Imaging through Wind an see electrode arrays reveals a small fraction of local neurons following surface MUA

  1. Martin Thunemann
  2. Lorraine Hossain
  3. Torbjørn V. Ness
  4. Nicholas Rogers
  5. Keundong Lee
  6. Sang Heon Lee
  7. Kıvılcım Kılıç
  8. Hongseok Oh
  9. Michael N. Economo
  10. Vikash Gilja
  11. Gaute T. Einevoll
  12. Shadi A. Dayeh
  13. Anna Devor

  • Curated by eLife

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    eLife assessment

    This study provides fundamental insights into the relationship between single neuron activity in superficial layers of the cortex and electrical signals recorded at the cortical surface. Based on solid measurements, the results indicate a weak correlation between individual layer 2/3 neuron activity and multiunit activity recorded at the surface, whose interpretation could be reinforced. In particular, a strong contribution of layer 1 axons to surface signals is suggested but relies on incomplete evidence.

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Abstract

Prior studies have shown that neuronal spikes can be recorded with microelectrode arrays placed on the cortical surface. However, the etiology of these spikes remains unclear. Because the top cortical layer (layer 1) contains very few neuronal cell bodies, it has been proposed that these spikes originate from neurons with cell bodies in layer 2. To address this question, we combined two-photon calcium imaging with electrophysiological recordings from the cortical surface in awake mice using chronically implanted PEDOT:PSS electrode arrays on transparent parylene C substrate.

Our electrode arrays (termed Wind an see) were integrated with cortical wind ows offering see -through optical access while also providing measurements of local field potentials (LFP) and multiunit activity (MUA) from the cortical surface. To enable longitudinal data acquisition, we have developed a mechanical solution for installation, connectorization, and protection of Wind an see devices aiming for an unhindered access for high numerical aperture microscope objectives and a lifetime of several months while worn by a mouse.

Contrary to the common notion, our measurements revealed that only a small fraction of layer 2 neurons from the sampled pool (~13%) faithfully followed MUA recorded from the surface above the imaging field-of-view. Surprised by this result, we turned to computational modeling for an alternative explanation of the MUA signal. Using realistic modeling of neurons with back-propagating dendritic properties, we computed the extracellular action potential at the cortical surface due to firing of local cortical neurons and compared the result to that due to axonal inputs to layer 1. Assuming the literature values for the cell/axon density and firing rates, our modeling results show that surface MUA due to axonal inputs is over an order of magnitude larger than that due to firing of layer 2 pyramidal neurons.

Thus, a combination of surface MUA recordings with two-photon calcium imaging can provide complementary information about the input to a cortical column and the local circuit response. Cortical layer I plays an important role in integration of a broad range of cortico-cortical, thalamocortical and neuromodulatory inputs. Therefore, detecting their activity as MUA while combining electrode recording with two-photon imaging using optically transparent surface electrode arrays would facilitate studies of the input/output relationship in cortical circuits, inform computational circuit models, and improve the accuracy of the next generation brain-machine interfaces.

Article activity feed

  1. eLife

    eLife assessment

    This study provides fundamental insights into the relationship between single neuron activity in superficial layers of the cortex and electrical signals recorded at the cortical surface. Based on solid measurements, the results indicate a weak correlation between individual layer 2/3 neuron activity and multiunit activity recorded at the surface, whose interpretation could be reinforced. In particular, a strong contribution of layer 1 axons to surface signals is suggested but relies on incomplete evidence.

  2. eLife

    Reviewer #1 (Public Review):

    This article describes simultaneous surface recordings with a transparent electrode array and two-photon calcium imaging in the mouse cortex. The study shows that spiking activity recorded by surface electrodes or imaged layer 2/3 activity is decoupled. Moreover, simulations indicate that this decoupling may be due to a dominance of L1 projecting axons (input to the cortex) in surface spiking activity.

    This is a rigorous study capitalizing on the new Windansee surface recording device, which provides extremely useful evidence that surface electrodes may not be able to capture information processed in the cortical layers. Recordings and simulations seem adequately performed. The indication that axons contribute significantly to multiunit activity is extremely important for the interpretation of multiunit activity in surface recordings. Here the claim is limited to surface recording, and one wonders to which extent this conclusion would transpose to recordings made with penetration electrodes.

