Neurophotonics beyond the Surface: Unmasking the Brain's Complexity Exploiting Optical Scattering.

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

Neurophotonics beyond the surface: unmasking the brain's complexity exploiting optical scattering.

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

Single-pixel transmission matrix recovery via two-photon fluorescence.

Imaging at depth in opaque materials has long been a challenge. Recently, wavefront shaping has enabled notable advance for deep imaging. Nevertheless, most noninvasive wavefront-shaping methods require cameras, lack the sensitivity for deep imaging under weak optical signals, or can only focus on a single "guidestar." Here, we retrieve the transmission matrix (TM) noninvasively using two-photon fluorescence exploiting a single-pixel detection combined with a computational framework, allowing to achieve single-target focus on multiple guidestars spread beyond the memory effect range. In addition, if we assume that memory effect correlations exist in the TM, we are able to substantially reduce the number of measurements needed.

Fast wavefront shaping for two-photon brain imaging with multipatch correction.

Nonlinear fluorescence microscopy promotes in-vivo optical imaging of cellular structure at diffraction-limited resolution deep inside scattering biological tissues. Active compensation of tissue-induced aberrations and light scattering through adaptive wavefront correction further extends the accessible depth by restoring high resolution at large depth. However, those corrections are only valid over a very limited field of view within the angular memory effect. To overcome this limitation, we introduce an acousto-optic light modulation technique for fluorescence imaging with simultaneous wavefront correction at pixel scan speed. Biaxial wavefront corrections are first learned by adaptive optimization at multiple locations in the image field. During image acquisition, the learned corrections are then switched on the fly according to the position of the excitation focus during the raster scan. The proposed microscope is applied to in vivo transcranial neuron imaging and demonstrates multi-patch correction of thinned skull-induced aberrations and scattering at 40-kHz data acquisition speed.

Linear optical random projections without holography.

We introduce what we believe to be a novel method to perform linear optical random projections without the need for holography. Our method consists of a computationally trivial combination of multiple intensity measurements to mitigate the information loss usually associated with the absolute-square non-linearity imposed by optical intensity measurements. Both experimental and numerical findings demonstrate that the resulting matrix consists of real-valued, independent, and identically distributed (i.i.d.) Gaussian random entries. Our optical setup is simple and robust, as it does not require interference between two beams. We demonstrate the practical applicability of our method by performing dimensionality reduction on high-dimensional data, a common task in randomized numerical linear algebra with relevant applications in machine learning.

Three-dimensional operando optical imaging of particle and electrolyte heterogeneities inside Li-ion batteries.

Understanding (de)lithiation heterogeneities in battery materials is key to ensure optimal electrochemical performance. However, this remains challenging due to the three-dimensional morphology of electrode particles, the involvement of both solid- and liquid-phase reactants and a range of relevant timescales (seconds to hours). Here we overcome this problem and demonstrate the use of confocal microscopy for the simultaneous three-dimensional operando measurement of lithium-ion dynamics in individual agglomerate particles, and the electrolyte in batteries. We examine two technologically important cathode materials: LixCoO2 and LixNi0.8Mn0.1Co0.1O2. The surface-to-core transport velocity of Li-phase fronts and volume changes are captured as a function of cycling rate. Additionally, we visualize heterogeneities in the bulk and at agglomerate surfaces during cycling, and image microscopic liquid electrolyte concentration gradients. We discover that surface-limited reactions and intra-agglomerate competing rates control (de)lithiation and structural heterogeneities in agglomerate-based electrodes. Importantly, the conditions under which optical imaging can be performed inside the complex environments of battery electrodes are outlined.

Quantifying high-dimensional spatial entanglement with a single-photon-sensitive time-stamping camera.

High-dimensional entanglement is a promising resource for quantum technologies. Being able to certify it for any quantum state is essential. However, to date, experimental entanglement certification methods are imperfect and leave some loopholes open. Using a single-photon-sensitive time-stamping camera, we quantify high-dimensional spatial entanglement by collecting all output modes and without background subtraction, two critical steps on the route toward assumptions-free entanglement certification. We show position-momentum Einstein-Podolsky-Rosen (EPR) correlations and quantify the entanglement of formation of our source to be larger than 2.8 along both transverse spatial axes, indicating a dimension higher than 14. Our work overcomes important challenges in photonic entanglement quantification and paves the way toward the development of practical quantum information processing protocols based on high-dimensional entanglement.

Direct imaging of micrometer-thick interfaces in salt-salt aqueous biphasic systems.

