Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Functional neuroimaging as a catalyst for integrated neuroscience

Nature volume 623, pages 263–273 (2023)Cite this article

Subjects

Abstract

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: fMRI as an integrative catalyst to dissolve modular boundaries in the existing neuroscience network.
Fig. 2: Benefits and challenges associated with fMRI.
Fig. 3: Potential paths towards a more integrative neuroscience.

Similar content being viewed by others

References

  1. Wagner, T., Rushmore, J., Eden, U. & Valero-Cabre, A. Biophysical foundations underlying TMS: setting the stage for an effective use of neurostimulation in the cognitive neurosciences. Cortex 45, 1025–1034 (2009).

    Article  PubMed  Google Scholar 

  2. Murphy, S. C., Palmer, L. M., Nyffeler, T., Müri, R. M. & Larkum, M. E. Transcranial magnetic stimulation (TMS) inhibits cortical dendrites. eLife 5, e13598 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Fitzgerald, P. B. Targeting repetitive transcranial magnetic stimulation in depression: do we really know what we are stimulating and how best to do it? Brain Stimul. 14, 730–736 (2021).

    Article  PubMed  Google Scholar 

  4. Lewis, L. D., Setsompop, K., Rosen, B. R. & Polimeni, J. R. Fast fMRI can detect oscillatory neural activity in humans. Proc. Natl Acad. Sci. USA 113, E6679–E6685 (2016). This study demonstrates a means for accelerating acquisition times in fMRI sequences, thus providing researchers access to rapid fluctuations in BOLD signals.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. NeuroImage 80, 169–189 (2013).

    Article  PubMed  Google Scholar 

  6. Markiewicz, C. J. et al. The OpenNeuro resource for sharing of neuroscience data. eLife 10, e71774 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zuo, X.-N. et al. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1, 140049 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Logothetis, N. K. et al. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  10. Bertolero, M. A., Yeo, B. T. T. & D’Esposito, M. The diverse club. Nat. Commun. 8, 1277 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shine, J. M. et al. The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92, 544–554 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Glover, G. H. Overview of functional magnetic resonance imaging. Neurosurg. Clin. N. Am. 22, 133–139 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Logothetis, N. K. & Wandell, B. A. Interpreting the BOLD Signal. Annu. Rev. Physiol. 66, 735–769 (2004).

    Article  CAS  PubMed  Google Scholar 

  15. MacVicar, B. A. & Newman, E. A. Astrocyte regulation of blood flow in the brain. Cold Spring Harb. Perspect. Biol. 7, a020388 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kahn, I. et al. Optogenetic drive of neocortical pyramidal neurons generates fMRI signals that are correlated with spiking activity. Brain Res. 1511, 33–45 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Huber, L. et al. Layer-dependent functional connectivity methods. Prog. Neurobiol. 207, 101835 (2020).

    Article  PubMed  Google Scholar 

  18. Zhang, Y. et al. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nat. Methods 20, 747–754 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Evans, M. H., Petersen, R. S. & Humphries, M. D. On the use of calcium deconvolution algorithms in practical contexts. Preprint at bioRxiv https://doi.org/10.1101/871137 (2019).

  20. Park, H.-J. & Friston, K. Structural and functional brain networks: from connections to cognition. Science 342, 1238411 (2013).

    Article  PubMed  Google Scholar 

  21. Betzel, R. F. & Bassett, D. S. Multi-scale brain networks. NeuroImage 160, 73–83 (2017).

    Article  PubMed  Google Scholar 

  22. Ekstrom, A. How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res. Rev. 62, 233–244 (2010).

    Article  PubMed  Google Scholar 

  23. Howarth, C., Mishra, A. & Hall, C. N. More than just summed neuronal activity: how multiple cell types shape the BOLD response. Phil. Trans. R. Soc. B 376, 20190630 (2021).

    Article  PubMed  Google Scholar 

  24. Takata, N. et al. Optogenetic astrocyte activation evokes BOLD fMRI response with oxygen consumption without neuronal activity modulation. Glia 66, 2013–2023 (2018).

