Brain-correlates of processing local dependencies within a statistical learning paradigm.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 09 2022
12 09 2022
Historique:
received:
08
03
2022
accepted:
25
08
2022
entrez:
13
9
2022
pubmed:
14
9
2022
medline:
15
9
2022
Statut:
epublish
Résumé
Statistical learning refers to the implicit mechanism of extracting regularities in our environment. Numerous studies have investigated the neural basis of statistical learning. However, how the brain responds to violations of auditory regularities based on prior (implicit) learning requires further investigation. Here, we used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of processing events that are irregular based on learned local dependencies. A stream of consecutive sound triplets was presented. Unbeknown to the subjects, triplets were either (a) standard, namely triplets ending with a high probability sound or, (b) statistical deviants, namely triplets ending with a low probability sound. Participants (n = 33) underwent a learning phase outside the scanner followed by an fMRI session. Processing of statistical deviants activated a set of regions encompassing the superior temporal gyrus bilaterally, the right deep frontal operculum including lateral orbitofrontal cortex, and the right premotor cortex. Our results demonstrate that the violation of local dependencies within a statistical learning paradigm does not only engage sensory processes, but is instead reminiscent of the activation pattern during the processing of local syntactic structures in music and language, reflecting the online adaptations required for predictive coding in the context of statistical learning.
Identifiants
pubmed: 36097186
doi: 10.1038/s41598-022-19203-7
pii: 10.1038/s41598-022-19203-7
pmc: PMC9468168
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
15296Informations de copyright
© 2022. The Author(s).
Références
Geisler, W. S. Visual perception and the statistical properties of natural scenes. Annu. Rev. Psychol. 59, 167–192 (2008).
pubmed: 17705683
doi: 10.1146/annurev.psych.58.110405.085632
Rohrmeier, M. & Rebuschat, P. Implicit learning and acquisition of music. Top. Cogn. Sci. 4, 525–553 (2012).
pubmed: 23060126
doi: 10.1111/j.1756-8765.2012.01223.x
Daikoku, T. Neurophysiological markers of statistical learning in music and language: Hierarchy, entropy and uncertainty. Brain Sci. 8, 114 (2018).
pmcid: 6025354
doi: 10.3390/brainsci8060114
Conway, C. M. How does the brain learn environmental structure? ten core principles for understanding the neurocognitive mechanisms of statistical learning. Neurosci. Biobehav. Rev. 112, 279–299 (2020).
pubmed: 32018038
pmcid: 7211144
doi: 10.1016/j.neubiorev.2020.01.032
Hohwy, J. New directions in predictive processing. Mind Language 35, 209–223 (2020).
doi: 10.1111/mila.12281
Pickering, M. J. & Clark, A. Getting ahead: forward models and their place in cognitive architecture. Trends Cogn. Sci. 18, 451–456 (2014).
pubmed: 24909775
doi: 10.1016/j.tics.2014.05.006
Dale, R., Duran, N. D. & Morehead, J. R. Prediction during statistical learning, and implications for the implicit/explicit divide. Adv. Cogn. Psychol. 8, 196 (2012).
pubmed: 22723817
pmcid: 3376885
doi: 10.5709/acp-0115-z
Karuza, E. A., Farmer, T. A., Fine, A. B., Smith, F. X. & Jaeger, T. F. On-line measures of prediction in a self-paced statistical learning task. In Proceedings of the annual meeting of the Cognitive Science Society, vol. 36 (2014).
Hasson, U. The neurobiology of uncertainty: implications for statistical learning. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160048 (2017).
doi: 10.1098/rstb.2016.0048
Perruchet, P. & Pacton, S. Implicit learning and statistical learning: One phenomenon, two approaches. Trends Cogn. Sci. 10, 233–238 (2006).
pubmed: 16616590
doi: 10.1016/j.tics.2006.03.006
Dienes, Z. Conscious versus unconscious learning of structure. Stat. Learn. Lang. Acquis. 1, 337–364 (2012).
