When we step out of our homes in the morning, we scan our surroundings to decide which path we should take. It is still unclear whether we use different brain areas to examine the environment, decide on a route, and then set our trajectory, or if a single region can play a role in all three processes. An area in the top of our brain, named the posterior parietal cortex (PPC), may be an intriguing candidate: some studies find that this region is involved in vision, others highlight that it takes part in decision-making, and a third group of experiments shows that it is important for setting paths. Could the PPC be integrating all three types of information, or might the activity of the neurons in this area be better explained by just one of these processes?

Here, Krumin et al. trained mice to use visual clues to navigate a virtual reality maze, where they have to ‘walk’ down a corridor and then choose to ‘go down’ the right or the left arm. The rodents move on a ball suspended in mid-air, which acts as a treadmill. Meanwhile, the head of the animal is kept still, making it easier to image the activity of hundreds of neurons in the PPC.

The experiments show that when the mouse uses its vision to choose its trajectory, the PPC does not encode visual signals or abstract decisions. Instead, two navigational attributes – the position of the animal along the corridor and its heading angle – activate neurons in this area. Knowing these two features was enough for Krumin et al. to accurately predict the activity of the neurons in the PPC.

This means that we can forecast the activity of neurons deep in the brain by recording simple behavioral features. The results also suggest that the PPC may be more important for setting trajectories than for processing visual images or making abstract decisions. If these findings were to be confirmed in humans, where the parietal cortex is much more complex, they might help understand better the problems that arise when this area is damaged, for example after a stroke.

Posterior parietal cortex (PPC) is recognized as a key nexus of sensorimotor integration (Milner and Goodale, 2006), and the large literature concerning its functions has led to disparate views as to its role. These views originate from a broad variety of experiments, some performed in primates and others in rodents.

A number of studies have related PPC activity to the control of body movement (Andersen and Buneo, 2002; Andersen and Cui, 2009; Andersen and Mountcastle, 1983; Bisley and Goldberg, 2010; Cohen and Andersen, 2002; Park et al., 2014). For instance, neurons in monkey PPC can be influenced by movements of eyes, head, limbs, and body (Cohen and Andersen, 2002), by the intention to execute such movements (Andersen and Buneo, 2002), or by the attention devoted to the resulting position (Bisley and Goldberg, 2010). Neurons in rat PPC, moreover, can encode attributes of body posture (Mimica et al., 2018).

Another set of experiments indicates a role of PPC in decision making, especially for decisions based on vision (Andersen and Cui, 2009; Erlich et al., 2015; Goard et al., 2016; Gold and Shadlen, 2007; Katz et al., 2016; Latimer et al., 2015; Licata et al., 2017; Platt and Glimcher, 1999; Raposo et al., 2014; Sugrue et al., 2004). Studies in rodents found decision signals to be widespread in PPC populations, where they are mixed with other signals (Goard et al., 2016; Pho et al., 2018; Raposo et al., 2014). One study, in particular, probed memory-based decision signals and found them to be remarkably common, with each PPC neuron firing only for a particular decision and only at a particular moment in a stereotyped sequence (Harvey et al., 2012).

A third set of studies, performed in rodents, suggested an important role of PPC in spatial navigation (McNaughton et al., 1994; Nitz, 2006; Nitz, 2012; Save and Poucet, 2000; Save and Poucet, 2009; Whitlock et al., 2012; Wilber et al., 2014). Neurons in rat PPC encode combinations of spatial location and body movement (McNaughton et al., 1994; Nitz, 2006; Nitz, 2012; Whitlock et al., 2012; Wilber et al., 2014). Inactivating it, moreover, can impair navigation (Save and Poucet, 2000; Save and Poucet, 2009).

In principle, all of these views may be correct. Neuronal populations may benefit from a strategy of ‘mixed selectivity’, where neurons are tuned to mixtures of task-related variables (Rigotti et al., 2013). Mixed selectivity has been observed in PPC neurons of both monkeys (Freedman and Assad, 2009; Park et al., 2014; Rishel et al., 2013) and rats (Raposo et al., 2014)

To investigate how spatial navigation, body movement, and decision-making are reflected in PPC activity, we took advantage of the capabilities allowed by virtual reality (Harvey et al., 2009). We trained mice in a virtual navigation task that relies on visual decision-making, and recorded from populations of PPC neurons. The results closely replicated the apparent selectivity of PPC activity on choice, including the arrangement of neurons in choice-dependent activation sequences (Harvey et al., 2012). However, we could predict the activity of PPC neurons based on simple spatial signals: the position of the mouse in the virtual environment and its heading angle. Given the trajectories taken by the mouse, the preferences of the neurons for these attributes predicted the activation of individual neurons and explained their arrangement in sequences. Selectivity for choice, in particular, was fully explained by preferred heading. The predictions improved only slightly when we explicitly added decision as a predictor, and they worsened when we used alternative models based on vision or body movement.

We trained mice in a virtual navigation task driven by perceptual decisions (Figure 1a–c). Head-fixed mice performed a visual two-alternative forced-choice (2AFC) contrast-detection task by walking on an air-suspended ball (Dombeck et al., 2010) through a virtual corridor (Figure 1a, Video 1). One of the corridor’s side walls, chosen randomly, contained a vertical grating, and mice indicated that side by turning into the left or right arms at the end of the corridor (Figure 1b). Successful trials were followed by a water reward, and unsuccessful ones by a brief white-noise sound. To control the task difficulty, we varied the grating’s contrast randomly across trials. Accordingly, contrast exerted a powerful influence on performance: mice frequently chose the correct side for high-contrast stimuli, and performed at chance when contrast was low or zero (Figure 1c).

Figure 1 with 1 supplement see all

Download asset Open asset

Video 1

Download asset

These visual decisions strongly influenced navigation throughout the corridor, as mice typically turned towards the intended side before reaching the end (Figure 1d–h). To describe the navigation trajectories, we considered two variables: position along the corridor (z) and heading angle (θ). In these coordinates, the paths that ended in left and right choices progressively deviated from each other: the animal started heading towards the chosen side before reaching the end of the corridor. This dependence of heading angle θ on decision was particularly clear for trials with higher contrast, which were easier (Figure 1f). The final choice could thus be predicted from the heading angle with increasing accuracy as the mouse reached the end of the corridor (Figure 1g,h).

While mice performed this task, we measured PPC population activity using 2-photon calcium imaging (Figure 1i,j). To identify the borders of visual cortical areas we obtained retinotopic maps using widefield imaging (Figure 1i), and identified PPC as a region anterior to the V1 region, along a contour of pixels that represent a retinotopic azimuth of 60–80 degrees. The average stereotaxic coordinates of this region (Figure 1—figure supplement 1) were close to the coordinates used in previous studies of mouse PPC (2.0 mm posterior, 1.7 mm lateral, Goard et al., 2016; Harvey et al., 2012). We then targeted 2-photon imaging to this region while the mouse was performing the task (Figure 1j). To obtain calcium traces from well-identified cell bodies we applied Suite2p, an image-processing pipeline that provides image registration, cell detection, and neuropil correction, followed by manual curation (Pachitariu et al., 2016).

In agreement with previous observations made in a memory-based task (Harvey et al., 2012), all the recorded PPC cells could be divided into two groups forming distinct, choice-dependent sequences of activation (Figure 1k–n). One group of cells responded primarily during trials that ended in a leftward choice (Figure 1k,l), and the other during trials that ended in a rightward choice (Figure 1m,n). Moreover, cells could be ordered so that the responses of each group of cells could be arranged in a sequence of activations (Harvey et al., 2012). While some cells that responded during the initial part of the task tended to fire in both trials that ended with left and right choices, the rest of the cells unambiguously fired only in one or the other of those trials.

