This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

1st Department of Neurology Faculty of Medicine of the Masaryk University and St Anne's University Hospital Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Department of Psychiatry University Hospital Brno and Faculty of Medicine Masaryk University Brno Czech Republic

Zobrazit více v PubMed

Arbabshirani, M. R. , Havlicek, M. , Kiehl, K. A. , Pearlson, G. D. , & Calhoun, V. D. (2013). Functional network connectivity during rest and task conditions: A comparative study. Human Brain Mapping, 34, 2959–2971. PubMed PMC

Baniqued, P. L. , Gallen, C. L. , Voss, M. W. , Burzynska, A. Z. , Wong, C. N. , Cooke, G. E. , … D'Esposito, M. (2018). Brain network modularity predicts exercise‐related executive function gains in older adults. Frontiers in Aging Neuroscience, 9. PubMed PMC

Barton, M. , Marecek, R. , Rektor, I. , Filip, P. , Janousova, E. , & Mikl, M. (2015). Sensitivity of PPI analysis to differences in noise reduction strategies. Journal of Neuroscience Methods, 253, 218–232. PubMed

Behzadi, Y. , Restom, K. , Liau, J. , & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37, 90–101. PubMed PMC

Birn, R. M. , Smith, M. A. , Jones, T. B. , & Bandettini, P. A. (2008). The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage, 40, 644–654. PubMed PMC

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. Magnetic Resonance in Medicine, 34, 537–541. PubMed

Bluhm, R. L. , Clark, C. R. , McFarlane, A. C. , Moores, K. A. , Shaw, M. E. , & Lanius, R. A. (2011). Default network connectivity during a working memory task. Human Brain Mapping, 32, 1029–1035. PubMed PMC

Caballero‐Gaudes, C. , & Reynolds, R. C. (2017). Methods for cleaning the BOLD fMRI signal. NeuroImage, 154, 128–149. PubMed PMC

Cavanna, A. E. , & Trimble, M. R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain, 129, 564–583. PubMed

Chang, C. , Cunningham, J. P. , & Glover, G. H. (2009). Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage, 44, 857–869. PubMed PMC

Chang, C. , & Glover, G. H. (2009). Effects of model‐based physiological noise correction on default mode network anti‐correlations and correlations. NeuroImage, 47, 1448–1459. PubMed PMC

Cheng, H. , Newman, S. D. , Kent, J. S. , Bolbecker, A. , Klaunig, M. J. , O'Donnell, B. F. , … Hetrick, W. P. (2015). White matter abnormalities of microstructure and physiological noise in schizophrenia. Brain Imaging and Behavior, 9, 868–877. PubMed PMC

Cole, M. W. , Bassett, D. S. , Power, J. D. , Braver, T. S. , & Petersen, S. E. (2014). Intrinsic and task‐evoked network architectures of the human brain. Neuron, 83, 238–251. PubMed PMC

Curtis, A. T. , & Menon, R. S. (2014). Highcor: A novel data‐driven regressor identification method for BOLD fMRI. NeuroImage, 98, 184–194. PubMed

DeDora, D. J. , Nedic, S. , Katti, P. , Arnab, S. , Wald, L. L. , Takahashi, A. , … Mujica‐Parodi, L. R. (2016). Signal fluctuation sensitivity: An improved metric for optimizing detection of resting‐state fMRI networks. Frontiers in Neuroscience, 10. PubMed PMC

Di, X. , Gohel, S. , Kim, E. H. , & Biswal, B. B. (2013). Task vs. rest‐different network configurations between the coactivation and the resting‐state brain networks. Frontiers in Human Neuroscience, 7. PubMed PMC

Ding, Z. H. , Huang, Y. L. , Bailey, S. K. , Gao, Y. R. , Cutting, L. E. , Rogers, B. P. , … Gore, J. C. (2018). Detection of synchronous brain activity in white matter tracts at rest and under functional loading. Proceedings of the National Academy of Sciences of the United States of America, 115, 595–600. PubMed PMC

