Evidence accumulation during perceptual decisions in humans varies as a function of dorsal frontoparietal organization

Abstract

Animal neurophysiological studies have identified neural signals within dorsal frontoparietal areas that trace a perceptual decision by accumulating sensory evidence over time and trigger action upon reaching a threshold. Although analogous accumulation-to-bound signals are identifiable on extracranial human electroencephalography, their cortical origins remain unknown. Here neural metrics of human evidence accumulation, predictive of the speed of perceptual reports, were isolated using electroencephalography and related to dorsal frontoparietal network (dFPN) connectivity using diffusion and resting-state functional magnetic resonance imaging. The build-up rate of evidence accumulation mediated the relationship between the white matter macrostructure of dFPN pathways and the efficiency of perceptual reports. This association between steeper build-up rates of evidence accumulation and the dFPN was recapitulated in the resting-state networks. Stronger connectivity between dFPN regions is thus associated with faster evidence accumulation and speeded perceptual decisions. Our findings identify an integrated network for perceptual decisions that may be targeted for neurorehabilitation in cognitive disorders.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Evidence accumulation (CPP build-up rate) and motor preparation (LHB latency) relate to the speed of perceptual reports.
Fig. 2: Relationship between evidence accumulation and white matter organization of the dorsal SLF.
Fig. 3: Relationship between dFPN connectivity and build-up rate of evidence accumulation recapitulated with rs-fMRI functional connectivity.

Data availability

The EEG dataset from the current study is available at the following repository: https://figshare.com/s/8d6f461834c47180a444. These data are open access and available under a Creative Commons attribution: NonCommercial-ShareAlike 3.0 international licence. Raw data for the diffusion and resting-state imaging were collected at the Turner Institute for Brain and Mental Health as part of a large-scale project. Derived diffusion and resting-state MRI data supporting the findings of this study are available from the corresponding author upon request.

Code availability

All EEG pre-processing scripts implemented in this study are available from https://github.com/gerontium/big_dots. These scripts are open access and available under a Creative Commons attribution: NonCommercial-ShareAlike international license. Custom code for the resting-state MRI and EEG data that the support the conclusion of this article are available from the corresponding author upon request.

References

  1. 1.

    Fish, S. et al. Modelling reaction time distribution of fast decision tasks in schizophrenia: evidence for novel candidate endophenotypes. Psychiatry Res. 269, 212–220 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

    Fosco, W. D., White, C. N. & Hawk, L. W. Acute stimulant treatment and reinforcement increase the speed of information accumulation in children with ADHD. J. Abnorm. Child Psychol. 45, 911–920 (2016).

    Article  Google Scholar 

  3. 3.

    Huang, Y.-T. et al. Different effects of dopaminergic medication on perceptual decision-making in Parkinson’s disease as a function of task difficulty and speed–accuracy instructions. Neuropsychologia 75, 577–587 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Kelly, S. P. & O’Connell, R. G. Internal and external influences on the rate of sensory evidence accumulation in the human brain. J. Neurosci. 33, 19434–19441 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Twomey, D. M., Kelly, S. P. & O’Connell, R. G. Abstract and effector-selective decision signals exhibit qualitatively distinct dynamics before delayed perceptual reports. J. Neurosci. 36, 7346–7352 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    White, C. N., Ratcliff, R., Vasey, M. W. & McKoon, G. Using diffusion models to understand clinical disorders. J. Math. Psychol. 54, 39–52 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Starns, J. J. The effects of aging on the speed–accuracy compromise: boundary optimality in the diffusion model. Psychol. Aging 2, 277–390 (2010).

    Google Scholar 

  8. 8.

    Hanks, T. D. & Summerfield, C. Perceptual decision making in rodents, monkeys, and humans. Neuron 93, 15–31 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Shadlen, M. N. & Shohamy, D. Decision making and sequential sampling from memory. Neuron 90, 927–939 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Ding, L. & Gold, J. I. Caudate encodes multiple computations for perceptual decisions. J. Neurosci. 30, 15747–15759 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Ratcliff, R., Cherian, A. & Segraves, M. A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J. Neurophysiol. 90, 1392–1407 (2003).

    PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Cisek, P. & Kalaska, J. F. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45, 801–814 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Pape, A.-A. & Siegel, M. Motor cortex activity predicts response alternation during sensorimotor decisions. Nat. Commun. 7, 13098 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Ding, L. & Gold, J. I. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22, 1052–1067 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  16. 16.

    Huk, A. C. Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. 25, 10420–10436 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Hanks, T., Kiani, R. & Shadlen, M. N. A neural mechanism of speed–accuracy tradeoff in macaque area LIP. eLife 3, e02260 (2014).

    PubMed Central  Article  Google Scholar 

  18. 18.

    Heitz, R. P. & Schall, J. D. Neural mechanisms of speed–accuracy tradeoff. Neuron 76, 616–628 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Thura, D. & Cisek, P. Modulation of premotor and primary motor cortical activity during volitional adjustments of speed–accuracy trade-offs. J. Neurosci. 36, 938–956 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    O’Connell, R. G., Dockree, P. M. & Kelly, S. P. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat. Neurosci. 15, 1729–1735 (2012).

    PubMed  Article  CAS  Google Scholar 

  21. 21.

    De Lange, F. P., Rahnev, D. A., Donner, T. H. & Lau, H. Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. J. Neurosci. 33, 1400–1410 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Steinemann, N. A., O’Connell, R. G. & Kelly, S. P. Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy. Nat. Commun. 9, 3627 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. 23.

    De Lange, F. P., Jensen, O. & Dehaene, S. Accumulation of evidence during sequential decision making: the importance of top–down factors. J. Neurosci. 30, 731–738 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Donner, T. H., Siegel, M., Fries, P. & Engel, A. K. Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr. Biol. 19, 1581–1585 (2009).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Loughnane, G. M. et al. Target selection signals influence perceptual decisions by modulating the onset and rate of evidence accumulation. Curr. Biol. 26, 496–502 (2016).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Wyart, V., de Gardelle, V., Scholl, J. & Summerfield, C. Rhythmic fluctuations in evidence accumulation during decision making in the human brain. Neuron 76, 847–858 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Murphy, P. R., Robertson, I. H., Harty, S. & O’Connell, R. G. Neural evidence accumulation persists after choice to inform metacognitive judgments. eLife 4, e11946 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Philiastides, M. G., Heekeren, H. R. & Sajda, P. Human scalp potentials reflect a mixture of decision-related signals during perceptual choices. J. Neurosci. 34, 16877–16889 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Rungratsameetaweemana, N., Itthipuripat, S., Salazar, A. & Serences, J. T. Expectations do not alter early sensory processing during perceptual decision-making. J. Neurosci. 38, 5632–5648 (2018).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Spitzer, B., Waschke, L. & Summerfield, C. Selective overweighting of larger magnitudes during noisy numerical comparison. Nature 1, 0145 (2017).

    Google Scholar 

  31. 31.

    Von Lautz, A., Herding, J. & Blankenburg, F. Neuronal signatures of a random-dot motion comparison task. NeuroImage 193, 57–66 (2019).

    PubMed  Article  Google Scholar 

  32. 32.

    Van Vugt, M. K., Beulen, M. A. & Taatgen, N. A. Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making. Brain Res. 1715, 1–12 (2019).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Afacan-Seref, K., Steinemann, N. A., Blangero, A. & Kelly, S. P. Dynamic interplay of value and sensory information in high-speed decision making. Curr. Biol. 28, 795–802.e6 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Herding, J., Ludwig, S., von Lautz, A., Spitzer, B. & Blankenburg, F. Centro-parietal EEG potentials index subjective evidence and confidence during perceptual decision making. NeuroImage 201, 116011 (2019).

    PubMed  Article  Google Scholar 

  35. 35.

    Tagliabue, C. F. et al. The EEG signature of sensory evidence accumulation during decision formation closely tracks subjective perceptual experience. Sci. Rep. 9, 4949 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Jurkiewicz, M. T., Gaetz, W. C., Bostan, A. C. & Cheyne, D. Post-movement beta rebound is generated in motor cortex: evidence from neuromagnetic recordings. NeuroImage 32, 1281–1289 (2006).

    PubMed  Article  Google Scholar 

  37. 37.

    Soltani, M. & Knight, R. T. Neural origins of the P300. Crit. Rev. Neurobiol. 14, 199–224 (2000).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Linden, D. E. J. The P300: where in the brain is it produced and what does it tell us? Neuroscientist 11, 563–576 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. 39.