To investigate this apparent dependence of activity on choice, we plotted the firing of individual neurons as a function of the animal’s position and heading (Figure 2a,b). Consider an example neuron, which fired mostly during trajectories that ended with rightward choices. Plotting the neuron’s activity on top of the individual trajectories reveals that the neuron tended to respond in precise combinations of position z (~60 cm into the corridor) and heading angle θ (~20 deg to the right). This combination occurred most frequently in trajectories that ended with rightward choices (Figure 2b) but also occasionally in trajectories that ended with leftward choices (Figure 2a). The neuron was active whenever a trajectory brought the mouse to the appropriate combinations of position and heading, regardless of final choice.

Figure 2 with 1 supplement see all

Download asset Open asset

Indeed, a simple ‘position-heading field’ was sufficient to accurately predict the calcium activity of the neuron (Figure 2c–g). We obtained an activity map of the neuron as a function of position and heading (Figure 2e), and found that it could be used effectively to predict responses as a function of time (Figure 2f). The model performed well across trials (Figure 2c,d), capturing not only the overall preference for trajectories that ended in rightward choices, but also detailed differences in responses in individual trials (Figure 2c,d). For instance, the position-heading field correctly predicted the occasional trials when the neuron responded during leftward choices (above the black bar in Figure 2c,d).

Position-heading fields provided a good account of the activity across the population (Figure 2h–j). High correlation between data and predictions was not associated with a particular preference for position or heading: cells whose responses were accurately predicted could have a variety of position-heading fields (Figure 2h–j). For all these cells, the model performed well in describing trial-by-trial activity (Figure 2—figure supplement 1). The median correlation between model predictions and calcium traces was 27% (±16% m.a.d., n = 7,646 cells) when pooling across seven mice, and ranged from 19% to 41% in individual mice (Figure 4—figure supplement 1). These values are high, given that all measures were cross-validated.

Position-heading fields were also largely sufficient to explain the arrangement of parietal cortex responses in choice-dependent sequences (Figure 3). For each PPC neuron we predicted responses for all trials and averaged these predictions depending on whether the trials ended in leftward choices (Figure 3a) or rightward choices (Figure 3b). The resulting predictions produce orderly, choice-dependent sequences of activation, replicating the essential features of those seen in the data (Figure 1k–n). These choice-dependent sequences of activation emerge from a combination of two factors: the fact that mice take different trajectories in trials that end with leftward vs. rightward choices (Figure 1d–f), and the fact that different cells prefer different combinations of position and heading (Figure 3c,d).

Figure 3

Download asset Open asset

The preferred heading of these PPC cells was thus sufficient to explain their selectivity for choice: cells with preferred leftward heading were more likely to fire when the animal headed leftward, which occurred most often when the animal ultimately chose the left arm. Indeed, cells that responded preferentially in trials ending in leftward choices almost invariably preferred negative (leftward) heading values (Figure 3c), and the same was true for rightward choices and positive (rightward) heading values (Figure 3d).

Decision was a worse predictor of PPC activity than heading (Figure 4a–c). A model f(z, d) where responses depend on position and decision alone is implicit in representations where neurons are arranged in choice-selective activation sequences (Harvey et al., 2012). It could in principle explain the sequences seen in our data (Figure 1k–n). However, it provided only a rough approximation of the trial-by-trial activity of individual cells, missing its graded dependence on heading angle θ (Figure 4a). To compare the model with the position-heading model f(z, θ), we calculated the correlation between trial-averaged measurements and model predictions, and we cross-validated the results. Before averaging activity in each trial, we excluded positions where decision and heading angle were so highly correlated as to be indistinguishable as predictors. The range of positions varied from session to session and invariably included the end of the corridor. This analysis gave a clear advantage to the position-heading model, with correlations higher by 5.6% than for the position-decision model (±9.8%, m.a.d., n = 7646 neurons, Figure 4b). This advantage was visible also in individual sessions (Figure 4c).

Figure 4 with 3 supplements see all

Download asset Open asset

Furthermore, a model with all three predictors performed only slightly better than the model with position and heading alone (Figure 4d–f). We extended the position-heading model to obtain a model f(z, θ, d) that includes explicit knowledge of the mouse decisions (d = left or right). This extended model effectively endows the cell with two position-heading fields for trajectories that end in leftward vs. rightward choices. The extended model thus predicted slightly different curves for trials ending in left vs. right choices (Figure 4d). Across all neurons, the extended model performed at a similar cross-validated level as the simple position-heading model, improving the correlations by only 0.4% (±2.9%, m.a.d., n = 7646 neurons, Figure 4e). Similar results were seen in individual sessions (Figure 4f). Conversely, adding heading angle θ to a model that already includes d considerably improved the fits, with a median increase in correlation of 6.1% (±6.1% m.a.d., n = 7646 neurons). Therefore, heading angle was a much better predictor of responses than decision.

Taken together, these results suggest that much of the activity of PPC neurons in our task can be explained by two spatial attributes: position and heading. A possible caveat in this conclusion, however, is that in our experiment those spatial attributes may covary with visual and motor factors. Position and heading determined the visual scene, and the visual scene could in turn determine the activity of PPC neurons, especially given that mouse PPC overlaps at least partially with regions of higher visual cortex (Wang and Burkhalter, 2007; Zhuang et al., 2017). Likewise, position in z-θ space is itself determined by the animal’s movement on the ball and therefore by motor factors such as linear and angular velocity, which may in turn determine PPC activity (McNaughton et al., 1994; Nitz, 2006; Whitlock, 2014; Whitlock et al., 2012).

To assess the role of visual factors, we ran a control experiment where the animal passively viewed a playback of visual scenes presented in the task on the same session. In this playback condition only a minority of PPC neurons maintained their preferences for position and heading, and even in those neurons the responses were much weaker (Figure 4—figure supplement 2a). Moreover, for the majority of PPC cells, activity in the playback condition was not predictable from the preferences for position and heading estimated during the task. This is perhaps remarkable, given that parietal areas of the mouse cortex are considered to overlap with visual areas RL, A, and AM (Hovde et al., 2018; Kirkcaldie, 2012), which contain retinotopic visual representations (Garrett et al., 2014; Wang and Burkhalter, 2007). By comparison, responses measured in primary visual cortex (V1) during the task and during playback were in better agreement (Figure 4—figure supplement 2b).

To assess the role of motor factors, we evaluated a model where PPC activity depends on the mouse’s movement, measured by the ball’s angular and linear velocities. These quantities are related to the derivatives of position z and heading θ, but they are not identical because they are in mouse-centered coordinates and are unconstrained by the boundaries of the virtual corridor. The model based on motor factors performed markedly worse than the model based on position and heading in virtual reality (Figure 4—figure supplement 3).

We finally asked if PPC population activity and its dependence on position and heading were sufficient to decode the details of the mouse’s trajectories and choices (Figure 5). Using established methods (Oram et al., 1998; Zhang et al., 1998) we implemented a simple Bayesian decoder (Figure 5—figure supplement 1). The decoder successfully predicted the position of the animal in position-heading space at individual time points (Figure 5a) and replicated the animal’s trajectory (Figure 5b; Video 2). In predicting the final choice, in fact, the population decoder was just as good as the animal’s actual heading: both showed a similar dependence on position z (Figure 5c) and stimulus contrast (Figure 5d). This result provides further support for the view that during visually-guided navigation, populations of PPC neurons encode spatial position and heading angle.

Figure 5 with 1 supplement see all

Download asset Open asset

Video 2

Download asset

We used a virtual reality task where mice use vision to decide and navigate, and we found that the activity of PPC neurons can be accurately predicted based on two simple spatial measures: position of the animal along the corridor, and heading angle. Using only these attributes, we could predict a large fraction of PPC activity during a complex task involving body movement, vision, decision, and navigation. These predictions are superior to those based purely on vision or on body movement.