Unlike the interface between two immiscible electrolyte solutions (ITIES) formed between water and polar solvents, molecular understanding of the liquid-liquid interface formed for aqueous biphasic systems (ABSs) is relatively limited and mostly relies on surface tension measurements and thermodynamic models. Here, high-resolution Raman imaging is used to provide spatial and chemical resolution of the interface of lithium chloride - lithium bis(trifluoromethanesulfonyl)imide - water (LiCl-LiTFSI-water) and HCl-LiTFSI-water, prototypical salt-salt ABSs found in a range of electrochemical applications. The concentration profiles of both TFSI anions and water are found to be sigmoidal thus not showing any signs of a positive adsorption for both salts and solvent. More striking, however, is the length at which the concentration profiles extend, ranging from 11 to 2 µm with increasing concentrations, compared to a few nanometers for ITIES. We thus reveal that unlike ITIES, salt-salt ABSs do not have a molecularly sharp interface but rather form an interphase with a gradual change of environment from one phase to the other. This knowledge represents a major stepping-stone in the understanding of aqueous interfaces, key for mastering ion or electron transfer dynamics in a wide range of biological and technological settings including novel battery technologies such as membraneless redox flow and dual-ion batteries.

Physics-based neural network for non-invasive control of coherent light in scattering media.

Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the propagation of different optical wavefronts are impacted by scattering. Based on this principle, several wavefront shaping techniques were successfully developed, but most of them are highly invasive and limited to proof-of-principle experiments. Here, we propose to use a neural network approach to non-invasively characterize and control light scattering inside the medium and also to retrieve information of hidden objects buried within it. Unlike most of the recently-proposed approaches, the architecture of our neural network with its layers, connected nodes and activation functions has a true physical meaning as it mimics the propagation of light in our optical system. It is trained with an experimentally-measured input/output dataset built from a series of incident light patterns and corresponding camera snapshots. We apply our physics-based neural network to a fluorescence microscope in epi-configuration and demonstrate its performance through numerical simulations and experiments. This flexible method can include physical priors and we show that it can be applied to other systems as, for example, non-linear or coherent contrast mechanisms.

Non-invasive chemically selective energy delivery and focusing inside a scattering medium guided by Raman scattering.

Raman scattering is a chemically selective probing mechanism with diverse applications in industry and clinical settings. Yet, most samples are optically opaque limiting the applicability of Raman probing at depth. Here, we demonstrate chemically selective energy deposition behind a scattering medium by combining prior information on the chemical's spectrum with the measurement of a spectrally resolved Raman speckle as a feedback mechanism for wavefront shaping. We demonstrate unprecedented sixfold signal enhancement in an epi-geometry, realizing targeted energy deposition and focusing on individual Raman active particles.

Large field-of-view non-invasive imaging through scattering layers using fluctuating random illumination.

Non-invasive optical imaging techniques are essential diagnostic tools in many fields. Although various recent methods have been proposed to utilize and control light in multiple scattering media, non-invasive optical imaging through and inside scattering layers across a large field of view remains elusive due to the physical limits set by the optical memory effect, especially without wavefront shaping techniques. Here, we demonstrate an approach that enables non-invasive fluorescence imaging behind scattering layers with field-of-views extending well beyond the optical memory effect. The method consists in demixing the speckle patterns emitted by a fluorescent object under variable unknown random illumination, using matrix factorization and a novel fingerprint-based reconstruction. Experimental validation shows the efficiency and robustness of the method with various fluorescent samples, covering a field of view up to three times the optical memory effect range. Our non-invasive imaging technique is simple, neither requires a spatial light modulator nor a guide star, and can be generalized to a wide range of incoherent contrast mechanisms and illumination schemes.

Scattering correcting wavefront shaping for three-photon microscopy.

Three-photon (3P) microscopy is getting traction due to its superior performance in deep tissues. Yet, aberrations and light scattering still pose one of the main limitations in the attainable depth ranges for high-resolution imaging. Here, we show scattering correcting wavefront shaping with a simple continuous optimization algorithm, guided by the integrated 3P fluorescence signal. We demonstrate focusing and imaging behind scattering layers and investigate convergence trajectories for different sample geometries and feedback non-linearities. Furthermore, we show imaging through a mouse skull and demonstrate a novel, to the best of our knowledge, fast phase estimation scheme that substantially increases the speed at which the optimal correction can be found.

Speckle Engineering through Singular Value Decomposition of the Transmission Matrix.

Speckle patterns are ubiquitous in optics and have multiple applications for which the control of their spatial correlations is essential. Here, we report on a method to engineer speckle correlations behind a scattering medium through the singular value decomposition of the transmission matrix. We not only demonstrate control over the speckle grain size and shape but also realize patterns with nonlocal correlations. Moreover, we show that the reach of our method extends also along the axial dimension, allowing volumetric speckle engineering behind scattering layers.

Engineering spatial correlations of entangled photon pairs by pump beam shaping.