    Article  PubMed  Google Scholar 

  25. Damoiseaux, J. S. et al. Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13848–13853 (2006).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Krienen, F. M., Yeo, B. T. T. & Buckner, R. L. Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture. Phil. Trans. R. Soc. B 369, 20130526 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Chang, C. et al. Tracking brain arousal fluctuations with fMRI. Proc. Natl Acad. Sci. USA 113, 4518–4523 (2016). In this paradigm-shifting study, the authors tracked covariance between BOLD signals and a range of physiological measures, highlighting the close inter-relationships between these markers.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bassett, D. S., Yang, M., Wymbs, N. F. & Grafton, S. T. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18, 744–751 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Uddin, L. Q. Typical and atypical development of functional human brain networks: insights from resting-state fMRI. Front. Syst. Neurosci. 4, 21 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Jack, C. R. et al. Magnetic resonance imaging in Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Dement. 11, 740–756 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Demirtaş, M. et al. Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron 101, 1181–1194.e13 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Vázquez-Rodríguez, B. et al. Gradients of structure–function tethering across neocortex. Proc. Natl Acad. Sci. USA 116, 21219–21227 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  35. Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl Acad. Sci. USA 113, 1435–1440 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Vértes, P. E. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Phil. Trans. R. Soc. B 371, 20150362 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Richiardi, J. et al. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lin, M. Z. & Schnitzer, M. J. Genetically encoded indicators of neuronal activity. Nat. Neurosci. 19, 1142–1153 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ferris, C. F. Applications in awake animal magnetic resonance imaging. Front. Neurosci. 16, 854377 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Gutierrez-Barragan, D. et al. Unique spatiotemporal fMRI dynamics in the awake mouse brain. Curr. Biol. 32, 631–644.e6 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zerbi, V., Grandjean, J., Rudin, M. & Wenderoth, N. Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification. NeuroImage 123, 11–21 (2015).

    Article  PubMed  Google Scholar 

  44. Zerbi, V. et al. Rapid reconfiguration of the functional connectome after chemogenetic locus coeruleus activation. Neuron 103, 702–718.e5 (2019). This study combined whole-brain BOLD signal detection with triggering of activity in the locus coeruleus of mice, demonstrating macroscopic network reconfigurations similar to those identified in humans during cognitive task performance, conceptually bridging the microscopic and macroscopic scales of analysis.

    Article  CAS  PubMed  Google Scholar 

  45. Lee, J. H. et al. Global and local fMRI signals driven by neurons defined optogenetically by type and wiring. Nature 465, 788–792 (2010). This landmark paper showed that combined optogenetic control of both the cerebral cortex and thalamus in the mouse brain elicits positive BOLD signals.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Oya, H. et al. Mapping effective connectivity in the human brain with concurrent intracranial electrical stimulation and BOLD-fMRI. J. Neurosci. Methods 277, 101–112 (2017).

    Article  PubMed  Google Scholar 

  47. Thompson, W. H. et al. A data resource from concurrent intracranial stimulation and functional MRI of the human brain. Sci. Data 7, 258 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Salvan, P. et al. Serotonin regulation of behavior via large-scale neuromodulation of serotonin receptor networks. Nat. Neurosci. 26, 53–63 (2023).

    Article  CAS  PubMed  Google Scholar 

  49. Lake, E. M. R. et al. Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI. Nat. Methods 17, 1262–1271 (2020). This article presents a method for concurrent widefield optical imaging and fMRI, enabling cell-type-specific investigations of how different neural populations contribute to the fMRI signal as well as more precise translation between mouse models and human studies.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Valdes-Sosa, P. A. et al. Model driven EEG/fMRI fusion of brain oscillations. Hum. Brain Mapp. 30, 2701–2721 (2009).

    Article  PubMed  Google Scholar 

  51. Mele, G. et al. Simultaneous EEG-fMRI for functional neurological assessment. Front. Neurol. 10, 848 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Destexhe, A., Rudolph, M. & Paré, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739–751 (2003).

    Article  CAS  PubMed  Google Scholar 

  53. Brown, E. N., Purdon, P. L. & Van Dort, C. J. General anesthesia and altered states of arousal: a systems neuroscience analysis. Annu. Rev. Neurosci. 34, 601–628 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. García-Cabezas, M. Á., Zikopoulos, B. & Barbas, H. The Structural Model: a theory linking connections, plasticity, pathology, development and evolution of the cerebral cortex. Brain Struct. Funct. 72, 429 (2019).

