Effects of extended pharmacological disruption of zebrafish embryonic heart biomechanical environment on cardiac function, morphology, and gene expression.

BACKGROUND: Biomechanical stimuli are known to be important to cardiac development, but the mechanisms are not fully understood. Here, we pharmacologically disrupted the biomechanical environment of wild-type zebrafish embryonic hearts for an extended duration and investigated the consequent effects on cardiac function, morphological development, and gene expression. RESULTS: Myocardial contractility was significantly diminished or abolished in zebrafish embryonic hearts treated for 72 hours from 2 dpf with 2,3-butanedione monoxime (BDM). Image-based flow simulations showed that flow wall shear stresses were abolished or significantly reduced with high oscillatory shear indices. At 5 dpf, after removal of BDM, treated embryonic hearts were maldeveloped, having disrupted cardiac looping, smaller ventricles, and poor cardiac function (lower ejected flow, bulboventricular regurgitation, lower contractility, and slower heart rate). RNA sequencing of cardiomyocytes of treated hearts revealed 922 significantly up-regulated genes and 1,698 significantly down-regulated genes. RNA analysis and subsequent qPCR and histology validation suggested that biomechanical disruption led to an up-regulation of inflammatory and apoptotic genes and down-regulation of ECM remodeling and ECM-receptor interaction genes. Biomechanics disruption also prevented the formation of ventricular trabeculation along with notch1 and erbb4a down-regulation. CONCLUSIONS: Extended disruption of biomechanical stimuli caused maldevelopment, and potential genes responsible for this are identified.

Hybrid wide-field and scanning microscopy for high-speed 3D imaging.

Wide-field optical microscopy is efficient and robust in biological imaging, but it lacks depth sectioning. In contrast, scanning microscopic techniques, such as confocal microscopy and multiphoton microscopy, have been successfully used for three-dimensional (3D) imaging with optical sectioning capability. However, these microscopic techniques are not very suitable for dynamic real-time imaging because they usually take a long time for temporal and spatial scanning. Here, a hybrid imaging technique combining wide-field microscopy and scanning microscopy is proposed to accelerate the image acquisition process while maintaining the 3D optical sectioning capability. The performance was demonstrated by proof-of-concept imaging experiments with fluorescent beads and zebrafish liver.

Analytic method to optimize aperture design in focal modulation microscopy.

Focal modulation microscopy (FMM) has been demonstrated more effective than confocal microscopy for imaging of thick biological tissues. To improve its penetration depth further, we propose a simple analytical method to enlarge the modulation depth, the unique property of FMM directly linked to its signal-to-noise ratio. The modulation depth increases as the excitation intensity of the binary phase aperture status is pushed further away from the focal region of the detection optics, thereby creating a dark region in the focal volume, which we call maximally flat crater (MFC). By direct algebraic manipulation, MFCs are achieved for both scalar and vector diffraction optics. Numerical results show that the modulation depth from MFC is very close to the maximum values, with a small difference less than 3% for the same number of subapertures. Applications of bifocus produced by MFC apertures are also discussed.

Real-time 3D particle manipulation visualized using volume holographic gratings.

Holographic optical tweezers (HOTs) extend optical trapping into three dimensions. Volume imaging then becomes a concern as trapped objects are easily moved out of focus of the imaging objective lens. Here we demonstrate a three-dimensional real-time interactive optical trapping, manipulating, and imaging system based on HOTs incorporated with volume holographic microscope. Intensity information about the trapped objects at multiple depths can be captured in a single measurement. This method is compatible with most imaging modes such as bright-field and fluorescence.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories. We design an autoencoder with a latent space where an orthogonal constraint is imposed to separate the space of global trajectories from individual trajectories. We also devise a cross-reconstruction loss to ensure consistency of global trajectories and enhance the orthogonality between representation spaces. To evaluate UOMETM, we conducted simulation experiments on images to verify that every component functions as intended. Furthermore, we evaluated its performance and robustness using longitudinal brain cortical thickness from two Alzheimer's disease (AD) datasets. Comparative analyses with state-of-the-art methods revealed UOMETM's superiority in identifying global and individual longitudinal patterns, achieving a lower reconstruction error, superior orthogonality, and higher accuracy in AD classification and conversion forecasting. Remarkably, we found that the space of global trajectories did not significantly contribute to AD classification compared to the space of individual trajectories, emphasizing their clear separation. Moreover, our model exhibited satisfactory generalization and robustness across different datasets. The study shows the outstanding performance and potential clinical use of UOMETM in the context of longitudinal data analysis.

