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Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
Published on: June 9, 2016
Magnetically compatible and fiberless fNIRS enables simultaneous multimodal imaging with optically pumped
Rui Yang1, Xingyu Ru1, Jingqi Song1
1Laboratory for Medical Physics and Engineering, School of Physics, Peking University, Beijing, 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; National Biomedical Imaging Center, Peking University, Beijing, 100871, China.
Abstract:
Simultaneous acquisition of functional near-infrared spectroscopy (fNIRS) and magnetoencephalography (MEG) provides complementary hemodynamic and electrophysiological information for studies of neurovascular coupling and has previously been demonstrated using fiber-based fNIRS implementations. Compared with fiber-based systems, fiberless fNIRS is lightweight and eliminates fiber-induced mechanical constraints; however, its integration with MEG remains challenging due to stringent magnetic compatibility requirements. Here, we present a magnetically compatible and fiberless fNIRS system enabling flexible and non-invasive multimodal imaging with optically pumped magnetometer (OPM) MEG. We developed magnetically compatible source/detector optodes and implemented a multipole moment flexible printed circuit design that suppresses driving-current-induced magnetic fields by more than 1000-fold. The optodes and cables generated less than 1 nT of magnetic field at ∼1 cm from the OPM sensor, with no measurable impact on OPM sensitivity. Simultaneous fiberless fNIRS and OPM-MEG acquisition was demonstrated in a somatosensory paradigm, capturing concurrent hemodynamic and evoked magnetic responses, thereby demonstrating the feasibility and robustness of our integrated multimodal system. By addressing a key magnetic-compatibility barrier between fiberless fNIRS and OPM-MEG, this work paves the way for neurovascular coupling studies using flexible multimodal platforms, and supports future developments in wearable multimodal neuroimaging and multimodal brain-computer interface systems.
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