These results resonate with the view that rodent PPC encodes combinations of spatial attributes (McNaughton et al., 1994; Nitz, 2006; Nitz, 2012; Save and Poucet, 2000; Save and Poucet, 2009; Whitlock et al., 2012; Wilber et al., 2014), but are also fully consistent with the observation that PPC cells can be divided into groups forming distinct sequences of activations depending on upcoming choice (Harvey et al., 2012). However, in our data, this division into choice-dependent sequences, and indeed the sequences themselves, could be explained by the effect on PPC of two measurable factors: the trajectories taken by the mouse in different trials, and the preferences of different cells for different combinations of position and heading. The selectivity of PPC cells for heading and position explained the cells’ apparent selectivity for the mouse’s decision.

Our results therefore appear to support a different view of PPC function to that proposed by Harvey et al. (2012). Perhaps this discrepancy is due to a difference between the tasks: for example in our task, unlike the main task by Harvey et al, the spatial cues indicating the appropriate decision were visible until the end of the corridor. This might have caused the animals to employ different strategies, resulting in genuinely different types of PPC coding across the two tasks.

It is also possible, however, that the same combination of spatial factors also contributed to the results of Harvey et al. (2012). Indeed, the animals’ trajectories in that study did exhibit differences in heading angle that correlated with the final decision, but trials showing such differences were progressively excluded from analysis until the difference in mean heading angle no longer reached statistical significance. It would therefore be interesting to test whether preferences for position and heading may also contribute to the apparent decision-selectivity in Harvey et al.’s full data set.

In conclusion, we found that the activity of neurons in PPC during a task involving movement, vision, decision, and navigation, can be accurately predicted based on the selectivity of the neurons for two spatial variables: the position of the mouse along the corridor, and its heading angle. Consideration of this selectivity, and the mouse’s trajectories through virtual space, fully accounts for the apparent formation of decision-dependent activity sequences in this task. In other words, when mice use vision to guide navigation, parietal cortex encodes navigational attributes such as position and heading rather than visual signals or abstract decisions.

Our results, however, do not completely rule out a possible role of decision signals in shaping the responses of PPC neurons. We found that adding decision as an explicit variable barely improved the fits of the position-heading model, but did not make them worse as it could have, given that the model fits were cross-validated.

Our task, in fact, cannot fully distinguish encoding of navigation variables and of decisions, because trajectory signals might strongly covary with an evolving decision variable. Such an evolving decision variable might be seen not only in covert neural signals, but also in ongoing body movements (Dotan et al., 2018; Resulaj et al., 2009; Song and Nakayama, 2009; Spivey et al., 2005). In our task, the clear dependence of trajectories on stimulus contrast is consistent with a close relationship between a decision variable and the animal’s trajectories. It may not be possible to distinguish the neural encoding of the two.

Moreover, PPC activity could be influenced by a multitude of postural attributes that we did not monitor, and some of these may correlate with the apparent decision signals that improved our fits. Indeed, there is mounting evidence that postural and other motor variables act as powerful determinants of activity throughout the cortex (Musall et al., 2018; Stringer et al., 2018) including PPC (Mimica et al., 2018). The same observation, in fact, can be made for the apparent selectivity of PPC neurons for heading: perhaps neurons in PPC are sensitive to postural variables that in turn predict heading.

The precise motor, postural, or cognitive correlates of heading and decision need further examination, ideally with tasks explicitly designed to distinguish these correlates. In the meantime, our results indicate that the activity of large populations of PPC neurons can be explained based on simple embodied quantities such as heading and position in the environment.

We report here on experiments performed in seven mice (two C57bl/6 mice, three Camk2a-tTA;Ai93(TITL-GCaMP6f);Emx1-IRES-Cre mice, and two Ai95(RCL-GCaMP6f)-D; Slc17a7-IRES2-Cre-D), of both sexes, aged 80–241 days during imaging sessions. Wild-type mice were acquired from The Jackson Laboratory (www.jax.org/strain/000664). Triple transgenic mice were bred by crossing Camk2a-tTA (www.jax.org/strain/007004), Ai93(TITL-GCaMP6f) (www.jax.org/strain/024103), and Emx1-IRES-Cre (www.jax.org/strain/005628). Double transgenic were bred by crossing Ai95(RCL-GCaMP6f)-D (www.jax.org/strain/024105) with Slc17a7-IRES2-Cre-D (www.jax.org/strain/023527).

The recordings from the three Ai93-Emx1 mice were made before we realized that this strain tends to show epileptiform activity (Steinmetz et al., 2017). We tested our recordings for this activity and the results were negative. However, we only imaged posterior regions of the cortex, where epileptiform activity can be missed (Steinmetz et al., 2017). For this reason, we recorded from the other strains and we ran a mouse-by-mouse analysis. This analysis did not reveal differences between the strains we used (Figure 4—figure supplement 1), so we pooled the data across all of them.

Behavioral and two-photon imaging data have been deposited in Dryad Digital Repository and are available at doi: 10.5061/dryad.ht3564h.

1. 1. Krumin M
   2. Lee JJ
   3. Harris KD
   4. Carandini M

   (2018) Dryad Digital Repository

   Data from: Decision and navigation in mouse parietal cortex.

   https://doi.org/10.5061/dryad.j1fd7

1. 1. Andersen RA
   2. Buneo CA

   (2002) Intentional maps in posterior parietal cortex

   Annual Review of Neuroscience 25:189–220.

   https://doi.org/10.1146/annurev.neuro.25.112701.142922

   • PubMed
   • Google Scholar
2. 1. Andersen RA
   2. Cui H

   (2009) Intention, action planning, and decision making in parietal-frontal circuits

   Neuron 63:568–583.

   https://doi.org/10.1016/j.neuron.2009.08.028

   • PubMed
   • Google Scholar
3. 1. Andersen RA
   2. Mountcastle VB

   (1983) The influence of the angle of gaze upon the excitability of the light-sensitive neurons of the posterior parietal cortex

   The Journal of Neuroscience 3:532–548.

   https://doi.org/10.1523/JNEUROSCI.03-03-00532.1983

   • PubMed
   • Google Scholar
4. 1. Bisley JW
   2. Goldberg ME

   (2010) Attention, intention, and priority in the parietal lobe

   Annual Review of Neuroscience 33:1–21.

   https://doi.org/10.1146/annurev-neuro-060909-152823

   • PubMed
   • Google Scholar
5. 1. Brainard DH

   (1997) The Psychophysics Toolbox

   Spatial Vision 10:433–436.

   https://doi.org/10.1163/156856897X00357

   • PubMed
   • Google Scholar
6. 1. Chen TW
   2. Wardill TJ
   3. Sun Y
   4. Pulver SR
   5. Renninger SL
   6. Baohan A
   7. Schreiter ER
   8. Kerr RA
   9. Orger MB
   10. Jayaraman V
   11. Looger LL
   12. Svoboda K
   13. Kim DS

   (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity

   Nature 499:295–300.

   https://doi.org/10.1038/nature12354

   • PubMed
   • Google Scholar
7. 1. Cohen YE
   2. Andersen RA

   (2002) A common reference frame for movement plans in the posterior parietal cortex

   Nature Reviews Neuroscience 3:553–562.

   https://doi.org/10.1038/nrn873

   • PubMed
   • Google Scholar
8. 1. Dombeck DA
   2. Harvey CD
   3. Tian L
   4. Looger LL
   5. Tank DW

   (2010) Functional imaging of hippocampal place cells at cellular resolution during virtual navigation

   Nature Neuroscience 13:1433–1440.

   https://doi.org/10.1038/nn.2648

   • PubMed
   • Google Scholar
9. 1. Dotan D
   2. Meyniel F
   3. Dehaene S

   (2018) On-line confidence monitoring during decision making

   Cognition 171:112–121.

   https://doi.org/10.1016/j.cognition.2017.11.001

   • PubMed
   • Google Scholar
10. 1. Erlich JC
    2. Brunton BW
    3. Duan CA
    4. Hanks TD
    5. Brody CD

    (2015) Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat

    eLife 4:e05457.

