For in vivo imaging, the short-wavelength infrared region (SWIR; 1000–2000 nm) provides several advantages over the visible and near-infrared regions: general lack of autofluorescence, low light absorption by blood and tissue, and reduced scattering. However, the lack of versatile and functional SWIR emitters has prevented the general adoption of SWIR imaging by the biomedical research community. Here, we introduce a class of high-quality SWIR-emissive indium-arsenide-based quantum dots (QDs) that are readily modifiable for various functional imaging applications, and that exhibit narrow and size-tunable emission and a dramatically higher emission quantum yield than previously described SWIR probes. To demonstrate the unprecedented combination of deep penetration, high spatial resolution, multicolor imaging and fast-acquisition-speed afforded by the SWIR QDs, we quantified, in mice, the metabolic turnover rates of lipoproteins in several organs simultaneously and in real time as well as heartbeat and breathing rates in awake and unrestrained animals, and generated detailed three-dimensional quantitative flow maps of the mouse brain vasculature.

Next-generation in vivo optical imaging with short-wave infrared quantum dots

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Computation Through Neural Population Dynamics.

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

Accurate Estimation of Neural Population Dynamics without Spike Sorting.

Artificial Neural Networks for Neuroscientists: A Primer.

Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

https://www.biorxiv.org/content/early/2018/04/28/211128.full.pdf

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Deep-tissue three-dimensional optical imaging of live mammals in vivo with high spatiotemporal resolution in non-invasive manners has been challenging due to light scattering. Here, we developed near-infrared (NIR) light sheet microscopy (LSM) with optical excitation and emission wavelengths up to ~ 1320 nm and ~ 1700 nm respectively, far into the NIR-II (1000-1700 nm) region for 3D optical sectioning through live tissues. Suppressed scattering of both excitation and emission photons allowed one-photon optical sectioning at ~ 2 mm depth in highly scattering brain tissues. NIR-II LSM enabled non-invasive in vivo imaging of live mice, revealing never-before-seen dynamic processes such as highly abnormal tumor microcirculation, and 3D molecular imaging of an important immune checkpoint protein, programmed-death ligand 1 (PD-L1) receptors at the single cell scale in tumors. In vivo two-color near-infrared light sheet sectioning enabled simultaneous volumetric imaging of tumor vasculatures and PD-L1 proteins in live mammals.

https://www.biorxiv.org/content/biorxiv/early/2018/10/18/447433.full.pdf

Optical Sectioning of Live Mammal with Near-Infrared Light Sheet

EEG has been central to investigations of the time course of various neural functions underpinning visual word recognition. Recently the steady-state visual evoked potential (SSVEP) paradigm has been increasingly adopted for word recognition studies due to its high signal-to-noise ratio. Such studies, however, have been typically framed around a single source in the left ventral occipitotemporal cortex (vOT). Here, we combine SSVEP recorded from 16 adult native English speakers with a data-driven spatial filtering approach--Reliable Components Analysis (RCA)--to elucidate distinct functional sources with overlapping yet separable time courses and topographies that emerge when contrasting words with pseudofont visual controls. The first component topography was maximal over left vOT regions with an early latency (approximately 180 msec). A second component was maximal over more dorsal parietal regions with a longer latency (approximately 260 msec). Both components consistently emerged across a range of parameter manipulations including changes in the spatial overlap between successive stimuli, and changes in both base and deviation frequency. We then contrasted word-in-nonword and word-in-pseudoword to test the hierarchical processing mechanisms underlying visual word recognition. Results suggest that these hierarchical contrasts fail to evoke a unitary component that might be reasonably associated with lexical access.

https://biorxiv.org/content/10.1101/2021.04.16.439729v1.full.pdf

Forea Wang

Papers

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Optical Sectioning of Live Mammal with Near-Infrared Light Sheet

Neural Sources Underlying Visual Word Form Processing as Revealed by Steady State Visual Evoked Potentials (SSVEP)