Remote photonic sensing of cerebral hemodynamic changes via temporal spatial analysis of acoustic vibrations. Issue 2 (15th November 2019)

• Record Type: Journal Article
• Authors: Ozana, Nisan; Noah, Jack Adam; Zhang, Xian; Ono, Yumie; Hirsch, Joy; Zalevsky, Zeev
• Is Part Of: Journal of biophotonics. Volume 13:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-15
• Subjects: biosensing -- laser speckles -- remote sensing; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201900201
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: A novel photonic method for remote monitoring of task‐related hemodynamic changes in human brain activation is presented. Physiological processes associated with neural activity, such as nano‐vibrations due to blood flow and tissue oxygenation in the brain, are detected by remote sensing of nano‐acoustic vibrations using temporal spatial analysis of defocused self‐interference random patterns. Temporal nanometric changes of the speckle pattern due to visual task‐induced hemodynamic responses were tracked by this method. Reversing visual checkerboard stimulation alternated with rest epochs, and responsive signals were identified in occipital lobe using near‐infrared spectroscopy. Temporal vibrations associated with these hemodynamic response functions were observed using three different approaches: (a) single spot illumination at active and control areas simultaneously, (b) subspots cross‐correlation‐based analysis, and (c) multiwavelength measurement using a magnitude‐squared wavelet coherence function. Findings show remote sensing of task‐specific neural activity in the human brain. Abstract : A novel photonic method for remote monitoring of hemodynamic changes in human brain is presented. Physiological processes associated with hemodynamic changes, such as nano‐vibrations due to blood flow and tissue oxygenation in the brain, are detected by remote sensing of acoustic vibrations using temporal spatial analysis of defocused self‐interference random patterns.