University of Minnesota
Department of Biomedical Engineering

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Bin He

Bin He








Biomedical Imaging and Neuroengineering

Functional Neuroimaging

Brain activation is a spatio-temporally-distributed process. Recent advances in medical imaging technology, especially functional MRI, have greatly increased our ability to image brain functions with high spatial resolution but with limited temporal resolution. Electrophysiological recordings such as EEG, on the other hand, offer millisecond temporal resolution in detecting and characterizing brain activity. Our approach is to achieve high resolution spatio-temporal functional neuroimaging by solving the “inverse” problem of the brain from scalp recorded EEG with the aid of MR images. Innovation in engineering methods has led to greatly enhanced spatial resolution of brain electrical imaging, which has been applied to aid presurgical planning in epilepsy patients. Furthermore, we are developing multimodal neuroimaging methods integrating EEG with BOLD functional MRI based on quantitative neurovascular coupling models. Experimental investigations are being carried out to study brain functions with sensory, motor and cognitive paradigms.

Brain-Computer Interface

There are currently over two million people in the United States suffering from various degrees of paralysis. A means to rehabilitate these individuals would thus have tremendous economic and social impact. The brain-computer interface has been developed as a means to “read” the minds of the individuals and translate these thoughts into actions performed via a computer, which aims at restoring function in paralytics by providing the brain with new output pathways. Our approach is to develop non-invasive brain-computer interface systems, which can perform complex tasks reliably and efficiently. These include development of practical BCI systems and to elucidate basic mechanisms underlying BCI applications. Of particular interest is the motor imagination based BCI applications and its fundamental research.

Cardiac Electrical Tomography

Another major area of research activity in our laboratory is in the investigation of advanced imaging technologies for assessing dynamic cardiac electrical activity. Research in this area is aimed at improving the understanding of the mechanisms of cardiac functions and dysfunctions and aiding clinical diagnosis and management of cardiac diseases. Our approach is to image cardiac activation and repolarization from noninvasive or minimally invasive recordings. We have proposed and developed 3-dimensional cardiac electrical imaging methods and been pursuing experimental validation of these novel methods in animal models. Clinical investigation is also under its way to assess the clinical applicability of our developed cardiac electrical tomography techniques. The ultimate goal is to establish high resolution cardiac functional electrical imaging methodology which can image and localize sites of arrhythmogenesis and its mechanisms in a clinical setting.

Electrical Impedance Imaging

In addition to imaging electrical sources, we are also pursuing imaging of electrical properties of tissues, such as electrical impedance. Such impedance imaging has important applications in cancer detection, functional brain imaging, and functional cardiac imaging. Our approach includes magnetoacoustic imaging with magnetic induction (MAT-MI), magnetic resonance electrical impedance imaging (MREIT), and magnetic resonance electrical property imaging (MREPT). We have proposed and developed a novel MAT-MI technique, integrating biomagnetism and ultrasound, to achieve high resolution imaging electrical impedance. Also under investigation is the use of MRI to image electrical properties of brain and other tissues.

Selected Publications

Yu K, Sohrabpour A, He B: “Electrophysiological Source Imaging of Brain Networks Perturbed by Low-intensity Transcranial Focused Ultrasound,” IEEE Transactions on Biomedical Engineering, 63(9): 1787-1794, 2016 (cover article).

Sohrabpour A, Lu Y, Worrell G, He B: “Imaging Brain Source Extent from EEG/MEG by Means of an Iteratively Reweighted Edge Sparsity Minimization (IRES) Strategy,” NeuroImage, e-published May 27, 2016.

Zhou Z, Jin Q, Chen LY, Yu L, Wu L, He B: “Noninvasive Imaging of High Frequency Drivers and Reconstruction of Global Dominant Frequency Maps in Patients with Paroxysmal and Persistent Atrial Fibrillation,” IEEE Transactions on Biomedical Engineering, 63(6): 1333-1340, 2016.

Mariappan L, Shao Q, Jiang C, Yu K, Ashkenazi S, Bischof J, He B: “Magneto acoustic tomography with short pulsed magnetic field for in-vivo imaging of magnetic iron oxide nanoparticles,” Nanomedicine: Nanotechnology, Biology, and Medicine, 12(3): 689–699, 2016.