    https://doi.org/10.7554/eLife.05457

    • Google Scholar
11. 1. Freedman DJ
    2. Assad JA

    (2009) Distinct encoding of spatial and nonspatial visual information in parietal cortex

    Journal of Neuroscience 29:5671–5680.

    https://doi.org/10.1523/JNEUROSCI.2878-08.2009

    • PubMed
    • Google Scholar
12. 1. Garrett ME
    2. Nauhaus I
    3. Marshel JH
    4. Callaway EM

    (2014) Topography and areal organization of mouse visual cortex

    Journal of Neuroscience 34:12587–12600.

    https://doi.org/10.1523/JNEUROSCI.1124-14.2014

    • PubMed
    • Google Scholar
13. 1. Goard MJ
    2. Pho GN
    3. Woodson J
    4. Sur M

    (2016) Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions

    eLife 5:e13764.

    https://doi.org/10.7554/eLife.13764

    • PubMed
    • Google Scholar
14. 1. Gold JI
    2. Shadlen MN

    (2007) The neural basis of decision making

    Annual Review of Neuroscience 30:535–574.

    https://doi.org/10.1146/annurev.neuro.29.051605.113038

    • PubMed
    • Google Scholar
15. 1. Harvey CD
    2. Collman F
    3. Dombeck DA
    4. Tank DW

    (2009) Intracellular dynamics of hippocampal place cells during virtual navigation

    Nature 461:941–946.

    https://doi.org/10.1038/nature08499

    • PubMed
    • Google Scholar
16. 1. Harvey CD
    2. Coen P
    3. Tank DW

    (2012) Choice-specific sequences in parietal cortex during a virtual-navigation decision task

    Nature 484:62–68.

    https://doi.org/10.1038/nature10918

    • PubMed
    • Google Scholar
17. Preprint

    1. Hovde K
    2. Gianatti M
    3. Witter MP
    4. Whitlock JR

    (2018) Architecture and organization of mouse posterior parietal cortex relative to extrastriate areas

    bioRxiv.

    https://doi.org/10.1101/361832

    • Google Scholar
18. 1. Katz LN
    2. Yates JL
    3. Pillow JW
    4. Huk AC

    (2016) Dissociated functional significance of decision-related activity in the primate dorsal stream

    Nature 535:285–288.

    https://doi.org/10.1038/nature18617

    • PubMed
    • Google Scholar
19. Book

    1. Kirkcaldie MTK

    (2012)

    Neocortex

    In: Watson C, Paxinos G, Puelles L, editors. The Mouse Nervous System. Academic Press. pp. 52–111.

    • Google Scholar
20. 1. Latimer KW
    2. Yates JL
    3. Meister ML
    4. Huk AC
    5. Pillow JW

    (2015) Neuronal modeling. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

    Science 349:184–187.

    https://doi.org/10.1126/science.aaa4056

    • PubMed
    • Google Scholar
21. 1. Licata AM
    2. Kaufman MT
    3. Raposo D
    4. Ryan MB
    5. Sheppard JP
    6. Churchland AK

    (2017) Posterior Parietal Cortex Guides Visual Decisions in Rats

    The Journal of Neuroscience 37:4954–4966.

    https://doi.org/10.1523/JNEUROSCI.0105-17.2017

    • PubMed
    • Google Scholar
22. Book

    1. Loader C

    (1999)

    Local Regression and Likelihood

    New York: Springer.

    • Google Scholar
23. 1. McNaughton BL
    2. Mizumori SJ
    3. Barnes CA
    4. Leonard BJ
    5. Marquis M
    6. Green EJ

    (1994) Cortical representation of motion during unrestrained spatial navigation in the rat

    Cerebral Cortex 4:27–39.

    https://doi.org/10.1093/cercor/4.1.27

    • PubMed
    • Google Scholar
24. Book

    1. Milner AD
    2. Goodale MA

    (2006)

    The Visual Brain in Action (Second edition)

    Oxford University Press.

    • Google Scholar
25. Preprint

    1. Mimica B
    2. Dunn BA
    3. Tombaz T
    4. Bojja V
    5. Whitlock JR

    (2018) Efficient cortical coding of 3D posture in freely behaving rats

    bioRxiv.

    https://doi.org/10.1101/307785

    • Google Scholar
26. Preprint

    1. Musall S
    2. Kaufman MT
    3. Gluf S
    4. Churchland AK

    (2018) Movement-related activity dominates cortex during sensory-guided decision making

    bioRxiv.

    https://doi.org/10.1101/308288

    • Google Scholar
27. 1. Nitz DA

    (2006) Tracking route progression in the posterior parietal cortex

    Neuron 49:747–756.

    https://doi.org/10.1016/j.neuron.2006.01.037

    • PubMed
    • Google Scholar
28. 1. Nitz DA

    (2012) Spaces within spaces: rat parietal cortex neurons register position across three reference frames

    Nature Neuroscience 15:1365–1367.

    https://doi.org/10.1038/nn.3213

    • PubMed
    • Google Scholar
29. 1. Oram MW
    2. Földiák P
    3. Perrett DI
    4. Sengpiel F

    (1998) The 'Ideal Homunculus': decoding neural population signals

    Trends in Neurosciences 21:259–265.

    https://doi.org/10.1016/S0166-2236(97)01216-2

    • PubMed
    • Google Scholar
30. Preprint

    1. Pachitariu M
    2. Stringer C
    3. Schröder S
    4. Rossi LF
    5. Dipoppa M
    6. Carandini M
    7. Harris KD

    (2016) Suite2p: beyond 10,000 neurons with standard two-photon microscopy

    bioRxiv.

    https://doi.org/10.1101/061507

    • Google Scholar
31. 1. Park IM
    2. Meister ML
    3. Huk AC
    4. Pillow JW

    (2014) Encoding and decoding in parietal cortex during sensorimotor decision-making

    Nature Neuroscience 17:1395–1403.

    https://doi.org/10.1038/nn.3800

    • PubMed
    • Google Scholar
32. 1. Pelli DG

    (1997) The VideoToolbox software for visual psychophysics: transforming numbers into movies

    Spatial Vision 10:437–442.

    https://doi.org/10.1163/156856897X00366

    • PubMed
    • Google Scholar
33. 1. Pho GN
    2. Goard MJ
    3. Woodson J
    4. Crawford B
    5. Sur M

    (2018) Task-dependent representations of stimulus and choice in mouse parietal cortex

    Nature Communications 9:2596.

    https://doi.org/10.1038/s41467-018-05012-y

    • PubMed
    • Google Scholar
34. 1. Pisauro MA
    2. Dhruv NT
    3. Carandini M
    4. Benucci A

    (2013) Fast hemodynamic responses in the visual cortex of the awake mouse

    Journal of Neuroscience 33:18343–18351.

    https://doi.org/10.1523/JNEUROSCI.2130-13.2013

    • PubMed
    • Google Scholar
35. 1. Platt ML
    2. Glimcher PW

    (1999) Neural correlates of decision variables in parietal cortex

    Nature 400:233–238.

    https://doi.org/10.1038/22268

    • PubMed
    • Google Scholar
36. 1. Pologruto TA
    2. Sabatini BL
    3. Svoboda K

    (2003) ScanImage: flexible software for operating laser scanning microscopes

    BioMedical Engineering OnLine 2:13.

    https://doi.org/10.1186/1475-925X-2-13

    • PubMed
    • Google Scholar
37. 1. Raposo D
    2. Kaufman MT
    3. Churchland AK

    (2014) A category-free neural population supports evolving demands during decision-making

    Nature Neuroscience 17:1784–1792.

    https://doi.org/10.1038/nn.3865

    • PubMed
    • Google Scholar
38. 1. Resulaj A
    2. Kiani R
    3. Wolpert DM
    4. Shadlen MN

    (2009) Changes of mind in decision-making

    Nature 461:263–266.

    https://doi.org/10.1038/nature08275

    • PubMed
    • Google Scholar
39. 1. Rigotti M
    2. Barak O
    3. Warden MR
    4. Wang XJ
    5. Daw ND
    6. Miller EK
    7. Fusi S