Kóbor, A., Janacsek, K., Takács, Á. & Nemeth, D. Statistical learning leads to persistent memory: Evidence for one-year consolidation. Sci. Rep. 7, 1–10 (2017).
doi: 10.1038/s41598-017-00807-3
Southwell, R. & Chait, M. Enhanced deviant responses in patterned relative to random sound sequences. Cortex 109, 92–103 (2018).
pubmed: 30312781
pmcid: 6259587
doi: 10.1016/j.cortex.2018.08.032
Naatanen, R. The mismatch negativity to intensity changes in an auditory stimulus sequence. Electroencephalogr. Clin. Neurophysiol. Suppl. 40, 125–131 (1987).
pubmed: 3480111
Näätänen, R., Paavilainen, P., Rinne, T. & Alho, K. The mismatch negativity (mmn) in basic research of central auditory processing: a review. Clin. Neurophysiol. 118, 2544–2590 (2007).
pubmed: 17931964
doi: 10.1016/j.clinph.2007.04.026
Forkstam, C., Hagoort, P., Fernandez, G., Ingvar, M. & Petersson, K. M. Neural correlates of artificial syntactic structure classification. Neuroimage 32, 956–967 (2006).
pubmed: 16757182
doi: 10.1016/j.neuroimage.2006.03.057
Petersson, K. M., Forkstam, C. & Ingvar, M. Artificial syntactic violations activate broca’s region. Cogn. Sci. 28, 383–407 (2004).
Petersson, K.-M., Folia, V. & Hagoort, P. What artificial grammar learning reveals about the neurobiology of syntax. Brain Lang. 120, 83–95 (2012).
pubmed: 20943261
doi: 10.1016/j.bandl.2010.08.003
Folia, V. & Petersson, K. M. Implicit structured sequence learning: an fmri study of the structural mere-exposure effect. Front. Psychol. 5, 41 (2014).
pubmed: 24550865
pmcid: 3912435
doi: 10.3389/fpsyg.2014.00041
Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I. & Anwander, A. The brain differentiates human and non-human grammars: functional localization and structural connectivity. Proc. Natl. Acad. Sci. 103, 2458–2463 (2006).
pubmed: 16461904
pmcid: 1413709
doi: 10.1073/pnas.0509389103
Opitz, B. & Friederici, A. D. Neural basis of processing sequential and hierarchical syntactic structures. Hum. Brain Mapp. 28, 585–592 (2007).
pubmed: 17455365
pmcid: 6871462
doi: 10.1002/hbm.20287
Skosnik, P. et al. Neural correlates of artificial grammar learning. Neuroimage 17, 1306–1314 (2002).
pubmed: 12414270
doi: 10.1006/nimg.2002.1291
Conway, C. M. et al. Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fmri study. Neurobiol. Learn. Mem. 169, 107175 (2020).
pubmed: 32018026
pmcid: 7064258
doi: 10.1016/j.nlm.2020.107175
Bahlmann, J., Schubotz, R. I. & Friederici, A. D. Hierarchical artificial grammar processing engages broca’s area. Neuroimage 42, 525–534 (2008).
pubmed: 18554927
doi: 10.1016/j.neuroimage.2008.04.249
Friederici, A. D. The neural basis for human syntax: Broca’s area and beyond. Curr. Opin. Behav. Sci. 21, 88–92 (2018).
doi: 10.1016/j.cobeha.2018.03.004
Friederici, A. D. The brain basis of language processing: from structure to function. Physiol. Rev. 91, 1357–1392 (2011).
pubmed: 22013214
doi: 10.1152/physrev.00006.2011
Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274, 1926–1928 (1996).
pubmed: 8943209
doi: 10.1126/science.274.5294.1926
McNealy, K., Mazziotta, J. C. & Dapretto, M. Cracking the language code: neural mechanisms underlying speech parsing. J. Neurosci. 26, 7629–7639 (2006).
pubmed: 16855090
pmcid: 3713232
doi: 10.1523/JNEUROSCI.5501-05.2006
Cunillera, T. et al. Time course and functional neuroanatomy of speech segmentation in adults. Neuroimage 48, 541–553 (2009).
