A posteriori model validation for the temporal order of directed functional connectivity maps

Frontiers in Neuroscience

Volumn 9, Issue AUG, 2015, Pages

  Beltz, Adriene M ^a   Molenaar, Peter C M ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

A posteriori model validation; Directed functional connectivity; Neuroimaging; Structural vector autoregression; Temporal order; Unified structural equation modeling

Indexed keywords

ADULT; ARTICLE; BIOLOGICAL MODEL; BOLD SIGNAL; BRAIN MAPPING; CONNECTOME; CONTROLLED STUDY; DECISION MAKING; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; MALE; MATHEMATICAL COMPUTING; MENTAL TASK; NEUROIMAGING; POSTERIORI MODEL VALIDATION; PROCESS MODEL; RESTING STATE NETWORK; SIGNAL NOISE RATIO; TASK PERFORMANCE; TEMPORAL ANALYSIS; VALIDATION PROCESS; VALIDATION STUDY;

EID: 84940784646     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2015.00304     Document Type: Article

References (56)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716-723. doi: 10.1109/tac.1974.1100705.
2. Alonso-Montes, C., Diez, I., Remaki, L., Escudero, I., Mateos, B., Rosseel, Y., et al. (2015). Lagged and instantaneous dynamical influences related to brain structural connectivity. Front. Psychol. 6:1024. doi: 10.3389/fpsyg.2015.01024.
3. Arbabshirani, M.R., Damaraju, E., Phlypo, R., Plis, S., Allen, E., Ma, S., et al. (2014). Impact of autocorrelation on functional connectivity. Neuroimage 102, 294-308. doi: 10.1016/j.neuroimage.2014.07.045.
4. Beltz, A.M., Gates, K.M., Engels, A.S., Molenaar, P.C. M., Pulido, C., Turrisi, R., et al. (2013). Changes in alcohol-related brain networks across the first year of college: a prospective pilot study using fMRI effective connectivity mapping. Addict. Behav. 38, 2052-2059. doi: 10.1016/j.addbeh.2012.12.023.
5. Biswal, B.B., Mennes, M., Zuo, X.N., Gohel, S., Kelly, C., Smith, S.M., et al. (2010). Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A. 107, 4734-4739. doi: 10.1073/pnas.0911855107.
6. Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., et al. (2011). OpenMx: an open source extended structural equation modeling framework. Psychometrika 76, 306-317. doi: 10.1007/s11336-010-9200-6.
7. Box, G.E. P., and Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day.
8. Brown, T.A. (2006). Confirmatory Factor Analysis for Applied Research. New York, NY: GuilfordPress.
9. Chang, C., and Glover, G.H. (2009). Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage 47, 1448-1459. doi: 10.1016/j.neuroimage.2009.05.012.
10. Chen, G., Glen, D.R., Saad, Z.S., Hamilton, J.P., Thomason, M.E., Gotlib, I.H., et al. (2011). Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis. Comput. Biol. Med., 41, 1142-1155. doi: 10.1016/j.compbiomed.2011.09.004.
11. Chou, C.P., and Bentler, P.M. (1990). Model modification in covariance structure modeling: a comparison among likelihood ratio, Lagrange Multiplier, and Wald tests. Multivariate Behav. Res. 25, 115-136. doi: 10.1207/s15327906mbr2501_13.
12. Christova, P., Lewis, S.M., Jerde, T.A., Lynch, J.K., and Georgopoulos, A.P. (2011). True associations between resting fMRI time series based on innovations. J. Neural Eng. 8:046025. doi: 10.1088/1741-2560/8/4/046025.
13. Cox, R.W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173. doi: 10.1006/cbmr.1996.0014.
14. Fox, M.D., Zhang, D., Snyder, A.Z., and Raichle, M.E. (2009). The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270-3283. doi: 10.1152/jn.90777.2008.
15. Friston, K.J., Josephs, O., Zarahn, E., Holmes, A.P., Rouquette, S., and Poline, J.B. (2000). To smooth or not to smooth? Bias and efficiency in fMRI time-series analysis. Neuroimage 12, 196-208. doi: 10.1006/nimg.2000.0609.
