Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study.

Pain empathy enables us to understand and share how others feel pain. Few studies have investigated pain empathy-related functional interactions at the whole-brain level across all networks. Additionally, women with primary dysmenorrhea (PDM) have abnormal pain empathy, and the association among the whole-brain functional network, pain, and pain empathy remain unclear. Using resting-state functional magnetic resonance imaging (fMRI) and machine learning analysis, we identified the brain functional network connectivity (FNC)-based features that are associated with pain empathy in two studies. Specifically, Study 1 examined 41 healthy controls (HCs), while Study 2 investigated 45 women with PDM. Additionally, in Study 3, a classification analysis was performed to examine the differences in FNC between HCs and women with PDM. Pain empathy was evaluated using a visual stimuli experiment, and trait and state of menstrual pain were recorded. In Study 1, the results showed that pain empathy in HCs relied on dynamic interactions across whole-brain networks and was not concentrated in a single or two brain networks, suggesting the dynamic cooperation of networks for pain empathy in HCs. In Study 2, PDM exhibited a distinctive network for pain empathy. The features associated with pain empathy were concentrated in the sensorimotor network (SMN). In Study 3, the SMN-related dynamic FNC could accurately distinguish women with PDM from HCs and exhibited a significant association with trait menstrual pain. This study may deepen our understanding of the neural mechanisms underpinning pain empathy and suggest that menstrual pain may affect pain empathy through maladaptive dynamic interaction between brain networks.

TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans.

Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.

Advancements in Micro-LED Performance through Nanomaterials and Nanostructures: A Review.

Micro-light-emitting diodes (µLEDs), with their advantages of high response speed, long lifespan, high brightness, and reliability, are widely regarded as the core of next-generation display technology. However, due to issues such as high manufacturing costs and low external quantum efficiency (EQE), µLEDs have not yet been truly commercialized. Additionally, the color conversion efficiency (CCE) of quantum dot (QD)-µLEDs is also a major obstacle to its practical application in the display industry. In this review, we systematically summarize the recent applications of nanomaterials and nanostructures in µLEDs and discuss the practical effects of these methods on enhancing the luminous efficiency of µLEDs and the color conversion efficiency of QD-µLEDs. Finally, the challenges and future prospects for the commercialization of µLEDs are proposed.

A portable solid sampling visualization nano-sensor for soil Cd based on "three-phase transforming" technique.

Direct analysis of solid samples is always challenging for ionic sensors due to solidified elemental presence and matrix interference. In this work, a "three-phase transforming" technique was first established to make solid sampling elemental sensors and visual detection possible in the future. For Cd transforming from soil samples, a metal ceramic heater (MCH) electrothermal vaporizer (ETV) coupled with a dielectric barrier discharge quartz trap (DBD-QT) was first utilized to fulfill the solid sampling and preconcentration of Cd in soil; for on-site analysis, a colorimetric sensor based on the trithiocyanuric acid (TMT) functionalized gold nanoparticles (AuNPs) was chosen as a chromogenic analysis model. The portable and miniature ETV-DBD apparatus directly introduced Cd from soil and then captured Cd, consuming only <130 W and 4.5 kg weight; finally, only 200 µL water was injected as eluent to dissolve Cd for the following colorimetric detection. Herein, the Cd analyte underwent a "three-phase transforming" from solid (Cd compounds in soil), to aerosol (vaporization and transportation), to solid (Cd oxides trapped on quartz surface) and to liquid (Cd2+ in eluate). Under optimized conditions, the method limit of detection (LOD) reached 0.04 mg/kg Cd (50 mg sample), fulfilling fast monitoring of Cd contamination in soil, with <20 % relative standard deviations (RSDs). The analysis time was <10 min excluding sample digestion and acid application, as well as the interference of Pb2+ on the AuNPs sensor can be eliminated via the "three-phase transforming" process, proving an excellent anti-interference for solid analysis. This "three-phase transforming" processing technique coupled with colorimetric sensor holds a great potential for direct and on-site analysis in solid samples without complicated handling, providing a fantastic methodology for the application of ionic sensors and making solid sampling elemental sensor and visual detection possible.

Individual differences of white matter characteristic along the anterior insula-based fiber tract circuit for pain empathy in healthy women and women with primary dysmenorrhea.