pubmed: 19580874
doi: 10.1016/j.neuroimage.2009.06.069
Karuza, E. A. et al. The neural correlates of statistical learning in a word segmentation task: An fmri study. Brain Lang. 127, 46–54 (2013).
pubmed: 23312790
pmcid: 3750089
doi: 10.1016/j.bandl.2012.11.007
Plante, E. et al. The nature of the language input affects brain activation during learning from a natural language. J. Neurolinguist. 36, 17–34 (2015).
doi: 10.1016/j.jneuroling.2015.04.005
Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J. & Chait, M. Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. Proc. Natl. Acad. Sci. 113, E616–E625 (2016).
pubmed: 26787854
pmcid: 4747708
doi: 10.1073/pnas.1508523113
Ordin, M., Polyanskaya, L. & Soto, D. Neural bases of learning and recognition of statistical regularities. Ann. N. Y. Acad. Sci. 1467, 60–76 (2020).
pubmed: 31919870
doi: 10.1111/nyas.14299
Fletcher, P., Büchel, C., Josephs, O., Friston, K. & Dolan, R. Learning-related neuronal responses in prefrontal cortex studied with functional neuroimaging. Cereb. Cortex 9, 168–178 (1999).
pubmed: 10220229
doi: 10.1093/cercor/9.2.168
Orpella, J., Mas-Herrero, E., Ripollés, P., Marco-Pallarés, J. & de Diego-Balaguer, R. Language statistical learning responds to reinforcement learning principles rooted in the striatum. PLoS Biol. 19, e3001119 (2021).
pubmed: 34491980
pmcid: 8448350
doi: 10.1371/journal.pbio.3001119
Takács, Á. et al. Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning. Hum. Brain Mapp. 42, 3182–3201 (2021).
pubmed: 33797825
pmcid: 8193527
doi: 10.1002/hbm.25427
Jost, E., Conway, C. M., Purdy, J. D., Walk, A. M. & Hendricks, M. A. Exploring the neurodevelopment of visual statistical learning using event-related brain potentials. Brain Res. 1597, 95–107 (2015).
pubmed: 25475992
doi: 10.1016/j.brainres.2014.10.017
Singh, S., Daltrozzo, J. & Conway, C. M. Effect of pattern awareness on the behavioral and neurophysiological correlates of visual statistical learning. Neurosci. Conscious. 2017, nix020 (2017).
Celsis, P. et al. Differential fmri responses in the left posterior superior temporal gyrus and left supramarginal gyrus to habituation and change detection in syllables and tones. Neuroimage 9, 135–144 (1999).
pubmed: 9918735
doi: 10.1006/nimg.1998.0389
Opitz, B., Rinne, T., Mecklinger, A., Von Cramon, D. Y. & Schröger, E. Differential contribution of frontal and temporal cortices to auditory change detection: fmri and erp results. Neuroimage 15, 167–174 (2002).
pubmed: 11771985
doi: 10.1006/nimg.2001.0970
Opitz, B., Schröger, E. & Von Cramon, D. Y. Sensory and cognitive mechanisms for preattentive change detection in auditory cortex. Eur. J. Neurosci. 21, 531–535 (2005).
pubmed: 15673452
doi: 10.1111/j.1460-9568.2005.03839.x
Doeller, C. F. et al. Prefrontal cortex involvement in preattentive auditory deviance detection: neuroimaging and electrophysiological evidence. Neuroimage 20, 1270–1282 (2003).
pubmed: 14568496
doi: 10.1016/S1053-8119(03)00389-6
Sabri, M., Kareken, D. A., Dzemidzic, M., Lowe, M. J. & Melara, R. D. Neural correlates of auditory sensory memory and automatic change detection. Neuroimage 21, 69–74 (2004).
pubmed: 14741643
doi: 10.1016/j.neuroimage.2003.08.033
Sabri, M., Liebenthal, E., Waldron, E., Medler, D. A. & Binder, J. R. Attentional modulation in the detection of irrelevant deviance: a simultaneous erp/fmri study. J. Cogn. Neurosci. 18, 689–700 (2006).
