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.
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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.
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.
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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.
The authors declare no competing interests.
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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.
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).
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).
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).
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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 4, 844–855 (2020). https://doi.org/10.1038/s41562-020-0863-4
Nature Human Behaviour (2020)