BACKGROUND AND HYPOTHESIS:Convergent evidence shows the presence of brain metabolic abnormalities in psychotic disorders. This study examined brain reductive stress and energy metabolism in people with psychotic disorders with impaired or average range cognition. We hypothesized that global cognitive impairment would be associated with greater brain metabolic dysregulation. STUDY DESIGN:Participants with affective and non-affective psychosis (n = 62) were administered the MATRICS Consensus Cognitive Battery (MCCB) and underwent a 31P-magnetic resonance spectroscopy scan at 4T. We used a cluster-analysis approach to identify 2 clusters of participants with and without cognitive dysfunction. We compared clusters on brain redox balance or reductive stress, measured by the ratio of nicotinamide adenine dinucleotide (NAD+) and its reduced form NADH, in addition to creatine kinase (CK) enzymatic activity and pH. STUDY RESULTS:The mean (SD) age of participants was 25.1 (6.3) years. The mean NAD+/NADH ratio differed between groups, with lower NAD+/NADH ratio, suggesting more reductive stress, in the impaired cognitive cluster (t = -2.60, P = .01). There was also a significant reduction in CK activity in the impaired cognitive cluster (t = -2.19, P = .03). Intracellular pH did not differ between the 2 cluster groups (t = 1.31, P = .19). The clusters did not significantly differ on severity of mood and psychotic symptomatology or other measures of illness severity. CONCLUSIONS:Our results demonstrate that psychotic disorders with greater cognitive impairment have greater brain metabolic dysregulation, with more reductive stress and decrease in energy metabolic rate markers. This provides new evidence for the potential of emerging metabolic therapies to treat cognitive deficits in psychotic disorders.

Neuroimaging studies have revealed altered functional connectome dynamics in autism spectrum disorder (ASD) and linked these alterations to clinical symptoms. However, most studies have emphasized population-level contrasts, leaving interindividual variability in connectome dynamics and its structural underpinnings poorly understood. To address this gap, we analyzed resting-state functional and structural MRI data from 939 male participants (440 with ASD, 499 typically developing controls) across 18 sites in the Autism Brain Imaging Data Exchange (ABIDE). Whole-brain functional state dynamics was characterized using five leading activity modes and their expressions via eigen-microstate analysis. Age-related trajectories of mode expressions were constructed for typically developing controls using normative modeling, enabling quantification of individual-level deviations in functional dynamics. Compared with controls, ASD individuals showed greater interindividual variability in functional deviation profiles. Unsupervised clustering of these profiles identified two robust ASD subtypes with distinct mode-specific dysfunctions. One subtype primarily involved the visual, default-mode, frontoparietal, and dorsal attention networks, whereas the other subtype primarily involved the somatomotor, visual, frontoparietal, and ventral attention networks. These subtypes were clinically dissociable, differing in restricted and repetitive behaviors and social impairments, and exhibited mode-specific brain-symptom associations. Furthermore, the subtypes exhibited distinct cortical thickness alterations, and individual subtype membership was predicted with high accuracy (83%) using a random forest classifier based on cortical thickness. The main findings were replicated in an independent cohort outside ABIDE. This study delineates two reproducible and clinically dissociable ASD subtypes and links functional connectome dynamics to structural substrates, offering novel insights into the neurobiological basis behind ASD heterogeneity.

