mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from an RGB image. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep-learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral tradeoff, offering simple hardware requirements and potential applications of various machine-learning techniques.Comment: This paper will appear in PNAS Nexu

Human CSF movement influenced by vascular low frequency oscillations and respiration

Cerebrospinal fluid (CSF) movement through the pathways within the central nervous system is of high significance for maintaining normal brain health and function. Low frequency hemodynamics and respiration have been shown to drive CSF in humans independently. Here, we hypothesize that CSF movement may be driven simultaneously (and in synchrony) by both mechanisms and study their independent and coupled effects on CSF movement using novel neck fMRI scans. Caudad CSF movement at the fourth ventricle and hemodynamics of the major neck blood vessels (internal carotid arteries and internal jugular veins) was measured from 11 young, healthy volunteers using novel neck fMRI scans with simultaneous measurement of respiration. Two distinct models of CSF movement (1. Low-frequency hemodynamics and 2. Respiration) and possible coupling between them were investigated. We show that the dynamics of brain fluids can be assessed from the neck by studying the interrelationships between major neck blood vessels and the CSF movement in the fourth ventricle. We also demonstrate that there exists a cross-frequency coupling between these two separable mechanisms. The human CSF system can respond to multiple coupled physiological forces at the same time. This information may help inform the pathological mechanisms behind CSF movement-related disorders