Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography.

Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require extensive computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for estimating partial differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is considerably accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network.

Cylindrical organs, e.g., blood vessels, airways, and intestines, are ubiquitous structures in biomedical optical imaging analysis. Image segmentation of these structures serves as a vital step in tissue physiology analysis. Traditional model-driven segmentation methods seek to fit the structure by constructing a corresponding topological geometry based on domain knowledge. Classification-based deep learning methods neglect the geometric features of the cylindrical structure and therefore cannot ensure the continuity of the segmentation surface. In this paper, by treating the cylindrical structures as a 3D graph, we introduce a novel contour-based graph neural network for 3D cylindrical structure segmentation in biomedical optical imaging. Our proposed method, which we named CylinGCN, adopts a novel learnable framework that extracts semantic features and complex topological relationships in the 3D volumetric data to achieve continuous and effective 3D segmentation. Our CylinGCN consists of a multiscale 3D semantic feature extractor for extracting inter-frame multiscale semantic features, and a residual graph convolutional network (GCN) contour generator that combines the semantic features and cylindrical topological priors to generate segmentation contours. We tested the CylinGCN framework on two types of optical tomographic imaging data, small animal whole body photoacoustic tomography (PAT) and endoscopic airway optical coherence tomography (OCT), and the results show that CylinGCN achieves state-of-the-art performance. Code will be released at https://github.com/lzc-smu/CylinGCN.git.

Unsupervised Fusion of Misaligned PAT and MRI Images via Mutually Reinforcing Cross-Modality Image Generation and Registration.

Photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) are two advanced imaging techniques widely used in pre-clinical research. PAT has high optical contrast and deep imaging range but poor soft tissue contrast, whereas MRI provides excellent soft tissue information but poor temporal resolution. Despite recent advances in medical image fusion with pre-aligned multimodal data, PAT-MRI image fusion remains challenging due to misaligned images and spatial distortion. To address these issues, we propose an unsupervised multi-stage deep learning framework called PAMRFuse for misaligned PAT and MRI image fusion. PAMRFuse comprises a multimodal to unimodal registration network to accurately align the input PAT-MRI image pairs and a self-attentive fusion network that selects information-rich features for fusion. We employ an end-to-end mutually reinforcing mode in our registration network, which enables joint optimization of cross-modality image generation and registration. To the best of our knowledge, this is the first attempt at information fusion for misaligned PAT and MRI. Qualitative and quantitative experimental results show the excellent performance of our method in fusing PAT-MRI images of small animals captured from commercial imaging systems.

Automatic 3D reconstruction of an anatomically correct upper airway from endoscopic long range OCT images.

Endoscopic airway optical coherence tomography (OCT) is a non-invasive and high resolution imaging modality for the diagnosis and analysis of airway-related diseases. During OCT imaging of the upper airway, in order to reliably characterize its 3D structure, there is a need to automatically detect the airway lumen contour, correct rotational distortion and perform 3D airway reconstruction. Based on a long-range endoscopic OCT imaging system equipped with a magnetic tracker, we present a fully automatic framework to reconstruct the 3D upper airway model with correct bending anatomy. Our method includes an automatic segmentation method for the upper airway based on dynamic programming algorithm, an automatic initial rotation angle error correction method for the detected 2D airway lumen contour, and an anatomic bending method combined with the centerline detected from the magnetically tracked imaging probe. The proposed automatic reconstruction framework is validated on experimental datasets acquired from two healthy adults. The result shows that the proposed framework allows the full automation of 3D airway reconstruction from OCT images and thus reveals its potential to improve analysis efficiency of endoscopic OCT images.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging. Recently, deep-learning-based image deconvolution approaches have shown promise for image recovery. However, PAT imaging presents unique challenges, including spatially varying resolution and the absence of ground truth data. Consequently, there is a pressing need for a novel learning strategy specifically tailored for PAT imaging. Herein, we propose a configurable network model named Deep hybrid Image-PSF Prior (DIPP) that builds upon the physical image degradation model of PAT. DIPP is an unsupervised and deeply learned network model that aims to extract the ideal PAT image from complex system degradation. Our DIPP framework captures the degraded information solely from the acquired PAT image, without relying on ground truth or labeled data for network training. Additionally, we can incorporate the experimentally measured Point Spread Functions (PSFs) of the specific PAT system as a reference to further enhance performance. To evaluate the algorithm's effectiveness in addressing multiple degradations in PAT, we conduct extensive experiments using simulation images, publicly available datasets, phantom images, and in vivo small animal imaging data. Comparative analyses with classical analytical methods and state-of-the-art deep learning models demonstrate that our DIPP approach achieves significantly improved restoration results in terms of image details and contrast.

Long non-coding RNAs in schizophrenia: Genetic variations, treatment markers and potential targeted signaling pathways.

Schizophrenia (SZ), a complex and debilitating spectrum of psychiatric disorders, is now mainly attributed to multifactorial etiology that includes genetic and environmental factors. Long non-coding RNAs (lncRNAs) are gaining popularity as a way to better understand the comprehensive mechanisms beneath the clinical manifestation of SZ. Only in recent years has it been elucidated that mammalian genomes encode thousands of lncRNAs. Strikingly, roughly 30-40% of these lncRNAs are extensively expressed in different regions across the brain, which may be closely associated with SZ. The therapeutic and adverse effects of atypical antipsychotic drugs (AAPDs) are partially reflected by their role in the regulation of lncRNAs. This begs the question directly, do any lncRNAs exist as biomarkers for AAPDs treatment? Furthermore, we comprehend a range of mechanistic investigations that have revealed the regulatory roles for lncRNAs both involved in the brain and the periphery of SZ. More crucially, we also combine insights from a variety of signaling pathways to argue that lncRNAs probably play critical roles in SZ via their interactive downstream factors. This review provides a thorough understanding regarding dysregulation of lncRNAs, corresponding genetic alternations, as well as their potential regulatory roles in the pathology of SZ, which might help reveal useful therapeutic targets in SZ.

In vivo co-registered hybrid-contrast imaging by successive photoacoustic tomography and magnetic resonance imaging.

Magnetic resonance imaging (MRI) and photoacoustic tomography (PAT) offer two distinct image contrasts. To integrate these two modalities, we present a comprehensive hardware-software solution for the successive acquisition and co-registration of PAT and MRI images in in vivo animal studies. Based on commercial PAT and MRI scanners, our solution includes a 3D-printed dual-modality imaging bed, a 3-D spatial image co-registration algorithm with dual-modality markers, and a robust modality switching protocol for in vivo imaging studies. Using the proposed solution, we successfully demonstrated co-registered hybrid-contrast PAT-MRI imaging that simultaneously displays multi-scale anatomical, functional and molecular characteristics on healthy and cancerous living mice. Week-long longitudinal dual-modality imaging of tumor development reveals information on size, border, vascular pattern, blood oxygenation, and molecular probe metabolism of the tumor micro-environment at the same time. The proposed methodology holds promise for a wide range of pre-clinical research applications that benefit from the PAT-MRI dual-modality image contrast.

Astrocyte dysfunction drives abnormal resting-state functional connectivity in depression.

Major depressive disorder (MDD) is a devastating mental disorder that affects up to 17% of the population worldwide. Although brain-wide network-level abnormalities in MDD patients via resting-state functional magnetic resonance imaging (rsfMRI) exist, the mechanisms underlying these network changes are unknown, despite their immense potential for depression diagnosis and management. Here, we show that the astrocytic calcium-deficient mice, inositol 1,4,5-trisphosphate-type-2 receptor knockout mice (Itpr2-/- mice), display abnormal rsfMRI functional connectivity (rsFC) in depression-related networks, especially decreased rsFC in medial prefrontal cortex (mPFC)-related pathways. We further uncover rsFC decreases in MDD patients highly consistent with those of Itpr2-/- mice, especially in mPFC-related pathways. Optogenetic activation of mPFC astrocytes partially enhances rsFC in depression-related networks in both Itpr2-/- and wild-type mice. Optogenetic activation of the mPFC neurons or mPFC-striatum pathway rescues disrupted rsFC and depressive-like behaviors in Itpr2-/- mice. Our results identify the previously unknown role of astrocyte dysfunction in driving rsFC abnormalities in depression.

Pixel-wise reconstruction of tissue absorption coefficients in photoacoustic tomography using a non-segmentation iterative method.

In Photoacoustic Tomography (PAT), the acquired image represents a light energy deposition map of the imaging object. For quantitative imaging, the PAT image is converted into an absorption coefficient ( µ a ) map by dividing the light fluence (LF). Previous methods usually assume a uniform tissue µ a distribution, and consequently degrade the LF correction results. Here, we propose a simple method to reconstruct the pixel-wise µ a map. Our method is based on a non-segmentation-based iterative algorithm, which alternately optimizes the LF distribution and the µ a map. Using simulation data, as well as phantom and animal data, we implemented our algorithm and compared it to segmentation-based correction methods. The results show that our method can obtain accurate estimation of the LF distribution and therefore improve the image quality and feature visibility of the µ a map. Our method may facilitate efficient calculation of the concentration distributions of endogenous and exogenous agents in vivo.

Diminished treatment response in relapsed versus first-episode schizophrenia as revealed by a panel of blood-based biomarkers: A combined cross-sectional and longitudinal study.

There is a paucity of biomarkers for the prediction of treatment response in schizophrenia. In this study, we aimed to investigate whether diminished antipsychotic treatment response in relapsed versus first-episode schizophrenia can be revealed and predicted by a panel of blood-based biomarkers. A cross-sectional cohort consisting of 655 schizophrenia patients at different episodes and 606 healthy controls, and a longitudinal cohort including 52 first-episode antipsychotic-naïve schizophrenia patients treated with the same antipsychotic drugs during the 5-year follow-up of their first three episodes were enrolled. Plasma biomarker changes and symptom improvement were compared between the drug-free phase of psychosis onset and after 4 weeks of atypical antipsychotic drug (AAPD) treatment. In response to treatment, the extent of changes in the biomarkers of bioenergetic, purinergic, phospholipid and neurosteroid metabolisms dwindled down as number of episode and illness duration increased in relapsed schizophrenia. The changes of creatine, inosine, progesterone, allopregnanolone, cortisol and PE(16:0/22:6) were significantly correlated with the improvement of symptomatology. Inosine and progesterone at baseline were shown to be strong predictive biomarkers of treatment response. The results suggest that AAPD treatment response is diminished in the context of relapse, and our findings open new avenues for understanding the pathophysiology of treatment-resistance schizophrenia.

Effect of Probiotic Supplements on Oxidative Stress Biomarkers in First-Episode Bipolar Disorder Patients: A Randomized, Placebo-Controlled Trial.

Background: Currently no study has examined the effects of probiotic administration on the symptoms of anxiety, depression, and mania, as well as their correlations with the biomarkers of oxidative stress in patients with bipolar disorder (BPD). The aim of this study is to determine the effects of probiotic supplementation on plasma oxidative stress-related biomarkers and different domains of clinical symptom in patients suffering from BPD. Methods: Eighty first-episode drug-naive patients with BPD were recruited. The subjects were randomized to receive psychotropic drugs supplementing with either probiotic or placebo and scheduled to evaluate with follow-ups for clinical symptom improvements and changes in the oxidative stress biomarkers. The Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Young Mania Rating Scale were used to assess the clinical symptomatology. The panel of plasma oxidative stress biomarkers were determined by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) at baseline and for 3 months of follow-up, i.e., at post-treatment month 1, 2, and 3. Results: After 3 months of intervention, decreased levels of plasma lysophosphatidylcholines (LPCs) were found in both placebo and probiotic groups. However, six other oxidative stress biomarkers (i.e., creatine, inosine, hypoxanthine, choline, uric acid, allantoic acid) increased in BPD patients after the two types of therapies. In addition, a positive correlation between changes of LPC (18:0) and YMRS scale was found in BPD patients and this association only existed in the probiotic group. Additionally, the mania symptom greatly alleviated (pretreatment-posttreatment, odds ratio = 0.09, 95%CI = 0.01, 0.64, p= 0.016) in patients who received probiotic supplements as compared with the placebo group. Conclusion: The changes in plasma biomarkers of oxidative stress in patients with BPD have a potential to be trait-like markers, and serve as prognostic indexes for bipolar patients. Daily intakes of probiotics have advantageous effects on BPD patients with certain clinical symptoms, especially manic symptoms. The treatment may be a promising adjunctive therapeutic strategy for BPD patients in manic episode.

MRI Information-Based Correction and Restoration of Photoacoustic Tomography.

As an emerging molecular imaging modality, Photoacoustic Tomography (PAT) is capable of mapping tissue physiological metabolism and exogenous contrast agent information with high specificity. Due to its ultrasonic detection mechanism, the precise localization of targeted lesions has long been a challenge for PAT imaging. The poor soft-tissue contrast of the PAT image makes this process difficult and inaccurate. To meet this challenge, in this study, we first make use of the rich and clear structural information brought about by another advanced imaging modality, Magnetic Resonance Imaging (MRI), to assist organ segmentation and correct for the light fluence attenuation of PAT. We demonstrate improved feature visibility and enhanced localization of endogenous and exogenous agents in the fluence corrected PAT images. Compared with PAT-based methods, the contrast-to-noise ratio (CNR) of our MRI-assisted method increases by 29.1% in live animal experiments. Furthermore, we show that the co-registered MRI image can also be incorporated into PAT image restoration, and achieves improved anatomical landscape and soft-tissue contrast (CNR increased by 25.36%) while preserving similar spatial resolution. This PAT-MRI combination provides excellent structural, functional and molecular images of the subject, and may enable more comprehensive analysis of various preclinical research applications.

Disturbance of neurotransmitter metabolism in drug-naïve, first-episode major depressive disorder: a comparative study on adult and adolescent cohorts.

Neurotransmitter metabolism plays a critical role in the pathophysiology of major depressive disorder (MDD). However, whether the neurotransmitter metabolism in adolescent MDD is differentiated from adult MDD is still elusive. In the current study, plasma concentrations of monoamine and amino acid neurotransmitters as well as their metabolites, including tryptophan (TRP), kynurenine (KYN), kynurenic acid (KYNA), serotonin (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), dopamine (DA), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), norepinephrine (NE), vanillylmandelic acid (VMA), 3-methoxy-4-hydroxyphenylglycol (MHPG), glutamine (GLN), glutamate (GLU) and gamma-aminobutyric acid (GABA), were measured and compared in two cohorts of subjects (adult cohort: 31 first-episode MDD vs. 35 healthy controls; adolescent cohort: 33 first-episode MDD vs. 30 healthy controls). To assess the effects of antidepressant treatment, we also analyzed the concentrations of these indexes pre- and post-treatment in adult and adolescent cohorts. At baseline, the deficits of neurotransmitter metabolism in adult MDD were manifested in all the neurotransmitter systems. In contrast, for adolescent MDD, the dysregulation of neurotransmission was mainly indicated in the catecholaminergic systems. After antidepressant treatment, adult MDD showed increased TRP, KYN, KYNA and GLU levels, together with decreased levels of 5-HIAA and DOPAC. Adolescent MDD illustrated an increased level of 5-HT and decreased levels of TRP and GABA. The improvements of Hamilton total scores correlated with the changes in plasma TRP and the turnover of KYN/TRP after treatment in all MDD patients. However, these correlations were only manifested in the adult MDD rather than in adolescent MDD patients. The findings highlight the shared and distinguished neurotransmitter pathways in MDD and emphasize the different antidepressant responses between adults and adolescents. Potentially, the neurotransmitters above could serve as diagnostic biomarkers and provide a novel pharmacological treatment strategy for MDD.

Multispectral interlaced sparse sampling photoacoustic tomography based on directional total variation.

BACKGROUND AND OBJECTIVE: Photoacoustic tomography (PAT) is capable of obtaining cross-sectional images of small animals that represent the optical absorption of biological tissues. The multispectral Interlaced Sparse Sampling PAT, or ISS-PAT, is a previously proposed PAT imaging method that offered high quality images with much sparser transducer angular coverage. Although it provides superior imaging performance, the original ISS-PAT method suffered from a heavy computation burden, which hinders its practical application. METHODS: Here, we propose a new regularization scheme based on the directional total variation (dTV) for ISS-PAT. This method efficiently imposes the structural information by considering both the edge position and direction information of the anatomical prior image in ISS-PAT. It does not require image segmentation, and can be conveniently solved by a modified alternating direction of multipliers (ADMM) algorithm. RESULTS: We perform simulation, tissue mimicking phantom and in vivo small animal experiments to evaluate the proposed scheme. The reconstructed PAT images showed image quality and spectral un-mixing accuracy close to those obtained by non-local means based ISS-PAT, but with much shorter image reconstruction time. For a 1/6 sparse sampling rate, the average efficiency improvement is nearly 16-folds. CONCLUSIONS: The experimental results demonstrate the feasibility of the dTV regularization scheme for ISS-PAT. Its efficient image reconstruction performance facilitates the potential of the hardware realization and practical applications of the ISS-PAT.

Automatic 3-D segmentation and volumetric light fluence correction for photoacoustic tomography based on optimal 3-D graph search.

Preclinical imaging with photoacoustic tomography (PAT) has attracted wide attention in recent years since it is capable of providing molecular contrast with deep imaging depth. The automatic extraction and segmentation of the animal in PAT images is crucial for improving image analysis efficiency and enabling advanced image post-processing, such as light fluence (LF) correction for quantitative PAT imaging. Previous automatic segmentation methods are mostly two-dimensional approaches, which failed to conserve the 3-D surface continuity because the image slices were processed separately. This discontinuity problem further hampers LF correction, which, ideally, should be carried out in 3-D due to spatially diffused illumination. Here, to solve these problems, we propose a volumetric auto-segmentation method for small animal PAT imaging based on the 3-D optimal graph search (3-D GS) algorithm. The 3-D GS algorithm takes into account the relation among image slices by constructing a 3-D node-weighted directed graph, and thus ensures surface continuity. In view of the characteristics of PAT images, we improve the original 3-D GS algorithm on graph construction, solution guidance and cost assignment, such that the accuracy and smoothness of the segmented animal surface were guaranteed. We tested the performance of the proposed method by conducting in vivo nude mice imaging experiments with a commercial preclinical cross-sectional PAT system. The results showed that our method successfully retained the continuous global surface structure of the whole 3-D animal body, as well as smooth local subcutaneous tumor boundaries at different development stages. Moreover, based on the 3-D segmentation result, we were able to simulate volumetric LF distribution of the entire animal body and obtained LF corrected PAT images with enhanced structural visibility and uniform image intensity.

Clozapine Induced Disturbances in Hepatic Glucose Metabolism: The Potential Role of PGRMC1 Signaling.

Newly emerging evidence has implicated that progesterone receptor component 1 (PGRMC1) plays a novel role not only in the lipid disturbance induced by atypical antipsychotic drugs (AAPD) but also in the deterioration of glucose homoeostasis induced by clozapine (CLZ) treatment. The present study aimed to investigate the role of PGRMC1 signaling on hepatic gluconeogenesis and glycogenesis in male rats following CLZ treatment (20 mg/kg daily for 4 weeks). Recombinant adeno-associated viruses (AAV) were constructed for the knockdown or overexpression of hepatic PGRMC1. Meanwhile, AG205, the specific inhibitor of PGRMC1 was also used for functional validation of PGRMC1. Hepatic protein expressions were measured by western blotting. Meanwhile, plasma glucose, insulin and glucagon, HbA1c and hepatic glycogen were also determined by assay kits. Additionally, concentrations of progesterone (PROG) in plasma, liver and adrenal gland were measured by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Our study demonstrated that CLZ promoted the process of gluconeogenesis and repressed glycogenesis, respectively mediated by PI3K-Akt-FOXO1 and GSK3ß signaling via inhibition of PGRMC1-EGFR/GLP1R in rat liver, along with an increase in fasting blood glucose, HbA1c levels and a decrease in insulin and hepatic glycogen levels. Furthermore, through PGRMC1-EGFR/GLP1R-PI3K-Akt pathway, knockdown or inhibition (by AG205) of PGRMC1 mimics, whereas its overexpression moderately alleviates CLZ-induced glucose disturbances. Potentially, the PGRMC1 target may be regarded as a novel therapeutic strategy for AAPD-induced hepatic glucose metabolism disorder.

Repurposing of Anti-Diabetic Agents as a New Opportunity to Alleviate Cognitive Impairment in Neurodegenerative and Neuropsychiatric Disorders.

Cognitive impairment is a shared abnormality between type 2 diabetes mellitus (T2DM) and many neurodegenerative and neuropsychiatric disorders, such as Alzheimer's disease (AD) and schizophrenia. Emerging evidence suggests that brain insulin resistance plays a significant role in cognitive deficits, which provides the possibility of anti-diabetic agents repositioning to alleviate cognitive deficits. Both preclinical and clinical studies have evaluated the potential cognitive enhancement effects of anti-diabetic agents targeting the insulin pathway. Repurposing of anti-diabetic agents is considered to be promising for cognitive deficits prevention or control in these neurodegenerative and neuropsychiatric disorders. This article reviewed the possible relationship between brain insulin resistance and cognitive deficits. In addition, promising therapeutic interventions, especially current advances in anti-diabetic agents targeting the insulin pathway to alleviate cognitive impairment in AD and schizophrenia were also summarized.

Photoacoustic Tomography Image Restoration With Measured Spatially Variant Point Spread Functions.

The spatial resolution of photoacoustic tomography (PAT) can be characterized by the point spread function (PSF) of the imaging system. Due to the tomographic detection geometry, the PAT image degradation model could be generally described by using spatially variant PSFs. Deconvolution of the PAT image with these PSFs could restore image resolution and recover object details. Previous PAT image restoration algorithms assume that the degraded images can be restored by either a single uniform PSF, or some blind estimation of the spatially variant PSFs. In this work, we propose a PAT image restoration method to improve image quality and resolution based on experimentally measured spatially variant PSFs. Using photoacoustic absorbing microspheres, we design a rigorous PSF measurement procedure, and successfully acquire a dense set of spatially variant PSFs for a commercial cross-sectional PAT system. A pixel-wise PSF map is further obtained by employing a multi-Gaussian-based fitting and interpolation algorithm. To perform image restoration, an optimization-based iterative restoration model with two kinds of regularizations is proposed. We perform phantom and in vivo mice imaging experiments to verify the proposed method, and the results show significant image quality and resolution improvement.

Wireless multilateral devices for optogenetic studies of individual and social behaviors.

Advanced technologies for controlled delivery of light to targeted locations in biological tissues are essential to neuroscience research that applies optogenetics in animal models. Fully implantable, miniaturized devices with wireless control and power-harvesting strategies offer an appealing set of attributes in this context, particularly for studies that are incompatible with conventional fiber-optic approaches or battery-powered head stages. Limited programmable control and narrow options in illumination profiles constrain the use of existing devices. The results reported here overcome these drawbacks via two platforms, both with real-time user programmability over multiple independent light sources, in head-mounted and back-mounted designs. Engineering studies of the optoelectronic and thermal properties of these systems define their capabilities and key design considerations. Neuroscience applications demonstrate that induction of interbrain neuronal synchrony in the medial prefrontal cortex shapes social interaction within groups of mice, highlighting the power of real-time subject-specific programmability of the wireless optogenetic platforms introduced here.