    Google Scholar 

  55. Hansen, J. Y. et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat. Neurosci. 25, 1569–1581 (2022). This study collated data from multiple different neuroimaging modalities to track the spatial relationships between different classes of neuromodulatory receptors, thus providing a benchmark for future studies to orient their results to a broader neuroanatomical framework.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Weiler, N., Wood, L., Yu, J., Solla, S. A. & Shepherd, G. M. G. Top-down laminar organization of the excitatory network in motor cortex. Nat. Neurosci. 11, 360–366 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Finn, E. S., Huber, L., Jangraw, D. C., Molfese, P. J. & Bandettini, P. A. Layer-dependent activity in human prefrontal cortex during working memory. Nat. Neurosci. 22, 1687–1695 (2019). This article represents the first application of layer-specific fMRI to human association cortex, demonstrating that the technique can go beyond primary sensory cortex to reveal cortical-depth-dependent processes during a higher-order cognitive task.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Bastos, A. M., Loonis, R., Kornblith, S., Lundqvist, M. & Miller, E. K. Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proc. Natl Acad. Sci. USA 115, 1117–1122 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stephan, K. E. et al. Laminar fMRI and computational theories of brain function. NeuroImage 197, 699–706 (2019).

    Article  CAS  PubMed  Google Scholar 

  60. Kosslyn, S. M., Ganis, G. & Thompson, W. L. Neural foundations of imagery. Nat. Rev. Neurosci. 2, 635–642 (2001).

    Article  CAS  PubMed  Google Scholar 

  61. Iamshchinina, P. et al. Perceived and mentally rotated contents are differentially represented in cortical depth of V1. Commun. Biol. 4, 1069 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Persichetti, A. S., Avery, J. A., Huber, L., Merriam, E. P. & Martin, A. Layer-specific contributions to imagined and executed hand movements in human primary motor cortex. Curr. Biol. 30, 1721–1725.e3 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Klein, B. P. et al. Cortical depth dependent population receptive field attraction by spatial attention in human V1. NeuroImage 176, 301–312 (2018).

    Article  PubMed  Google Scholar 

  64. Gau, R., Bazin, P.-L., Trampel, R., Turner, R. & Noppeney, U. Resolving multisensory and attentional influences across cortical depth in sensory cortices. eLife 9, e46856 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Liu, C. et al. Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex. Prog. Neurobiol. 207, 101897 (2021).

    Article  PubMed  Google Scholar 

  66. Doeller, C. F., Barry, C. & Burgess, N. Evidence for grid cells in a human memory network. Nature 463, 657–661 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  67. Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016). This article uses model-based fMRI to show that the grid-like organization used by the brain to represent physical space also extends to conceptual knowledge, and is present in the entorhinal cortex, prefrontal cortex and several other brain regions.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  68. Deuker, L., Bellmund, J. L., Navarro Schröder, T. & Doeller, C. F. An event map of memory space in the hippocampus. eLife 5, e16534 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Gläscher, J. P. & O’Doherty, J. P. Model‐based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. WIREs Cogn. Sci. 1, 501–510 (2010).

    Article  Google Scholar 

  70. Williams, T. B. et al. Testing models at the neural level reveals how the brain computes subjective value. Proc. Natl Acad. Sci. USA 118, e2106237118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhang, L. & Gläscher, J. A brain network supporting social influences in human decision-making. Sci. Adv. 6, eabb4159 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  72. McDougle, S. D. et al. Neural signatures of prediction errors in a decision-making task are modulated by action execution failures. Curr. Biol. 29, 1606–1613.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lee, S. W., Shimojo, S. & O’Doherty, J. P. Neural computations underlying arbitration between model-based and model-free learning. Neuron 81, 687–699 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Schuck, N. W. & Niv, Y. Sequential replay of nonspatial task states in the human hippocampus. Science 364, eaaw5181 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  75. Kurth-Nelson, Z., Economides, M., Dolan, R. J. & Dayan, P. Fast sequences of non-spatial state representations in humans. Neuron 91, 194–204 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Abe, Y. et al. Opto-fMRI analysis for exploring the neuronal connectivity of the hippocampal formation in rats. Neurosci. Res. 74, 248–255 (2012).

    Article  PubMed  Google Scholar 

  77. Basu, J. & Siegelbaum, S. A. The corticohippocampal circuit, synaptic plasticity, and memory. Cold Spring Harb. Perspect. Biol. 7, a021733 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Schapiro, A. C., Turk-Browne, N. B., Botvinick, M. M. & Norman, K. A. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Phil. Trans. R. Soc. B 372, 20160049 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Chanales, A. J. H., Oza, A., Favila, S. E. & Kuhl, B. A. Overlap among spatial memories triggers repulsion of hippocampal representations. Curr. Biol. 27, 2307–2317.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Double, K. L. et al. Topography of brain atrophy during normal aging and Alzheimer’s disease. Neurobiol. Aging 17, 513–521 (1996).