Objective:Hippocampal dysfunction is implicated in posttraumatic stress disorder (PTSD), particularly intrusive reexperiencing symptoms, and may be mediated by glutamatergic excitotoxicity. Markers of glutamate dysfunction (higher glutamate to N-acetyl aspartate levels; Glu/NAA) in the hippocampus (HPC) have been linked to reexperiencing symptoms. However, the HPC demonstrates heterogeneity along its anterior-posterior axis, with different functional connectivity patterns and PTSD symptom associations, motivating investigations into glutamate metabolism in anterior and posterior HPC subregions (a/pHPC). Methods:121 symptomatic trauma-exposed adults (93 female) with current trauma reexperiencing symptoms completed magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging to examine the regional specificity of HPC Glu/NAA associations with reexperiencing, and the link to a/pHPC functional connectivity. PTSD symptoms were assessed with the Clinician-Administered PTSD Scale for DSM-5. Results:Reexperiencing symptom severity was associated with greater Glu/NAA in the pHPC, but not aHPC. pHPC Glu/NAA was further linked to stronger functional connectivity between the pHPC and visual cortex (VC), which in turn correlated with more severe reexperiencing symptoms. This strengthened pHPC-VC connectivity explained the shared variance between pHPC Glu/NAA and reexperiencing severity, suggesting dysregulated glutamate metabolism in the pHPC may contribute to reexperiencing symptoms through functional connectivity with the VC. Conclusions:These findings replicate prior work linking HPC Glu/NAA to trauma reexperiencing symptoms and provide novel evidence this association may be specific to the pHPC and mediated by its functional connectivity with the VC. This multimodal investigation supports translational models of glutamatergic dysfunction in trauma-related disorders and highlights new targets for pharmacological and neuromodulatory interventions.

Background:Abnormal liver function has often been associated with epilepsy, but there is a lack of large-scale clinical evidence to support the potential correlation between liver function and post-stroke epilepsy. Objective:We aimed to explore a real correlation between liver function and secondary epilepsy after stroke. Methods:From the Dryad database, we retrospectively identified 21,459 patients who had experienced acute ischemic stroke. Multivariate regression was used to clarify the correlation between liver function and secondary epilepsy post-stroke via cross-sectional analysis. Results:Our multivariate nonlinear analysis, adjusted for age and gender, suggested that a significant correlation exists between liver function (alanine aminotransferase [ALT] and aspartate aminotransferase [AST]) and the risk of secondary epilepsy post-stroke. Saturation effect analysis showed that a fold point (K) of blood AST level of 39.3 U/L was related to an epilepsy event. An increased AST level was related to a higher rate of epilepsy (1.2 [1.2-1.3], <0.001) among patients with AST levels of less than 39.3 U/L. Significance was weaker among patients with AST levels of more than 39.3 U/L. As for ALT, the fold point of blood ALT level revealed an association with epilepsy at 23.3 U/L. Similarly, elevated AST levels were related to a significantly higher risk of secondary epilepsy post-stroke (1.2 [1.1-1.2], <0.001) when they were less than 23.3 U/L. The significant correlation was weaker when blood AST levels were higher than 23.3 U/L. Importantly, ROC curve analysis displayed a high AUC value (0.8101) of blood AST in terms of detecting epilepsy risk, with a sensitivity of 0.7254 and specificity of 0.7997, respectively. Conclusion:Our study provided potential evidence for the correlation between liver function and secondary epilepsy after acute stroke, indicating that early protection and management of liver dysfunction among post-stroke patients might be beneficial for reducing epileptic seizures. More studies with a high level of evidence are needed to confirm this finding, however.

BackgroundPsychotic disorders are characterized by white matter (WM) abnormalities; however, their relationship with the various aspects of illness presentation remains unclear. Sleep disturbances are common in psychosis, and emerging evidence suggests that sleep plays a critical role in WM physiology. Therefore, it is plausible that sleep disturbances are associated with impaired WM integrity in these disorders. To test this hypothesis, we examined the association of self-reported sleep disturbances with WM transverse (T2) relaxation times in a cross-diagnostic sample of patients with psychosis.MethodsA total of 28 patients with psychosis (11 schizophrenia spectrum disorders and 17 bipolar disorder with psychotic features) were included. Metabolite (N-acetyl aspartate, choline, and creatine) and water T2 relaxation times were measured in the anterior corona radiata at 4T. Sleep was evaluated using the Pittsburgh Sleep Quality Index (PSQI).ResultsPSQI total score showed a moderate to strong positive correlation with water T2 (r = 0.64, p< 0.001). Linear regressions showed that this association was independent of the overall severity of depressive, manic, or psychotic symptoms. In our exploratory analysis, sleep disturbance was correlated with free water percentage, suggesting that increased extracellular water may be a mechanism underlying the association of disturbed sleep and prolonged water T2 relaxation.ConclusionOur results highlight the connection between poor sleep and WM abnormalities in psychotic disorders. Future research using objective sleep measures and neuroimaging techniques suitable to probe free water is needed to further our insight into this relationship.

