Dissociation of task engagement and arousal effects in auditory cortex and midbrain

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Abstract

Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.

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  1. ###Reviewer #3:

    Saderi and colleagues study the effects of arousal and task engagement on sound responses in the (primary) auditory cortex and inferior colliculus of ferrets. Arousal is measured by pupillometry, task engagement by contrasting an auditory detection task with passive sound exposure, and effects are quantitatively dissociated using a general linear model and multiple regression. The authors find that the sound responses of about half of the recorded neurons are modulated by task engagement and/or by arousal, with IC neurons most frequently modulated by arousal and AC neurons modulated by both factors. Increased arousal was associated with enhanced sound responses. In AC, task engagement was associated both with enhanced and suppressed sound responses. In IC, task engagement was associated with suppressed sound responses.

    Major comments:

    1. Some of the main conclusions of the results from AC are not novel. Using a different experimental approach, the study of Knyazeva et al., 2020, Front Neurosci. 14: 306 already suggested that the discharges of many neurons in AC are affected by arousal, that task effects can disappear if effects of arousal have been accounted for, and that there is no systematic difference in response modulation between neurons tuned, or not tuned, to task-relevant sounds. Dissociations of the effects of different non-auditory factors on sound responses in AC have also been described by Zhou et al., 2014, Nat Neurosci. 17:841-850 and by Carcea et al., 2017, Nat Comm. 8:14412.

    2. The study is based on a relatively small number of neurons and behavioral sessions, potentially reducing the strength of the statistical inference, e. g., that IC was more strongly affected by arousal than AC. It appears that data from about 20 behavioral sessions entered analyses. This estimate is based on the information that 1-3 behavioral blocks were tested during individual sessions (line 611) and that Figure 1F shows the results of about 36 active-passive comparisons in four animals. This indicates that, on average, about 10 neurons were simultaneously recorded in individual sessions. Therefore these neurons were statistically more dependent than neurons recorded in different sessions. This needs to be considered for potentially global effects such as arousal and task engagement. The authors should include this information, together with the number of trials in active and passive blocks and whether the responses to different TORCs were averaged.

    3. The authors did not distinguish single unit and multiunit data. This difference should be considered in detail because it could affect the interpretation of whether there are units that are affected both by arousal and task engagement.

    4. The authors should include a statement that the results on the effects of task engagement may not apply to all types of auditory tasks. This is highly important because the authors used an auditory detection task, which is a task that may not require AC at all.

  2. ###Reviewer #2:

    Main Review:

    Saderi and her colleagues have performed a cool study that attempts to determine whether and how two behavior-related variables - arousal and task engagement - differently influence activity in two stages of the auditory neuraxis, IC and A1. They define arousal as pupil diameter and task-engagement as a binary variable determined by the experimental block design. They find that although these two parameters often co-vary, they sometimes do not. They find that IC was more influenced by arousal and A1 was modulated by both arousal and engagement. One of their main findings is that previous reports of task-engagement effects may in fact be attributed to arousal state.

    This is a nice quantification of neural activity and behavior. My major concerns are all thematically linked and they stem from the use of a continuous readout of arousal (i.e. pupil diameter) but a binary readout of task-engagement (i.e. the block the animal is in at any moment). Relatedly, I am interested in knowing whether neural effects can be accounted for by the animals from which they were recorded (and from that particular animal's behavior). I expect that my enthusiasm for this paper will not be diminished in any way regardless of any changes that come out of the deeper analyses outlined below. Also, I do not intend that responses to these concerns will require any new experimentation.

    Major concerns:

    1. Can task engagement be explained more rigorously as a continuous rather than binary variable? In my experience training and testing animals on appetitive behaviors, task engagement can wax and wane within a single block, across an experimental recording session, or across days of behavioral testing. Such changes in engagement can be inferred, for example, as strings of (seemingly) easy trials in which the animal does not answer correctly. The authors should attempt to quantify through behavioral analysis (running lapse rate, lick latency, etc) whether and how task engagement may be changing within and across task blocks. Alternatively, the authors could clearly explain that their binary encoding of engagement has limitations and may not actually describe the animal's engagement at any given moment.

    2. Can a continuous readout of task engagement better explain neural activity? For many neurons, task-engagement does not provide unique predictive information, yet for others it does (e.g. Fig. 3C). If task engagement can be modeled as a continuous rather than binary variable, is it still true that "some apparent effects of task engagement should in fact be attributed to fluctuations in arousal" (Abstract)? In general, I worry that the current analysis is effectively a floor on task-related modulations since it assumes constant engagement throughout a task block.

    3. Can neural heterogeneity be attributed to animal-to-animal behavioral variability? Even if task engagement does not vary within a task block for any one animal, it may indeed vary across animals. In theory, the actual task engagement of some animals might more closely mirror the block design that the experimenters are imposing, and some animals may simply have a higher level of engagement than others. This could mean that some results that are currently attributed to population-level heterogeneity (e.g. some A1 neurons do this, while others do that) might actually be attributed to animal-to-animal heterogeneity as opposed to distinct neural populations. For example, the authors state that for a subset of neurons, persistent task-like activity after a block change can be accounted for by pupil, whereas for other neurons this effect cannot (Fig. 7, line 452). The authors should confirm that key findings are consistent across animals and not related to degrees of task engagement (see point #1). If the findings are not consistent across animals but can be explained by each animals' unique behavior, this would also be really cool.

  3. ###Reviewer #1:

    This study distinguishes effects of generalized arousal and specific task engagement on the activity of neurons in the inferior colliculus and auditory cortex of ferrets as they engaged in a tone detection task, while monitoring arousal via pupillometry. The authors found that arousal effects were more prominent in IC, while arousal and engagement effects were equally likely in A1. Task engagement was correlated with increased arousal. They propose that there is a hierarchy such that generalized arousal enhances activity in the midbrain, and task engagement effects are more prominent in cortex. I have no major concerns, but two points to consider:

    I would like to know how the model would perform if task engagement were modeled as a continuous regressor.

    The authors state that they separated single units and stable multi units from the electrode signal, but I do not see where these data are separately reported.

  4. ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

    ###Summary:

    Saderi and her colleagues have attempted to determine whether and how two behavior-related variables - arousal and task engagement - differently influence activity in two stages of the auditory neuraxis, IC and A1. They define arousal as pupil diameter and task-engagement as a binary variable determined by the experimental block design. They find that although these two parameters often co-vary, they sometimes do not. They find that IC was more influenced by arousal and A1 was modulated by both arousal and engagement. One of their main findings is that previous reports of task-engagement effects may in fact be attributed to arousal state.