A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data.

Background: Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information.

Methods: We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data.

Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data.

Background: Real-valued mutual information (MI) has been used in spatial functional network connectivity (FNC) to measure high-order and nonlinear dependence between spatial maps extracted from magnitude-only functional magnetic resonance imaging (fMRI). However, real-valued MI cannot fully capture the group differences in spatial FNC from complex-valued fMRI data with magnitude and phase dependence.

Methods: We propose a complete complex-valued MI method according to the chain rule of MI.

A Millimeter-Wave Broadband Multi-Mode Substrate-Integrated Gap Waveguide Traveling-Wave Antenna with Orbit Angular Momentum.

Orbit angular momentum (OAM) has been considered a new dimension for improving channel capacity in recent years. In this paper, a millimeter-wave broadband multi-mode waveguide traveling-wave antenna with OAM is proposed by innovatively utilizing the transmitted electromagnetic waves (EMWs) characteristic of substrate-integrated gap waveguides (SIGWs) to introduce phase delay, resulting in coupling to the radiate units with a phase jump. Nine "L"-shaped slot radiate elements are cut in a circular order at a certain angle on the SIGW to generate spin angular momentum (SAM) and OAM.

Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia.

Background: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information.

Methods: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase.

Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data.

Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex-valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude-only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex-valued data.

Constrained CPD of Complex-Valued Multi-Subject fMRI Data via Alternating Rank-R and Rank-1 Least Squares.

Complex-valued shift-invariant canonical polyadic decomposition (CPD) under a spatial phase sparsity constraint (pcsCPD) shows excellent separation performance when applied to band-pass filtered complex-valued multi-subject fMRI data. However, some useful information may also be eliminated when using a band-pass filter to suppress unwanted noise. As such, we propose an alternating rank- R and rank-1 least squares optimization to relax the CPD model.

SSPNet: An interpretable 3D-CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data.

Convolutional neural networks (CNNs) have shown promising results in classifying individuals with mental disorders such as schizophrenia using resting-state fMRI data. However, complex-valued fMRI data is rarely used since additional phase data introduces high-level noise though it is potentially useful information for the context of classification. As such, we propose to use spatial source phase (SSP) maps derived from complex-valued fMRI data as the CNN input.

Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint.

Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data.

Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts.

Background: Spatial sparsity has been found to be in line with the intrinsic characteristic of brain activation. However, identifying a sparse representation of complex-valued fMRI data is challenging due to high noise within the phase data.

New Methods: We propose to reduce the noise by combining real and imaginary parts of complex-valued fMRI data along spatial and temporal dimensions to form a real-valued spatiotemporal concatenation model.

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint.

Canonical polyadic decomposition (CPD) of multi-subject complex-valued fMRI data can be used to provide spatially and temporally shared components among groups with both magnitude and phase information. However, the CPD model is not well formulated due to the large subject variability in the spatial and temporal modalities, as well as the high noise level in complex-valued fMRI data. Considering that the shift-invariant CPD can model temporal variability across subjects, we propose to further impose a phase sparsity constraint on the shared spatial maps to denoise the complex-valued components and to model the inter-subject spatial variability as well.

Spatial source phase: A new feature for identifying spatial differences based on complex-valued resting-state fMRI data.

Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data-driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex-valued resting-state fMRI data from 82 individuals.

Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia.

Background: Component splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders.

New Method: This study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients.

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing.

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data.

Background: Complex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution.

Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition.

Background: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability.

New Method: This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way.

ICA of full complex-valued fMRI data using phase information of spatial maps.

Background: ICA of complex-valued fMRI data is challenging because of the ambiguous and noisy nature of the phase. A typical solution is to remove noisy regions from fMRI data prior to ICA. However, it may be more optimal to carry out ICA of full complex-valued fMRI data, since any filtering or voxel-based processing may disrupt information that can be useful to ICA.

How to validate similarity in linear transform models of event-related potentials between experimental conditions?

Background: It is well-known that data of event-related potentials (ERPs) conform to the linear transform model (LTM). For group-level ERP data processing using principal/independent component analysis (PCA/ICA), ERP data of different experimental conditions and different participants are often concatenated. It is theoretically assumed that different experimental conditions and different participants possess the same LTM.

Simultaneous source localization and polarization estimation via non-orthogonal joint diagonalization with vector-sensors.

Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation.

Semiblind spatial ICA of fMRI using spatial constraints.

Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task-related components. Considering that prior information about spatial patterns is also available, a semiblind spatial ICA algorithm utilizing the spatial information was proposed within the framework of constrained ICA with fixed-point learning.

[Proteomic study of paclitaxel on human cervical carcinoma HCE1].

Objective: To explore the mechanism of paclitaxel on the protein expression of human cervical carcinoma cell line HCE1.

Methods: The total proteins extracted from paclitaxel-treated HCE1 cells were analyzed by 2-dimensional gel electrophoresis (2-DE), and compared with those from untreated HCE1 cells. The differential proteins were identified by mass spectrometry.

[Expression of caveolin-1 and the invasion of choriocarcinoma].

Objective: To investigate the association between the expression of caveolin-1(CAV-1) and the invasion of choriocarcinoma, and to explore the effect of CAV-1 small interfering RNA(siRNA) on the invasion of choriocarcinoma cell line JEG-3.

Methods: (1) Matrigel invasion assay and 3-(4,4)-dimethylthiahiazo (-z-yl)-3,5-di-phenytetrazoliumormide (MTT) assay were used to examine the difference in invasion and proliferation ability between JEG-3 cells and JAR cells;(2) Expression of caveolin-1 gene in the human chorionic villi tissues and chorionicnoma cell lines (JEG-3 cells and JAR cells) were detected by semi-quantitative RT-PCR. (3) The effect of CAV-1 siRNA transfection on the expression of CAV-1 mRNA, and the invasion and proliferation ability of JEG-3 cells were measured by RT-PCR, Matrigel invasion assay and MTT assay.

[Effect of carbon dioxide pneumoperitoneum-laparoscopic surgery on tumor seeding and metastases in endometrial cancer].

Objective: To explore the influence of carbon dioxide pneumoperitoneum-laparoscopic surgery on tumor cell seeding and metastases in endometrial cancer.

Methods: Twenty patients with endometrial cancer who underwent laparoscopic surgery and 10 patients with endometrial cancer who underwent laparotomic surgery were enrolled. Each patient was in preoperative clinical StageIand the uterus size in each patient was less than 12 weeks of pregnancy.

Application of the Empirical Mode Decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease.

The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and non-stationary time series. The central idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). An IMF is defined as any function having the number of extrema and the number of zero-crossings equal (or differing at most by one), and also having symmetric envelopes defined by the local minima, and maxima respectively.