    Stuss, D. T. & Knight, R. T. Principles of Frontal Lobe Function (Oxford Univ. Press, 2002).

  40. 40.

    Tang, Y.-Y. et al. Short-term meditation induces white matter changes in the anterior cingulate. Proc. Natl Acad. Sci. USA 107, 15649–15652 (2010).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Tang, Y.-Y., Lu, Q., Fan, M., Yang, Y. & Posner, M. I. Mechanisms of white matter changes induced by meditation. Proc. Natl Acad. Sci. USA 109, 10570–10574 (2012).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Dienes, Z. How Bayes factors change scientific practice. J. Math. Psychol. 72, 78–89 (2016).

    Article  Google Scholar 

  44. 44.

    De Schotten, M. T. et al. A lateralized brain network for visuospatial attention. Nat. Neurosci. 14, 1245–1246 (2011).

    Article  CAS  Google Scholar 

  45. 45.

    Hayes, A. F. PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling White Paper. https://api.semanticscholar.org/CorpusID:22220661 (2012).

  46. 46.

    Hayes, A. F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Guilford Press, 2013).

  47. 47.

    Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L. & Raichle, M. E. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl Acad. Sci. USA 103, 10046–10051 (2006).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 215–229 (2002).

    Article  CAS  Google Scholar 

  49. 49.

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

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Nelson, S. M. et al. A parcellation scheme for human left lateral parietal cortex. Neuron 67, 156–170 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Grefkes, C., Wang, L. E., Eickhoff, S. B. & Fink, G. R. Noradrenergic modulation of cortical networks engaged in visuomotor processing. Cereb. Cortex 20, 783–797 (2010).

    PubMed  Article  Google Scholar 

  52. 52.

    Corbetta, M., Patel, G. & Shulman, G. L. The reorienting system of the human brain: from environment to theory of mind. Neuron 58, 306–324 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Corbetta, M. & Shulman, G. L. Spatial neglect and attention networks. Annu. Rev. Neurosci. 34, 569–599 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    O’Connell, R. G., Shadlen, M. N., Wong-Lin, K. & Kelly, S. P. Bridging neural and computational viewpoints on perceptual decision-making. Trends Neurosci. 41, 838–852 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Romo, R. & de Lafuente, V. Conversion of sensory signals into perceptual decisions. Prog. Neurobiol. 103, 41–75 (2013).

    PubMed  Article  Google Scholar 

  57. 57.

    Shadlen, M. N. & Kiani, R. Decision making as a window on cognition. Neuron 80, 791–806 (2013).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Assaf, Y., Johansen-Berg, H. & de Schotten, M. T. The role of diffusion MRI in neuroscience. NMR Biomed. 11, e3762 (2017).

    Google Scholar 

  59. 59.

    Johansen-Berg, H. Behavioural relevance of variation in white matter microstructure. Curr. Opin. Neurol. 23, 351–358 (2010).

    PubMed  Google Scholar 

  60. 60.

    Kanai, R. & Rees, G. The structural basis of inter-individual differences in human behaviour and cognition. Nat. Rev. Neurosci. 12, 231–242 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Zatorre, R. J., Fields, R. D. & Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Hursh, J. B. Conduction velocity diameter of nerve fibers. Am. J. Physiol. 127, 131–139 (1939).

    Article  Google Scholar 

  63. 63.

    Waxman, S. G. & Bennett, M. V. L. Relative conduction velocities of small myelinated and non-myelinated fibres in the central nervous system. Nat. New Biol. 238, 217–219 (1972).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  64. 64.

    Selen, L. P. J., Shadlen, M. N. & Wolpert, D. M. Deliberation in the motor system: reflex gains track evolving evidence leading to a decision. J. Neurosci. 32, 2276–2286 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Barbas, H. & Pandya, D. N. Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey. J. Comp. Neurol. 256, 211–228 (1987).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  66. 66.