Pain empathy, defined as the ability of one person to understand another person's pain, shows large individual variations. The anterior insula is the core region of the pain empathy network. However, the relationship between white matter (WM) properties of the fiber tracts connecting the anterior insula with other cortical regions and an individual's ability to modulate pain empathy remains largely unclear. In this study, we outline an automatic seed-based fiber streamline (sFS) analysis method and multivariate pattern analysis (MVPA) to predict the levels of pain empathy in healthy women and women with primary dysmenorrhoea (PDM). Using the sFS method, the anterior insula-based fiber tract network was divided into five fiber cluster groups. In healthy women, interindividual differences in pain empathy were predicted only by the WM properties of the five fiber cluster groups, suggesting that interindividual differences in pain empathy may rely on the connectivity of the anterior insula-based fiber tract network. In women with PDM, pain empathy could be predicted by a single cluster group. The mean WM properties along the anterior insular-rostroventral area of the inferior parietal lobule further mediated the effect of pain on empathy in patients with PDM. Our results suggest that chronic periodic pain may lead to maladaptive plastic changes, which could further impair empathy by making women with PDM feel more pain when they see other people experiencing pain. Our study also addresses an important gap in the analysis of the microstructural characteristics of seed-based fiber tract network.

Graph Fourier transform for spatial omics representation and analyses of complex organs.

Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.

Relationship between individual differences in pain empathy and task- and resting-state EEG.

Pain empathy is a complex form of psychological inference that enables us to understand how others feel in the context of pain. Since pain empathy may be grounded in our own pain experiences, it exhibits huge inter-individual variability. However, the neural mechanisms behind the individual differences in pain empathy and its association with pain perception are still poorly understood. In this study, we aimed to characterize brain mechanisms associated with individual differences in pain empathy in adult participants (n = 24). The 32-channel electroencephalography (EEG) was recorded at rest and during a pain empathy task, and participants viewed static visual stimuli of the limbs submitted to painful and nonpainful stimulation to solicit empathy. The pain sensitivity of each participant was measured using a series of direct current stimulations. In our results, the N2 of Fz and the LPP of P3 and P4 were affected by painful pictures. We found that both delta and alpha bands in the frontal and parietal cortex were involved in the regulation of pain empathy. For the delta band, a close relationship was found between average power, either in the resting or task state, and individual differences in pain empathy. It suggested that the spectral power in Fz's delta band may reflect subjective pain empathy across individuals. For the alpha band, the functional connectivity between Fz and P3 under painful picture stimulation was correlated to individuals' pain sensitivity. It indicated that the alpha band may reflect individual differences in pain sensitivity and be involved in pain empathy processing. Our results suggested the distinct role of the delta and alpha bands of EEG signals in pain empathy processing and may deepen our understanding of the neural mechanisms underpinning pain empathy.

Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region.

BACKGROUND: Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. PURPOSE: To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). MATERIAL AND METHODS: A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. RESULTS: Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. CONCLUSION: The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.

Evaluation of a Clustering Approach to Define Distinct Subgroups of Patients With Migraine to Select Electroacupuncture Treatments.

BACKGROUND AND OBJECTIVES: The objective of this study was to propose a clustering approach to identify migraine subgroups and test the clinical usefulness of the approach by providing prognostic information for electroacupuncture treatment selection. METHODS: Participants with migraine without aura (MWoA) were asked to complete a daily headache diary, self-rating depression and anxiety, and quality-of-life questionnaires. Whole-brain functional connectivities (FCs) were assessed on resting-state functional MRI (fMRI). By integrating clinical measurements and fMRI data, partial least squares correlation and hierarchical clustering analysis were used to cluster participants with MWoA. Multivariate pattern analysis was applied to validate the proposed subgrouping strategy. Some participants had an 8-week electroacupuncture treatment, and the response rate was compared between different MWoA subgroups. RESULTS: In study 1, a total of 97 participants (age of 28.2 ± 1.0 years, 70 female participants) with MWoA and 77 healthy controls (HCs) (age of 26.8 ± 0.1 years, 61 female participants) were enrolled (dataset 1), and 2 MWoA subgroups were defined. The participants in subgroup 1 had a significantly lower headache frequency (times/month of 4.4 ± 1.1) and significantly higher self-ratings of depression (depression score of 49.5 ± 2.3) when compared with participants in subgroup 2 (times/month of 7.0 ± 0.6 and depression score of 43.4 ± 1.2). The between-group differences of FCs were predominantly related to the amygdala, thalamus, hippocampus, and parahippocampal area. In study 2, 33 participants with MWoA (age of 30.9 ± 2.0 years, 28 female participants) and 23 HCs (age of 29.8 ± 1.1 years, 13 female participants) were enrolled as an independent dataset (dataset 2). The classification analysis validated the effectiveness of the 2-cluster solution of participants with MWoA in datasets 1 and 2. In study 3, 58 participants with MWoA were willing to receive electroacupuncture treatment and were assigned to different subgroups. Participants in different subgroups exhibited different response rates (p = 0.03, OR CI 0.086-0.93) to electroacupuncture treatment (18% and 44% for subgroups 1 and 2, respectively). DISCUSSION: Our study proposed a novel clustering approach to define distinct MWoA subgroups, which could be useful for refining the diagnosis of participants with MWoA and guiding individualized strategies for pain prophylaxis and analgesia.