    (2013) The importance of mixed selectivity in complex cognitive tasks

    Nature 497:585–590.

    https://doi.org/10.1038/nature12160

    • PubMed
    • Google Scholar
40. 1. Rishel CA
    2. Huang G
    3. Freedman DJ

    (2013) Independent category and spatial encoding in parietal cortex

    Neuron 77:969–979.

    https://doi.org/10.1016/j.neuron.2013.01.007

    • PubMed
    • Google Scholar
41. 1. Save E
    2. Poucet B

    (2000) Hippocampal-parietal cortical interactions in spatial cognition

    Hippocampus 10:491–499.

    https://doi.org/10.1002/1098-1063(2000)10:4<491::AID-HIPO16>3.0.CO;2-0

    • PubMed
    • Google Scholar
42. 1. Save E
    2. Poucet B

    (2009) Role of the parietal cortex in long-term representation of spatial information in the rat

    Neurobiology of Learning and Memory 91:172–178.

    https://doi.org/10.1016/j.nlm.2008.08.005

    • PubMed
    • Google Scholar
43. 1. Song JH
    2. Nakayama K

    (2009) Hidden cognitive states revealed in choice reaching tasks

    Trends in Cognitive Sciences 13:360–366.

    https://doi.org/10.1016/j.tics.2009.04.009

    • PubMed
    • Google Scholar
44. 1. Spivey MJ
    2. Grosjean M
    3. Knoblich G

    (2005) Continuous attraction toward phonological competitors

    PNAS 102:10393–10398.

    https://doi.org/10.1073/pnas.0503903102

    • PubMed
    • Google Scholar
45. 1. Steinmetz NA
    2. Buetfering C
    3. Lecoq J
    4. Lee CR
    5. Peters AJ
    6. Jacobs EAK
    7. Coen P
    8. Ollerenshaw DR
    9. Valley MT
    10. de Vries SEJ
    11. Garrett M
    12. Zhuang J
    13. Groblewski PA
    14. Manavi S
    15. Miles J
    16. White C
    17. Lee E
    18. Griffin F
    19. Larkin JD
    20. Roll K
    21. Cross S
    22. Nguyen TV
    23. Larsen R
    24. Pendergraft J
    25. Daigle T
    26. Tasic B
    27. Thompson CL
    28. Waters J
    29. Olsen S
    30. Margolis DJ
    31. Zeng H
    32. Hausser M
    33. Carandini M
    34. Harris KD

    (2017) Aberrant Cortical Activity in Multiple GCaMP6-Expressing Transgenic Mouse Lines

    Eneuro 4:ENEURO.0207-17.2017.

    https://doi.org/10.1523/ENEURO.0207-17.2017

    • PubMed
    • Google Scholar
46. Preprint

    1. Stringer C
    2. Pachitariu M
    3. Steinmetz N
    4. Bai Reddy C
    5. Carandini M
    6. Harris KD

    (2018) Spontaneous behaviors drive multidimensional. brain-wide population activity

    bioRxiv.

    https://doi.org/10.1101/306019

    • Google Scholar
47. 1. Sugrue LP
    2. Corrado GS
    3. Newsome WT

    (2004) Matching behavior and the representation of value in the parietal cortex

    Science 304:1782–1787.

    https://doi.org/10.1126/science.1094765

    • PubMed
    • Google Scholar
48. 1. Vogelstein JT
    2. Packer AM
    3. Machado TA
    4. Sippy T
    5. Babadi B
    6. Yuste R
    7. Paninski L

    (2010) Fast nonnegative deconvolution for spike train inference from population calcium imaging

    Journal of Neurophysiology 104:3691–3704.

    https://doi.org/10.1152/jn.01073.2009

    • PubMed
    • Google Scholar
49. 1. Wang Q
    2. Burkhalter A

    (2007) Area map of mouse visual cortex

    The Journal of Comparative Neurology 502:339–357.

    https://doi.org/10.1002/cne.21286

    • PubMed
    • Google Scholar
50. 1. Whitlock JR
    2. Pfuhl G
    3. Dagslott N
    4. Moser MB
    5. Moser EI

    (2012) Functional split between parietal and entorhinal cortices in the rat

    Neuron 73:789–802.

    https://doi.org/10.1016/j.neuron.2011.12.028

    • PubMed
    • Google Scholar
51. 1. Whitlock JR

    (2014) Navigating actions through the rodent parietal cortex

    Frontiers in Human Neuroscience 8:293.

    https://doi.org/10.3389/fnhum.2014.00293

    • PubMed
    • Google Scholar
52. 1. Wilber AA
    2. Clark BJ
    3. Forster TC
    4. Tatsuno M
    5. McNaughton BL

    (2014) Interaction of egocentric and world-centered reference frames in the rat posterior parietal cortex

    Journal of Neuroscience 34:5431–5446.

    https://doi.org/10.1523/JNEUROSCI.0511-14.2014

    • PubMed
    • Google Scholar
53. 1. Zhang K
    2. Ginzburg I
    3. McNaughton BL
    4. Sejnowski TJ

    (1998) Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells

    Journal of Neurophysiology 79:1017–1044.

    https://doi.org/10.1152/jn.1998.79.2.1017

    • PubMed
    • Google Scholar
54. 1. Zhuang J
    2. Ng L
    3. Williams D
    4. Valley M
    5. Li Y
    6. Garrett M
    7. Waters J

    (2017) An extended retinotopic map of mouse cortex

    eLife 6:e18372.

    https://doi.org/10.7554/eLife.18372

    • PubMed
    • Google Scholar

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Decision and navigation in mouse parietal cortex" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

As you can see from the reviews below, all three reviewers found merit in your study, but they also shared a number of major concerns. These concerns included: 1) potentially epileptic mice and overall low animal numbers that would likely require substantial new experiments; 2) the ability to identify decision-related signals given the task design and analysis methods used, particularly relative to the richer navigation-centric framework; and 3) unclear comparisons to Harvey et al. and other previous results. The reviewers were confident that you would be able to carry out additional experiments effectively. However, they also agreed that the manuscript is not suitable for eLife in its current form.

If you are able to substantially expand the dataset and address the reviewers' concerns, then we would be willing to reconsider the work as a new submission to eLife. In that case, please include in the cover letter a description of the changes that you have made.

Reviewer #1:

This study uses an impressive combination of in vivo calcium imaging and navigation/choice behavior in a virtual-reality environment to try to distinguish different representations (body movement and navigation versus visual-driven decisions) in mouse parietal cortex. The data show a fairly strong and clear relationship between activation of neurons in the PPC and navigation trajectory defined by position and heading angle. These factors explain the PPC population data much better than factors related to the visual stimulus, the binary decision, and the motor patterns.

The conclusions of this study are in contrast with those reported previously that measured similar patterns of activity under very similar conditions (Harvey et al., 2012). That study focused on "choice-related activity," and noted that the activity could not be explained by the mouse's running trajectory ("The choice-specific activity could result if mice experienced different visual stimuli and running patterns on right and left trials and if PPC activity was modulated by those differences. To examine this, we first performed a multiple regression analysis to determine the potential effects of the parameters defining the mouse's running trajectory on the fluorescence changes during the delay period (Supplementary Table 1). These parameters could not explain the choice-specific activity patterns, suggesting that any differences in running trajectories between right and left trials did not trigger the activity we observed.").

In contrast, here the claim is that "we could explain these observations parsimoniously by the effect on PPC neurons of two simple navigational parameters: position in the environment and heading."