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

BACKGROUND: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms. AIM: Establish an objective measurement and evaluation of MDD. METHODS: Functional near-infrared spectroscopy (fNIRS) was used to investigate the brain activity of MDD patients and healthy controls (HCs). Leveraging a sizeable fNIRS dataset of 263 HCs and 251 patients with MDD, including mild to moderate MDD (mMDD; n = 139) and severe MDD (sMDD; n = 77), we developed an interpretable deep learning model for screening MDD and staging its severity. RESULTS: The proposed deep learning model achieved an accuracy of 80.9% in diagnostic classification and 78.6% in severity staging for MDD. We discerned five channels with the most significant contribution to MDD identification through Shapley additive explanations (SHAP), located in the right medial prefrontal cortex, right dorsolateral prefrontal cortex, right superior temporal gyrus, and left posterior superior frontal cortex. The findings corresponded closely to the features of haemoglobin responses between HCs and individuals with MDD, as we obtained a good discriminative ability for MDD using cortical channels that are related to the disorder, namely the frontal and temporal cortical channels with areas under the curve of 0.78 and 0.81, respectively. CONCLUSION: Our study demonstrated the potential of integrating the fNIRS system with artificial intelligence algorithms to classify and stage MDD in clinical settings using a large dataset. This approach can potentially enhance MDD assessment and provide insights for clinical diagnosis and intervention.

Recent advances in optical microscopy methods for subcellular imaging of thick biological tissues.

Optical microscopy has been widely applied in cellular and subcellular imaging. Conventional light microscopes, however, have rather limited imaging depth and are limited to imaging only mechanically sectioned thin samples. Multiphoton microscopy and optical coherence microscopy are common techniques for diffraction-limited imaging beyond an imaging depth of 0.5 mm. Focal modulation microscopy is a novel method that combines confocal spatial filtering with focal modulation to reject out-of-focus backgrounds. Focal modulation microscopy has demonstrated an imaging depth comparable to those of multiphoton microscopy and optical coherence microscopy, near-real-time image acquisition, and capability with a multiple contrast mechanism.

Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction.

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks. We design the ML-Joint-Att network with edge and node convolutional operators, an adaptive inception module, and three attention modules, including network-wise, region-wise, and region-wise joint attention modules. The adaptive inception learns brain functional connectivity at multiple spatial scales. The network-wise and region-wise attention modules take the multi-scale functional connectivities as input and learn features at the network and regional levels for individual tasks. Moreover, the joint attention module is designed as region-wise joint attention to learn shared brain features that contribute to and compensate for the prediction of multiple tasks. We employed the Adolescent Brain Cognitive Development (ABCD) dataset (n =9092) to evaluate the ML-Joint-Att network for the prediction of cognitive flexibility and inhibition. Our experiments demonstrated the usefulness of the three attention modules and identified brain functional connectivities and regions specific and common between cognitive flexibility and inhibition. In particular, the joint attention module can significantly improve the prediction of both cognitive functions. Moreover, leave-one-site cross-validation showed that the ML-Joint-Att network is robust to independent samples obtained from different sites of the ABCD study. Our network outperformed existing machine learning techniques, including Brain Bias Set (BBS), spatio-temporal graph convolution network (ST-GCN), and BrainNetCNN. We demonstrated the generalization of our method to other applications, such as the prediction of fluid intelligence and crystallized intelligence, which also outperformed the ST-GCN and BrainNetCNN.

Light-sheet laser speckle imaging for cilia motility assessment.

Mucociliary clearance is an important innate defense mechanism predominantly mediated by ciliated cells in the upper respiratory tract. Ciliary motility on the respiratory epithelium surface and mucus pathogen trapping assist in maintaining healthy airways. Optical imaging methods have been used to obtain several indicators for assessing ciliary movement. Light-sheet laser speckle imaging (LSH-LSI) is a label-free and non-invasive optical technique for three-dimensional and quantitative mapping of velocities of microscopic scatterers. Here, we propose to use an inverted LSH-LSI platform to study cilia motility. We have experimentally confirmed that LSH-LSI can reliably measure the ciliary beating frequency and has the potential to provide many additional quantitative indicators for characterizing the ciliary beating pattern without labeling. For example, the asymmetry between the power stroke and the recovery stroke is apparent in the local velocity waveform. PIV (particle imaging velocimetry) analysis of laser speckle data could determine the cilia motion directions in different phases.