Dipasquale, O. , Sethi, A. , Lagana, M. M. , Baglio, F. , Baselli, G. , Kundu, P. , … Cercignani, M. (2017). Comparing resting state fMRI de‐noising approaches using multi‐ and single‐echo acquisitions. PLoS One, 12, e0173289. PubMed PMC

Erhardt, E. B. , Rachakonda, S. , Bedrick, E. J. , Allen, E. A. , Adali, T. , & Calhoun, V. D. (2011). Comparison of multi‐subject ICA methods for analysis of fMRI data. Human Brain Mapping, 32, 2075–2095. PubMed PMC

Fan, J. , Xu, P. , Van Dam, N. T. , Eilam‐Stock, T. , Gu, X. , Luo, Y. J. , & Hof, P. R. (2012). Spontaneous brain activity relates to autonomic arousal. Journal of Neuroscience, 32, 11176–11186. PubMed PMC

Fratini, M. , Moraschi, M. , Maraviglia, B. , & Giove, F. (2014). On the impact of physiological noise in spinal cord functional MRI. Journal of Magnetic Resonance Imaging, 40, 770–777. PubMed

Friston, K. J. , Williams, S. , Howard, R. , Frackowiak, R. S. J. , & Turner, R. (1996). Movement‐related effects in fMRI time‐series. Magnetic Resonance in Medicine, 35, 346–355. PubMed

Gargouri, F. , Kallel, F. , Delphine, S. , Ben Hamida, A. , Lehericy, S. , & Valabregue, R. (2018). The influence of preprocessing steps on graph theory measures derived from resting state fMRI. Frontiers in Computational Neuroscience, 12. PubMed PMC

Gawryluk, J. R. , Mazerolle, E. L. , & D'Arcy, R. C. N. (2014). Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions. Frontiers in Neuroscience, 8. PubMed PMC

Glover, G. H. , Li, T. Q. , & Ress, D. (2000). Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44, 162–167. PubMed

Golestani, A. M. , Kwinta, J. B. , Khatamian, Y. B. , & Chen, J. J. A. (2017). The effect of low‐frequency physiological correction on the reproducibility and specificity of resting‐state fMRI metrics: Functional connectivity, ALFF, and ReHo. Frontiers in Neuroscience, 11. PubMed PMC

Gonzalez‐Castillo, J. , Roopchansingh, V. , Bandettini, P. A. , & Bodurka, J. (2011). Physiological noise effects on the flip angle selection in BOLD fMRI. NeuroImage, 54, 2764–2778. PubMed PMC

Hagberg, G. E. , Bianciardi, M. , Brainovich, V. , Cassara, A. M. , & Maraviglia, B. (2012). Phase stability in fMRI time series: Effect of noise regression, off‐resonance correction and spatial filtering techniques. NeuroImage, 59, 3748–3761. PubMed

Jiang, Y. , Luo, C. , Li, X. , Li, Y. , Yang, H. , Li, J. , … Yao, D. (2018). White‐matter functional networks changes in patients with schizophrenia. NeuroImage. 10.1016/j.neuroimage.2018.04.018. [Epub ahead of print]. PubMed DOI

Jo, H. J. , Saad, Z. S. , Simmons, W. K. , Milbury, L. A. , & Cox, R. W. (2010). Mapping sources of correlation in resting state FMRI, with artifact detection and removal. NeuroImage, 52, 571–582. PubMed PMC

Jones, T. B. , Bandettini, P. A. , & Birn, R. M. (2008). Integration of motion correction and physiological noise regression in fMRI. NeuroImage, 42, 582–590. PubMed PMC

Jorge, J. , Figueiredo, P. , van der Zwaag, W. , & Marques, J. P. (2013). Signal fluctuations in fMRI data acquired with 2D‐EPI and 3D‐EPI at 7 tesla. Magnetic Resonance Imaging, 31, 212–220. PubMed

Khalili‐Mahani, N. , Chang, C. , van Osch, M. J. , Veer, I. M. , van Buchem, M. A. , Dahan, A. , … Rombouts, S. (2013). The impact of “physiological correction” on functional connectivity analysis of pharmacological resting state fMRI. Neuroimage, 65, 499–510. PubMed