    Muakkassa, K. F. & Strick, P. L. Frontal lobe inputs to primate motor cortex: evidence for four somatotopically organized ‘premotor’ areas. Brain Res. 177, 176–182 (1979).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Shushruth, S., Mazurek, M. & Shadlen, M. N. Comparison of decision-related signals in sensory and motor preparatory responses of neurons in area LIP. J. Neurosci. 38, 6350–6365 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Wyart, V., Myers, N. E. & Summerfield, C. Neural mechanisms of human perceptual choice under focused and divided attention. J. Neurosci. 35, 3485–3498 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Lorteije, J. A. M. et al. The formation of hierarchical decisions in the visual cortex. Neuron 87, 1344–1356 (2015).

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Ditterich, J., Mazurek, M. E. & Shadlen, M. N. Microstimulation of visual cortex affects the speed of perceptual decisions. Nat. Neurosci. 6, 891–898 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  71. 71.

    Gazzaley, A. & Nobre, A. C. Top-down modulation: bridging selective attention and working memory. Trends Cogn. Sci. 16, 129–135 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  72. 72.

    Brosnan, M. B. et al. Prefrontal modulation of visual processing and sustained attention in aging, a transcranial direct current stimulation–electroencephalogram coregistration approach. J. Cogn. Neurosci. 30, 1–16 (2018).

    Article  Google Scholar 

  73. 73.

    Twomey, D. M., Murphy, P. R., Kelly, S. P. & O’Connell, R. G. The classic P300 encodes a build‐to‐threshold decision variable. Eur. J. Neurosci. 42, 1636–1643 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  74. 74.

    Hillyard, S. A., Squires, K. C., Bauer, J. W. & Science, P. L. Evoked potential correlates of auditory signal detection. Science 172, 1357–1360 (1971).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75.

    Squires, K. C., Hillyard, S. A. & Lindsay, P. H. Vertex potentials evoked during auditory signal detection: relation to decision criteria. Percept. Psychophys. 14, 265–272 (1973).

    Article  Google Scholar 

  76. 76.

    Sutton, S., Braren, M., Zubin, J. & John, E. R. Evoked-potential correlates of stimulus uncertainty. Science 150, 1187–1188 (1965).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  77. 77.

    Courchesne, E., Hillyard, S. A. & Courchesne, R. Y. P3 waves to the discrimination of targets in homogeneous and heterogeneous stimulus sequences. Psychophysiology 14, 590–597 (1977).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  78. 78.

    Johnson, C. C. D. & Donchin, E. On quantifying surprise: the variation of event‐related potentials with subjective probability. Psychophysiology 14, 456–467 (1977).

    Article  Google Scholar 

  79. 79.

    Polich, J. Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118, 2128–2148 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Nieuwenhuis, S., Aston-Jones, G. & Cohen, J. D. Decision making, the P3, and the locus coeruleus–norepinephrine system. Psychol. Bull. 131, 510–532 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  81. 81.

    Szuromi, B., Czobor, P., Komlósi, S. & Bitter, I. P300 deficits in adults with attention deficit hyperactivity disorder: a meta-analysis. Psychol. Med. 41, 1529–1538 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Ford, J. M., Mathalon, D. H., Kalba, S., Marsh, L. & Pfefferbaum, A. N1 and P300 abnormalities in patients with schizophrenia, epilepsy, and epilepsy with schizophrenialike features. Biol. Psychiatry 49, 848–860 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  83. 83.

    Frodl, T. et al. Value of event-related P300 subcomponents in the clinical diagnosis of mild cognitive impairment and Alzheimer’s disease. Psychophysiology 39, 175–181 (2002).

    PubMed  Article  PubMed Central  Google Scholar 

  84. 84.

    Ziegler, D. A. et al. Closed-loop digital meditation improves sustained attention in young adults. Nat. Hum. Behav. 3, 746–757 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  85. 85.

    Storebø, O. J. et al. Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents. Cochrane Database Syst. Rev. https://doi.org//10.1002/14651858.CD012069.pub2 (2012)

  86. 86.

    Loughnane, G. M. et al. Catecholamine modulation of evidence accumulation during perceptual decision formation: a randomized trial. J. Cogn. Neurosci. 31, 1044–1053 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  87. 87.

    Chiang, M.-C. et al. Genetics of brain fiber architecture and intellectual performance. J. Neurosci. 29, 2212–2224 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Newman, D. P., Loughnane, G. M., Kelly, S. P., O’Connell, R. G. & Bellgrove, M. A. Visuospatial asymmetries arise from differences in the onset time of perceptual evidence accumulation. J. Neurosci. 37, 3378–3385 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Marshall, T. R., Bergmann, T. O. & Jensen, O. Frontoparietal structural connectivity mediates the top-down control of neuronal synchronization associated with selective attention. PLoS Biol. 13, e1002272 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  90. 90.

