Extended field-of-view ultrathin microendoscopes for high-resolution two-photon imaging with minimal invasiveness
Abstract
Imaging neuronal activity with high and homogeneous spatial resolution across the field-of-view (FOV) and limited invasiveness in deep brain regions is fundamental for the progress of neuroscience, yet is a major technical challenge. We achieved this goal by correcting optical aberrations in gradient index lens-based ultrathin (< 500 μm) microendoscopes using aspheric microlenses generated through 3D-microprinting. Corrected microendoscopes had extended FOV (eFOV) with homogeneous spatial resolution for two-photon fluorescence imaging and required no modification of the optical set-up. Synthetic calcium imaging data showed that, compared to uncorrected endoscopes, eFOV-microendoscopes led to improved signal-to-noise ratio and more precise evaluation of correlated neuronal activity. We experimentally validated these predictions in awake head-fixed mice. Moreover, using eFOV-microendoscopes we demonstrated cell-specific encoding of behavioral state-dependent information in distributed functional subnetworks in a primary somatosensory thalamic nucleus. eFOV-microendoscopes are, therefore, small-crosssection ready-to-use tools for deep two-photon functional imaging with unprecedentedly high and homogeneous spatial resolution.
Data availability
The datasets shown in Figures 4, 6, 7 and corresponding figure supplements are available at:https://data.mendeley.com/datasets/wm6c5wzs4c/draft?a=56f1660e-a036-40ee-83ef-458dc2457b6aThe software used in this paper to generate and analyze artificial t-series is available at: https://github.com/moni90/eFOV_microendoscopes_simNumerical data for graphs represented in figures 3-7, figure 2-figure supplement 2-4, figure 3-figure supplement 4, figure 4-figure supplement 1-2 are provided as source data.
Article and author information
Author details
Funding
European Research Council (NEURO-PATTERNS)
- Tommaso Fellin
IIT interdisciplinary grant
- Tommaso Fellin
IIT interdisciplinary grant
- Carlo Liberale
KAUST (BAS/1/1064-01-01)
- Carlo Liberale
NIH Brain Initiative (NS090576)
- Tommaso Fellin
NIH Brain Initiative (NS107464)
- Tommaso Fellin
NIH Brain Initiative (NS107464)
- Stefano Panzeri
NIH Brain Initiative (NS109961)
- Stefano Panzeri
FP7 (DESIRE)
- Tommaso Fellin
FIRB (RBAP11X42L)
- Tommaso Fellin
Flag-Era JTC Human Brain Project (SLOW-DYN)
- Tommaso Fellin
Flag-Era JTC Human Brain Project (SLOW-DYN)
- Stefano Panzeri
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Experimental procedures involving animals have been approved by the Istituto Italiano di Tecnologia Animal Health Regulatory Committee, by the National Council on Animal Care of the Italian Ministry of Health (authorization # 1134/2015-PR, # 689/2018-PR) and carried out according to the National legislation (D.Lgs. 26/2014) and to the legislation of the European Communities Council Directive (European Directive 2010/63/EU).
Copyright
© 2020, Fellin et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.
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