The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability

. 2019 ; 13 () : 1249. [epub] 20191126

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid31849578

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies frequently applied the spatial normalization on fMRI time series before the calculation of temporal features (here referred to as "Prenorm"). We hypothesized that calculating the rs-fMRI features, for example, functional connectivity (FC), regional homogeneity (ReHo), or amplitude of low-frequency fluctuation (ALFF) in individual space, before the spatial normalization (referred to as "Postnorm") can be an improvement to avoid artifacts and increase the results' reliability. We utilized two datasets: (1) simulated images where temporal signal-to-noise ratio (tSNR) is kept a constant and (2) an empirical fMRI dataset with 50 healthy young subjects. For simulated images, the tSNR is constant as generated in individual space but increased after Prenorm and intersubject variability of tSNR was induced. In contrast, tSNR was kept constant after Postnorm. Consistently, for empirical images, higher tSNR, ReHo, and FC (default mode network, seed in precuneus) and lower ALFF were found after Prenorm compared to those of Postnorm. Coefficient of variability of tSNR and ALFF was higher after Prenorm compared to those of Postnorm. Moreover, the significant correlation was found between simulated tSNR after Prenorm and empirical tSNR, ALFF, and ReHo after Prenorm, indicating algorithmic variation in empirical rs-fMRI features. Furthermore, comparing to Prenorm, ALFF and ReHo showed higher intraclass correlation coefficients between two serial scans after Postnorm. Our results indicated that Prenorm may induce algorithmic intersubject variability on tSNR and reduce its reliability, which also significantly affected ALFF and ReHo. We suggest using Postnorm instead of Prenorm for future rs-fMRI studies using ALFF/ReHo.

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Archer J. A., Lee A., Qiu A., Chen S. A. (2018). Working memory, age and education: a lifespan fMRI study. PLoS One 13:e0194878. 10.1371/journal.pone.0194878 PubMed DOI PMC

Ashburner J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage 38 95–113. 10.1016/j.neuroimage.2007.07.007 PubMed DOI

Ashburner J., Friston K. J. (2005). Unified segmentation. Neuroimage 26 839–851. 10.1016/j.neuroimage.2005.02.018 PubMed DOI

Bennett C. M., Miller M. B. (2010). How reliable are the results from functional magnetic resonance imaging? Ann. N. Y. Acad. Sci. 1191 133–155. 10.1111/j.1749-6632.2010.05446.x PubMed DOI

Biswal B., Yetkin F. Z., Haughton V. M., Hyde J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34 537–541. 10.1002/mrm.1910340409 PubMed DOI

Button K. S., Ioannidis J. P., Mokrysz C., Nosek B. A., Flint J., Robinson E. S., et al. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14 365–376. 10.1038/nrn3475 PubMed DOI

Calhoun V. D., Wager T. D., Krishnan A., Rosch K. S., Seymour K. E., Nebel M. B., et al. (2017). The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. Hum. Brain Mapp. 38 5331–5342. 10.1002/hbm.23737 PubMed DOI PMC

Collins D. L., Neelin P., Peters T. M., Evans A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 18 192–205. 10.1097/00004728-199403000-00005 PubMed DOI

Cooper R. A., Richter F. R., Bays P. M., Plaisted-Grant K. C., Baron-Cohen S., Simons J. S. (2017). Reduced hippocampal functional connectivity during episodic memory retrieval in Autism. Cereb. Cortex 27 888–902. 10.1093/cercor/bhw417 PubMed DOI PMC

Cox R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29 162–173. 10.1006/cbmr.1996.0014 PubMed DOI

De Vos F., Koini M., Schouten T. M., Seiler S., Van Der Grond J., Lechner A., et al. (2018). A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer’s disease. Neuroimage 167 62–72. 10.1016/j.neuroimage.2017.11.025 PubMed DOI

Di X., Kannurpatti S. S., Rypma B., Biswal B. B. (2013). Calibrating BOLD fMRI activations with neurovascular and anatomical constraints. Cereb. Cortex 23 255–263. 10.1093/cercor/bhs001 PubMed DOI PMC

Duff E. P., Makin T., Cottaar M., Smith S. M., Woolrich M. W. (2018). Disambiguating brain functional connectivity. Neuroimage 173 540–550. 10.1016/j.neuroimage.2018.01.053 PubMed DOI PMC

Fox M. D., Snyder A. Z., Vincent J. L., Corbetta M., Van Essen D. C., Raichle M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U.S.A. 102 9673–9678. 10.1073/pnas.0504136102 PubMed DOI PMC

Garrett D. D., Samanez-Larkin G. R., Macdonald S. W., Lindenberger U., Mcintosh A. R., Grady C. L. (2013). Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci. Biobehav. Rev. 37 610–624. 10.1016/j.neubiorev.2013.02.015 PubMed DOI PMC

Haak K. V., Marquand A. F., Beckmann C. F. (2018). Connectopic mapping with resting-state fMRI. Neuroimage 170 83–94. 10.1016/j.neuroimage.2017.06.075 PubMed DOI

Hedge C., Powell G., Sumner P. (2018). The reliability paradox: why robust cognitive tasks do not produce reliable individual differences. Behav. Res. Methods 50 1166–1186. 10.3758/s13428-017-0935-1 PubMed DOI PMC

Jenkinson M., Beckmann C. F., Behrens T. E., Woolrich M. W., Smith S. M. (2012). Fsl. Neuroimage 62 782–790. 10.1016/j.neuroimage.2011.09.015 PubMed DOI

Ji G. J., Wei L., Chen F. F., Zhang L., Wang K. (2017). Low-frequency blood oxygen level-dependent fluctuations in the brain white matter:more than just noise. Sci. Bull. 62 656–657. 10.1016/j.scib.2017.03.021 PubMed DOI