The ability to engineer the properties of quantum optical states is essential for quantum information processing applications. Here, we demonstrate tunable control of spatial correlations between photon pairs produced by spontaneous parametric down-conversion, and measure them using an electron multiplying charge coupled device (EMCCD) camera. By shaping the spatial pump beam profile in a type-I collinear configuration, we tailor the spatial structure of coincidences between photon pairs entangled in high dimensions without effect on intensity. The results highlight fundamental aspects of spatial coherence and hold potential for the development of quantum technologies based on high-dimensional spatial entanglement.

Deeply Subwavelength Localization with Reverberation-Coded Aperture.

Accessing subwavelength information about a scene from the far-field without invasive near-field manipulations is a fundamental challenge in wave engineering. Yet it is well understood that the dwell time of waves in complex media sets the scale for the waves' sensitivity to perturbations. Modern coded-aperture imagers leverage the degrees of freedom (d.o.f.) offered by complex media as natural multiplexor but do not recognize and reap the fundamental difference between placing the object of interest outside or within the complex medium. Here, we show that the precision of localizing a subwavelength object can be improved by several orders of magnitude simply by enclosing it in its far field with a reverberant passive chaotic cavity. We identify deep learning as a suitable noise-robust tool to extract subwavelength localization information encoded in multiplexed measurements, achieving resolutions well beyond those available in the training data. We demonstrate our finding in the microwave domain: harnessing the configurational d.o.f. of a simple programmable metasurface, we localize a subwavelength object along a curved trajectory inside a chaotic cavity with a resolution of λ/76 using intensity-only single-frequency single-pixel measurements. Our results may have important applications in photoacoustic imaging as well as human-machine interaction based on reverberating elastic waves, sound, or microwaves.

Visualization of Directional Beaming of Weakly Localized Raman from a Random Network of Silicon Nanowires.

Disordered optical media are an emerging class of materials that can strongly scatter light. These materials are useful to investigate light transport phenomena and for applications in imaging, sensing and energy storage. While coherent light can be generated using such materials, its directional emission is typically hampered by their strong scattering nature. Here, the authors directly image Rayleigh scattering, photoluminescence and weakly localized Raman light from a random network of silicon nanowires via real-space microscopy and Fourier imaging. Direct imaging enables us to gain insight on the light transport mechanisms in the random material, to visualize its weak localization length and to demonstrate out-of-plane beaming of the scattered coherent Raman light. The direct visualization of coherent light beaming in such random networks of silicon nanowires offers novel opportunities for fundamental studies of light propagation in disordered media. It also opens venues for the development of next generation optical devices based on disordered structures, such as sensors, light sources, and optical switches.

Spectrally resolved point-spread-function engineering using a complex medium.

Propagation of an ultrashort pulse of light through strongly scattering media generates an intricate spatio-spectral speckle that can be described by means of the multi-spectral transmission matrix (MSTM). In conjunction with a spatial light modulator, the MSTM enables the manipulation of the pulse leaving the medium; in particular focusing it at any desired spatial position and/or time. Here, we demonstrate how to engineer the point-spread-function of the focused beam both spatially and spectrally, from the measured MSTM. It consists of numerically filtering the spatial content at each wavelength of the matrix prior to focusing. We experimentally report on the versatility of the technique through several examples, in particular as an alternative to simultaneous spatial and temporal focusing, with potential applications in multiphoton microscopy.

Three-dimensional broadband light beam manipulation in forward scattering samples.

Focusing light into highly disordered biological tissue is a major challenge in optical microscopy and biomedical imaging due to scattering. However, correlations in the scattering matrix, known as "memory effects", can be used to improve imaging capabilities. Here we discuss theoretically and numerically the possibility to achieve three-dimensional ultrashort laser focusing and scanning inside forward scattering media, beyond the scattering mean free path, by simultaneously taking advantage of the angular and the chromato-axial memory effects. The numerical model is presented in details, is validated within the state of the art theoretical and experimental framework and is finally used to propose a scheme for focusing ultra-short laser pulses in depth through forward scattering media.

Accelerating ptychographic reconstructions using spectral initializations.

Ptychography is a promising phase retrieval technique for label-free quantitative phase imaging. Recent advances in phase retrieval algorithms witnessed the development of spectral methods to accelerate gradient descent algorithms. Using spectral initializations on experimental data, for the first time, we report three times faster ptychographic reconstructions than with a standard gradient descent algorithm and improved resilience to noise. Coming at no additional computational cost compared to gradient-descent-based algorithms, spectral methods have the potential to be implemented in large-scale iterative ptychographic algorithms.

Inference in artificial intelligence with deep optics and photonics.

Artificial intelligence tasks across numerous applications require accelerators for fast and low-power execution. Optical computing systems may be able to meet these domain-specific needs but, despite half a century of research, general-purpose optical computing systems have yet to mature into a practical technology. Artificial intelligence inference, however, especially for visual computing applications, may offer opportunities for inference based on optical and photonic systems. In this Perspective, we review recent work on optical computing for artificial intelligence applications and discuss its promise and challenges.