Rapid and Portable Detection of Hg and Cd in Grain Samples Based on Novel Catalytic Pyrolysis Composite Trap Coupled with Miniature Atomic Absorption Spectrometry.

As toxic metals, Hg and Cd are a concern for food safety and human health; their rapid and portable analysis is still a challenge. A portable and rapid Hg-Cd analyzer constructed from a metal-ceramic heater (MCH)-based electrothermal vaporizer (ETV), an on-line catalytic pyrolysis furnace (CPF), a composite Pt/Ni trap, and a homemade miniature atomic absorption spectrometer (AAS) was proposed for grain analysis in this work. To enhance sensitivity, a new folded light path was designed for simultaneous Hg and Cd analysis using charge coupled device (CCD) in AAS. To eliminate the grain matrix interference, a catalytic pyrolysis furnace with aluminum oxide fillers was utilized to couple with a composite Pt/Ni trap. The method limits of detection (LODs) were 1.1 µg/kg and 0.3 µg/kg for Hg and Cd using a 20 mg grain sample, fulfilling the real sample analysis to monitor the grain contamination quickly; linearity R2 > 0.995 was reached only using standard solution calibration, indicating the sample was free of grain matrix interference. The favorable analytical accuracy and precision were validated by analyzing real and certified reference material (CRM) grains with recoveries of 97-103% and 96-111% for Hg and Cd, respectively. The total analysis time was less than 5 min without sample digestion or use of any chemicals, and the instrumental size and power consumption were <14 kg and 270 W, respectively. Compared with other rapid methods, this newly designed Hg-Cd analyzer is proven to be simple, portable, and robust and is, thus, suitable to quickly monitor Hg and Cd contamination in the field to protect grain and food safety.

Progress in Bioengineering of Myotropic Adeno-Associated Viral Gene Therapy Vectors.

The ability to specifically, safely, and efficiently transfer therapeutic payloads to the striated musculature via a minimally invasive delivery route remains one of the most important but also most ambitious aims in human gene therapy. Over the past two decades, a flurry of groups have harnessed recombinant adeno-associated viruses (AAVs) for this purpose, carrying cargoes that were packaged either in one of the various wild-type capsids or in a synthetic protein shell derived by molecular bioengineering. In this study, we provide an overview over the most commonly used techniques for the enrichment of muscle-specific (myotropic) AAV capsids, typically starting off with the genetic diversification of one or more extant wild-type sequences, followed by the stratification of the ensuing capsid libraries in different muscle types in small or large animals. These techniques include the shuffling of multiple parental capsid genes, peptide display in exposed capsid loops, mutagenesis of individual capsid residues, creation of chimeras between two viral parents, or combinations thereof. Moreover, we highlight alternative experimental or bioinformatic strategies such as ancestral reconstruction or rational design, all of which have already been employed successfully to derive synthetic AAV capsids or vectors with unprecedented in vivo efficiency and/or specificity in the musculature. Most recently, these efforts have culminated in the isolation of unique clades of myotropic vectors called AAVMYO or MyoAAV that have in common the display of the amino acid motif RGD (arginine-glycine-aspartate) on the capsid surface and that exhibit the highest transduction rate in striated muscles of mice or nonhuman primates reported to date. Finally, we note essential looming improvements that will facilitate and accelerate clinical translation of these latest generations of myotropic AAVs, including the identification and utilization of capsid selection or validation schemes that promise optimal translation in humans, and continued efforts to enhance patient safety by minimizing hepatic off-targeting.