pubmed: 16768370
pmcid: 1769347
doi: 10.1162/jocn.2006.18.5.689
Molholm, S., Martinez, A., Ritter, W., Javitt, D. C. & Foxe, J. J. The neural circuitry of pre-attentive auditory change-detection: an fmri study of pitch and duration mismatch negativity generators. Cereb. Cortex 15, 545–551 (2005).
pubmed: 15342438
doi: 10.1093/cercor/bhh155
Cacciaglia, R., Costa-Faidella, J., Zarnowiec, K., Grimm, S. & Escera, C. Auditory predictions shape the neural responses to stimulus repetition and sensory change. Neuroimage 186, 200–210 (2019).
pubmed: 30414982
doi: 10.1016/j.neuroimage.2018.11.007
Tsogli, V., Jentschke, S., Daikoku, T. & Koelsch, S. When the statistical mmn meets the physical mmn. Sci. Rep. 9, 1–12 (2019).
Daikoku, T. et al. Neural correlates of statistical learning in developmental dyslexia: An electroencephalography study. bioRxiv. https://doi.org/10.1101/2022.07.06.498909 (2022). https://www.biorxiv.org/content/early/2022/07/07/2022.07.06.498909.full.pdf .
Koelsch, S., Busch, T., Jentschke, S. & Rohrmeier, M. Under the hood of statistical learning: A statistical mmn reflects the magnitude of transitional probabilities in auditory sequences. Sci. Rep. 6, 19741 (2016).
pubmed: 26830652
pmcid: 4735647
doi: 10.1038/srep19741
Friederici, A. D., Rüschemeyer, S.-A., Hahne, A. & Fiebach, C. J. The role of left inferior frontal and superior temporal cortex in sentence comprehension: localizing syntactic and semantic processes. Cereb. Cortex 13, 170–177 (2003).
pubmed: 12507948
doi: 10.1093/cercor/13.2.170
Bubic, A., Von Cramon, D. Y. & Schubotz, R. I. Prediction, cognition and the brain. Front. Hum. Neurosci. 4, 25 (2010).
pubmed: 20631856
pmcid: 2904053
Koelsch, S., Fritz, T., Schulze, K., Alsop, D. & Schlaug, G. Adults and children processing music: An fmri study. Neuroimage 25, 1068–1076 (2005).
pubmed: 15850725
doi: 10.1016/j.neuroimage.2004.12.050
Koelsch, S. Music-syntactic processing and auditory memory: Similarities and differences between eran and mmn. Psychophysiology 46, 179–190 (2009).
pubmed: 19055508
doi: 10.1111/j.1469-8986.2008.00752.x
Friederici, A. D. The cortical language circuit: from auditory perception to sentence comprehension. Trends Cogn. Sci. 16, 262–268 (2012).
pubmed: 22516238
doi: 10.1016/j.tics.2012.04.001
Gruber, O., Diekhof, E. K., Kirchenbauer, L. & Goschke, T. A neural system for evaluating the behavioural relevance of salient events outside the current focus of attention. Brain Res. 1351, 212–221 (2010).
pubmed: 20599810
doi: 10.1016/j.brainres.2010.06.056
Friston, K. A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 815–836 (2005).
pubmed: 15937014
pmcid: 1569488
doi: 10.1098/rstb.2005.1622
Henin, S. et al. Learning hierarchical sequence representations across human cortex and hippocampus. Sci. Adv.7, eabc4530 (2021).