Multi-contrast focal modulation microscopy for in vivo imaging of thick biological tissues.

In vivo high resolution imaging of biological tissues is desirable for a wide range of biomedical applications. Recently focal modulation microscopy (FMM) has been developed and an imaging depth comparable to multi-photon microscopy (MPM) and optical coherence microscopy (OCM) has been achieved. Here we report the first focal modulation microscope that is capable of performing real-time fluorescence and scattering imaging simultaneously on thick biological tissues. A novel spatiotemporal phase modulator (STPM) has been designed and integrated into such a microscope to achieve high performances in terms of imaging speed, contrast, effective spatial resolution, signal to noise ratio, and compatibility with multiple excitation wavelengths.

Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches.

BACKGROUND: Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established. METHODS: Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD. Supervised classifiers (e.g., support vector machine (SVM), k-nearest neighbors (KNN)) cooperated with cross-validation were implemented to evaluate the predictive capability of selected features in a training set. Another test set that was not involved in developing the algorithms enabled the independent assessment of the model's generalization. FINDINGS: For the classification with the optimal fusion features, the SVM classifier achieved the highest accuracy of 75.6% ± 4.7% in the nested cross-validation, and the correct prediction rate of 78.0% with a sensitivity of 75.0% and a specificity of 81.4% in the test set. Moreover, the multiway ANOVA test on clinical and demographic factors confirmed that twenty out of 39 optimal features were significantly correlated with the MDD-distinctive consequence. INTERPRETATION: The abnormal prefrontal activity of MDD may be quantified as diminished relative intensity and inappropriate activation timing of hemodynamic response, resulting in an objectively measurable biomarker for assessing cognitive deficits and screening MDD at the early stage. FUNDING: This study was funded by NUS iHeathtech Other Operating Expenses (R-722-000-004-731).

Plasmonic chiral contrast agents for optical coherence tomography: numerical study.

Optical coherence tomography (OCT) is a widely used morphological imaging modality. Various contrast agents, which change localized optical properties, are used to extend the applicability of OCT, where intrinsic contrast is not sufficient. In this paper we propose the use of a dual-rod gold nano-structure as a polarization sensitive contrast agent. Using numerical simulation, we demonstrated that the proposed structure has tunable chiral response. Enhanced cross-section due to Plasmon resonance in gold nanoparticles, along with the chiral behavior can provide enhanced detection sensitivity. The proposed contrast agents may extend the applicability of OCT to the problems that require the molecular contrast with enhanced sensitivity.

Considerations of aperture configuration in focal modulation microscopy from the standpoint of modulation depth.

Focal modulation microscopy (FMM) is a simple, yet efficient, method to preserve image quality in terms of signal-to-background ratio by selecting ballistic photons for image formation. The aim of this paper is to investigate the effect of the various aperture configurations of the spatial phase modulator on the modulation depth of the FMM signal. The definition of modulation depth in FMM and its calculation method are introduced. According to two brief principles of choosing aperture configuration, three types of configurations with different numbers of zones ranging from two to six (totaling eight aperture configurations) are selected, and their corresponding modulation depths and attainable spatial resolutions are simulated. The results show that the modulation depth increases significantly when the number of zones varies from two to six, with a slight or no sacrifice in resolution. In summary, the annular configuration is superior to the fan- and stripe-shaped configurations in modulation depth and spatial resolution.

Optical breast atlas as a testbed for image reconstruction in optical mammography.

We present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation. A finite element-based method is used to deform the breast vasculature models from their natural shapes to generate the second atlas, compressed breast models. Breast compression is typically done in X-ray mammography but also necessary for some optical mammography systems. Technical validation is presented to demonstrate how the atlases can be used to study the image reconstruction algorithms. Optical measurements are generated numerically with compressed breast models and a predefined configuration of light sources and photodetectors. The simulated data is fed into three standard image reconstruction algorithms to reconstruct optical images of the vasculature, which can then be compared with the ground truth to evaluate their performance.

Focal modulation microscopy: a theoretical study.