    Article  CAS  PubMed  Google Scholar 

  81. Engel, J. Mesial temporal lobe epilepsy: what have we learned? Neuroscientist 7, 340–352 (2001).

    Article  PubMed  Google Scholar 

  82. Dennis, E. L. & Thompson, P. M. Functional brain connectivity Using fMRI in aging and Alzheimer’s disease. Neuropsychol. Rev. 24, 49–62 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Friston, K. J., Frith, C. D., Liddle, P. F. & Frackowiak, R. S. Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14 (1993).

    Article  CAS  PubMed  Google Scholar 

  84. McIntosh, A. R. Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics 2, 175–182 (2004).

    Article  PubMed  Google Scholar 

  85. Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  86. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Shine, J. M. & Poldrack, R. A. Principles of dynamic network reconfiguration across diverse brain states. NeuroImage 180, 396–405 (2017).

    Article  PubMed  Google Scholar 

  88. Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

    Article  CAS  PubMed  Google Scholar 

  90. Bubeck, S. et al. Sparks of artificial general intelligence: early experiments with GPT-4. Preprint at arXiv https://doi.org/10.48550/ARXIV.2303.12712 (2023).

  91. Doerig, A. et al. The neuroconnectionist research programme. Nat. Rev. Neurosci. 24, 431–450 (2023).

    Article  CAS  PubMed  Google Scholar 

  92. Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (MIT Press, 2010).

  93. Poldrack, R. A. & Yarkoni, T. From brain maps to cognitive ontologies: informatics and the search for mental structure. Annu. Rev. Psychol. 67, 587–612 (2016).

    Article  PubMed  Google Scholar 

  94. Bolt, T., Anderson, M. L. & Uddin, L. Q. Beyond the evoked/intrinsic neural process dichotomy. Netw. Neurosci. https://doi.org/10.1162/netn_a_00028 (2018).

  95. Lindsay, G. W. Convolutional neural networks as a model of the visual system: past, present, and future. J. Cogn. Neurosci. 33, 2017–2031 (2021).

    Article  PubMed  Google Scholar 

  96. Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  97. Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1, 417–446 (2015).

    Article  PubMed  Google Scholar 

  98. Bowers, J. S. et al. Deep problems with neural network models of human vision. Behav. Brain Sci. https://doi.org/10.1017/S0140525X22002813 (2022).

  99. Allen, E. J. et al. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat. Neurosci. 25, 116–126 (2022).

    Article  CAS  PubMed  Google Scholar 

  100. Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25, 369–380 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T. & Wang, X.-J. Task representations in neural networks trained to perform many cognitive tasks. Nat. Neurosci. 22, 297–306 (2019).

    Article  CAS  PubMed  Google Scholar 

  102. Mastrogiuseppe, F. & Ostojic, S. Linking connectivity, dynamics, and computations in low-rank recurrent neural networks. Neuron 99, 609–623.e29 (2018). This landmark study demonstrated how the low-rank yet noisy structure of a connectome can directly shape the low-dimensional manifold that characterizes the emergent behaviour of a recurrent neural network.

    Article  CAS  PubMed  Google Scholar 

  103. Thomas, A. W., Ré, C. & Poldrack, R. A. Interpreting mental state decoding with deep learning models. Trends Cogn. Sci. 26, 972–986 (2022).

    Article  PubMed  Google Scholar 

  104. Ito, T., Yang, G. R., Laurent, P., Schultz, D. H. & Cole, M. W. Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior. Nat. Commun. 13, 673 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  105. Bonnen, T., Yamins, D. L. K. & Wagner, A. D. When the ventral visual stream is not enough: a deep learning account of medial temporal lobe involvement in perception. Neuron 109, 2755–2766.e6 (2021).

    Article  CAS  PubMed  Google Scholar 

  106. Bonnen, T. & Eldridge, M. A. G. Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream. eLife 12, e84357 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Zador, A. M. A critique of pure learning and what artificial neural networks can learn from animal brains. Nat. Commun. 10, 3770 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  108. Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).

    Article  CAS  PubMed  Google Scholar 

  109. Hasson, U., Nastase, S. A. & Goldstein, A. Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron 105, 416–434 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Silva, M. A., See, A. P., Essayed, W. I., Golby, A. J. & Tie, Y. Challenges and techniques for presurgical brain mapping with functional MRI. NeuroImage 17, 794–803 (2018).