CsHscB Derived from a Liver Fluke Clonorchis Sinensis Ameliorates Cholestatic Hepatic Fibrosis in a Mouse Model of Sclerosing Cholangitis.

BACKGROUND: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by inflammatory fibrosis usually involving the whole biliary tree. However, there are very limited treatment options to treat this disease. Our previous study found a lipid-protein rCsHscB from a liver fluke - Clonorchis sinensis, which had full capacities of immune regulation. Therefore, we investigated the role of rCsHscB in a mouse model of sclerosing cholangitis induced by xenobiotic 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) to explore whether this protein had potential therapeutic value for PSC. METHODS: Mice were fed 0.1% DDC for 4 weeks and treated with CsHscB (30 µg/mouse, intraperitoneal injection, once every 3 days); the control group was given an equal amount of PBS or CsHscB under normal diet conditions. All the mice were sacrificed at 4 weeks for the evaluation of biliary proliferation, fibrosis, and inflammation. RESULTS: rCsHscB treatment attenuated DDC-induced liver congestion and enlargement and significantly decreased the upregulation of serum AST and ALT levels. The administration of rCsHscB to DDC-fed mice significantly decreased cholangiocyte proliferation and pro-inflammatory cytokine production compared to mice fed with DDC alone. Also, rCsHscB treatment showed a decreased expression of α-SMA in the liver and other markers of liver fibrosis (Masson staining, Hydroxyproline content, and collagen deposit). More interestingly, DDC-fed mice treated with rCsHscB showed a significant up-regulation of PPAR-γ expression, which was similar to control mice, indicating the involvement of PPAR-γ signaling in the protective action of rCsHscB. CONCLUSION: Overall, our data show that rCsHscB attenuates the progression of cholestatic fibrosis induced by DDC and supports the potential for manipulating the parasite-derived molecule to treat certain immune-mediated disorders.

Rapid detection of ultratrace urinary arsenic by direct sampling microplasma vaporization based on silicon nitride.

At present, immediate monitoring urinary arsenic is still a challenge for treating arsenic poisoning patients. Thus, a fast, reliable and accurate analytical approach is indispensable to monitor ultratrace arsenic in urine sample for health warning. In this work, a silicon nitride (SN) rod was first integrally utilized as a sample carrier for ≤50 µL urinary aliquot, an electric heater for removing water and ashing sample as well as a high voltage electrode for dielectric barrier discharge vaporization (DBDV). The direct analytical method of arsenic in urine without sample digestion was thus developed using atomic fluorescence spectrometer (AFS) as a model detector. After 4 V electrically heating the SN rod for 60 s, urine sample was dehydrated and ashed outside; then, DBD was exerted under 0.8 A with 0.8 L/min H2 + Ar (1:9, v:v) for 20 s to vaporize arsenic analyte from the SN rod. After optimization, 0.014 µg/L arsenic detection limit (LOD) was reached with favorable analytical precision (RSD <5%) and accuracy (91-110% recoveries) for real sample analysis. As a result, the whole analysis process only consumes <3 min to exclude complicated sample preparation; furthermore, the designed DBDV system only occupies 25 W and <2 kg, which renders a miniature sampling component to hyphenate with a miniature detector to detect arsenic. Thus, this direct sampling DBDV method extremely fulfills the fast, sensitive and precise detection of ultratrace arsenic in urine sample.

Effects on resting-state EEG phase-amplitude coupling in insomnia disorder patients following 1 Hz left dorsolateral prefrontal cortex rTMS.

