Although animals can accumulate sensory evidence over considerable time scales to appropriately select behavior, little is known about how the vertebrate brain as a whole accomplishes this. In this study, we developed a new sensorimotor decision-making assay in larval zebrafish based on whole-field visual motion. Fish responded by swimming in the direction of perceived motion, such that the latency to initiate swimming and the fraction of correct turns were modulated by motion strength. Using whole-brain functional imaging, we identified neural activity relevant to different stages of the decision-making process, including the momentary evaluation and accumulation of sensory evidence. This activity is distributed in functional clusters across different brain regions and is characterized by a wide range of time constants. In addition, we found that the caudal interpeduncular nucleus (IPN), a circular structure located ventrally on the midline of the brain, reliably encodes the left and right turning rates.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Processed imaging data are available on Zenodo (https://doi.org/10.5281/zenodo.3453488). Original imaging data and behavioral data are available upon reasonable request from the corresponding author.
Code for Figs. 4 and 5 is available on GitHub (https://github.com/portugueslab/Dragomir-et-al-2019-modelfit). Parts of the analysis can be explored online via Binder. Stimulus code is available as part of Stytra v0.8.22 (http://portugueslab.com/stytra and https://doi.org/10.5281/zenodo.3451302), and explicit experimental protocols are available on GitHub (https://github.com/portugueslab/Dragomir-et-al-2019-protocols). MATLAB code is available upon reasonable request from the corresponding author.
Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).
Stone, M. Models for choice-reaction time. Psychometrika 25, 251–260 (1960).
Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).
Shadlen, M. N. & Newsome, W. T. Motion perception: seeing and deciding. Proc. Natl Acad. Sci. USA 93, 628–633 (1996).
Hanes, D. P. & Schall, J. D. Neural control of voluntary movement initiation. Science 274, 427–430 (1996).
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
Scott, B. B. et al. Fronto–parietal cortical circuits encode accumulated evidence with a diversity of timescales. Neuron 95, 385–398 (2017).
Akrami, A., Kopec, C. D., Diamond, M. E. & Brody, C. D. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554, 368–372 (2018).
Licata, A. M. et al. Posterior parietal cortex guides visual decisions in rats. J. Neurosci. 37, 4954–4966 (2017).
Brunton, B. W., Botvinick, M. M. & Brody, C. D. Rats and humans can optimally accumulate evidence for decision-making. Science 340, 95–98 (2013).
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).
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).
DasGupta, S., Ferreira, C. H. & Miesenböck, G. FoxP influences the speed and accuracy of a perceptual decision in Drosophila. Science 344, 901–904 (2014).
Newsome, W. T. & Paré, E. B. A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J. Neurosci. 8, 2201–2211 (1988).
Horwitz, G. D. & Newsome, W. T. Separate signals for target selection and movement specification in the superior colliculus. Science 284, 1158–1161 (1999).
Kim, J. N. & Shadlen, M. N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999).
Ding, L. & Gold, J. I. Separate, causal roles of the caudate in saccadic choice and execution in a perceptual decision task. Neuron 75, 865–874 (2012).
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).
Hanks, T. D., Ditterich, J. & Shadlen, M. N. Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nat. Neurosci. 9, 682–689 (2006).
Katz, L., Yates, J., Pillow, J. W. & Huk, A. Dissociated functional significance of choice-related activity across the primate dorsal stream. Nature 535, 285–288 (2016).
Brody, C. D. & Hanks, T. D. Neural underpinnings of the evidence accumulator. Curr. Opin. Neurobiol. 37, 149–157 (2016).
Neuhauss, S. C. et al. Genetic disorders of vision revealed by a behavioral screen of 400 essential loci in zebrafish. J. Neurosci. 19, 8603–8615 (1999).
Orger, M. B., Smear, M. C., Anstis, S. M. & Baier, H. Perception of Fourier and non-Fourier motion by larval zebrafish. Nat. Neurosci. 3, 1128–1133 (2000).