16. Friston, K., Moran, R., and Seth, A.K. (2013). Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurol. 23, 172-178. doi: 10.1016/j.conb.2012.11.010.
17. Gates, K.M., and Molenaar, P.C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage 63, 310-319. doi: 10.1016/j.neuroimage.2012.06.026.
18. Gates, K.M., Molenaar, P.C. M., Hillary, F.G., Ram, N., and Rovine, M.J. (2010). Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. Neuroimage 50, 1118-1125. doi: 10.1016/j.neuroimage.2009.12.117.
19. Gates, K.M., Molenaar, P.C. M., Hillary, F.G., and Slobounov, S. (2011). Extended unified SEM approach for modeling event-related fMRI data. Neuroimage 54, 1151-1158. doi: 10.1016/j.neuroimage.2010.08.051.
20. Granger, C.W. J., and Newbold, P. (1974). Spurious regressions in econometrics. J. Econom. 2, 111-120. doi: 10.1016/0304-4076(74)90034-7.
21. Grant, A.M., Fang, S.-Y., and Li, P. (2015). Second language lexical development and cognitive control: a longitudinal fMRI study. Brain Lang. 144, 35-47. doi: 10.1016/j.bandl.2015.03.010.
22. Handwerker, D.A., Ollinger, J.M., and D'Esposito, M. (2004). Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 21, 1639-1651. doi: 10.1016/j.neuroimage.2003.11.029.
23. Hillary, F.G., Medaglia, J.D., Gates, K.M., Molenaar, P.C., and Good, D.C. (2014). Examining network dynamics after traumatic brain injury using the extended unified SEM approach. Brain Imaging Behav. 8, 435-445. doi: 10.1007/s11682-012-9205-0.
24. Hillary, F.G., Medaglia, J.D., Gates, K., Molenaar, P.C., Slocomb, J., Peechatka, A., et al. (2011). Examining working memory task acquisition in a disrupted neural network. Brain 134, 1555-1570. doi: 10.1093/brain/awr043.
25. Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., and Smith, S.M. (2012). FSL. Neuroimage 62, 782-790. doi: 10.1016/j.neuroimage.2011.09.015.
26. Jöreskog, K.G., and Sörbom, D. (1992). LISREL. Chicago, IL: Scientific Software International, Inc.
27. Kaneoke, Y., Donishi, T., Iwatani, J., Ukai, S., Shinosaki, K., and Terada, M. (2012). Variance and autocorrelation of the spontaneous slow brain activity. PLoS ONE 7:e38131. doi: 10.1371/journal.pone.0038131.
28. Karunanayaka, P., Eslinger, P.J., Wang, J.L., Weitekamp, C.W., Molitoris, S., Gates, K.M., et al. (2014). Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling. Hum. Brain Mapp. 35, 2055-2072. doi: 10.1002/hbm.22312.
29. Kim, J., Zhu, W., Chang, L., Bentler, P.M., and Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Hum. Brain Mapp. 28, 85-93. doi: 10.1002/hbm.20259.
30. Loehlin, J.C. (1996). The Cholesky approach: a cautionary note. Behav. Genet. 26, 65-69. doi: 10.1007/BF02361160.
31. Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin: Springer.
32. Mathworks. (2013). Matlab. Natick, MA: The Mathworks, Inc.
33. McIntosh, A.R., and Gonzalez-Lima, F.;1. (1994). Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 2, 2-22. doi: 10.1002/hbm.460020104.
34. Molenaar, P.C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika 50, 181-202. doi: 10.1007/BF02294246.
35. Molenaar, P.C. M., and Lo, L.L. (2015). "Alternative forms of granger causality, heterogeneity and non-stationarity," in Statistics and Causality, eds A. von Eye and W. Wiedermann (Hoboken, NJ: Wiley).
36. Murphy, K., Birn, R.M., and Bandettini, P.A. (2013). Resting-state fMRI confounds and cleanup. Neuroimage 80, 349-359. doi: 10.1016/j.neuroimage.2013.04.001.
37. Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., and Bandettini, P.A. (2009). The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44, 893-905. doi: 10.1016/j.neuroimage.2008.09.036.
38. Nichols, T.T., Gates, K.M., Molenaar, P.C. M., and Wilson, S.J. (2014). Greater BOLD activity but more efficient connectivity is associated with better cognitive performance within a sample of nicotine-deprived smokers. Addict. Biol. 19, 931-940. doi: 10.1111/adb.12060.