  3. eLife

    Reviewer #2 (Public Review):

    The manuscript describes a novel transparent electrode array and demonstrates its combination with two-photon calcium imaging in mouse neocortex. Using a computational model, the authors propose that surface multi-unit activity mainly reflects L1 axonal activity and they find a small population of L2/3 neurons that correlates with this activity. While the multi-modal approach with the innovative device in our view is interesting and potentially useful, we have several technical and scientific concerns that should be addressed by the authors.

    Strengths:
    We find the general scope of this manuscript, to establish a hybrid electrophysiological and optical approach for studying neocortical activity, very interesting and relevant. The authors provide a compelling use case for combined ECoG and two-photon imaging. While extracellular action potentials have been recorded from the cortical surface, the underlying source is unknown and the device and techniques introduced by the authors are appropriate to address this question. The introduced device can be implanted chronically and has good long-term stability, providing longitudinal optical and electrical recordings from the cortex. The authors perform recordings in awake, head-fixed animals which provides the opportunity to relate ECoG and single-cell data to the animal's behavioral state. The combination of empirical data and biophysical modelling is a powerful means by which to answer such questions.

    Weaknesses:
    The central claim of the paper relies heavily on the computational model and the physiological data could be more completely analyzed. Based on a sample of 136 L2/3 neurons the authors find a small proportion (13%) that correlates with the ECoG MUA (eMUA). Based on this, they use a model to show that ECoG MUA likely reflects axonal spikes. They then posit that these layer 2/3 neurons are tightly correlated to the layer 1 input. The presentation of their data and the specifics of their model makes it difficult to assess the validity of this claim. They do not sufficiently discuss possible confounds in the data, caveats of their model, or alternative explanations of the observed low proportion of L2/3 neurons that correlate with the ECoG MUA.

    Most relevantly, the authors do not measure single units with their ECoG. The eMUA is a complex mixture of many neuronal sources, and interpretation is therefore difficult. They relate the calcium transients of small populations of single L2/3 neurons with the aggregate measure of population activity reflected in eMUA. It is possible that the eMUA reflects population activity in the local circuit and might therefore have a low correlation with individual single units. Critically, there is no information on the sensitivity of calcium recordings. Do the imaging data detect single action potentials, or are they biased to bursts of more than 1 AP?

    The analysis pipeline and values used for computing the correlation coefficients are counterintuitive. The fluorescence data are first interpolated from 15 Hz to 4 kHz and then both eMUA and imaging data are effectively down-sampled to 2 Hz. A single correlation coefficient is then estimated for each neuron, regardless of behavioral state, even though the authors themselves show that the activity of single neurons and the ECoG signal depend on the state of the animal.

    There is also insufficient information on the weight of the implant and its effect on mouse behavior. How does the movement of implanted and non-implanted mice differ? Must mice be singly housed? Finally, the modeling parameters are highly specific, using independently driving spikes, while the activity of neurons can be highly correlated. Likewise, the contribution of tangentially oriented axons that could relate to long-range connections conveying information related to the animal's motion or level of arousal is not considered. The manuscript would benefit from further analysis of the physiological data, consideration of alternative explanations and forthright discussion of limitations and caveats of their device and approach.

  4. eLife

    Reviewer #3 (Public Review):

    The authors have developed a new form of transparent surface multielectrode integrated into an imaging window, enabling simultaneous recording of electrical activity at the surface of the cortex combined with two-photon imaging through the window and electrode. The authors characterise the electrical signals and use simulations to argue that they reflect the activity of axons in layer 1. This is then correlated with calcium imaging signals from layer 2/3 pyramidal cells. A subset of these displayed strong correlations with the layer 1 activity.

    The raw electrical recordings appear to be contaminated by large movement artefacts. The authors attempt to decompose the signal into neuronal activity and artefact. The independent component analysis (ICA) employed yields plausible results. However, there is no definitive validation of this procedure.

    The simulations strongly suggest that only layer 1 axons will generate significant neuronal signals at the surface, but the authors have not attempted to reconstruct the multiunit activity in the simulations, which could provide additional assurance for their interpretation.

    A small fraction of pyramidal cells has activity strongly correlated with the signal at the surface electrode. However, the authors have not examined whether the distance from neuron to the electrode influences the strength of correlation. It remains possible that the differential correlation reflects a distance effect rather than the existence of two populations.

  5. Version published to 10.1101/2022.09.01.506113 on bioRxiv