I found this study for the most part to be carefully designed, executed, and analyzed and thus represents an important step forward in our understanding of the role of this circuit in navigation and decision-related behaviors. However, I also have several major concerns:

1) My biggest concern is about how the present study distinguished navigation encoding from decision encoding. In particular, they have a reasonably sophisticated description of those two navigation parameters at all points within a trial. In contrast, they describe decision as just the binary outcome and have no model for what the evolving decision process actually looks like on a given trial. For example, is it a gradual process of evidence accumulation that might modulate activity throughout a trial? Or does it happen abruptly at random times, making it difficult to know if/when/how then neural activity might be affected? Given such issues, it doesn't seem surprising that the decision term didn't do a very good job of accounting for the neural data.

2) It seems like it would be worthwhile to directly compare the responses of units that were activated by comparable position/heading pairs but that ultimately led to different choices, or the same choice but on correct versus error trials.

Reviewer #2:

This is a somewhat intriguing study from a group of investigators who have developed a number of really nice experiments for examining neural signals in behaving animals. Although I think the key issue the authors pose-the functional role of PPC for navigation and decision making-is a really important one, this study seems to seriously miss the mark in three major ways.

1) The original Tigre (Titl; Ai93 line) mice from the Allen Institute have a gorgeous level of signal at the single-cell and wide-field levels, and are easy to use. However, it is now generally known in the field that when the Ai93 line is combined with a pan-neuronal Tet enhancer line, such as the CamKII line used here, and an excitatory Cre line, such as the Emx1 line used here, all the resulting mice are epileptic. In fact, these same investigators have published a pre-print of an article on bioRxiv (https://www.biorxiv.org/content/early/2017/05/16/138511.1) showing epileptiform activity in the cortex of this same three-line cross. There is thus a serious technical issue in the interpretation of the neural data from these animals. At the barest minimum, these results should be fully replicated in a larger set of animals using a different Tet line cross or viral expression of gCAMP6f.

2) Related to the issue of mice, I find the numbers here to be troubling. First, the authors used only 5 mice total for the study, 2 of which were wild-types and 3 of which were the Ai93 line. This is quite a low number of animals, given the first major issue above. In addition, the authors appear to be using cells as the 'n' for statistical analysis, which is a completely inappropriate approach. They should instead be using animals as the 'n', either alone or with weighting of the data from each animal. However, because the 'n' and stats for each analysis are not well described, it is almost impossible to understand from the manuscript what criteria were used.

3) Last, although the authors use the Harvey et al., 2012 paper as a foil for re-examining whether PPC neurons encode decisions, the task used in the current study is quite different. In that original paper, the cue in the virtual T maze was only presented for a short section of the animal's approach, and the animal had only a short period of time to integrate information and make a decision. In the current study, the cue is presented continuously along the approach corridor, and the animal has a much longer period of time in which to decide. The version in the current study may simply be much easier for the mouse to perform, and it is possible that either easy tasks do not require PPC circuits to be engaged or that the representation of the decision under these circumstances is relatively sparse.

Reviewer #3:

This paper examines the relationship of mouse PPC responses to body position and choice during a virtual navigation visual decision task. Mice navigated a virtual T-maze and were rewarded for navigating towards the side that displayed a grating stimulus on its wall. Mice were head-fixed in this virtual setup, facilitating 2-photon calcium imaging. These responses were selective for position and heading angle. Furthermore, responses were not better explained when including information about the mouse's choice. The authors conclude that PPC encodes navigational attributes rather than decisions when mice use vision to make spatial choices.

This paper is nicely written with a clear presentation of the data and analyses performed. The topic is also likely to be of interest to a wide range of researchers. My major reservation is that I am not sure that the experimental design allows for as strong a conclusion as the authors have drawn. Nonetheless, I think these findings are important, especially in light of their contrast to other high profile results reported elsewhere.

1) The authors are trying to determine the extent to which PPC encodes heading angle versus choices. However, they have trained mice in a way that heading angle embodies choice, as they show in Figure 1. Furthermore, with the visual stimulus present along the full length of the T-maze, the mouse may decide at any point before the end and/or change its mind. In this situation, if PPC were perfectly reflecting the mouse's decision process that was then embodied in its heading angle, I think that would give the same results as the authors found. So while I am reasonably convinced that PPC encodes heading in this task, I don't think the data allow the authors to exclude the possibility that it is also encoding an evolving choice.

2) Clearly, the contrast with the results of Harvey et al., 2012, are a critical component of this paper. The authors rightly consider task differences as a potential explanation of the differing results. However, I do not think the authors have done this adequate justice. Harvey et al. used a memory-guided task as opposed to a visually-guided task in the present paper. This difference is the basis of an extensive literature examining neural mechanisms of working memory. Furthermore, Harvey et al. reported this difference as key for PPC's role, having found it to be necessary when they used a memory-guided task but not a visually-guided task. The authors here describe this as follows: "…unlike Harvey et al.'s, the spatial cues indicating appropriate decision were visible until the end of the corridor. This might have caused the animals to employ different neural strategies…" In fact, Harvey et al. had already shown the task difference to cause animals to employ a different neural strategy, to use the phrasing of the present authors. Bottom line, I think this point is potentially much less subtle than portrayed.

3) The methods are not up to par for an eLife manuscript. The standard should be ability to reproduce the experiments. Some examples: The authors should fully describe the stimuli employed. There is incomplete description of the grating stimulus (Figure 1 seems to show a noise component to it) and no description of the competing stimuli on the other wall. The analyses require better descriptions. For instance, I found no mention of how their model incorporates choice into its predictions, an absolutely critical point for the manuscript. As a second example, there are many ways one could calculate variance explained but the reader is left guessing. For each figure panel, there should be a corresponding section of the methods that states exactly the computational steps taken to generate that panel.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "Decision and navigation in mouse parietal cortex" for further consideration at eLife. Your revised article has been favorably evaluated by three peer reviewers, including Joshua Gold as the Senior and Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

In this revised manuscript, the authors have done an excellent job of responding to many of the concerns raised in the first round of reviews. In particular, they have ruled out the possibility of epilepsy in their animals, and they have substantially increased the amount of data. They also more clearly delineate key differences between this study and that of Harvey et al.

However, the reviewers expressed a few lingering concerns that we would like you to address. All of these concerns are related to points that were brought up in the first round of reviews and should not require substantial new work.

Specifically:

1) There still are concerns that the authors' interpretation of the results does not adequately address the possibility that because position and trajectory signals might covary strongly with an evolving decision variable, it might not be possible to distinguish navigation versus decision encoding. There is precedent for an evolving decision variable that can be seen in not just covert motor-planning signals in the brain, but in actual, ongoing movements, as well:

Review: Song and Nakayama, 2009; Spivey, Grosjean and Knoblich, 2005.

Put another way, it seems possible that a close relationship between navigation and the decision variable exists, which would imply that it is not possible to completely rule out that the neuronal encoding of navigation signals also at least partly reflects encoding of the ongoing decision variable – despite the strong statement that "nowhere in the paper do we claim to be studying such a thing" [i.e., an evolving decision variable].

On a related note, given the close relationship between heading and final choice, it seems likely that adding the final choice to a model with both position and heading is not expected to have much of an effect, even for a truly decision-encoding neuron. Therefore, the interpretation of "decision" (here implemented as just the final, binary choice, not an evolving decision variable) as having little effect on the neural encoding seems overly dismissive. Figure 4E, F seems to show fairly reliable effects. It might be that might be useful to provide a more nuanced discussion of how these signals might represent a combination of both navigation and decision encoding in PPC.

2) In Figure 4C and 4F, the authors plot data by sessions, but it is unclear why this is the appropriate presentation of data. Why not plot by animal to show that variance across animals does not drive the result? Or, at a minimum, color-code the sessions in 4C and 4F by animal to demonstrate that the result is not driven by mis-weighting of the data due to over-representation of a particular animal.

[Editors’ note: the author responses to the first round of peer review follow.]

We thank the editor and the reviewers of our previous submission (summer 2017) for their suggestions. We have now addressed all of these comments, and we believe the resulting paper is even stronger.

Specifically, we have made the following major changes:

1) We added a third strain of mice, bringing the total number of mice to 7, and we confirmed with a mouse-by-mouse analysis that the results are consistent across mice (new Figure 4—figure supplement 1).