Edelman B, Baxter B, He B: “EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks,” IEEE Transactions on Biomedical Engineering, 63(1): 4-14, 2016.

He B, Baxter B, Edelman B, Cline C, Ye W: “Noninvasive brain-computer interfaces based on sensorimotor rhythms,” Proceedings of the IEEE, 103(6): 907-925, 2015.

Han C, Pogwizd SM, Yu L, Zhou Z, Killingsworth CR, He B: “Imaging Cardiac Activation Sequence during Ventricular Tachycardia in a Canine Model of Nonischemic Heart Failure,” American Journal of Physiology-Heart and Circulatory Physiology, 308(2): H108-114, 2015 (cover article).

Liu J, Zhang XT, Schmitter S, Van de Moortele PF, He B: “Gradient-based electrical properties tomography (gEPT): A robust method for mapping electrical properties of biological tissues in vivo using magnetic resonance imaging,” Magnetic Resonance in Medicine, 74(3):634-646, 2015.

LaFleur K, Cassady K, Doud A, Shades K, Rogin E, He B: “Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface,” Journal of Neural Engineering, 10: 046003, 2013 (Featured in Nature).

Johnson MD, Lim HH, Netoff TI, Connolly AT, Johnson N, Roy A, Holt A, Lim KO, Carey JR, Vitek JL, and He B: “Neuromodulation for Brain Disorders: Challenges and Opportunities,” IEEE Transactions on Biomedical Engineering. 60(3): 610-624, 2013 (cover article).

Yang L, Worrell G, Nelson C, Brinkmann B, He B: “Spectral and spatial shifts of postictal slow waves in temporal lobe seizures,” Brain, 135(10): 3134-3143, 2012.

Aarabi A, He B: “A rule-based seizure prediction method for focal neocortical epilepsy,” Clinical Neurophysiology, Clin Neurophysiol. 123(6):1111-22, 2012.

Zhang P, Jamison K, Engel S, He B, He S: "Binocular rivalry requires visual attention,” Neuron 71, 362–369, 2011.

Yuan H, Perdoni C, Yang L, He B: “Differential Electrophysiological Coupling for Positive and Negative BOLD Responses during Unilateral Hand Movements,” Journal of Neuroscience, 31(26):9585-93, 2011.

Yang L, Wilke C, Brinkmann B, Worrell GA, He B: “Dynamic Imaging of Ictal Oscillations Using Non-invasive High-Resolution EEG,” Neuroimage. 56(4):1908-17, 2011.

Bao M, Yang L, Rios C, He B, Engel S: “Perceptual learning increases the strength of the earliest signals in visual cortex,” Journal of Neuroscience, 30(45): 15080-15084, 2010.

Yuan H, Liu T, Szarkowski R, Savage M, Ashe J, He B: “An EEG and fMRI Study of Motor Imagery: Negative Correlation of BOLD and EEG Activity in Primary Motor Cortex,” NeuroImage, 49: 2596-2606, 2010.

Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala L, de Vico Fallani F, Salinari S, Ursino M, Zavaglia M, Ding L, Edgar JC, Miller GA , He B, Babiloni F: “A comparison of different cortical connectivity estimators for high resolution EEG recordings,” Human Brain Mapping, 28(2):143-57, 2007.

Xu Y, He B: “Magnetoacoustic Tomography with Magnetic Induction (MAT-MI),” Physics in Medicine and Biology, 50:5175-5187, 2005.

Babiloni F, Babiloni C, Carducci F, Cincotti F, Astolfi L, Basilisco A, Rossini PM, Ding L, Ni Y, Cheng J, Christine K, Sweeney J, and He B: “Assessing time-varying cortical functional connectivity with the multimodal integration of high resolution EEG and fMRI data by Directed Transfer Function,” NeuroImage, 24(1):118-131, 2005.

Lai Y, van Drongelen W, Ding L, Hecox KE, Towle VL, Frim DM, He B: “In vivo human skull conductivity estimation from simultaneous extra- and intra-cranial electrical potential recordings,” Clinical Neurophysiology, 116(2):456-465, 2005.

Qin L, Ding L, He B: "Motor Imagery Classification by Means of Source Analysis for Brain Computer Interface Applications," J of Neural Engineering, 1:135-141, 2004.