Friston, K. Hierarchical models in the brain. PLoS Comput. Biol. 4, e1000211 (2008).
pubmed: 18989391
pmcid: 2570625
doi: 10.1371/journal.pcbi.1000211
Schultz, W. & Dickinson, A. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000).
pubmed: 10845072
doi: 10.1146/annurev.neuro.23.1.473
O’Neill, M. & Schultz, W. Predictive coding of the statistical parameters of uncertain rewards by orbitofrontal neurons. Behav. Brain Res. 355, 90–94 (2018).
pubmed: 29709608
pmcid: 6152578
doi: 10.1016/j.bbr.2018.04.041
Nobre, A., Coull, J., Frith, C. & Mesulam, M. Orbitofrontal cortex is activated during breaches of expectation in tasks of visual attention. Nat. Neurosci. 2, 11–12 (1999).
pubmed: 10195173
doi: 10.1038/4513
Koelsch, S. et al. The quartet theory of human emotions: An integrative and neurofunctional model. Phys. Life Rev. 13, 1–27 (2015).
pubmed: 25891321
doi: 10.1016/j.plrev.2015.03.001
Koelsch, S. A coordinate-based meta-analysis of music-evoked emotions. Neuroimage 223, 117350 (2020).
pubmed: 32898679
doi: 10.1016/j.neuroimage.2020.117350
Kim, C., Johnson, N. F., Cilles, S. E. & Gold, B. T. Common and distinct mechanisms of cognitive flexibility in prefrontal cortex. J. Neurosci. 31, 4771–4779 (2011).
pubmed: 21451015
pmcid: 3086290
doi: 10.1523/JNEUROSCI.5923-10.2011
Zarr, N. & Brown, J. W. Hierarchical error representation in medial prefrontal cortex. Neuroimage 124, 238–247 (2016).
pubmed: 26343320
doi: 10.1016/j.neuroimage.2015.08.063
Iannaccone, R. et al. Conflict monitoring and error processing: New insights from simultaneous eeg-fmri. Neuroimage 105, 395–407 (2015).
pubmed: 25462691
doi: 10.1016/j.neuroimage.2014.10.028
Alexander, W. H. & Brown, J. W. The role of the anterior cingulate cortex in prediction error and signaling surprise. Top. Cogn. Sci. 11, 119–135 (2019).
pubmed: 29131512
doi: 10.1111/tops.12307
Botvinick, M. M., Cohen, J. D. & Carter, C. S. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn. Sci. 8, 539–546 (2004).
pubmed: 15556023
doi: 10.1016/j.tics.2004.10.003
Ullsperger, M. & von Cramon, D. Y. Decision making, performance and outcome monitoring in frontal cortical areas. Nat. Neurosci. 7, 1173–1174 (2004).
pubmed: 15508012
doi: 10.1038/nn1104-1173
Rolls, E. T. The orbitofrontal cortex (Oxford University Press, 2019).
Walton, M. E., Devlin, J. T. & Rushworth, M. F. Interactions between decision making and performance monitoring within prefrontal cortex. Nat. Neurosci. 7, 1259–1265 (2004).
pubmed: 15494729
doi: 10.1038/nn1339
Ullsperger, M., Nittono, H. & Von Cramon, D. Y. When goals are missed: dealing with self-generated and externally induced failure. Neuroimage 35, 1356–1364 (2007).
pubmed: 17350291
doi: 10.1016/j.neuroimage.2007.01.026
Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 214, 655–667 (2010).
pubmed: 20512370
pmcid: 2899886
doi: 10.1007/s00429-010-0262-0
Kotz, S. A., Schwartze, M. & Schmidt-Kassow, M. Non-motor basal ganglia functions: A review and proposal for a model of sensory predictability in auditory language perception. Cortex 45, 982–990 (2009).
pubmed: 19361785
doi: 10.1016/j.cortex.2009.02.010
Reber, P. J. The neural basis of implicit learning and memory: A review of neuropsychological and neuroimaging research. Neuropsychologia 51, 2026–2042 (2013).
pubmed: 23806840
doi: 10.1016/j.neuropsychologia.2013.06.019
Graybiel, A. M. The basal ganglia: learning new tricks and loving it. Curr. Opin. Neurobiol. 15, 638–644 (2005).
pubmed: 16271465
doi: 10.1016/j.conb.2005.10.006
den Ouden, H. E., Daunizeau, J., Roiser, J., Friston, K. J. & Stephan, K. E. Striatal prediction error modulates cortical coupling. J. Neurosci. 30, 3210–3219 (2010).
