Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems.
Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device.
Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head.
Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results.
Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.
Ethan Pratt, Micah Ledbetter, Ricardo Jiménez-Martínez, Benjamin Shapiro, Amelia Solon, Geoffrey Iwata, Steve Garber, Jeff Gormley, Dakota Decker, David Delgadillo, Argyrios Dellis, Jake Phillips, Guhan Sundar, Jerry Leung, Jim Coyne, Mike McKinley, Gilbert Lopez, Scott Homan, Lucas Marsh, Mary Zhang, Vincent Maurice, Benjamin Siepser, Teresa Giovannoli, Brandon Leverett, Gabriel Lerner, Scott Seidman, Vicente DeLuna, Kayla Wright-Freeman, Julian Kates-Harbeck, Teague Lasser, Hooman Mohseni, T.J. Sharp, Anthony Zorzos, Antonio Lara, Ali Kouhzadi, Alejandro Ojeda, Pronoy Chopra, Zachary Bednarke, Michael Henninger, Jamu Alford
MEG based on optically-pumped magnetometry (OP-MEG) operates with miniaturized, wearable insulation, in contrast to massive cryogenic dewars for SQUID-MEG, and allows placement of the sensors close to the scalp. This allows more natural head motion during data recording and localized signal quality comparable to, or surpassing, SQUID-MEG. However no OP-MEG system to date has offered full-head coverage with dense sensor packing, and existing systems - as with SQUID-MEG - require the subject to be sealed in a multilayer, passively-shielded vault in order to suppress ambient magnetic fields. Here we present Kernel Flux, which overcomes these limitations. Kernel Flux uses a collection of alkali vapor sensors in a unique array architecture to directly detect the magnetic fields generated by collective neural activity in the brain, while allowing for comfortable head motion. Each Kernel Flux OP-MEG system was designed from the ground up to work as an integrated system optimized around the user's experience, with relevance to natural home and office contexts.
Han Ban, Geoffrey Barrett, Alex Borisevich, Ashutosh Chaturvedi, Jacob Dahle, Hamid Dehghani, Bruno DoValle, Julien Dubois, Ryan Field, Viswanath Gopalakrishnan, Andrew Gundran, Michael Henninger, Wilson Ho, Howard Hughes, Rong Jin, Julian Kates-Harbeck, Thanh Landy, Antonio Lara, Michael Leggiero, Gabriel Lerner, Zahra Aghajan, Michael Moon, Alejandro Ojeda, Isai Olvera, Meric Ozturk, Sangyong Park, Milin Patel, Katherine Perdue, Wing Poon, Zachary Sheldon, Benjamin Siepser, Sebastian Sorgenfrei, Nathan Sun, Victor Szczepanski, Mary Zhang, Zhenye Zhu
Time-Domain Near-Infrared Spectroscopy (TD-NIRS) has been considered as the gold standard of non-invasive optical brain imaging devices. However, due to the high cost, complexity, and large form-factor, it has not been as widely adopted as Continuous Wave (CW) NIRS systems. Kernel Flow is a TD-NIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-NIRS system while integrating all of the components into a small modular device. The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. We show performance similar to benchtop systems with our miniaturized device.
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