Bob Smith, and the endowment provided by Phyllis Green and Randolph Cōwen. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References (51)

  • M. Jenkinson et al.

    A global optimisation method for robust affine registration of brain images

    Med. Image Anal.

    (2001)
  • A.M. Kelly et al.

    Competition between functional brain networks mediates behavioral variability

    Neuroimage

    (2008)
  • V. Maxim et al.

    Fractional Gaussian noise, functional MRI and Alzheimer's disease

    Neuroimage

    (2005)
  • M. Mennes et al.

    Inter-individual differences in resting state functional connectivity predict task-induced BOLD activity

    Neuroimage

    (2010)
  • M.P. Milham et al.

    Practice-related effects demonstrate complementary roles of anterior cingulate and prefrontal cortices in attentional control

    Neuroimage

    (2003)
  • N. Petridou et al.

    Phase vs. magnitude information in functional magnetic resonance imaging time series: toward understanding the noise

    Magn. Reson. Imaging

    (2009)
  • M.E. Raichle

    Two views of brain function

    Trends Cogn. Sci.

    (2010)
  • A.K. Roy et al.

    Functional connectivity of the human amygdala using resting state fMRI

    Neuroimage

    (2009)
  • M.F. Rushworth et al.

    Action sets and decisions in the medial frontal cortex

    Trends Cogn. Sci.

    (2004)
  • R.N. Spreng et al.

    Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition

    Neuroimage

    (2010)
  • M.L. Steyn-Ross et al.

    Modeling brain activation patterns for the default and cognitive states

    Neuroimage

    (2009)
  • Q. Zou et al.

    Static and dynamic characteristics of cerebral blood flow during the resting state

    Neuroimage

    (2009)
  • Q.H. Zou et al.

    An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF

    J. Neurosci. Meth.

    (2008)
  • X.N. Zuo et al.

    The oscillating brain: complex and reliable

    Neuroimage

    (2010)
  • J.L.R. Andersson et al.

    TR07JA2: non-linear registration, aka Spatial normalisation. FMRIB Analysis Group Technical Reports

  • Cited by (185)

    • Altered dynamic amplitude of low-frequency fluctuation between bipolar type I and type II in the depressive state

      2022, NeuroImage: Clinical
      Citation Excerpt :

      These conventional indicators are temporally stationary, of which linear dependence measures are computed over the entire scan. However, unlike sleeping, the participants enduring resting-state fMRI scanning are on stand-by, in which the patterns of brain activity are distinguishable from those in sleep or during goal-directed activity (Deco et al., 2014; Mennes et al., 2011). The sliding-window technique is constantly sensitive to functional activation changes during the entire scan (Gembris et al., 2000), so it can precisely depict the dynamic features of brain activity according to time.

    View all citing articles on Scopus
    View full text