Jiang L., Zuo X. N. (2016). Regional homogeneity: a multimodal, multiscale neuroimaging marker of the human connectome. Neuroscientist 22 486–505. 10.1177/1073858415595004 PubMed DOI PMC

Krüger G., Glover G. H. (2001). Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magn. Reson. Med. 46:631. 10.1002/mrm.1240 PubMed DOI

Makedonov I., Black S. E., Macintosh B. J. (2013). BOLD fMRI in the white matter as a marker of aging and small vessel disease. PLoS One 8:e67652. 10.1371/journal.pone.0067652 PubMed DOI PMC

Makedonov I., Chen J. J., Masellis M., Macintosh B. J. Alzheimer’s Disease Neuroimaging Initiative. (2016). Physiological fluctuations in white matter are increased in Alzheimer’s disease and correlate with neuroimaging and cognitive biomarkers. Neurobiol. Aging 37 12–18. 10.1016/j.neurobiolaging.2015.09.010 PubMed DOI

Mazziotta J., Toga A., Evans A., Fox P., Lancaster J., Zilles K., et al. (2001). A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 356 1293–1322. 10.1098/rstb.2001.0915 PubMed DOI PMC

Poldrack R. A., Baker C. I., Durnez J., Gorgolewski K. J., Matthews P. M., Munafo M. R., et al. (2017). Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18 115–126. 10.1038/nrn.2016.167 PubMed DOI PMC

Qing Z., Gong G. (2016). Size matters to function: brain volume correlates with intrinsic brain activity across healthy individuals. Neuroimage 139 271–278. 10.1016/j.neuroimage.2016.06.046 PubMed DOI

Qing Z., Li W., Nedelska Z., Wu W., Wang F., Liu R., et al. (2017). Spatial navigation impairment is associated with alterations in subcortical intrinsic activity in mild cognitive impairment: a resting-State fMRI study. Behav. Neurol. 2017:6364314. 10.1155/2017/6364314 PubMed DOI PMC

Raichle M. E. (2010). The brain’s dark energy. Sci. Am. 302 44–49. PubMed

Raichle M. E. (2015). The brain’s default mode network. Annu. Rev. Neurosci. 38 433–447. 10.1146/annurev-neuro-071013-014030 PubMed DOI

Strother S., La Conte S., Kai Hansen L., Anderson J., Zhang J., Pulapura S., et al. (2004). Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis. Neuroimage 23(Suppl. 1), S196–S207. PubMed

Tzourio-Mazoyer N., Landeau B., Papathanassiou D., Crivello F., Etard O., Delcroix N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15 273–289. PubMed

Wang J., Wang X., Xia M., Liao X., Evans A., He Y. (2015). GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front. Hum. Neurosci. 9:386. 10.3389/fnhum.2015.00386 PubMed DOI PMC

Worsley K. J. (2005). Spatial smoothing of autocorrelations to control the degrees of freedom in fMRI analysis. Neuroimage 26 635–641. 10.1016/j.neuroimage.2005.02.007 PubMed DOI

Wu C. W., Chen C. L., Liu P. Y., Chao Y. P., Biswal B. B., Lin C. P. (2011). Empirical evaluations of slice-timing, smoothing, and normalization effects in seed-based, resting-state functional magnetic resonance imaging analyses. Brain Connect. 1 401–410. 10.1089/brain.2011.0018 PubMed DOI

Xing X. X., Zuo X. N. (2018). The anatomy of reliability: a must read for future human brain mapping. Sci. Bull. Vol. 63 1606–1607. 10.1016/j.scib.2018.12.010 PubMed DOI

Xing X. X. Z. X. N. (2018). The anatomy of reliability: a must read for future human brain mapping. Sci. Bull. Vol. 63 606–607. PubMed

Yan C. G., Wang X. D., Zuo X. N., Zang Y. F. (2016). DPABI: data processing & analysis for (Resting-State) brain imaging. Neuroinformatics 14 339–351. 10.1007/s12021-016-9299-4 PubMed DOI

Yan C. G., Zang Y. F. (2010). DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci. 4:13. 10.3389/fnsys.2010.00013 PubMed DOI PMC

Zang Y., Jiang T., Lu Y., He Y., Tian L. (2004). Regional homogeneity approach to fMRI data analysis. Neuroimage 22 394–400. 10.1016/j.neuroimage.2003.12.030 PubMed DOI

Zang Y. F., He Y., Zhu C. Z., Cao Q. J., Sui M. Q., Liang M., et al. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 29 83–91. 10.1016/j.braindev.2006.07.002 PubMed DOI

Zhang D., Raichle M. E. (2010). Disease and the brain’s dark energy. Nat. Rev. Neurol. 6 15–28. 10.1038/nrneurol.2009.198 PubMed DOI

Zuo X. N., Anderson J. S., Bellec P., Birn R. M., Biswal B. B., Blautzik J., et al. (2014). An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1:140049. 10.1038/sdata.2014.49 PubMed DOI PMC

Zuo X. N., Biswal B. B., Poldrack R. A. (2019a). Editorial: reliability and reproducibility in functional connectomics. Front. Neurosci. 13:117. 10.3389/fnins.2019.00117 PubMed DOI PMC

Zuo X. N., Xu T., Milham M. P. (2019b). Harnessing reliability for neuroscience research. Nat. Hum. Behav. 3 768–771. 10.1038/s41562-019-0655-x PubMed DOI

Zuo X. N., Xu T., Jiang L., Yang Z., Cao X. Y., He Y., et al. (2013). Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage 65 374–386. 10.1016/j.neuroimage.2012.10.017 PubMed DOI PMC

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