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A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI

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Abstract

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.

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References

  1. Matthews PM, Honey GD, Bullmore ET (2006) Applications of fMRI in translational medicine and clinical practice. Nat Rev Neurosci 7(9):732–744

    Article  PubMed  CAS  Google Scholar 

  2. Vlieger EJ et al (2004) Functional magnetic resonance imaging for neurosurgical planning in neurooncology. Eur Radiol 14(7):1143–1153

    Article  PubMed  Google Scholar 

  3. Adcock JE et al (2003) Quantitative fMRI assessment of the differences in lateralization of language-related brain activation in patients with temporal lobe epilepsy. Neuroimage 18(2):423–438

    Article  PubMed  CAS  Google Scholar 

  4. Haberg A et al (2004) Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery 54(4):902–14

    Article  PubMed  Google Scholar 

  5. Lang S, Duncan N, Northoff G (2014) Resting-state functional magnetic resonance imaging: review of neurosurgical applications. Neurosurgery 74(5):453–64

    Article  PubMed  Google Scholar 

  6. Pujol J et al (1998) Clinical application of functional magnetic resonance imaging in presurgical identification of the central sulcus. J Neurosurg 88(5):863–869

    Article  PubMed  CAS  Google Scholar 

  7. Zhang D et al (2009) Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with functional magnetic resonance imaging: initial experience. Neurosurgery 65(6 Suppl):226–236

    PubMed  Google Scholar 

  8. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700–711

    Article  PubMed  CAS  Google Scholar 

  9. Biswal B et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541

    Article  PubMed  CAS  Google Scholar 

  10. Fox MD et al (2006) Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses. Nat Neurosci 9(1):23–25

    Article  PubMed  CAS  Google Scholar 

  11. Cordes D et al (2000) Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am J Neuroradiol 21(9):1636–1644

    PubMed  PubMed Central  CAS  Google Scholar 

  12. Hampson M et al (2002) Detection of functional connectivity using temporal correlations in MR images. Hum Brain Mapp 15(4):247–262

    Article  PubMed  PubMed Central  Google Scholar 

  13. Smith SM et al (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A 106(31):13040–13045

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Birbaumer N et al (1990) Slow potentials of the cerebral cortex and behavior. Physiol Rev 70(1):1–41

    Article  PubMed  CAS  Google Scholar 

  15. Mitzdorf U (1985) Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol Rev 65(1):37–100

    Article  PubMed  CAS  Google Scholar 

  16. Liu H et al (2009) Task-free presurgical mapping using functional magnetic resonance imaging intrinsic activity. J Neurosurg 111(4):746–754

    Article  PubMed  PubMed Central  Google Scholar 

  17. Birn RM et al (2013) The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83:550–558

    Article  PubMed  Google Scholar 

  18. Patriat R et al (2013) The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated. Neuroimage 78:463–473

    Article  PubMed  Google Scholar 

  19. Liang X et al (2012) Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study. PLoS ONE 7(3):e32766

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Vergara VM et al (2017) The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA. Neuroimage 145:365–376

    Article  PubMed  Google Scholar 

  21. Wu CW et al (2011) Empirical evaluations of slice-timing, smoothing, and normalization effects in seed-based, resting-state functional magnetic resonance imaging analyses. Brain connectivity 1(5):401–410

    Article  PubMed  Google Scholar 

  22. Pizoli CE et al (2011) Resting-state activity in development and maintenance of normal brain function. Proc Natl Acad Sci U S A 108(28):11638–11643

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Hutchison RM et al (2012) Functional connectivity of the frontal eye fields in humans and macaque monkeys investigated with resting-state fMRI. J Neurophysiol 107(9):2463–2474

    Article  PubMed  Google Scholar 

  24. Schwarz AJ et al (2013) Anti-correlated cortical networks of intrinsic connectivity in the rat brain. Brain Connect 3(5):503–511

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nasiriavanaki M et al (2014) High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain. Proc Natl Acad Sci U S A 111(1):21–26

    Article  PubMed  CAS  Google Scholar 

  26. Nasrallah FA, Tay HC, Chuang KH (2014) Detection of functional connectivity in the resting mouse brain. Neuroimage 86:417–424

    Article  PubMed  Google Scholar 

  27. Spreng RN (2012) The fallacy of a “task-negative” network. Front Psychol 3:145

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lee MH, Smyser CD, Shimony JS (2013) Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 34(10):1866–1872

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Leuthardt EC et al (2015) Resting-state blood oxygen level-dependent functional MRI: a paradigm shift in preoperative brain mapping. Stereotact Funct Neurosurg 93(6):427–439

    Article  PubMed  Google Scholar 

  30. Klingbeil J et al (2017) Resting-state functional connectivity: an emerging method for the study of language networks in post-stroke aphasia. Brain Cogn

