[T] A Temporal Dynamics perspective towards to the coupling of the intrinsic regional and global information integration

[may serve as a template for the “Raise research ideas” section at rfmri.org]

 

Idea: A Temporal Dynamics perspective towards to the coupling of the intrinsic regional and global information integration of spontaneous activities.

 

Raise date: August 8, 2013.

 

Already picked up? When and who: Yes, August 8, 2013 by Chao-Gan Yan, Ph.D.

 

Still allow other nodes to follow: yes.

 

Want other nodes to cite when publishing: yes.

 

Want to be an author when other nodes publishing: no, but could be, depend on how much I am involved in the study.

 

How can other researchers discuss with you: reply this topic or contact me at ycg.yan#gmail.com

 

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Recent studies have demonstrated temporally dynamic changes in intrinsic functional connectivity (iFC) patterns - the constituents of intrinsic connectivity networks, as well as their within- and between-network connectivity varies over time (Chang and Glover, 2010; Handwerker et al., 2012; Kang et al., 2011; Kiviniemi et al., 2011; Smith et al., 2012; see Hutchison et al., In press, for a review). However, the dynamic properties of the regional activity (e.g., frequency characteristics), as well as the dynamic coupling of regional and global profiles, remain largely unknown. The regional/global coupling may vary with age across life span, and might be a critical biomarker in brain disorders.

 

 

1.     Investigate how the regional frequency characteristics (e.g., amplitude of low frequency fluctuations (ALFF) (Zang et al., 2007),  fractional ALFF (fALFF) (Zou et al., 2008)) change across time, and where the regions demonstrated more stability are. Explore how the pattern changes with age across life-span, especially with development or aging (e.g., based on the NKI Enhanced Rockland Sample).

 

2.     From a regional integration view, where are the regions showed more variation in temporal dynamics in regional homogeneity (ReHo) (Zang et al., 2004)? And how does the stability change across life-span?

 

3.     From a global integration view, where are the regions showed stable information integration with the whole brain (degree centrality, DC, (Buckner et al., 2009; Tomasi and Volkow, 2010; Zuo et al., 2012)), and how does the temporal variation change with age?

 

4.     From an inter-hemispheric interaction view, how does the homotopic connectivity (measured by voxel mirrored homotopic connectivity, VMHC (Anderson et al., 2011; Zuo et al., 2010)) vary across time? And how is this variation related to age?

 

5.     More importantly, what's the coupling relationship between amplitude, regional integration / global integration across time? How does the coupling relationship (correlation across temporal windows) change with age in life-span sample?

 

6.     Extended analyses: iFC style analyses based on these temporal dynamics of fALFF/ReHo/DC/VMHC, i.e., seed based correlation analysis (SCA), independent component analysis (ICA), and principle component analysis (PCA). Will the common networks based on time series analyses emerge here?

 

Issues:

 

1.     Window size

 

Start from 50s (0.02Hz). Examine the effects of window size in supplementary analyses. Overlap: 90%

 

2.     Window type

 

Hamming window, because of its clear main lobe.

 

3.     Head motion

 

Examine the impact of sliding window-wise mean framewise displacement (FD) on the dynamic profiles

 

4.     Respirational and cardiac signals

 

Analyze the time-frequency characteristics of respirational and cardiac signals (available with the NKI Enhanced Rockland Sample), and estimate their relationship with temporal dynamics of fALFF/ReHo/DC/VMHC.

 

 

1.     Data

 

NKI Enhanced Rockland Sample: testing the life span trajectory of temporal dynamics

 

NKI TRT Dataset: testing the reliability of the temporally dynamic integration.

 

2.     Preprocessing

 

Realign -> Friston 24 + white matter (WM)/ cerebrospinal fluid (CSF) signals regression (with/without global signal regression) -> Normalize: in MNI space

 

3.     Windowing

 

Hamming window 50s (~78 TRs, could sample one cycle of fluctuation as low as 0.02Hz). Examine the window effects (type and length) in supplementary analyses.