Morley, B. J. The interpeduncular nucleus. Int. Rev. Neurobiol. 28, 157–182 (1986).
Sharp, P. E., Turner-Williams, S. & Tuttle, S. Movement-related correlates of single cell activity in the interpeduncular nucleus and habenula of the rat during a pellet-chasing task. Behav. Brain Res. 166, 55–70 (2006).
Clark, B. J. & Taube, J. S. Deficits in landmark navigation and path integration after lesions of the interpeduncular nucleus. Behav. Neurosci. 123, 490–503 (2009).
Orger, M. B., Kampff, A. R., Severi, K. E., Bollmann, J. H. & Engert, F. Control of visually guided behavior by distinct populations of spinal projection neurons. Nat. Neurosci. 11, 327–333 (2008).
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).
Hwang, E. J., Dahlen, J. E., Mukundan, M. & Komiyama, T. History-based action selection bias in posterior parietal cortex. Nat. Commun. 8, 1242 (2017).
Gold, J. I., Law, C.-T., Connolly, P. & Bennur, S. The relative influences of priors and sensory evidence on an oculomotor decision variable during perceptual learning. J. Neurophysiol. 100, 2653–2668 (2008).
Portugues, R., Feierstein, C. E., Engert, F. & Orger, M. B. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81, 1328–1343 (2014).
Kubo, F. et al. Functional architecture of an optic flow-responsive area that drives horizontal eye movements in zebrafish. Neuron 81, 1344–1359 (2014).
Naumann, E. A. et al. From whole-brain data to functional circuit models: the zebrafish optomotor response. Cell 167, 947–960 (2016).
Huang, K.-H. H., Ahrens, M. B., Dunn, T. W. & Engert, F. Spinal projection neurons control turning behaviors in zebrafish. Curr. Biol. 23, 1566–1573 (2013).
Dunn, T. W. et al. Neural circuits underlying visually evoked escapes in larval zebrafish. Neuron 89, 613–628 (2016).
Wolf, S. et al. Sensorimotor computation underlying phototaxis in zebrafish. Nat. Commun. 8, 651 (2017).
Bianco, I. H. & Wilson, S. W. The habenular nuclei: a conserved asymmetric relay station in the vertebrate brain. Philos. Trans. R. Soc. B. Biol. Sci. 364, 1005–1020 (2009).
Bogacz, R. Optimal decision-making theories: linking neurobiology with behaviour.Trends Cogn. Sci. 11, 118–125 (2007).
Shadlen, M. N. & Newsome, W. T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001).
Usher, M. & McClelland, J. L. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108, 550–592 (2001).
Mazurek, M. E., Roitman, J. D., Ditterich, J. & Shadlen, M. N. A role for neural integrators in perceptual decision making. Cerebral Cortex 13, 1257–1269 (2003).
Teodorescu, A. R. & Usher, M. Disentangling decision models: from independence to competition. Psychol. Rev. 120, 1–38 (2013).
Portugues, R., Haesemeyer, M., Blum, M. L. & Engert, F. Whole-field visual motion drives swimming in larval zebrafish via a stochastic process. J. Exp. Biol. 218, 1433–1443 (2015).
Severi, K. E. et al. Neural control and modulation of swimming speed in the larval zebrafish. Neuron 83, 692–707 (2014).
Robinson, D. A. Integrating with neurons. Annu. Rev. Neurosci. 12, 33–45 (1989).
Aksay, E., Baker, R., Seung, H. S. & Tank, D. W. Anatomy and discharge properties of pre-motor neurons in the goldfish medulla that have eye-position signals during fixations. J. Neurophysiol. 84, 1035–1049 (2000).
Miri, A. et al. Spatial gradients and multidimensional dynamics in a neural integrator circuit. Nat. Neurosci. 14, 1150–1161 (2011).