Despite burgeoning evidence for cortical hyperarousal in insomnia disorder, the existing results on electroencephalography spectral features are highly heterogeneous. Phase-amplitude coupling, which refers to the modulation of the low-frequency phase to a high-frequency amplitude, is probably a more sensitive quantitative measure for characterizing abnormal neural oscillations and explaining the therapeutic effect of repetitive transcranial magnetic stimulation in the treatment of patients with insomnia disorder. Sixty insomnia disorder patients were randomly divided into the active and sham treatment groups to receive 4 weeks of repetitive transcranial magnetic stimulation treatment. Behavioral assessments, resting-state electroencephalography recordings, and sleep polysomnography recordings were performed before and after repetitive transcranial magnetic stimulation treatment. Forty good sleeper controls underwent the same assessment. We demonstrated that phase-amplitude coupling values in the frontal and temporal lobes were weaker in Insomnia disorder patients than in those with good sleeper controls at baseline and that phase-amplitude coupling values near the intervention area were significantly enhanced after active repetitive transcranial magnetic stimulation treatment. Furthermore, the enhancement of phase-amplitude coupling values was significantly correlated with the improvement of sleep quality. This study revealed the potential of phase-amplitude coupling in assessing the severity of insomnia disorder and the efficacy of repetitive transcranial magnetic stimulation treatment, providing new insights on the abnormal physiological mechanisms and future treatments for insomnia disorder.

The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder.

BACKGROUND: It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes. METHODS: In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects. RESULTS: In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID. CONCLUSION: rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.

Reversal of neurovascular decoupling and cognitive impairment in patients with end-stage renal disease during a hemodialysis session: Evidence from a comprehensive fMRI analysis.

Neurovascular (NV) decoupling is a potential neuropathologic mechanism of cognitive impairment in patients with end-stage renal disease (ESRD). Hemodialysis improves cognitive impairment at 24 h post-dialysis, which suggests a potential neuroprotective effect of hemodialysis treatment on the brain. We investigated the effects of hemodialysis treatment on the reversal of NV decoupling associated with cognitive improvement. A total of 39 patients with ESRD and 39 healthy controls were enrolled. All patients were imaged twice during a dialysis session: before hemodialysis (T1pre-dialysis ) and at 24 h after dialysis (T2post-dialysis ). The healthy controls were imaged once. NV coupling was characterized based on correlation coefficients between four types of blood oxygen level-dependent signals and cerebral blood flow (CBF). A battery of neuropsychological and blood tests was performed before the imaging. Patients with ESRD showed improvements in memory and executive function at T2post-dialysis compared with that at T1pre-dialysis . At both T1pre-dialysis and T2post-dialysis , patients with ESRD had lower amplitude of low-frequency fluctuation (ALFF)-CBF coupling than healthy controls. Additionally, patients with ESRD had higher ALFF-CBF coupling at T2post-dialysis than at T1pre-dialysis . Higher memory scores, higher hemoglobin level, lower total plasma homocysteine level, lower systolic blood pressure variance, and lower ultrafiltration volume were associated with higher ALFF-CBF coupling in patients with ESRD after a hemodialysis session. These findings indicate that partial correction of anemia and hyperhomocysteinemia, stable systolic blood pressure, and fluid restriction may be closely linked to the reversal of NV decoupling and improvement in cognition in patients with ESRD.

Brain recovery of the NAc fibers and prediction of craving changes in person with heroin addiction: A longitudinal study.

BACKGROUND: Progress have been made in brain function recovery after long-term abstinence in person with heroin addiction (PHA). However, less is known about whether the nucleus accumbens (NAc) white matter pathways can recover in PHA by prolonged abstinence. METHODS: Forty-two PHA and Thirty-nine age- and gender- matched healthy controls (HCs) were recruited. Two MRI scans were obtained at baseline (PHA1) and 8-month follow-up (PHA2). We employed tractography atlas-based analysis (TABS) method to investigate fractional anisotropy (FA) changes in NAc fiber tracts (i.e., Insula-NAc, ventral tegmental area (VTA)-NAc, medial prefrontal cortex (MPFC)-NAc) in PHA. A partial least square regression (PLSR) analysis was carried to explore whether FA of NAc fiber tracts can predict longitudinal craving changes. RESULTS: Relative to HCs, lower FA was found in the right Insula-NAc and VTA-NAc fiber tracts in PHA1, and PHA2 showed increased FA values in these tracts compared with PHA1. Furthermore, changes of FA of NAc fiber tracts can predict longitudinal craving changes (r = 0.51). Additionally, craving changes can also be predicted from FA changes in the left Insula-NAc (r = 0.601) and VTA-NAc (r = 0.384) fiber alone. CONCLUSIONS: Results indicated that the right Insula-NAc and VTA-NAc fiber tracts are potential biomarkers for brain recovery. Prediction of craving changes highlighted the utility of structural markers to inform clinical decision-making of treatment for PHA.