2) Thanks to the new mice and to new analyses to more data sets in the previous mice, we substantially increased the sample from 1,922 neurons to 7,646 neurons.

3) We performed tests designed to detect epilepsy in the recordings from triple-transgenic mice at risk of epilepsy (Emx1-Cre;Camk2a-tTA;Ai93), and we found negative results (Materials and methods).

4) We introduced a more intuitive explanation of our results, including examples of trajectories where the animal first heads in a direction consistent with one choice and then makes the opposite choice (new Figure 2A, B).

5) We added an explanation that in our experiment decision does not help to predict activity over what can be predicted by position and heading (Figure 4).

6) We now explain that the strength of our study lies in its ability to predict with high precision the activity of neurons in an association area of the cortex. We can do a good job at predicting this ability, and we show that we do not need to add “decision” as a regressor.

7) We now highlight the fact that we replicate the results of Harvey et al. in terms of choice-selective sequences of activation. Ours is the first and only confirmation of such sequences by an independent laboratory since the Harvey study was published (2012).

Reviewer #1:

This study uses an impressive combination of in vivo calcium imaging and navigation/choice behavior in a virtual-reality environment to try to distinguish different representations (body movement and navigation versus visual-driven decisions) in mouse parietal cortex. The data show a fairly strong and clear relationship between activation of neurons in the PPC and navigation trajectory defined by position and heading angle. These factors explain the PPC population data much better than factors related to the visual stimulus, the binary decision, and the motor patterns.

The conclusions of this study are in contrast with those reported previously that measured similar patterns of activity under very similar conditions (Harvey et al., 2012). That study focused on "choice-related activity," and noted that the activity could not be explained by the mouse's running trajectory ("The choice-specific activity could result if mice experienced different visual stimuli and running patterns on right and left trials and if PPC activity was modulated by those differences. To examine this, we first performed a multiple regression analysis to determine the potential effects of the parameters defining the mouse's running trajectory on the fluorescence changes during the delay period (Supplementary Table 1). These parameters could not explain the choice-specific activity patterns, suggesting that any differences in running trajectories between right and left trials did not trigger the activity we observed.").

In contrast, here the claim is that "we could explain these observations parsimoniously by the effect on PPC neurons of two simple navigational parameters: position in the environment and heading."

We see the reviewer’s desire to compare the two studies, but one must keep in mind that the tasks were different. In our paper, and especially in our revised text, we take extra care to indicate that the strength of the result is that we can predict the activity of neurons in an associative area of the cortex. We mainly replicate Harvey’s results, in a different task. Moreover, we have an explanation for the results we obtain: in our task, the neurons are selective for combinations of position and heading. This explains their apparent preference for decision.

I found this study for the most part to be carefully designed, executed, and analyzed and thus represents an important step forward in our understanding of the role of this circuit in navigation and decision-related behaviors. However, I also have several major concerns:

1) My biggest concern is about how the present study distinguished navigation encoding from decision encoding. In particular, they have a reasonably sophisticated description of those two navigation parameters at all points within a trial. In contrast, they describe decision as just the binary outcome and have no model for what the evolving decision process actually looks like on a given trial. For example, is it a gradual process of evidence accumulation that might modulate activity throughout a trial? Or does it happen abruptly at random times, making it difficult to know if/when/how then neural activity might be affected? Given such issues, it doesn't seem surprising that the decision term didn't do a very good job of accounting for the neural data.

From an operational standpoint, the models where decision is a variable are models that have two functions (of position z and possibly of heading theta), one for trajectories that ended on the left and one for trajectories that ended on the right. We thus have a valid operational method to ask whether decision is a variable that needs to be taken into consideration to predict the firing of a neuron.

Keeping in mind that our task and Harvey’s task are not identical, we have used the model that Harvey et al. implicitly proposed to explain their data (where responses depend on position z and decision d) and compared it head-to-head with our model, where responses depend on position z and heading theta. This comparison appears in new Figure 4. In this comparison we have taken care to exclude data obtained in positions where decision and heading angle are so correlated that they have to have equal predictive power.

Regarding the “evolving decision process”: nowhere in the paper do we claim to be studying such a thing: we simply study what the mouse does and we relate it successfully to what thousands of PPC neurons do.

2) It seems like it would be worthwhile to directly compare the responses of units that were activated by comparable position/heading pairs but that ultimately led to different choices, or the same choice but on correct versus error trials.

We thank the reviewer for this suggestion. We have added an analysis of this kind, first for an example cell, and then for all cells. For the example cell, we show that there are clear cases of trajectories when the cell fired even though the animal ended up making the non-preferred choice; this occurred when the heading angle was the preferred one for the neuron (Figure 2A, B). As for the population, we show that a cell’s “preferred decision” can be very well predicted based on their preferred heading (Figure 3C, D). Finally, when pitching models against each other, we now analyze each session by excluding all positions z when decision d and heading angle theta were too correlated to be distinguished (new Figure 4). This approach enhances our ability to distinguish models, because in the positions where d and theta are highly correlated, it would be impossible to dissociate the two. All these analyses confirm that there is no need to add “decision” as an explanation for our data in our experiment.

Reviewer #2:

This is a somewhat intriguing study from a group of investigators who have developed a number of really nice experiments for examining neural signals in behaving animals. Although I think the key issue the authors pose-the functional role of PPC for navigation and decision making-is a really important one, this study seems to seriously miss the mark in three major ways.

1) The original Tigre (Titl; Ai93 line) mice from the Allen Institute have a gorgeous level of signal at the single-cell and wide-field levels, and are easy to use. However, it is now generally known in the field that when the Ai93 line is combined with a pan-neuronal Tet enhancer line, such as the CamKII line used here, and an excitatory Cre line, such as the Emx1 line used here, all the resulting mice are epileptic. In fact, these same investigators have published a pre-print of an article on bioRxiv (https://www.biorxiv.org/content/early/2017/05/16/138511.1) showing epileptiform activity in the cortex of this same three-line cross. There is thus a serious technical issue in the interpretation of the neural data from these animals. At the barest minimum, these results should be fully replicated in a larger set of animals using a different Tet line cross or viral expression of gCAMP6f.

We see the reviewer’s concern: the potential for epilepsy in some transgenic mouse strains crossed with the Emx1 line was indeed highlighted by a publication from our own laboratory (Steinmetz et al., 2017). It makes sense to ask whether this problem occurred in our mice and potentially influenced the results. We have now addressed this issue in two ways. First, we performed tests designed to detect epilepsy in the recordings from triple-transgenic mice at risk of epilepsy (Emx1-Cre;Camk2a-tTA;Ai93), and we found negative results (see Materials and methods). Second, we added data from 2 additional mice where a different transgenic strategy was used (VGLut1-IRES2-Cre-D;Ai95). The results are confirmed in these mice, as well as in the two C57 mice that were already in our study. The total number of mice is now 7, and the results are consistent across them (new Figure 4—figure supplement 1).

2) Related to the issue of mice, I find the numbers here to be troubling. First, the authors used only 5 mice total for the study, 2 of which were wild-types and 3 of which were the Ai93 line. This is quite a low number of animals, given the first major issue above.

The total number of mice is now 7, and the results are consistent across them (new Figure 4—figure supplement 1). The number of neurons is now above 7,000. These are large samples of mice and neurons.

In addition, the authors appear to be using cells as the 'n' for statistical analysis, which is a completely inappropriate approach. They should instead be using animals as the 'n', either alone or with weighting of the data from each animal. However, because the 'n' and stats for each analysis are not well described, it is almost impossible to understand from the manuscript what criteria were used.

We see the reviewer’s point and we now provide a better explanation of the analyses. We believe the reviewer is referring to the figures where we compare the different models. We now summarize the results not only by looking across neurons (throughout the main text) but also on a session-by-session basis (Figure 4C, F) and in individual mice (Figure 4—figure supplement 1).

