Our study examined test-retest reliability for a relatively new method, rs-fMRI, which may prove invaluable for assessing large-scale, functional neural networks in children and adolescents. Considerable anatomical evidence shows that human brain maturation is gradual and continuous, characterized by steadily increasing white matter, a general reduction in grey matter, extensive synaptic pruning, and elaboration through dendritic arborization (Changeux and Danchin, 1976
; Giedd et al., 1999
; Huttenlocher, 1990
; Paus et al., 1999
). Less is known, however, about human functional
By measuring function and connectivity in multiple large-scale brain networks, concurrently and without requiring task-compliance, rs-fMRI studies differ from task-based fMRI studies that have been the foundation of systems neuroscience imaging research over the past two decades. Rs-fMRI studies sample activity in spatially segregated brain regions. This activity (measured by BOLD signal) apparently occurs spontaneously, but with a coordinated temporal pattern. Rs-fMRI may be useful for generating data relevant to the development of functional neural systems, and may increase our knowledge of developmental neuropsychiatric disorders. Already, rs-fMRI has been applied to infants to measure early neural network function (Fransson et al., 2007
; Lin et al., 2008
), to children with developmental disorders (reviewed by Uddin et al., 2010
) and has recently been proposed as a measure for predicting brain developmental age (Dosenbach et al., 2010
). Indeed, analysis of resting-state brain activity has already been useful in other populations in which task compliance is not possible, including individuals with disorders of consciousness (Vanhaudenhuyse et al., 2010
) and chimpanzees (Rilling et al., 2007
). This tool is made even more powerful as it can be linked to complementary data about the anatomy of the brain assessed in the same scan session (Honey et al., 2009
The early and extraordinary success of rs-fMRI as a useful method for measuring systems-level brain organization has occurred sufficiently quickly that some of the assumptions currently being made have yet to be tested. We have experience examining hemodynamic responses pertaining to BOLD confounds (e.g., physiology, blood flow, breathing-rate) and have contributed to the methodological literature in that area (Chang et al., 2009
; Chang and Glover, 2009
; Thomason et al., 2007
). Given our past work showing that BOLD in children is inherently noisier than it is in adults (Thomason et al., 2005
), and given that the best practices for acquiring and analyzing resting-state data are still being developed (Van Dijk et al., 2009
), it is critical that we determine whether resting-state network measurements are stable in children. The present data set would have been useful for a developmental study, but there have already been important contributions in that area (Dosenbach et al., 2010
; Fair et al., 2009
; Fair et al., 2007
; Kelly et al., 2009
; Supekar et al., 2009
). The present study contributes to this literature by being the first to examine temporal stability in rs-fMRI measurements in children. Our results indicate that rs-fMRI is likely to primarily reflect features of the underlying biology (i.e., is stable within individual even over 2–3 years), with some lesser contributions from aspects of the acquisition process (i.e., is even more stable when within session).
Using group ICA in a large sample of youth, we identified rs-fMRI networks that have been found in previous research on adults. We reported the peak locations for networks composed of regions important for executive functioning, salience processing, motor, visual and auditory processing, and the default mode. These peaks may be useful for seed-based connectivity analyses in future studies of children.
ICC and Kendall’s W values were predominantly positive across the whole brain volume, indicating that participant differentiability outweighed scan variance for most brain areas. The general pattern was one of moderately high concordance across spatial ICNs (), but there were some small areas of non-concordance (i.e., where these statistics were negative). These could reflect genuine neural developmental changes across the scan interval. For rs-fMRI to be effective, it should reliably measure stable features of the underlying biology, but still be sensitive to true biological differences. Investigators who conduct longitudinal rs-fMRI studies of children may find it useful to assess change using a mutual information approach that would quantify change across the interval and test its correspondence to behavioral measures, time, or developmental age, for example.
Concordance measured for spatial maps was greater for within-session than for between-session comparisons (measured by Kendall’s W and ICC statistics). The distribution ICC coefficients across all brain voxels within each network are presented in . It is apparent that concordance was higher within sessions than between sessions in this study. Both distributions are significantly different than zero, indicating that the networks are stable within individuals, but the consistency is greater within sessions. Differences in these distributions could be driven by a number of factors that cannot be distinguished within this experimental design, including scan session specific biological factors (e.g., temperature), MRI technology factors (e.g., machine SNR, field shim), developmental maturation, and psychological factors (e.g., mood).
In this study we extended the investigation of the spatial reliability of ICNs to examine stability in temporal and frequency domains. We obtained a significant correlation across all T1/T2 z-converted time-course correlation measures (i.e., scatterplot in , N = 28). The correlation measure used here (global network coherence) may be interpreted as the amount of total relatedness between networks, computed by averaging the pairwise correlations between networks. We found that measures of the relatedness of network time-course data are reliable for individual participants across time. This is among the first work to demonstrate empirically that network dynamics are stable reflections of individual differences, indicating that the study of network dynamics is a key area for future investigation.
Resting state low-frequency fluctuations are thought to reflect cyclic modulation of gross cortical excitability and network neuronal synchronization (Balduzzi et al., 2008
). Here, we examined stability of the computed low-frequency power (range 0.008 < f < 0.08 Hz) for each ICN at each measurement time. Time one to time two comparisons within network showed smaller differences than those observed across ICNs; this relation was significant, however, only in the visual network for the T1/T2 within-session comparisons where across participants the frequency from time 1 to time 2 was correlated (p < .05). Prior work in adults has shown frequency oscillations in visual cortex are impacted by eyes-open versus eyes-closed scanning (Yang et al., 2007
), and also shown that coherent low-frequency fluctuations are particularly strong in visual cortex and posterior midline structures (Zuo et al., 2010a
). Having obtained a significant result in the frequency domain in the visual cortex could therefore reflect aspects of development (i.e. early maturation in sensorimotor cortical networks), or could relate to qualities inherent to the visual network that persist across the life-span. It will be useful for future work in large samples of children to measure the regional specificity and developmental timing of BOLD-derived low-frequency fluctuations to refine what is understood about frequency dynamics within large-scale brain networks across development. Consistent with what has been observed in adults (Zuo et al., 2010a
), we obtained significant results in both the temporal and frequency domains, further supporting stability in rs-fMRI data.
The present results indicate that, if motion is restrained and physiologically generated noise is appropriately controlled, rs-fMRI data in children are robust, and reflect meaningful characteristics of the underlying neurobiology. This is consistent with adult studies (Shehzad et al., 2009
; van de Ven et al., 2004
; Zuo et al., 2010a
; Zuo et al., 2010b
), and is the first indication that ICN maps are relatively stable in children and adolescents. We found that rs-fMRI measurements across spatial, temporal, and frequency domains were reproducible in children. This work provides an important demonstration that rs-fMRI measures are viable for studying developmental progress and of disease. We provide a critical foundation for using the resting state as a marker of large-scale neural network development, and as a basis to compare clinical and healthy population samples.
- Test-retest reliability of rs-fMRI networks is demonstrated for children across time.
- Peaks of six ICA-derived networks are summarized for N=65 children ages 9–15.
- Results in spatial maps and timecourse data are correlated across repeat measures.
- Scans taken within-scan and scans separated 2–3 years are compared in a youth sample.
- Concordance measures and correlations are tested over multiple domains.