Focal modulation microscopy is an emerging fluorescence microscopy technique for in vivo imaging of thick biological tissues. Here, we present a theoretical study to assess its performance. The scalar diffraction theory is combined with Monte Carlo simulation to evaluate the signal-to-background ratio at various depths. The performance of confocal microscopy with a similar optical setup is also evaluated for comparison.

Quadrature demodulation in line-scan focal modulation microscopy for imaging three-dimensional zebrafish neural structure.

Visualizing biological processes in neuroscience requires in vivo functional imaging at single-neuron resolution, high image acquisition speed and strong optical sectioning ability. However, due to light scattering of in tissue, very often conventional wide-field fluorescence microscopes are unable to resolve cells in the presence of a strong out-of-focus background. Line-scan focal modulation microscopy enables high temporal resolution and good optical sectioning ability at the same time. Here we demonstrate a quadrature demodulation method to extract the focal information with an extended frequency bandwidth and therefore higher spatial resolution. The performance of the demodulation scheme in line-scan focal modulation microscope has been evaluated by performing imaging experiments with fluorescence beads and zebrafish neural structure. Reduced background, reduced artifacts and more detailed morphological information are evident in the obtained images.

Multifunctional laser speckle imaging.

We have developed a multi-functional laser speckle imaging system, which can be operated in both the surface illumination laser speckle contrast imaging (SI-LSCI) mode and the line scan laser speckle contrast imaging (LS-LSCI) mode. The system has been applied to imaging the chicken embryos to visualize both the blood flow and morphological details of the vasculature. The experimental results demonstrated that LS-LSCI is capable of detecting and quantifying blood flow in blood vessels smaller and deeper than those detectable by conventional SI-LSCI. Furthermore, the line scan mode is also capable of producing depth-resolved absorption-based morphological images of tissue, augmenting flow-based functional images.

4D modelling of fluid mechanics in the zebrafish embryonic heart.

Abnormal blood flow mechanics can result in pathological heart malformation, underlining the importance of understanding embryonic cardiac fluid mechanics. In the current study, we performed image-based computational fluid dynamics simulation of the zebrafish embryonic heart ventricles and characterized flow mechanics, organ dynamics, and energy dynamics in detail. 4D scans of 5 days post-fertilization embryonic hearts with GFP-labelled myocardium were acquired using line-scan focal modulation microscopy. This revealed that the zebrafish hearts exhibited a wave-like contractile/relaxation motion from the inlet to the outlet during both systole and diastole, which we showed to be an energy efficient configuration. No impedance pumping effects of pressure and velocity waves were observed. Due to its tube-like configuration, inflow velocities were higher near the inlet and smaller at the outlet and vice versa for outflow velocities. This resulted in an interesting spatial wall shear stress (WSS) pattern where WSS waveforms near the inlet and those near the outlet were out of phase. There was large spatial variability in WSS magnitudes. Peak WSS was in the range of 47.5-130 dyne/cm2 at the inflow and outflow tracts, but were much smaller, in the range of 4-11 dyne/cm2, in the mid-ventricular segment. Due to very low Reynolds number and the highly viscous environment, intraventricular pressure gradients were high, suggesting substantial energy losses of flow through the heart.

Improved spatial resolution in fluorescence focal modulation microscopy.

We show that the focal modulation microscopy (FMM), which combines a spatial phase modulator with confocal microscopy, results in an improvement in spatial resolution. This technique was introduced to increase imaging depth into tissue and rejection of background from a thick scattering object. A theory for image formation in FMM is presented, and the effects of detecting the in-phase modulated fluorescence signal are discussed. Compared with conventional confocal microscopy, the width of the point-spread function for the in-phase fluorescence signal is improved by 16.4%. When applied to saturable fluorescence, the half-width at half-maximum is improved by 33.6%, 50.0%, and 62.9%, at demodulation frequencies 2omega, 4omega, and 8omega, respectively.

Edge enhancement for in-phase focal modulation microscope.

In-phase focal modulation microscopy (IPFMM) with single photon excited fluorescence is presented. Optical transfer functions and images of thin and thick fluorescent edges in IPFMM are investigated. The results show that, compared with confocal microscopy, using IPFMM can result in a sharper image of the edge, and the edge gradient can be increased up to 75.4% and 58.9% for a thick edge and a thin edge, respectively. Signal level is also discussed, and the results show that, to obtain high transverse resolution with IPFMM, the normalized detector pinhole radius should not exceed 2.8.