 

Detrend and High-pass filtering 0.01Hz for the whole time series.

 

4.     Calculate fALFF/ALFF (like Spectrogram using short-time Fourier transform), ReHo, DC and VMHC within each window.

 

5.     Smooth after calculation of window-wise measures.

 

6.     Examine the variance (SD) and CV across time for these measures.

 

7.     Examine the correlation between amplitude, regional and global measures to measure the coupling relationship.

 

8.     Examine the age-related change in variance (analysis 6) and coupling (analysis 7) across life span.

 

9.     Investigate the test-retest reliability of the variance and coupling based on NKI TRT dataset.

 

10.  To clarify the current findings are not driven by the physiological noise, correlation analyses are performed between the power (calculated for each window) of respirational and cardiac recordings and the measures (fALFF/ReHo/DC/VMHC).

 

11.  Extended analyses: iFC style analyses based on these temporal dynamics of fALFF/ReHo/DC/VMHC, i.e., SCA, ICA, PCA, to evaluate if the common intrinsic networks (e.g., the default mode network) could also be acquired based on the temporal dynamics of fALFF/ReHo/DC/VMHC.

 

 

 

Anderson, J.S., Druzgal, T.J., Froehlich, A., DuBray, M.B., Lange, N., Alexander, A.L., Abildskov, T., Nielsen, J.A., Cariello, A.N., Cooperrider, J.R., Bigler, E.D., Lainhart, J.E., 2011. Decreased interhemispheric functional connectivity in autism. Cerebral cortex 21, 1134-1146.

 

Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., Andrews-Hanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 29, 1860-1873.

 

Chang, C., Glover, G.H., 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50, 81-98.

 

Handwerker, D.A., Roopchansingh, V., Gonzalez-Castillo, J., Bandettini, P.A., 2012. Periodic changes in fMRI connectivity. Neuroimage 63, 1712-1719.

 

Hutchison, R.M., Womelsdorf, T., Allen, E.a., Bandettini, P.A., Calhoun, V.D., Corbetta, M., Penna, S.D., duyn, J.H., Glover, G.H., Gonzalez-Castillo, J., Handwerker, D.A., Keilholz, S., Kiviniemi, V., Leopold, D.A., de Pasquale, F., Sporns, O., Walter, M., Chang, C., In press. Dynamic functional connectivity: Promises, issues, and interpretations. NeuroImage.

 

Kang, J., Wang, L., Yan, C., Wang, J., Liang, X., He, Y., 2011. Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. NeuroImage 56, 1222-1234.

 

Kiviniemi, V., Vire, T., Remes, J., Elseoud, A.A., Starck, T., Tervonen, O., Nikkinen, J., 2011. A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain connectivity 1, 339-347.

 

Smith, S.M., Miller, K.L., Moeller, S., Xu, J., Auerbach, E.J., Woolrich, M.W., Beckmann, C.F., Jenkinson, M., Andersson, J., Glasser, M.F., Van Essen, D.C., Feinberg, D.A., Yacoub, E.S., Ugurbil, K., 2012. Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci U S A 109, 3131-3136.

 

Tomasi, D., Volkow, N.D., 2010. Functional connectivity density mapping. Proc Natl Acad Sci U S A 107, 9885-9890.

 

Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang, Y.F., 2007. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29, 83-91.

 

Zang, Y.F., Jiang, T.Z., Lu, Y.L., He, Y., Tian, L.X., 2004. Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 394-400.

 

Zou, Q.H., Zhu, C.Z., Yang, Y., Zuo, X.N., Long, X.Y., Cao, Q.J., Wang, Y.F., Zang, Y.F., 2008. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172, 137-141.

 

Zuo, X.N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F.X., Sporns, O., Milham, M.P., 2012. Network Centrality in the Human Functional Connectome. Cereb Cortex 22, 1862-1875.

 

Zuo, X.N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D.S., Bangaru, S., Grzadzinski, R., Evans, A.C., Zang, Y.F., Castellanos, F.X., Milham, M.P., 2010. Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J Neurosci 30, 15034-15043.