BACKGROUND: Neurocognitive factors including aberrant reward learning, blunted GABA (gamma-aminobutyric acid), and potentiated stress sensitivity have been linked to anhedonia, a hallmark depressive symptom, possibly in a sex-dependent manner. However, previous research has not investigated the putative associations among these factors or the extent to which they represent trait- or state-based vulnerabilities for depression. METHODS: Young adults with current major depressive disorder (MDD) (n = 44), remitted MDD (n = 42), and healthy control participants (HCs) (n = 44), stratified by sex assigned at birth, underwent magnetic resonance spectroscopy to assess macromolecular contaminated GABA (GABA+) and then a reward learning task before and after acute stress. We assessed changes in reward learning after stress and associations with GABA+. RESULTS: Results revealed blunted baseline reward learning in participants with remitted MDD versus participants with current MDD and HCs but, surprisingly, no differences between participants with current MDD and HCs. Reward learning was reduced following acute stress regardless of depressive history. GABA+ in the rostral anterior cingulate cortex, but not the dorsolateral prefrontal cortex, was associated with reduced baseline reward learning only in female participants. GABA+ did not predict stress-related changes in reward learning. CONCLUSIONS: To our knowledge, this is the first study to investigate associations among GABA, reward learning, and stress reactivity in current versus past depression. Hypothesized depression-related differences in reward learning did not emerge, precluding claims about state versus trait vulnerabilities. However, our finding that blunted GABA was associated with greater reward learning in female participants provides novel insights into sexselective associations between the frontal GABAergic inhibitory system and reward processing.

Background:Schizophrenia (SZ) and bipolar disorder (BD) are characterized by white matter (WM) abnormalities, however, their relationship with illness presentation is not clear. Sleep disturbances are common in both disorders, and recent evidence suggests that sleep plays a critical role in WM physiology. Therefore, it is plausible that sleep disturbances are associated with impaired WM integrity in these disorders. To test this hypothesis, we examined the association of self-reported sleep disturbances with WM transverse (T2) relaxation times in patients with SZ spectrum disorders and BD with psychotic features. Methods:28 patients with psychosis (17 BD-I, with psychotic features and 11 SZ spectrum disorders) were included. Metabolite and water T2 relaxation times were measured in the anterior corona radiata at 4T. Sleep was evaluated using the Pittsburgh Sleep Quality Index. Results:PSQI total score showed a moderate to strong positive correlation with water T2 (r = 0.64, p<0.001). Linear regressions showed that this association was specific to sleep disturbance but was not a byproduct of exacerbation in depressive, manic, or psychotic symptoms. In our exploratory analysis, sleep disturbance was correlated with free water percentage, suggesting that increased extracellular water may be a mechanism underlying the association of disturbed sleep and prolonged water T2 relaxation. Conclusion:Our results highlight the connection between poor sleep and WM abnormalities in psychotic disorders. Future research using objective sleep measures and neuroimaging techniques suitable to probe free water is needed to further our insight into this relationship.

BACKGROUND:We examined the acute effects of intranasal insulin on cognitive function and brain glutathione (GSH), a central factor in resistance to oxidative stress, in both participants with early psychosis and healthy control (HC) participants. METHODS:Twenty-one patients with early-stage psychotic disorders and 18 HC participants underwent magnetic resonance spectroscopy (MRS) scans and cognitive assessments before and after administration of intranasal insulin 40 IU. We conducted proton MRS (1H-MRS) in the prefrontal cortex at 4T to measure GSH and glutamate metabolites. We assessed cognition using the Brief Assessment of Cognition in Schizophrenia symbol coding, digit sequencing, and verbal fluency tasks, in addition to the Stroop task. RESULTS:The mean (SD) age of participants was 25.7 (4.6) years; 51.3% were female. There were no significant group differences at baseline in age, sex, body mass index, homeostatic model assessment of insulin resistance (HOMA-IR), or cognition. Patients had higher baseline GSH (p < .001) and glutamate (p = .007). After insulin administration, GSH increased in HC participants (mean change, 0.15; 95% CI 0.03 to 0.26; p = .015), but not in patients. Symbol coding improved in both patients (0.74; 95% CI 0.37 to 1.11; p < .001) and HC participants (0.83; 95% CI 0.58 to 1.09; p < .001), and verbal fluency improved in HC participants (0.43; 95% CI 0.14 to 0.72; p = .006). Lower baseline HOMA-IR was associated with greater change in GSH (coefficient -0.22; 95% CI -0.40 to -0.04; p = .017). CONCLUSIONS:Intranasal insulin increased brain GSH in HC participants, but not in patients with early psychotic disorders. These novel findings demonstrate that intranasal insulin enhances antioxidant capacity and resilience to oxidative stress in HC individuals in contrast to an absent antioxidant response in those with early psychotic disorders.

