Fluorescence microendoscopy for in vivo deep-brain imaging of neuronal circuits

Imagining the future of optical microscopy: everything, everywhere, all at once

The optical microscope has revolutionized biology since at least the 17th Century. Since then, it has progressed from a largely observational tool to a powerful bioanalytical platform. However, realizing its full potential to study live specimens is hindered by a daunting array of technical challenges. Here, we delve into the current state of live imaging to explore the barriers that must be overcome and the possibilities that lie ahead. We venture to envision a future where we can visualize and study everything, everywhere, all at once - from the intricate inner workings of a single cell to the dynamic interplay across entire organisms, and a world where scientists could access the necessary microscopy technologies anywhere.

Using deep learning to study emotional behavior in rodent models

Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging

Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky microscope objective inside the brain. By creating a self-consistent deep neural network that is trained to reconstruct anthropocentric images from the raw signal transported by the cannula, we demonstrate a single-cell resolution (< 10μm), depth sectioning resolution of 40 μm, and field of view of 200 μm, all with green-fluorescent-protein labelled neurons imaged at depths as large as 1.4 mm from the brain surface. Since ground-truth images at these depths are challenging to obtain in vivo, we propose a novel ensemble method that averages the reconstructed images from disparate deep-neural-network architectures. Finally, we demonstrate dynamic imaging of moving GCaMp-labelled C. elegans worms. Our approach dramatically simplifies deep-brain microscopy.

Anatomical Methods to Study the Suprachiasmatic Nucleus

The mammalian suprachiasmatic nucleus (SCN) functions as a master circadian pacemaker. In order to examine mechanisms by which it keeps time, entrains to periodic environmental signals (zeitgebers), and regulates subordinate oscillators elsewhere in the brain and in the periphery, a variety of molecular methods have been applied. Multiple label immunocytochemistry and in situ hybridization provide anatomical insights that complement physiological approaches (such as ex vivo electrophysiology and luminometry) widely used to study the SCN.The anatomical methods require interpretation of data gathered from groups of individual animals sacrificed at different time points. This imposes constraints on the design of the experiments that aim to observe changes that occur with circadian phase in free-running conditions. It is essential in such experiments to account for differences in the periods of the subjects. Nevertheless, it is possible to resolve intracellular colocalization and regional expression of functionally important transcripts and/or their peptide products that serve as neuromodulators or neurotransmitters. Armed with these tools and others, understanding of the mechanisms by which the hypothalamic pacemaker regulates circadian function is progressing apace.

Circuit Investigation of Social Interaction and Substance Use Disorder Using Miniscopes

Substance use disorder (SUD) is comorbid with devastating health issues, social withdrawal, and isolation. Successful clinical treatments for SUD have used social interventions. Neurons can encode drug cues, and drug cues can trigger relapse. It is important to study how the activity in circuits and embedded cell types that encode drug cues develop in SUD. Exploring shared neurobiology between social interaction (SI) and SUD may explain why humans with access to social treatments still experience relapse. However, circuitry remains poorly characterized due to technical challenges in studying the complicated nature of SI and SUD. To understand the neural correlates of SI and SUD, it is important to: (1) identify cell types and circuits associated with SI and SUD, (2) record and manipulate neural activity encoding drug and social rewards over time, (3) monitor unrestrained animal behavior that allows reliable drug self-administration (SA) and SI. Miniaturized fluorescence microscopes (miniscopes) are ideally suited to meet these requirements. They can be used with gradient index (GRIN) lenses to image from deep brain structures implicated in SUD. Miniscopes can be combined with genetically encoded reporters to extract cell-type specific information. In this mini-review, we explore how miniscopes can be leveraged to uncover neural components of SI and SUD and advance potential therapeutic interventions.