    Chechlacz, M., Gillebert, C. R., Vangkilde, S. A., Petersen, A. & Humphreys, G. W. Structural variability within frontoparietal networks and individual differences in attentional functions: an approach using the theory of visual attention. J. Neurosci. 35, 10647–10658 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Pelli, D. G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spatial Vis. 10, 437–442 (1997).

    CAS  Article  Google Scholar 

  92. 92.

    Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    CAS  PubMed  Article  Google Scholar 

  93. 93.

    Cornelissen, F. W., Peters, E. M. & Palmer, J. The Eyelink Toolbox: eye tracking with MATLAB and the Psychophysics Toolbox. Behav. Res. Methods Instrum. Comput. 34, 613–617 (2002).

    PubMed  Article  Google Scholar 

  94. 94.

    Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    Article  Google Scholar 

  95. 95.

    Morel, P. Gramm: grammar of graphics plotting in Matlab. J. Open. Source. Softw. 3, 568 (2018).

    Article  Google Scholar 

  96. 96.

    Kayser, J. & Tenke, C. E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin. Neurophysiol. 117, 348–368 (2006).

    PubMed  Article  Google Scholar 

  97. 97.

    Foxe, J. J. & Simpson, G. V. Flow of activation from V1 to frontal cortex in humans. Exp. Brain Res. 142, 139–150 (2002).

    PubMed  Article  Google Scholar 

  98. 98.

    Kelly, S. P., Gomez-Ramirez, M. & Foxe, J. J. Spatial attention modulates initial afferent activity in human primary visual cortex. Cereb. Cortex 18, 2629–2636 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    Thut, G., Nietzel, A., Brandt, S. A. & Pascual-Leone, A. α-Band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J. Neurosci. 26, 9494–9502 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image. Anal. 2, 143–156 (2001).

    Article  Google Scholar 

  102. 102.

    Smith, R. E., Tournier, J.-D., Calamante, F. & Connelly, A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62, 1924–1938 (2012).

    PubMed  Article  Google Scholar 

  103. 103.

    Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154 (2012).

    PubMed  Article  Google Scholar 

  104. 104.

    Ball, G. et al. Multimodal structural neuroimaging markers of brain development and ADHD symptoms. Am. J. Psychiatry 176, 57–66 (2019).

    PubMed  Article  Google Scholar 

  105. 105.

    D’Albis, M.-A. Local structural connectivity is associated with social cognition in autism spectrum disorder. Brain 141, 3472–3481 (2018).

    PubMed  Article  Google Scholar 

  106. 106.

    Esteban, O. et al. MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12, e0184661 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107.

    Griffanti, L. et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232–247 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Boyacioğlu, R., Schulz, J., Koopmans, P. J., Barth, M. & Norris, D. G. Improved sensitivity and specificity for resting state and task fMRI with multiband multi-echo EPI compared to multi-echo EPI at 7T. NeuroImage 119, 352–361 (2015).

    PubMed  Article  Google Scholar 

  110. 110.

    Griffanti, L. et al. Hand classification of fMRI ICA noise components. NeuroImage 154, 188–205 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  111. 111.

    Parkes, L., Fulcher, B., Yücel, M. & Fornito, A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415–436 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  112. 112.

    Burgess, G. C. et al. Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome project. Brain Connect. 6, 669–680 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174–187 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Power, J. D., Plitt, M., Laumann, T. O. & Martin, A. Sources and implications of whole-brain fMRI signals in humans. NeuroImage 146, 609–625 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  115. 115.

    Fox, M. D., Zhang, D., Snyder, A. Z. & Raichie, M. E. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Saad, Z. S. et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2, 25–32 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    Li, J. et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. NeuroImage 196, 126–141 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  118. 118.

    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  Article  PubMed Central  Google Scholar 

  119. 119.

    Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. 121.

    Dai, H. et al. Resting‐state functional MRI: functional connectivity analysis of the visual cortex in primary open‐angle glaucoma patients. Hum. Brain Mapp. 34, 2455–2463 (2013).