3) Last, although the authors use the Harvey et al., 2012 paper as a foil for re-examining whether PPC neurons encode decisions, the task used in the current study is quite different. In that original paper, the cue in the virtual T maze was only presented for a short section of the animal's approach, and the animal had only a short period of time to integrate information and make a decision. In the current study, the cue is presented continuously along the approach corridor, and the animal has a much longer period of time in which to decide. The version in the current study may simply be much easier for the mouse to perform, and it is possible that either easy tasks do not require PPC circuits to be engaged or that the representation of the decision under these circumstances is relatively sparse.

We thank the reviewer for this comment. Our study resembles the study by Harvey et al. in many ways, and our results recapitulate their results. Specifically, we replicate their finding of choice-selective sequences of activation. Ours is the first and only confirmation of such sequences by an independent laboratory since their study was published (2012). This said, the two tasks are not identical, and we say this throughout the paper.

Reviewer #3:

This paper examines the relationship of mouse PPC responses to body position and choice during a virtual navigation visual decision task. Mice navigated a virtual T-maze and were rewarded for navigating towards the side that displayed a grating stimulus on its wall. Mice were head-fixed in this virtual setup, facilitating 2-photon calcium imaging. These responses were selective for position and heading angle. Furthermore, responses were not better explained when including information about the mouse's choice. The authors conclude that PPC encodes navigational attributes rather than decisions when mice use vision to make spatial choices.

This paper is nicely written with a clear presentation of the data and analyses performed. The topic is also likely to be of interest to a wide range of researchers. My major reservation is that I am not sure that the experimental design allows for as strong a conclusion as the authors have drawn. Nonetheless, I think these findings are important, especially in light of their contrast to other high profile results reported elsewhere.

1) The authors are trying to determine the extent to which PPC encodes heading angle versus choices. However, they have trained mice in a way that heading angle embodies choice, as they show in Figure 1. Furthermore, with the visual stimulus present along the full length of the T-maze, the mouse may decide at any point before the end and/or change its mind. In this situation, if PPC were perfectly reflecting the mouse's decision process that was then embodied in its heading angle, I think that would give the same results as the authors found. So while I am reasonably convinced that PPC encodes heading in this task, I don't think the data allow the authors to exclude the possibility that it is also encoding an evolving choice.

We thank the reviewer for these insightful comments. We agree that the main conclusion of our study is the joint encoding of position and heading, and the main result is that we are able to predict the activity of neurons during navigation in an associative area of the neocortex. In addition, the data allow us to ask whether there are signals that we cannot explain simply with position and heading, which might be due to decision. We thus ask the question, and the answer we get is no. However, because we don’t know exactly when the animal makes a decision, we can only speculate that the signals we are not seeing are those associated with decision.

2) Clearly, the contrast with the results of Harvey et al., 2012, are a critical component of this paper. The authors rightly consider task differences as a potential explanation of the differing results. However, I do not think the authors have done this adequate justice. Harvey et al. used a memory-guided task as opposed to a visually-guided task in the present paper. This difference is the basis of an extensive literature examining neural mechanisms of working memory. Furthermore, Harvey et al. reported this difference as key for PPC's role, having found it to be necessary when they used a memory-guided task but not a visually-guided task. The authors here describe this as follows: "…unlike Harvey et al.'s, the spatial cues indicating appropriate decision were visible until the end of the corridor. This might have caused the animals to employ different neural strategies…" In fact, Harvey et al. had already shown the task difference to cause animals to employ a different neural strategy, to use the phrasing of the present authors. Bottom line, I think this point is potentially much less subtle than portrayed.

We see the reviewer’s point, and we have now changed the paper so its main goal is to illustrate the dependence of PPC responses on position and heading. The comparison with the results of Harvey et al. (which we are the first to replicate, 6 years after their publication) is not given as much importance in the paper.

3) The methods are not up to par for an eLife manuscript. The standard should be ability to reproduce the experiments. Some examples: The authors should fully describe the stimuli employed. There is incomplete description of the grating stimulus (Figure 1 seems to show a noise component to it) and no description of the competing stimuli on the other wall. The analyses require better descriptions. For instance, I found no mention of how their model incorporates choice into its predictions, an absolutely critical point for the manuscript. As a second example, there are many ways one could calculate variance explained but the reader is left guessing. For each figure panel, there should be a corresponding section of the methods that states exactly the computational steps taken to generate that panel.

We thank the reviewer for this pointing this out. We completely agree. We have now substantially improved the Materials and methods section to provide this information.

[Editors' note: the author responses to the re-review follow.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

In this revised manuscript, the authors have done an excellent job of responding to many of the concerns raised in the first round of reviews. In particular, they have ruled out the possibility of epilepsy in their animals, and they have substantially increased the amount of data. They also more clearly delineate key differences between this study and that of Harvey et al.

However, the reviewers expressed a few lingering concerns that we would like you to address. All of these concerns are related to points that were brought up in the first round of reviews and should not require substantial new work.

Specifically:

1) There still are concerns that the authors' interpretation of the results does not adequately address the possibility that because position and trajectory signals might covary strongly with an evolving decision variable, it might not be possible to distinguish navigation versus decision encoding. There is precedent for an evolving decision variable that can be seen in not just covert motor-planning signals in the brain, but in actual, ongoing movements, as well:

Review: Song and Nakayama, 2009; Spivey, Grosjean and Knoblich, 2005.

Put another way, it seems possible that a close relationship between navigation and the decision variable exists, which would imply that it is not possible to completely rule out that the neuronal encoding of navigation signals also at least partly reflects encoding of the ongoing decision variable – despite the strong statement that "nowhere in the paper do we claim to be studying such a thing" [i.e., an evolving decision variable].

We thank the reviewers for clarifying this point and for indicating these two very interesting papers. We had not understood it in the first round of reviews, and now we understand it. Indeed, we agree with it: it is entirely possible that by observing the physical trajectories of the mice we are observing an evolving decision process that is instantiated in their body position. We added a paragraph in Discussion where we explain this, and where we cite not only those two papers but also other two papers that seem relevant.

On a related note, given the close relationship between heading and final choice, it seems likely that adding the final choice to a model with both position and heading is not expected to have much of an effect, even for a truly decision-encoding neuron. Therefore, the interpretation of "decision" (here implemented as just the final, binary choice, not an evolving decision variable) as having little effect on the neural encoding seems overly dismissive. Figure 4E, F seems to show fairly reliable effects. It might be that might be useful to provide a more nuanced discussion of how these signals might represent a combination of both navigation and decision encoding in PPC.

We see the point. We cannot exclude that the small improvement in fit quality due to adding “decision” may be due to neural signals encoding decisions (rather than other physical attributes that we did not happen to measure). In addition to the new paragraph mentioned above, we have made some word changes in this direction.

2) In Figure 4C and 4F, the authors plot data by sessions, but it is unclear why this is the appropriate presentation of data. Why not plot by animal to show that variance across animals does not drive the result? Or, at a minimum, color-code the sessions in 4C and 4F by animal to demonstrate that the result is not driven by mis-weighting of the data due to over-representation of a particular animal.

We thank the reviewers for this suggestion. We have now changed panels C and F in Figure 4 to distinguish across mice, and we provide a legend for this in Figure 4—figure supplement 1. The results clearly show that there is no over-representation of any particular animal.

Author details

1. Michael Krumin

   UCL Institute of Ophthalmology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7356-6994

2. Julie J Lee

   UCL Institute of Ophthalmology, University College London, London, United Kingdom

   Contribution

   Data curation, Formal analysis, Funding acquisition, Validation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7293-8538

3. Kenneth D Harris

   UCL Institute of Neurology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Resources, Funding acquisition, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5930-6456

4. Matteo Carandini

   UCL Institute of Ophthalmology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   m.carandini@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4880-7682

• 4,896

  Page views

• 720

  Downloads

• 42

  Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.