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1. Alpha-180 spin-echo-based line-scanning method for high-resolution laminar-specific fMRI in animals

   https://doi.org/10.1162/imag_a_00120  

   Choi, Sangcheon; Hike, David; Pohmann, Rolf; Avdievich, Nikolai; Gomez-Cid, Lidia; Man, Weitao; Scheffler, Klaus; Yu, Xin (March 2024, Imaging Neuroscience)

   Abstract Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given the limitation of spatial and temporal resolution. Previously, a gradient-echo-based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 µm) resolution to better characterize the fMRI onset time across cortical layers by employing two saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose an α (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method in animals to resolve this issue by employing a refocusing 180˚ RF pulse perpendicular to the excitation slice (without any saturation RF pulse) and also achieve high spatiotemporal resolution. In contrast to GELINE signals which peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers using the SELINE method, indicating the well-defined exclusion of the large draining-vein effect using the spin-echo sequence. Furthermore, we applied the SELINE method with a 200 ms repetition time (TR) to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth. 

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2. Investigation of electro-vascular phase-amplitude coupling during an auditory task

   https://doi.org/10.1016/j.compbiomed.2023.107902  

   McLinden, J; Rahimi, N; Kumar, C; Krusienski, DJ; Shao, M; Spencer, KM; Shahriari, Y (February 2024, Computers in Biology and Medicine)

   Multimodal neuroimaging using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides complementary views of cortical processes, including those related to auditory processing. However, current multimodal approaches often overlook potential insights that can be gained from nonlinear interactions between electrical and hemodynamic signals. Here, we explore electro-vascular phase-amplitude coupling (PAC) between low-frequency hemodynamic and high-frequency electrical oscillations during an auditory task. We further apply a temporally embedded canonical correlation analysis (tCCA)-general linear model (GLM)-based correction approach to reduce the possible effect of systemic physiology on fNIRS recordings. Before correction, we observed significant PAC between fNIRS and broadband EEG in the frontal region (p ≪ 0.05), β (p ≪ 0.05) and γ (p = 0.010) in the left temporal/temporoparietal (left auditory; LA) region, and γ (p = 0.032) in the right temporal/temporoparietal (right auditory; RA) region across the entire dataset. Significant differences in PAC across conditions (task versus silence) were observed in LA (p = 0.023) and RA (p = 0.049) γ sub-bands and in lower frequency (5-20 Hz) frontal activity (p = 0.005). After correction, significant fNIRS-γ-band PAC was observed in the frontal (p = 0.021) and LA (p = 0.025) regions, while fNIRS-α (p = 0.003) and fNIRS-β (p = 0.041) PAC were observed in RA. Decreased frontal γ-band (p = 0.008) and increased β-band (p ≪ 0.05) PAC were observed during the task. These outcomes represent the first characterization of electro-vascular PAC between fNIRS and EEG signals during an auditory task, providing insights into electro-vascular coupling in auditory processing. 

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3. Generalized Tensor Decomposition With Features on Multiple Modes

   https://doi.org/10.1080/10618600.2021.1978471  

   Hu, Jiaxin; Lee, Chanwoo; Wang, Miaoyan (September 2021, Journal of computational and graphical statistics)

   Higher-order tensors have received increased attention across science and engineering. While most tensor decomposition methods are developed for a single tensor observation, scientific studies often collect side information, in the form of node features and interactions thereof, together with the tensor data. Such data problems are common in neuroimaging, network analysis, and spatial-temporal modeling. Identifying the relationship between a high-dimensional tensor and side information is important yet challenging. Here, we develop a tensor decomposition method that incorporates multiple feature matrices as side information. Unlike unsupervised tensor decomposition, our supervised decomposition captures the effective dimension reduction of the data tensor confined to feature space of interest. An efficient alternating optimization algorithm with provable spectral initialization is further developed. Our proposal handles a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and multi-relational political network data. We identify the key global connectivity pattern and pinpoint the local regions that are associated with available features. The package and data used are available at https://CRAN.R-project.org/package=tensorregress. Supplementary materials for this article are available online. 

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4. Learning Multiple Networks via Supervised Tensor Decomposition

   Hu, Jiaxin; Lee, Chanwoo; Wang, Miaoyan (December 2020, Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada.)

   We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress. 

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5. Learning Multiple Networks via Supervised Tensor Decomposition

   Hu, Jiaxin; Lee, Chanwoo; Wang, Miaoyan (December 2020, NeurIPS 2023, Machine Learning and the Physical Sciences Workshop)

   We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress. 

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