OBJECTIVE:Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. METHODS:Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen in training compared to the end-to-end deep learning method. RESULTS:Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. CONCLUSION:This study provides an intelligent, reliable and robust MRS quantification. SIGNIFICANCE:QNet is the first LLS quantification aided by deep learning.

There is a burgeoning need for high-power-density sodium-ion batteries (SIBs) with compressed size, whereas it is very challenging to integrate fast kinetics and high volumetric capacity into one material. Herein, we demonstrate an effective strategy to jointly promote the power and volumetric energy densities from the perspective of materials' electronic conductivity. Via semiconductor-to-metal transition, we successfully develop the first intrinsic pseudocapacitive chalcogenide material, metallic FeS1-xSex(x >= 0.5), which shows inherent capacitor-like faradic charge storage, even when crystallizes in micrometer bulk material and operates in the absence of conducting additives. Therefore, it enables conducting-additive-free (CA-free) electrodes with high volumetric capacities of 965 mA h cm-3, which also delivers exceptional rate capability with a capacity above 392 mA h cm-3 at 50C-rate, among the best state-of-the-art SIB anodes. These superior performances can be attributed to a rare intrinsic intercalation pseudocapacitive mechanism, which is related to the nature of metallic behavior and unique layered structure. This discovery not only provides a valuable complement to our understanding of pseudocapacitive processes, but also facilitates the design of high-rate electrode materials with compressed sizes in the future.

In both preclinical and clinical settings, dysregulated frontostriatal circuits have been identified as the underlying neural substrates of compulsive seeking/taking behaviors manifested in substance use disorders and behavioral addictions including internet gaming disorder (IGD). However, the neurochemical substrates for these disorders remain elusive. The lack of comprehensive cognitive assessments in animal models has hampered our understanding of neural plasticity in addiction from these models. In this study, combining data from a rat model of compulsive taking/seeking and human participants with various levels of IGD severity, we investigated the relationship between regional glutamate (Glu) concentration and addictive behaviors. We found that Glu levels were significantly lower in the prelimbic cortex (PrL) of rats after 20-days of methamphetamine self-administration (SA), compared to controls. Glu concentration after a punishment phase negatively correlated with acute drug-seeking behavior. In addition, changes in Glu levels from a drug naïve state to compulsive drug taking patterns negatively correlated with drug-seeking during both acute and prolonged abstinence. The human data revealed a significant negative correlation between Glu concentration in the dorsal anterior cingulate cortex (dACC), the human PrL counterpart, and symptoms of IGD. Interestingly, there was a positive correlation between Glu levels in the dACC and self-control, as well as mindful awareness. Further analysis revealed that the dACC Glu concentration mediated the relationship between self-control/mindful awareness and IGD symptoms. These results provide convergent evidence for a protective role of dACC/PrL in addiction, suggesting interventions to enhance dACC glutamatergic functions as a potential strategy for addiction prevention and treatment.