    Article  PubMed  Google Scholar 

  111. Boutet, A. et al. Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning. Nat. Commun. 12, 3043 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  112. Cole, E. J. et al. Stanford neuromodulation therapy (SNT): a double-blind randomized controlled trial. AJP 179, 132–141 (2022).

    Article  Google Scholar 

  113. Abi-Dargham, A. et al. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 22, 236–262 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Dinga, R. et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of. NeuroImage 22, 101796 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Dukart, J., Weis, S., Genon, S. & Eickhoff, S. B. Towards increasing the clinical applicability of machine learning biomarkers in psychiatry. Nat. Hum. Behav. 5, 431–432 (2021).

    Article  PubMed  Google Scholar 

  116. Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022). This article calls attention to the need for very large sample sizes when performing mass univariate correlations between measures of structural and/or functional brain connectivity and trait-like behaviours across individuals.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  117. Rosenberg, M. D. et al. Functional connectivity predicts changes in attention observed across minutes, days, and months. Proc. Natl Acad. Sci. USA 117, 3797–3807 (2020). This article shows that the same functional brain connections that predict overall sustained attention ability also predict within-subject changes in attention observed over several timescales as well as during pharmacological manipulation, shedding light on the relationship between trait- and state-level factors in a manner with implications for studying symptom fluctuations in clinical conditions.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  118. Podvalny, E., Flounders, M. W., King, L. E., Holroyd, T. & He, B. J. A dual role of prestimulus spontaneous neural activity in visual object recognition. Nat. Commun. 10, 3910 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  119. Sadaghiani, S., Poline, J.-B., Kleinschmidt, A. & D’Esposito, M. Ongoing dynamics in large-scale functional connectivity predict perception. Proc. Natl Acad. Sci. USA 112, 8463–8468 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  120. Burciu, R. G. et al. Functional MRI of disease progression in Parkinson disease and atypical parkinsonian syndromes. Neurology 87, 709–717 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Finn, E. S., Corlett, P. R., Chen, G., Bandettini, P. A. & Constable, R. T. Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative. Nat. Commun. 9, 2043 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  122. Rikandi, E. et al. Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland. Psychol. Med. 47, 495–506 (2017).

    Article  CAS  PubMed  Google Scholar 

  123. Salmi, J. et al. ADHD desynchronizes brain activity during watching a distracted multi-talker conversation. NeuroImage 216, 116352 (2020).

    Article  PubMed  Google Scholar 

  124. Sterzer, P. et al. The predictive coding account of psychosis. Biol. Psychiatry 84, 634–643 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Jardri, R., Pouchet, A., Pins, D. & Thomas, P. Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. Am. J. Psychiatry 168, 73–81 (2011).

    Article  PubMed  Google Scholar 

  126. Haarsma, J., Kok, P. & Browning, M. The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis. Schizophr. Res. 245, 68–76 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Powers, A. R., Mathys, C. & Corlett, P. R. Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science 357, 596–600 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  128. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  129. Huys, Q. J. M., Maia, T. V. & Frank, M. J. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19, 404–413 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Christensen, A. J., Ott, T. & Kepecs, A. Cognition and the single neuron: How cell types construct the dynamic computations of frontal cortex. Curr. Opin. Neurobiol. 77, 102630 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Misaki, M. et al. Beyond synchrony: the capacity of fMRI hyperscanning for the study of human social interaction. Soc. Cogn. Affect. Neurosci. 16, 84–92 (2021).

    Article  PubMed  Google Scholar 

  132. Shine, J. M. et al. The low-dimensional neural architecture of cognitive complexity is related to activity in medial thalamic nuclei. Neuron 104, 849–855.e3 (2019).

    Article  CAS  PubMed  Google Scholar 

  133. Deisseroth, K. Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci. 18, 1213–1225 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Yacoub, E., Harel, N. & Uğurbil, K. High-field fMRI unveils orientation columns in humans. Proc. Natl Acad. Sci. USA 105, 10607–10612 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  135. De Martino, F. et al. Frequency preference and attention effects across cortical depths in the human primary auditory cortex. Proc. Natl Acad. Sci. USA 112, 16036–16041 (2015).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  136. Wittkuhn, L. & Schuck, N. W. Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nat. Commun. 12, 1795 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  137. Rocca, R. et al. Language beyond the language system: Dorsal visuospatial pathways support processing of demonstratives and spatial language during naturalistic fast fMRI. NeuroImage 216, 116128 (2020).

    Article  PubMed  Google Scholar 

  138. Sonkusare, S., Breakspear, M. & Guo, C. Naturalistic stimuli in neuroscience: critically acclaimed. Trends Cogn. Sci. 23, 699–714 (2019).