  31. Tomasi D, Volkow ND (2012) Resting functional connectivity of language networks: characterization and reproducibility. Mol Psychiatry 17(8):841–854

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Beckmann CF et al (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 360(1457):1001–1013

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rosazza C et al (2012) Functional connectivity during resting-state functional MR imaging: study of the correspondence between independent component analysis and region-of-interest-based methods. AJNR Am J Neuroradiol 33(1):180–187

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Cordes D et al (2002) Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging 20(4):305–317

    Article  PubMed  Google Scholar 

  35. Salvador R et al (2005) Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 15(9):1332–1342

    Article  PubMed  Google Scholar 

  36. Lee MH et al (2012) Clustering of resting state networks. PLoS ONE 7(7):e40370

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Mitchell TJ et al (2013) A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging. Neurosurgery 73(6):969–82

    Article  PubMed  Google Scholar 

  38. Bengio Y (2009) Learning deep architectures for AI. Foundations and trends® in Machine Learning 2(1):1–127

    Article  Google Scholar 

  39. Page MJ et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906

    Article  PubMed  Google Scholar 

  40. Munn Z et al (2018) Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 18:1–7

    Article  Google Scholar 

  41. Barry ES, Merkebu J, Varpio L (2022) State-of-the-art literature review methodology: a six-step approach for knowledge synthesis. Perspect Med Educ 11(5):281–288

    Article  PubMed  PubMed Central  Google Scholar 

  42. Barry ES, Merkebu J, Varpio L (2022) Understanding state-of-the-art literature reviews. J Grad Med Educ 14(6):659–662

    Article  PubMed  PubMed Central  Google Scholar 

  43. Luckett P et al (2020) Mapping of the language network with deep learning. Front Neurol 11:819

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lee JJ et al (2021) Resting state functional MR imaging of language function. Neuroimaging Clin 31(1):69–79

    Article  Google Scholar 

  45. Luckett PH et al (2023) Resting state network mapping in individuals using deep learning. Front Neurol 13:1055437

    Article  PubMed  PubMed Central  Google Scholar 

  46. Luckett PH et al (2023) Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning. J Neurosurg 1:1–12

    Article  Google Scholar 

  47. Nandakumar N et al (2021) A multi-scale spatial and temporal attention network on dynamic connectivity to localize the eloquent cortex in brain tumor patients. Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings 27. Springer

    Google Scholar 

  48. Nandakumar N et al (2021) Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks. Med Image Anal 74:102203

    Article  PubMed  PubMed Central  Google Scholar 

  49. Holmes AJ et al (2015) Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Scientific data 2(1):1–16

    Article  Google Scholar 

  50. Yarkoni T et al (2011) Large-scale automated synthesis of human functional neuroimaging data. Nat Methods 8(8):665–670

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Fischl B (2012) FreeSurfer. Neuroimage 62(2):774–781

    Article  PubMed  Google Scholar 

  52. Seghier ML, Price CJ (2018) Interpreting and utilising intersubject variability in brain function. Trends Cogn Sci 22(6):517–530

    Article  PubMed  PubMed Central  Google Scholar 

  53. Keller SS et al (2007) Sulcal variability, stereological measurement and asymmetry of Broca’s area on MR images. J Anat 211(4):534–555

    Article  PubMed  PubMed Central  Google Scholar 

  54. Amunts K et al (1999) Broca’s region revisited: cytoarchitecture and intersubject variability. J Comp Neurol 412(2):319–341

    Article  PubMed  CAS  Google Scholar 

  55. Gordon EM et al (2017) Precision functional mapping of individual human brains. Neuron 95(4):791-807. e7

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Van Essen DC et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79

    Article  PubMed  Google Scholar 

  57. Nandakumar N et al (2019) A novel graph neural network to localize eloquent cortex in brain tumor patients from resting-state fmri connectivity. Connectomics in NeuroImaging: Third International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 3. Springer

    Google Scholar 

  58. Han K et al (2022) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45(1):87–110

    Article  PubMed  Google Scholar 

  59. Hlinka J et al (2011) Functional connectivity in resting-state fMRI: is linear correlation sufficient? Neuroimage 54(3):2218–2225

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Integrated Program in Neuroscience at McGill University, Montreal, Canada

    Daniel A. Di Giovanni

  2. Department of Biomedical Engineering and Department of Neurology and Neurosurgery in McGill University, Montreal, Canada

    D. Louis Collins

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  1. Daniel A. Di Giovanni
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  2. D. Louis Collins
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Daniel A. Di Giovanni wrote the main body of text and prepared the table. D. L. Collins edited and revised the manuscript where appropriate.

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Correspondence to Daniel A. Di Giovanni.

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Di Giovanni, D.A., Collins, D.L. A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI. Neurosurg Rev 46, 249 (2023). https://doi.org/10.1007/s10143-023-02154-6

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