    PubMed  Article  PubMed Central  Google Scholar 

  122. 122.

    Forstmann, B. U. et al. Cortico-subthalamic white matter tract strength predicts interindividual efficacy in stopping a motor response. NeuroImage 60, 370–375 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  123. 123.

    Rouder, J. N. & Morey, R. D. The nature of psychological thresholds. Psychol. Rev. 116, 655–660 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  124. 124.

    Baron, R. M. & Kenny, D. A. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the Australian Research Council (including DP150100986 and DP180102066) to M.A.B. and R.G.O. M.A.B. is supported by a Senior Research Fellowship from the Australian National Health and Medical Research Council (APP1154378). A.F. was supported by the Sylvia and Charles Viertel Foundation, National Health and Medical Research Council (1050504) and Australian Research Council (FT130100589). We thank N. Steinemann and D. McGovern for providing the additional data reported in Supplementary Results 1 and Supplementary Fig. 1, and J. Wiley and J. Matthews for statistical advice. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

M.B.B., D.P.N., G.M.L., R.G.O. and M.A.B. conceived of the study. M.B.B., K.S., T.S., D.P.N., G.M.L., A.F., R.G.O. and M.A.B. developed the methodology. M.B.B., K.S., T.S., S.G., D.P.N. and G.M.L. developed the software. M.B.B., K.S. and T.S. performed the formal analysis. D.P.N. and G.M.L. performed the investigation. M.B.B. wrote the original draft of the manuscript. M.B.B., K.S., D.P.N., G.M.L., S.G., T.S., R.G.O. and M.A.B. reviewed and edited the manuscript. M.B.B. and T.S. visualized the data. A.F., R.G.O. and M.A.B. supervised the study. A.F., R.G.O. and M.A.B. acquired funding.

Corresponding authors

Correspondence to Méadhbh B. Brosnan or Mark A. Bellgrove.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Marike Schiffer.

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

Extended data

Extended Data Fig. 1 Consistency of an individual’s CPP build-up rate across experimental manipulations.

a, Steinemann et al.22 Nature Communications. b, McGovern et al (2018) Nature Human Behaviour. c, Loughnane et al.25 Current Biology. Note. For each sub-plot every data point depicts an individual’s CPP build-up rate for condition a (as described on the x-axis), and condition b (as described on the y-axis). r denotes the correlation between the two experimental conditions using Pearson’s r.

Extended Data Fig. 2 EEG signals of relevance for RT but not accounting for independent variance.

The EEG signals (pre-target alpha a, N2c (latency and amplitude) b, and FCN amplitude (c)) that offered a significant improvement in model fit for RT, over and above the signals that temporally preceded them but did not independently account for variance in RT. Please note data have been binned using a median split of participants’ RT for visualisation purposes only. Given the window of interest is the pre-stimulus epoch, alpha power has not been baseline-corrected here (but see Supplementary Fig. 3).

Extended Data Fig. 3 The dorsal and ventral FPN by hemisphere.

The left and right hemispheres of the dFPN (a) and vFPN (b) respectively depicted in the upper and lower panels. Note. For the purpose of visualisation, these images were thresholded using a clusterwise FWE rate of p < 0.001 (extent threshold of 10 voxels).

Extended Data Fig. 4 Regions of interest (ROI) used for the tractography of the three branches of the left and right superior longitudinal fasciculus on the cohort-specific T1 template.

At the level of the AC, three ROIs delineate the superior (light blue), middle (purple) and inferior frontal gyri (pink) in each hemisphere. At the level of the PC is a single large parietal ROI (green).

Extended Data Fig. 5 Lateralisation of the SLF.

In line with previous work, the most ventral branch (SLF3) shows specific right lateralisation (Note, x-axis depicts right-left hemisphere volume, controlling for overall white matter volume, *** indicates p < 0.0005).

Supplementary information

Supplementary Information

Supplementary Results 1–7, Supplementary Figs. 1–5, Supplementary Tables 1–3, Supplementary Discussion and Supplementary References.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brosnan, M.B., Sabaroedin, K., Silk, T. et al. Evidence accumulation during perceptual decisions in humans varies as a function of dorsal frontoparietal organization. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-020-0863-4

Download citation

Further reading