At A Glance
- Brain imaging research: develop hardware, acquisition, and analysis to combine data from multiple brain imaging modalities, including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), to better understand brain functions and dysfunctions.
- Patents: MRI receiver technology, data acquisition and image reconstruction strategies.
Technical development of magnetic resonance imaging
Using a multi-channel coil array allows simultaneous large field-of-view and high signal-to-noise ratio magnetic resonance imaging (MRI) acquisition, which can also be traded for spatiotemporal resolution enhancements. We have applied parallel MRI to improve the quality and speed of structural MRI, functional MRI (fMRI), and magnetic resonance spectroscopic imaging of the human brain. In particular, a receiver head coil array with distributed and localized sensitivity enabled recording fMRI with whole-brain coverage and 5 mm isotropic spatial resolution with 10 Hz sampling rate. Such a fast fMRI acquisition can be used to monitor as well as suppress physiological noise and to reveal fine timing features in the hemodynamic responses modulated by stimuli conditions and mental states.
In parallel to the development of RF coils, my lab has also engaged in the research of using nonlinear gradients to spatially encode MRI data. We have developed methods to combine linear and quadratic gradient fields to improve the spatial encoding efficiency and to tailor the spatial resolution within the field-of-view. Nonlinear gradients can also be used to efficiently reduce the inhomogeneity of the transmitted RF field in order to improve patient safety and to facilitate the use of high field (>7T) MRI in clinical diagnosis.
Technical development of multimodal imaging using MRI and EEG/MEG
Different from fMRI, which uses a hemodynamic contrast to observe vascular responses after neuronal events, EEG/MEG is directly sensitive to neuronal activity and has millisecond temporal resolution. However, EEG/MEG suffers from uncertainty in estimating the source locations. The mathematical ill-posedness of this inverse problem can be alleviated by using high spatial resolution anatomical MRI data to constrain the EEG/MEG source locations and orientations. Building on these ideas, we have created widely used methods to integrate high spatial resolution anatomical MRI with distributed EEG/MEG source modeling. These efforts have also allowed us to investigate oscillatory neuronal activity in cortical source space. In addition to using the anatomical MRI information, we have developed strategies to use fMRI to help reduce ambiguity in EEG/MEG source modeling by taking advantage of fMRI's higher spatial resolution and homogeneous sensitivity over the whole brain.
Applications in neuroscience
We have been applying the fast fMRI method to neuroscience research. Due to its high temporal resolution, whole-brain coverage, and sufficient spatial resolution, the fast fMRI technique can effectively monitor and suppress respiratory and cardiac fluctuations that are the dominant sources of physiological noise in fMRI experiments, therefore greatly improving the SNR in fMRI studies. We have also used fast fMRI to reveal the surprisingly fine-graded neuronal timing information (tens to hundreds of milliseconds) contained in hemodynamic responses. Such information has been neglected previously because of the lack of an enabling technology to observe fine temporal differences.
We have also used the method of constraining EEG/MEG source with structural MRI information in neuroscience studies of the human auditory system and learning. We have additionally extended these techniques to clinical applications, such as epilepsy and studying individual responsiveness to drugs.
Understanding how the brain supports behavior and cognition necessitates unraveling the interactions among brain areas. This requires analyzing both functional (synchronization) and effective (causal modulations) connectivity. Using fMRI data, we have developed multivariate analysis methods to reveal connectivity patterns in human motor and auditory/language systems. We have also developed dynamic Granger causality analysis methods for EEG/MEG data and applied these in studies of causal modulations in human language networks and epileptic spike propagation.
- Pu-Yeh Wu, Ying-Hua Chu, Jo-Fu Lotus Lin, Wen-Jui Kuo, Fa-Hsuan Lin, “Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex”, Sci Rep (2018), Vol. 8, Article number: 13287, doi: 10.1038/s41598-018-31292-x.
- Fa-Hsuan Lin, Jonathan Polimeni; Jo-Fu Lin; Kevin W Tsai; Ying-Hua Chu; Pu-Yeh Wu; Yi-Tien Li; Yi-Cheng Hsu; Shang-Yueh Tsai; Wen-Jui Kuo, “Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex”, NeuroImage (2018), Vol. 164, pp. 194-201, doi: 10.1016/j.neuroimage.2017.01.041.).
- Yi-Cheng Hsu, Riccardo Lattanzi, Ying-Hua Chu, Martijn A. Cloos, Daniel K. Sodickson, Fa-Hsuan Lin, “Mitigation of B1+ inhomogeneity using spatially selective excitation with jointly designed quadratic spatial encoding magnetic fields and RF shimming”, Magn Reson Med (2017), Vol. 78(2), pp. 577-587.
- Ying-Hua Chu, Yi-Cheng Hsu, Fa-Hsuan Lin, "Decoupled dynamic magnetic field measurements improves diffusion-weighted magnetic resonance images", Sci Rep (2017), Vol. 7, Article number: 11630, doi:10.1038/s41598-017-11138-8.
- Fa-Hsuan Lin, Ying-Hua Chu, Yi-Cheng Hsu, Jo-Fu Lin, Kevin W.-K. Tsai, Shang-Yueh Tsai, Wen-Jui Kuo, “Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals“, NeuroImage (2015), Vol 121, pp 69-77.
- Fa-Hsuan Lin, Thomas Witzel, Tommi Raij, Jyrki Ahveninen, Kevin Wen-Kai Tsai, Yin-Hua Chu, Wei-Tang Chang, Aapo Nummenmaa, Jonathan R. Polimeni, Wen-Jui Kuo, Jen-Chuen Hsieh, Bruce R. Rosen, John W. Belliveau, “fMRI hemodynamics accurately reflect neuronal timing in the human brain measured by MEG”, NeuroImage (2013), Vol. 78, pp. 372-384.
- Fa-Hsuan Lin, Panu T. Vesanen, Yi-Cheng Hsu, Jaakko O. Nieminen, Koos C.J. Zevenhoven, Juhani Dabek, Lauri T. Parkkonen, Juha Simola, Antti I. Ahonen, Risto J. Ilmoniemi, “Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity”, PLoS ONE (2013), Vol. 8 (4), pp. e61652.
- Fa-Hsuan Lin, “Multidimensionally encoded magnetic resonance imaging”, Mag Reson Med, (2013), Vol. 70 (1), pp. 86-96.