39. Pashler, H., and Wagenmakers, E.-J. (2012). Editors' introduction to the special section on replicability in psychological science: a crisis of confidence? Perspect. Psychol. Sci. 7, 528-530. doi: 10.1177/1745691612465253.
40. Penny, W., and Harrison, L. (2007). "Multivariate autoregressive models," in Statistical Parametric Mapping: The Analysis of Functional Brain Images, eds K. J. Friston, J.T. Ashburner, S.J. Kiebel, T.E. Nichols, and W. D. Penny (London: Elsevier), 534-540.
41. Penny, W., Kiebel, S., and Friston, K.J. (2003). Variational Bayesian inference for fMRI time series. Neuroimage 19, 727-741. doi: 10.1016/s1053-8119(03)00071-5.
42. Poldrack, R.A. (2000). Imaging brain plasticity: conceptual and methodological issues - A theoretical review. Neuroimage 12, 1-13. doi: 10.1006/nimg.2000.0596.
43. Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., and Shulman, G.L. (2001). A default mode of brain function. Proc. Natl. Acad. Sci. U.S.A. 98, 676-682. doi: 10.1073/pnas.98.2.676.
44. Ramsey, J.D., Hanson, S.J., Hanson, C., Halchenko, Y.O., Poldrack, R.A., and Glymour, C. (2010). Six problems for causal inference from fMRI. Neuroimage 49, 1545-1558. doi: 10.1016/j.neuroimage.2009.08.065.
45. Rosseel, Y. (2012). lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1-36.
46. Smith, S.M. (2012). The future of FMRI connectivity. Neuroimage 62, 1257-1266. doi: 10.1016/j.neuroimage.2012.01.022.
47. Smith, S.M., Miller, K.L., Salimi-Khorshidi, G., Webster, M., Beckmann, C.F., Nichols, T.E., et al. (2011). Network modelling methods for fMRI. Neuroimage 54, 875-891. doi: 10.1016/j.neuroimage.2010.08.063.
48. Sörbom, D. (1989). Model modification. Psychometrika 54, 371-384. doi: 10.1007/bf02294623.
49. Turrisi, R., Mallett, K.A., Cleveland, M.J., Varvil-Weld, L., Abar, C., Scaglione, N., et al. (2013). Evaluation of timing and dosage of a parent-based intervention to minimize college students' alcohol consumption. J. Stud. Alcohol Drugs 74, 30-40. doi: 10.15288/jsad.2013.74.30.
50. Van Dijk, K.R. A., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., and Buckner, R.L. (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103, 297-321. doi: 10.1152/jn.00783.2009.
51. Varvil-Weld, L., Mallett, K.A., Turrisi, R., and Abar, C.C. (2012). Using parental profiles to predict membership in a subset of college students experiencing excessive alcohol consequences: findings from a longitudinal study. J. Stud. Alcohol Drugs 73, 434-443. doi: 10.15288/jsad.2012.73.434.
52. Varvil-Weld, L., Scaglione, N., Cleveland, M.J., Mallett, K.A., Turrisi, R., and Abar, C.C. (2014). Optimizing timing and dosage: does parent type moderate the effects of variations of a parent-based intervention to reduce college student drinking? Prev. Sci. 15, 94-102. doi: 10.1007/s11121-012-0356-4.
53. Wilson, S.J., Sayette, M.A., and Fiez, J.A. (2012). Quitting-unmotivated and quitting-motivated cigarette smokers exhibit different patterns of cue-elicited brain activation when anticipating an opportunity to smoke. J. Abnorm. Psychol. 121, 198-211. doi: 10.1037/a0025112.
54. Woolrich, M.W., Ripley, B.D., Brady, M., and Smith, S.M. (2001). Temporal autocorrelation in univariate linear modeling of fMRI data. Neuroimage 14, 1370-1386. doi: 10.1006/nimg.2001.0931.
55. Wu, G.R., Liao, W., Stramaglia, S., Ding, J.R., Chen, H., and Marinazzo, D. (2013). A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17, 365-374. doi: 10.1016/j.media.2013.01.003.
56. Yang, J., Gates, K.M., Molenaar, P., and Li, P. (2015). Neural changes underlying successful second language word learning: an fMRI study. J. Neurolinguistics 33, 29-49. doi: 10.1016/j.jneuroling.2014.09.004.