doi: 10.1523/JNEUROSCI.4458-09.2010
Den Ouden, H. E., Friston, K. J., Daw, N. D., McIntosh, A. R. & Stephan, K. E. A dual role for prediction error in associative learning. Cereb. Cortex 19, 1175–1185 (2009).
doi: 10.1093/cercor/bhn161
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J. & Frith, C. D. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature 442, 1042–1045 (2006).
pubmed: 16929307
pmcid: 2636869
doi: 10.1038/nature05051
Polimeni, J. R. & Lewis, L. D. Imaging faster neural dynamics with fast fmri: A need for updated models of the hemodynamic response. Prog. Neurobiol. 207, 102174 (2021).
pubmed: 34525404
doi: 10.1016/j.pneurobio.2021.102174
Shepard, R. N. Attention and the metric structure of the stimulus space. J. Math. Psychol. 1, 54–87 (1964).
doi: 10.1016/0022-2496(64)90017-3
Jäger, G. & Rogers, J. Formal language theory: Refining the chomsky hierarchy. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 1956–1970 (2012).
pubmed: 22688632
pmcid: 3367686
doi: 10.1098/rstb.2012.0077
Weiskopf, N., Hutton, C., Josephs, O., Turner, R. & Deichmann, R. Optimized epi for fmri studies of the orbitofrontal cortex: Compensation of susceptibility-induced gradients in the readout direction. Magn. Reson. Mater. Phys., Biol. Med. 20, 39 (2007).
doi: 10.1007/s10334-006-0067-6
Esteban, O. et al. fmriprep: A robust preprocessing pipeline for functional mri. Nat. Methods 16, 111–116 (2019).
pubmed: 30532080
doi: 10.1038/s41592-018-0235-4
Tustison, N. J. et al. N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).
pubmed: 20378467
pmcid: 3071855
doi: 10.1109/TMI.2010.2046908
Avants, B. B., Epstein, C. L., Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008).
pubmed: 17659998
doi: 10.1016/j.media.2007.06.004
Zhang, Y., Brady, M. & Smith, S. Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001).
pubmed: 11293691
doi: 10.1109/42.906424
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis: I. segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
Klein, A. et al. Mindboggling morphometry of human brains. PLoS Comput. Biol. 13, e1005350 (2017).
pubmed: 28231282
pmcid: 5322885
doi: 10.1371/journal.pcbi.1005350
Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. & Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 54, S102 (2009).
doi: 10.1016/S1053-8119(09)70884-5
Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72 (2009).
pubmed: 19573611
doi: 10.1016/j.neuroimage.2009.06.060
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Cox, R. W. & Hyde, J. S. Software tools for analysis and visualization of fmri data. NMR Biomed. Int. J. Devot. Dev. Appl. Magn. Reson. In Vivo 10, 171–178 (1997).
Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fmri. Neuroimage 84, 320–341 (2014).
pubmed: 23994314
doi: 10.1016/j.neuroimage.2013.08.048
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (compcor) for bold and perfusion based fmri. Neuroimage 37, 90–101 (2007).
pubmed: 17560126
doi: 10.1016/j.neuroimage.2007.04.042
Satterthwaite, T. D. et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64, 240–256 (2013).
pubmed: 22926292
doi: 10.1016/j.neuroimage.2012.08.052
Lanczos, C. Evaluation of noisy data. J. Soc. Ind. Appl. Math. Ser. B Numer. Anal. 1, 76–85 (1964).
doi: 10.1137/0701007
Spisák, T. et al. Probabilistic tfce: a generalized combination of cluster size and voxel intensity to increase statistical power. Neuroimage 185, 12–26 (2019).
pubmed: 30296561
doi: 10.1016/j.neuroimage.2018.09.078
Lohmann, G. et al. Lisa improves statistical analysis for fmri. Nat. Commun. 9, 1–9 (2018).
doi: 10.1038/s41467-018-06304-z