Daie, K., Goldman, M. S. & Aksay, E. R. F. Spatial patterns of persistent neural activity vary with the behavioral context of short-term memory. Neuron 85, 847–860 (2015).
Joshua, M. & Lisberger, S. G. A tale of two species: neural integration in zebrafish and monkeys. Neuroscience 296, 80–91 (2015).
Kim, D. H. et al. Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish. Nat. Methods 14, 1107–1114 (2017).
Štih, V., Petrucco, L., Kist, A. M. & Portugues, R. Stytra: an open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments. PLoS Comput. Biol. 15, e1006699 (2019).
Rohlfing, T. & Maurer, C. R. Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans. Inf. Technol. Biomed. 7, 16–25 (2003).
Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
Mogensen, P. K. & Riseth, A. N. Optim: a mathematical optimization package for Julia. J. Open Source Softw. 3, 615 (2018).
We thank M. Wulliman for advice on anatomic references. We thank A. Kist and D. Markov for technical help. We thank A. Borst and N. Gogolla for conversations and useful comments throughout the project and J. Fitzgerald for comments on the manuscript. We thank D.G.C. Hildebrand, I.H. Bianco and F. Engert (Harvard University) for sharing the Tg(elavl3:GCaMP6f) transgenic fish before publication. E.I.D. was partly funded by the Graduate School of Systemic Neurosciences, and all authors were funded by the Max Planck Gesellschaft. This research was also partially funded by Human Frontier Science Program grant RGP0027/2016 and a Life? grant from the Volkwagen Stiftung.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Two-dimensional contour plot showing the distance moved and angle turned averaged across all 51 fish when presented with coherence >0.5. Most swim bout events are clustered around 0 degrees, and represent forward swims, while lateralized turns begin around after 15 degrees. b, Number of left, right and forward swims as a function of stimulus coherence.
a, Schematic of experimental paradigm involving stimulus pulses. 6 seconds of coherence 0 were followed by a pulse coherence 0.3, 0.6 or 1. The stimulus pulse lasted 1, 2, 3, 4, 6, 8 or 10 seconds. After the pulse is over 6 seconds of coherence 0 were presented again. b, Forward swimming and turning rates for the three different coherences and the seven different pulse durations presented. The vertical grey lines denote pulse start and end. Instantaneous rates were computed by averaging over a 200 ms window. c, Average behavioral rates for forward swimming (black), turning in the direction of the stimulus (red), and against the stimulus (blue) averaged over the whole pulse as a function of the total pulse duration. In dotted lines the average baseline rates are shown computed over the 5 second window from second 1 to second 6 right before pulse onset (see panel a).
a, Turning behavior during trials of coherence 0.3 (top) and 1 (bottom) as a function of the coherence presented during the preceding trial. Trajectories are split by both magnitude and direction of the preceding trial. b, Latency to first correct turn in trials of coherence 0.3 (top) and 1 (bottom) as a function of the coherence of the preceding trial. c, Turning behavior during trials of coherence 0.3 (top) and 1 (bottom) as a function of both the direction of the coherence presented during the preceding trial and the direction of the last turn in the preceding trial. Only preceding trials with coherence of magnitude 0.6 were included. d, Fraction of correct first turns during trials of coherence 0.3 (top) and 1 (bottom) as a function of both the direction of the coherence of the preceding trial and the direction of the last turn in the preceding trial. Only preceding trials with coherence of magnitude 0.3 (top) and 1 (bottom) were included.