    Article  Google Scholar 

  139. Hamilton, L. S. & Huth, A. G. The revolution will not be controlled: natural stimuli in speech neuroscience. Lang. Cogn. Neurosci. 35, 573–582 (2020).

    Article  PubMed  Google Scholar 

  140. Lenormand, D. & Piolino, P. In search of a naturalistic neuroimaging approach: exploration of general feasibility through the case of VR-fMRI and application in the domain of episodic memory. Neurosci. Biobehav. Rev. 133, 104499 (2022).

    Article  PubMed  Google Scholar 

  141. Tomov, M. S., Tsividis, P. A., Pouncy, T., Tenenbaum, J. B. & Gershman, S. J. The neural architecture of theory-based reinforcement learning. Neuron 111, 1331–1344.e8 (2023).

  142. Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  143. Mitchell, T. M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).

    Article  ADS  CAS  PubMed  Google Scholar 

  144. Haxby, J. V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).

    Article  ADS  CAS  PubMed  Google Scholar 

  145. Polyn, S. M., Natu, V. S., Cohen, J. D. & Norman, K. A. Category-specific cortical activity precedes retrieval during memory search. Science 310, 1963–1966 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  146. Kriegeskorte, N. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. https://doi.org/10.3389/neuro.06.004.2008 (2008).

  147. Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject synchronization of cortical activity during natural vision. Science 303, 1634–1640 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  148. Nastase, S. A., Gazzola, V., Hasson, U. & Keysers, C. Measuring shared responses across subjects using intersubject correlation. Soc. Cogn. Affect. Neurosci. 14, 667–685 (2019).

    PubMed  PubMed Central  Google Scholar 

  149. Song, H., Shim, W. M. & Rosenberg, M. D. Large-scale neural dynamics in a shared low-dimensional state space reflect cognitive and attentional dynamics. eLife 12, e85487 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  150. van der Meer, J. N., Breakspear, M., Chang, L. J., Sonkusare, S. & Cocchi, L. Movie viewing elicits rich and reliable brain state dynamics. Nat. Commun. 11, 5004 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  151. Munn, B. R., Müller, E. J., Wainstein, G. & Shine, J. M. The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states. Nat. Commun. 12, 6016 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  152. Gordon, E. M. et al. A somato-cognitive action network alternates with effector regions in motor cortex. Nature 617, 351–359 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  153. Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Bennett, C., Miller, M. & Wolford, G. Neural correlates of interspecies perspective taking in the post-mortem Atlantic salmon: an argument for multiple comparisons correction. NeuroImage 47, S125 (2009).

    Article  Google Scholar 

  155. Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl Acad. Sci. USA 113, 7900–7905 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  156. Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  157. Zuo, X.-N., Xu, T. & Milham, M. P. Harnessing reliability for neuroscience research. Nat. Hum. Behav. 3, 768–771 (2019).

    Article  PubMed  Google Scholar 

  158. Gorgolewski, K. J. & Poldrack, R. A. A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biol. 14, e1002506 (2016). This important benchmark paper highlights goals for improving standard practice in neuroimaging research.

    Article  PubMed  PubMed Central  Google Scholar 

  159. Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  160. Karcher, N. R. & Barch, D. M. The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46, 131–142 (2021).

    Article  PubMed  Google Scholar 

  161. Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  162. Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).

    Article  CAS  PubMed  Google Scholar 

  163. Kiar, G. et al. Align with the NMIND consortium for better neuroimaging. Nat. Hum. Behav. 7, 1027–1028 (2023).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank D. Fair for his engagement on earlier versions of this manuscript and K. Nautiyal for helpful discussions.

Author information

Authors and Affiliations

  1. Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH, USA

    Emily S. Finn

  2. Department of Psychology, Stanford University, Stanford, CA, USA

    Russell A. Poldrack

  3. School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia

    James M. Shine

Authors
  1. Emily S. Finn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Russell A. Poldrack
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. James M. Shine
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed in the generation, execution and revision of the manuscript.

Corresponding authors

Correspondence to Emily S. Finn, Russell A. Poldrack or James M. Shine.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Thomas Nickl-Jockschat, Nicholas Turk-Browne, Lucina Uddin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Finn, E.S., Poldrack, R.A. & Shine, J.M. Functional neuroimaging as a catalyst for integrated neuroscience. Nature 623, 263–273 (2023). https://doi.org/10.1038/s41586-023-06670-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06670-9

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing