[18F]FDG PET integrated with structural MRI for accurate brain age prediction.

PURPOSE: Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS: The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS: Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION: The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.

Efficient degradation of sulfamethoxazole in various waters with peroxymonosulfate activated by magnetic-modified sludge biochar: Surface-bound radical mechanism.

First time, this study synthesized a magnetic-modified sludge biochar (MSBC) as an activator of peroxymonosulfate (PMS) to eliminate sulfamethoxazole (SMX). The removal efficiency of SMX reached 96.1% at t = 60 min by PMS/MSBC system. The larger surface area and magnetic Fe3O4 of MSBC surface enhanced its activation performance for PMS. The PMS decomposition, premixing and reactive oxygen species (ROS) identification experiments combined with Raman spectra analysis demonstrated that the degradation process was dominated by surface-bound radicals. The transformed products (TPs) of SMX and the main degradation pathways were identified and proposed. The ecotoxicity of all TPs was lower than that of SMX. The magnetic performance was beneficial for its reuse and the removal efficiency of SMX was 83.3% even after five reuse cycles. Solution pH, HCO3- and CO32- were the critical environmental factors affecting the degradation process. MSBC exhibited environmental safety for its low heavy metal leaching. PMS/MSBC system also performed excellent removal performance for SMX in real waters including drinking water (88.1%), lake water (84.3%), Yangtze River water (83.0%) and sewage effluent (70.2%). This study developed an efficient PMS activator for SMX degradation in various waters and provided a workable way to reuse and recycle municipal sludge.

The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study.

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

Phenomic Imaging.

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

Protocol for Brain Magnetic Resonance Imaging and Extraction of Imaging-Derived Phenotypes from the China Phenobank Project.

Imaging-derived phenotypes (IDPs) have been increasingly used in population-based cohort studies in recent years. As widely reported, magnetic resonance imaging (MRI) is an important imaging modality for assessing the anatomical structure and function of the brain with high resolution and excellent soft-tissue contrast. The purpose of this article was to describe the imaging protocol of the brain MRI in the China Phenobank Project (CHPP). Each participant underwent a 30-min brain MRI scan as part of a 2-h whole-body imaging protocol in CHPP. The brain imaging sequences included T1-magnetization that prepared rapid gradient echo, T2 fluid-attenuated inversion-recovery, magnetic resonance angiography, diffusion MRI, and resting-state functional MRI. The detailed descriptions of image acquisition, interpretation, and post-processing were provided in this article. The measured IDPs included volumes of brain subregions, cerebral vessel geometrical parameters, microstructural tracts, and function connectivity metrics.

Mitolysosome exocytosis, a mitophagy-independent mitochondrial quality control in flunarizine-induced parkinsonism-like symptoms.

Mitochondrial quality control plays an important role in maintaining mitochondrial homeostasis and function. Disruption of mitochondrial quality control degrades brain function. We found that flunarizine (FNZ), a drug whose chronic use causes parkinsonism, led to a parkinsonism-like motor dysfunction in mice. FNZ induced mitochondrial dysfunction and decreased mitochondrial mass specifically in the brain. FNZ decreased mitochondrial content in both neurons and astrocytes, without affecting the number of nigral dopaminergic neurons. In human neural progenitor cells, FNZ also induced mitochondrial depletion. Mechanistically, independent of ATG5- or RAB9-mediated mitophagy, mitochondria were engulfed by lysosomes, followed by a vesicle-associated membrane protein 2- and syntaxin-4-dependent extracellular secretion. A genome-wide CRISPR knockout screen identified genes required for FNZ-induced mitochondrial elimination. These results reveal not only a previously unidentified lysosome-associated exocytosis process of mitochondrial quality control that may participate in the FNZ-induced parkinsonism but also a drug-based method for generating mitochondria-depleted mammal cells.

Microbiota-microglia connections in age-related cognition decline.

Aging is an inevitable process that all individuals experience, of which the extent differs among individuals. It has been recognized as the risk factor of neurodegenerative diseases by affecting gut microbiota compositions, microglia, and cognition abilities. Aging-induced changes in gut microbiota compositions have a critical role in orchestrating the morphology and functions of microglia through the gut-brain axis. Gut microbiota communicates with microglia by its secreted metabolites and neurotransmitters. This is highly associated with age-related cognitive declines. Here, we review the main composition of microbiota in the aged individuals, outline the changes of the brain in age-related cognitive decline from a neuroinflammation perspective, especially the changes of morphology and functions of microglia, discuss the crosstalk between microbiota and microglia in the aged brain and further highlight the role of microbiota-microglia connections in neurodegenerative diseases (Alzheimer's disease and Parkinson's disease).

Processing speed dysfunction is associated with functional corticostriatal circuit alterations in childhood epilepsy with centrotemporal spikes: a PET and fMRI study.

PURPOSE: Epilepsy with centrotemporal spikes (ECTS) is the most common epilepsy syndrome in children and usually presents with cognitive dysfunctions. However, little is known about the processing speed dysfunction and the associated neuroimaging mechanism in ECTS. This study aims to investigate the brain functional abnormality of processing speed dysfunction in ECTS patients by using the 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: This prospective study recruited twenty-eight ECTS patients who underwent the 18F-FDG PET, rs-fMRI, and neuropsychological examinations. Twenty children with extracranial tumors were included as PET controls, and 20 healthy children were recruited as MRI controls. The PET image analysis investigated glucose metabolism by determining standardized uptake value ratio (SUVR). The MRI image analysis explored abnormal functional connectivity (FC) within the cortical-striatal circuit through network-based statistical (NBS) analysis. Correlation analysis was performed to explore the relationship between SUVR, FC, and processing speed index (PSI). RESULTS: Compared with healthy controls, ECTS patients showed normal intelligence quotient but significantly decreased PSI (P = 0.04). PET analysis showed significantly decreased SUVRs within bilateral caudate, putamen, pallidum, left NAc, right rostral middle frontal gyrus, and frontal pole of ECTS patients (P < 0.05). Rs-fMRI analysis showed absolute values of 20 FCs were significantly decreased in ECTS patients compared with MRI controls, which connected 16 distinct ROIs. The average SUVR of right caudate and the average of 20 FCs were positively correlated with PSI in ECTS patients (P = 0.034 and P = 0.005, respectively). CONCLUSION: This study indicated that ECTS patients presented significantly reduced PSI, which is closely associated with decreased SUVR and FC of cortical-striatal circuit. Caudate played an important role in processing speed dysfunction. CLINICAL TRIAL REGISTRATION: NCT04954729; registered on July 8, 2021, public site, https://clinicaltrials.gov/ct2/show/NCT04954729.

Positron Emission Tomography Molecular Imaging for Phenotyping and Management of Lymphoma.

Positron emission tomography (PET) represents molecular imaging for non-invasive phenotyping of physiological and biochemical processes in various oncological diseases. PET imaging with 18F-fluorodeoxyglucose (18F-FDG) for glucose metabolism evaluation is the standard imaging modality for the clinical management of lymphoma. One of the 18F-FDG PET applications is the detection and pre-treatment staging of lymphoma, which is highly sensitive. 18F-FDG PET is also applied during treatment to evaluate the individual chemo-sensitivity and accordingly guide the response-adapted therapy. At the end of the therapy regiment, a negative PET scan is indicative of a good prognosis in patients with advanced Hodgkin's lymphoma and diffuse large B-cell lymphoma. Thus, adjuvant radiotherapy may be alleviated. Future PET studies using non-18F-FDG radiotracers, such as 68Ga-labeled pentixafor (a cyclic pentapeptide that enables sensitive and high-contrast imaging of C-X-C motif chemokine receptor 4), 68Ga-labeled fibroblast activation protein inhibitor (FAPI) that reflects the tumor microenvironment, and 89Zr-labeled atezolizumab that targets the programmed cell death-ligand 1 (PD-L1), may complement 18F-FDG and offer essential tools to decode lymphoma phenotypes further and identify the mechanisms of lymphoma therapy.

A Mitochondria-targeted AIEgen Labelled with 18 F for Breast Cancer Cell Imaging and Therapy.

A lack of efficient diagnostic tools for early and noninvasive diagnosis of breast cancer has restricted the clinical treatment effect. This problem might be addressed by the combination of aggregation-induced emission (AIE) fluorescence imaging and positron emission tomography (PET) with the dual advantages of high resolution and easy operation, and unlimited penetration and high sensitivity. Here, a mitochondria-targeted AIE luminogen (AIEgen) radiolabeled with 18 F was developed through a two-step radiochemical reaction by virtue of a prosthetic group. The obtained 18/19 F-Bz-CP imaging probe was examined by in vitro cell uptake and cell proliferation inhibition in two breast cancer cell lines, showing that the probe can efficiently target and locate in the mitochondria through the analysis of fluorescence imaging and PET simultaneously. Additionally, the probe can induce cancer cell apoptosis with the half maximal inhibitory concentration (IC50) of 4.8 µM for MCF-7 cells and 7.2 µM for T47D cells, indicating its potential application for breast cancer therapy.

Positron emission tomography in the COVID-19 pandemic era.

Coronavirus disease 2019 (COVID-19) has become a major public health problem worldwide since its outbreak in 2019. Currently, the spread of COVID-19 is far from over, and various complications have roused increasing awareness of the public, calling for novel techniques to aid at diagnosis and treatment. Based on the principle of molecular imaging, positron emission tomography (PET) is expected to offer pathophysiological alternations of COVID-19 in the molecular/cellular perspectives and facilitate the clinical management of patients. A number of PET-related cases and research have been reported on COVID-19 over the past one year. This article reviews the current studies of PET in the diagnosis and treatment of COVID-19, and discusses potential applications of PET in the development of management strategy for COVID-19 patients in the pandemic era.

Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data.

It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.

Links Between the Amplitude Modulation of Low-Frequency Spontaneous Fluctuation Across Resting State Conditions and Thalamic Functional Connectivity.

A comparison of the different types of resting state reveals some interesting characteristics of spontaneous brain activity that cannot be found in a single condition. Differences in the amplitude of low-frequency fluctuation (ALFF) between the eyes open (EO) and the eyes closed (EC) almost have a spatially distinct pattern with traditional EO-EC activation within sensory systems, suggesting the divergent functional roles of ALFF and activation. However, the underlying mechanism is far from clear. Since the thalamus plays an essential role in sensory processing, one critical step toward understanding the divergences is to depict the relationships between the thalamus and the ALFF modulation in sensory regions. In this preliminary study, we examined the association between the changes of ALFF and the changes of thalamic functional connectivity (FC) between EO and EC. We focused on two visual thalamic nuclei, the lateral geniculate nucleus (LGN) and the pulvinar (Pu). FC results showed that LGN had stronger synchronization with regions in lateral but not in medial visual networks, while Pu had a weaker synchronization with auditory and sensorimotor areas during EO compared with EC. Moreover, the patterns of FC modulation exhibited considerable overlaps with the ALFF modulation, and there were significant correlations between them across subjects. Our findings support the crucial role of the thalamus in amplitude modulation of low-frequency spontaneous activity in sensory systems, and may pave the way to elucidate the mechanisms governing distinction between evoked activation and modulation of low-frequency spontaneous brain activity.

Size-Dependent Bacterial Toxicity of Hematite Particles.

Submicron-sized iron oxide particles can influence the activity of bacteria, but the exact mechanisms of oxide toxicity toward bacteria remain elusive. By using atomic force microscopy (AFM), soft X-ray tomography (Nano-CT), and Fourier transform infrared (FTIR) spectrometry, we show how the size-dependent interfacial interactions between hematite particles and bacteria in the absence of any ligands contribute to the antimicrobial properties against Gram-positive and Gram-negative bacterial strains. We found that surface adhesion between hematite particles and bacterial cells is initially dominated by Lifshitz van der Waals and electrostatic forces. Subsequently, the rapid formation of P-O-Fe bonds occurs, followed by changes in the structures of membrane proteins in 2 h, resulting in the loss of the structural integrity of the membrane within 10 h. Thus, particles can migrate into the cells. After contact with bacterial cells, reactive oxygen species are generated on the surface of hematite particles, leading to cell permeabilization. G- bacteria appear to be more susceptible to this process than G+ bacteria because the latter exhibit weaker adhesion forces toward hematite and benefit from the protective effects of the peptidoglycan layers. Our work revealed that hematite nanoparticles are more toxic to bacteria than microscaled particles due to their strong interfacial physicochemical interactions with the cells.

Distinction Between Variability-Based Modulation and Mean-Based Activation Revealed by BOLD-fMRI and Eyes-Open/Eyes-Closed Contrast.

Recent BOLD-fMRI studies have revealed spatial distinction between variability- and mean-based between-condition differences, suggesting that BOLD variability could offer complementary and even orthogonal views of brain function with traditional activation. However, these findings were mainly observed in block-designed fMRI studies. As block design may not be appreciate for characterizing the low-frequency dynamics of BOLD signal, the evidences suggesting the distinction between BOLD variability and mean are less convincing. Based on the high reproducibility of signal variability modulation between continuous eyes-open (EO) and eyes-closed (EC) states, here we employed EO/EC paradigm and BOLD-fMRI to compare variability- and mean-based EO/EC differences while the subjects were in light. The comparisons were made both on block-designed and continuous EO/EC data. Our results demonstrated that the spatial patterns of variability- and mean-based EO/EC differences were largely distinct with each other, both for block-designed and continuous data. For continuous data, increases of BOLD variability were found in secondary visual cortex and decreases were mainly in primary auditory cortex, primary sensorimotor cortex and medial nuclei of thalamus, whereas no significant mean-based differences were observed. For the block-designed data, the pattern of increased variability resembled that of continuous data and the negative regions were restricted to medial thalamus and a few clusters in auditory and sensorimotor networks, whereas activation regions were mainly located in primary visual cortex and lateral nuclei of thalamus. Furthermore, with the expanding window analyses we found variability results of continuous data exhibited a rather slower dynamical process than typically considered for task activation, suggesting block design is less optimal than continuous design in characterizing BOLD variability. In sum, we provided more solid evidences that variability-based modulation could represent orthogonal views of brain function with traditional mean-based activation.