a, Example tail trace during a full head restrained experiment with the coherence stimuli sequence (positive and negative numbers represent coherences to the left and right direction, respectively). b, Average tail traces of all bouts elicited during presentation of stimuli with coherence +1 (red), 0 (black) and −1 (green) for the fish shown in a above. Leftward turns, forward swims and rightward turns, which are the predominant behaviors during these stimuli presentations, can be clearly distinguished by computing the sum of the cumulative tail angle during the first 60 ms of the bout, which we refer to as the laterality index. c, Histogram of all laterality indices for the fish in a, showing a distribution with three peaks corresponding to left- and right-ward turns and forward swims. Thresholds can be imposed to distinguish between these behaviors, in this case −7.34 separates rightward and forward swims and 2.61 separates forward swims and left-ward turns. d, Individual histograms for all bouts elicited during stimulus presentations of the different coherences for the sample fish in a. e, Left: Average fraction of correct turns (in the direction of presented coherence). Right: Average latency (time from stimulus onset) to first correct turn as a fraction of stimulus coherence. Averages over N = 18 fish; error bars denote SEM.
Sum of projection footprints along the three axes for all brains that were imaged and could be registered to the reference brain.
a, The regressors are constructed from the stimulus coherence shown and the tail movement recorded (both underlined) b, ROI maps color coded according to individual sensory and motor related regressors displayed in Fig. 3 (each regressor and corresponding thresholds for ROI shown here are displayed at the bottom). Each map has views from lateral left (left), dorsoventral (top central), lateral right (right) and rostro-caudal (bottom) ROI projections.
a, Variance of ROIs explained by the model. The orange line is the cut of threshold for ROIs displayed in Fig. 4, chosen empirically to discard spurious fits due to artifacts. b, Simulated behavioral response of the integrator model (dashed line) to transitions in coherence and direction superimposed on data shown in Fig. 2a. The relative weights of excitation and inhibition and the nonlinearity P where extracted from data presented in Fig. 1c, whereas the time constant was extracted from behavioral data presented in Fig. 2a.
a, Left: motor triggers corresponding to stepwise increases (green) and decreases (magenta) concurrent with leftward turns. The average activity of all ROIs with correlation >0.7 with the corresponding motor trigger is superimposed in black. Right: anatomical location of the motor triggers throughout the brain. Ro – rostral, c – caudal, l – left, r – right and scale bar = 300 microns. b, c, Similar to a but for neuronal activity which ramps up or down after a left turn (b) or neuronal activity which has a maximum or minimum coincident with the left turn (c). d, Motor trigger corresponding to neuronal activity that decreases steadily and is reset upon a left turn. The number of ROIs with activity that increased steadily and was reset upon a left turn was negligible. e, All ROIs with activity coincident with a forward swim.
Freely swimming behavior of zebrafish during presentation of the RDM stimulus. The zebrafish larva is swimming in an arena consisting of an 88-mm Petri dish and being shown dark-on-white coherent dots. The larva (shown on the top right for clarity) is tracked in real time, and each stimulus is updated to appear always in the same direction with respect to its body axis. The angle of the fish’s axis is recorded (bottom right).
Whole-brain projection of sensory- and motor-based regression ROIs. The video shows planes from dorsal to ventral of all the ROIs identified and displayed in Fig. 3 (using the same color code, except for the motor ROIs, which are displayed here in dark blue for both directions) superimposed on the anatomy.
Activity in the IPN correlates with both the stimulus and directional turning behavior. Activity in the IPN averaged over three trials (rostral up). Red and green colors denote activity above and below baseline, respectively. The RDM stimulus is present on top. The trace in the bottom displays the coherence of the visual stimulus presented as a function of time in seconds (black) together with the behavior directional tail behavior (red). Positive values denote left. The acquisition rate was ~ 3 Hz. The movie is sped up eight times.
About this article
Cite this article
Dragomir, E.I., Štih, V. & Portugues, R. Evidence accumulation during a sensorimotor decision task revealed by whole-brain imaging. Nat Neurosci 23, 85–93 (2020). https://doi.org/10.1038/s41593-019-0535-8
Brain-wide perception of the emotional valence of light is regulated by distinct hypothalamic neurons
Molecular Psychiatry (2022)
Nature Reviews Physics (2022)
Nature Communications (2021)
Nature Communications (2021)
Animal Cognition (2021)