Kiviniemi, V. , Wang, X. D. , Korhonen, V. , Keinanen, T. , Tuovinen, T. , Autio, J. , … Nedergaard, M. (2016). A ultra‐fast magnetic resonance encephalography of physiological brain activity—Glymphatic pulsation mechanisms? Journal of Cerebral Blood Flow and Metabolism, 36, 1033–1045. PubMed PMC

Kruger, G. , Kastrup, A. , & Glover, G. H. (2001). Neuroimaging at 1.5 T and 3.0 T: Comparison of oxygenation‐sensitive magnetic resonance imaging. Magnetic Resonance in Medicine, 45, 595–604. PubMed

Laird, A. R. , Fox, P. M. , Eickhoff, S. B. , Turner, J. A. , Ray, K. L. , McKay, D. R. , … Fox, P. T. (2011). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience, 23, 4022–4037. PubMed PMC

Li, Y. O. , Adali, T. , & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28, 1251–1266. PubMed PMC

Liu, T. T. (2016). Noise contributions to the fMRI signal: An overview. NeuroImage, 143, 141–151. PubMed

Mazerolle, E. L. , Gawryluk, J. R. , Dillen, K. N. H. , Patterson, S. A. , Feindel, K. W. , Beyea, S. D. , … D'Arcy, R. C. N. (2013). Sensitivity to white matter fMRI activation increases with field strength. PLoS One, 8, e58130. PubMed PMC

McLaren, D. G. , Ries, M. L. , Xu, G. , & Johnson, S. C. (2012). A generalized form of context‐dependent psychophysiological interactions (gPPI): A comparison to standard approaches. NeuroImage, 61, 1277–1286. PubMed PMC

Murphy, K. , Birn, R. M. , & Bandettini, P. A. (2013). Resting‐state fMRI confounds and cleanup. NeuroImage, 80, 349–359. PubMed PMC

Muschelli, J. , Nebel, M. B. , Caffo, B. S. , Barber, A. D. , Pekar, J. J. , & Mostofsky, S. H. (2014). Reduction of motion‐related artifacts in resting state fMRI using a CompCor. NeuroImage, 96, 22–35. PubMed PMC

O'Reilly, J. X. , Woolrich, M. W. , Behrens, T. E. J. , Smith, S. M. , & Johansen‐Berg, H. (2012). Tools of the trade: Psychophysiological interactions and functional connectivity. Social Cognitive and Affective Neuroscience, 7, 604–609. PubMed PMC

Ogawa, S. , Lee, T. M. , Kay, A. R. , & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87, 9868–9872. PubMed PMC

Peer, M. , Nitzan, M. , Bick, A. S. , Levin, N. , & Arzyt, S. (2017). Evidence for functional networks within the Human Brain's white matter. Journal of Neuroscience, 37, 6394–6407. PubMed PMC

Perlbarg, V. , Bellec, P. , Anton, J. L. , Pelegrini‐Issac, M. , Doyon, J. , & Benali, H. (2007). CORSICA: Correction of structured noise in fMRI by automatic identification of ICA components. Magnetic Resonance Imaging, 25, 35–46. PubMed

Petridou, N. , Schafer, A. , Gowland, P. , & Bowtell, R. (2009). Phase vs. magnitude information in functional magnetic resonance imaging time series: Toward understanding the noise. Magnetic Resonance Imaging, 27, 1046–1057. PubMed

Raichle, M. E. , & Snyder, A. Z. (2007). A default mode of brain function: A brief history of an evolving idea. NeuroImage, 37, 1083–1090 discussion 1097‐9. PubMed

Renvall, V. , Nangini, C. , & Hari, R. (2014). All that glitters is not BOLD: Inconsistencies in functional MRI. Scientific Reports, 4. PubMed PMC

Reynaud, O. , Jorge, J. , Gruetter, R. , Marques, J. P. , & van der Zwaag, W. (2017). Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7T. Magnetic Resonance in Medicine, 78, 888–896. PubMed

Salimi‐Khorshidi, G. , Douaud, G. , Beckmann, C.F. , Glasser, M.F. , Griffanti, L. , Smith, S.M. (2014) Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. 90:449–468. PubMed PMC

Seto, E. , Sela, G. , McIlroy, W. E. , Black, S. E. , Staines, W. R. , Bronskill, M. J. , … Graham, S. J. (2001). Quantifying head motion associated with motor tasks used in fMRI. NeuroImage, 14, 284–297. PubMed

Shmueli, K. , van Gelderen, P. , de Zwart, J. A. , Horovitz, S. G. , Fukunaga, M. , Jansma, J. M. , & Duyn, J. H. (2007). Low‐frequency fluctuations in the cardiac rate as a source of variance in the resting‐state fMRI BOLD signal. NeuroImage, 38(2), 306–320. PubMed PMC

Shirer, W. R. , Jiang, H. , Price, C. M. , Ng, B. , & Greicius, M. D. (2015). Optimization of rs‐fMRI pre‐processing for enhanced signal‐noise separation, test‐retest reliability, and group discrimination. NeuroImage, 117, 67–79. PubMed

Smith, A. M. , Lewis, B. K. , Ruttimann, U. E. , Ye, F. Q. , Sinnwell, T. M. , Yang, Y. H. , … Frank, J. A. (1999). Investigation of low frequency drift in fMRI signal. NeuroImage, 9, 526–533. PubMed

Soltysik, D. A. , Thomasson, D. , Rajan, S. , & Biassou, N. (2015). Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task‐based fMRI. Journal of Neuroscience Methods, 241, 18–29. PubMed PMC

Sweetman, B. , & Linninger, A. A. (2011). Cerebrospinal fluid flow dynamics in the central nervous system. Annals of Biomedical Engineering, 39, 484–496. PubMed

Thomas, C. G. , Harshman, R. A. , & Menon, R. S. (2002). Noise reduction in BOLD‐based fMRI using component analysis. NeuroImage, 17, 1521–1537. PubMed

Tillman, R. M. , Stockbridge, M. D. , Nacewicz, B. M. , Torrisi, S. , Fox, A. S. , Smith, J. F. , & Shackman, A. J. (2018). Intrinsic functional connectivity of the central extended amygdala. Human Brain Mapping, 39, 1291–1312. PubMed PMC

Tzourio‐Mazoyer, N. , Landeau, B. , Papathanassiou, D. , Crivello, F. , Etard, O. , Delcroix, N. , … Joliot, M. (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

Utevsky, A. V. , Smith, D. V. , & Huettel, S. A. (2016). Precuneus is a functional core of the default‐mode network (vol 34, pg 932, 2014). Journal of Neuroscience, 36, 12066–12068. PubMed PMC

Vincent, J. L. , Kahn, I. , Snyder, A. Z. , Raichle, M. E. , & Buckner, R. L. (2008). Evidence for a Frontoparietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology, 100, 3328–3342. PubMed PMC

Weissenbacher, A. , Kasess, C. , Gerstl, F. , Lanzenberger, R. , Moser, E. , & Windischberger, C. (2009). Correlations and anticorrelations in resting‐state functional connectivity MRI: A quantitative comparison of preprocessing strategies. NeuroImage, 47, 1408–1416. PubMed

Zahneisen, B. , Grotz, T. , Lee, K. J. , Ohlendorf, S. , Reisert, M. , Zaitsev, M. , & Hennig, J. (2011). Three‐dimensional MR‐encephalography: Fast volumetric brain imaging using rosette trajectories. Magnetic Resonance in Medicine, 65, 1260–1268. PubMed

Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression

Contribution of the multi-echo approach in accelerated functional magnetic resonance imaging multiband acquisition

Functional neuroanatomy of reading in Czech: Evidence of a dual-route processing architecture in a shallow orthography

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines

The role of the striatum in visuomotor integration during handwriting: an fMRI study