Breast cancer ranks the first in the incidence of female cancer and is the most common cancer threatening the life and health of women worldwide.Tumor protein p53-regulated apoptosis-inducing protein 1 (TP53AIP1) is a pro-apoptotic gene downstream of p53. However, the role of TP53AIP1 in BC needs to be investigated. In vitro and in vivo experiments were conducted to assess the biological functions and associated mechanisms. Several bioinformatics analyses were made, CCK8 assay, wound healing, transwell assays, colony formation assay, EDU, flow cytometry, Immunofluorescence, qRT-PCR and Western-blotting were performed. In our study, we discovered that BC samples had low levels of TP53AIP1 expression, which correlated with a lower survival rate in BC patients. When TP53AIP1 was up-regulated, it caused a decrease in cell proliferation, migration, and invasion. It also induced epithelial-to-mesenchymal transition (EMT) and protective autophagy. Furthermore, the over-expression of TP53AIP1 suppressed tumor growth when tested in vivo. We also noticed that TP53AIP1 up-regulation resulted in decreased levels of phosphorylation in AKT and mTOR, suggesting a mechanistic role. In addition, we performed functional rescue experiments where the activation of AKT was able to counteract the impact of TP53AIP1 on the survival and autophagy in breast cancer cell lines. This suggests that TP53AIP1 acts as an oncogene by controlling the AKT/mTOR pathway. These findings reveal TP53AIP1 as a gene that suppresses tumor growth and triggers autophagy through the AKT/mTOR pathway in breast cancer cells. As a result, TP53AIP1 presents itself as a potential target for novel therapeutic approaches in treating breast cancer.

Objective: Preclinical work suggests that excess glucocorticoids and reduced cortical gamma-aminobutyric acid (GABA) may affect sex-dependent differences in brain regions implicated in stress regulation and depressive phenotypes. The authors sought to address a critical gap in knowledge, namely, how stress circuitry is functionally affected by glucocorticoids and GABA in current or remitted major depressive disorder (MDD). Methods: Multimodal imaging data were collected from 130 young adults (ages 18-25), of whom 44 had current MDD, 42 had remitted MDD, and 44 were healthy comparison subjects. GABA+ (gamma-aminobutyric acid and macromolecules) was assessed using magnetic resonance spectroscopy, and task-related functional MRI data were collected under acute stress and analyzed using data-driven network modeling. Results: Across modalities, trait-related abnormalities emerged. Relative to healthy comparison subjects, both clinical groups were characterized by lower rostral anterior cingulate cortex (rACC) GABA+ and frontoparietal network amplitude but higher amplitude in salience and stress-related networks. For the remitted MDD group, differences from the healthy comparison group emerged in the context of elevated cortisol levels, whereas the MDD group had lower cortisol levels than the healthy comparison group. In the comparison group, frontoparietal and stress-related network connectivity was positively associated with cortisol level (highlighting putative top-down regulation of stress), but the opposite relationship emerged in the MDD and remitted MDD groups. Finally, rACC GABA+ was associated with stress-induced changes in connectivity between overlapping default mode and salience networks. Conclusions: Lifetime MDD was characterized by reduced rACC GABA+ as well as dysregulated cortisol-related interactions between top-down control (frontoparietal) and threat (task-related) networks. These findings warrant further investigation of the role of GABA in the vulnerability to and treatment of MDD.

Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.

Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of 1H-MRS is the low Signal-Noise Ratio (SNR). To improve the SNR, a typical approach is to perform a large number of Signal Averaging (SA). The data acquisition time, however, is proportional to the number of SA accordingly. A complete clinical MRS scan takes approximately 10 minutes in a common setting with the number of SA of 128. Recently, deep learning has been introduced to improve the SNR, but mostly the simulated data were used as the training set. This may hinder the MRS applications since some potential differences, such as acquisition system imperfections, physiological and psychologic conditions may exist between the simulated and in vivo data. In this paper, a new scheme that purely used repeated samples of realistic data was proposed. A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the time domain data with low SNR (24 SA) to the one with high SNR (128 SA). Experiments on the in vivo brain spectra of 7 healthy, 2 tumor and 1 cerebral infarction region show that using SA of 24, only 20% of a common setting, the spectra denoised by ReLSTM can provide the estimated concentrations of metabolites with the reliability comparable to those of the high-SNR spectra obtained commonly with 128 SA. Furthermore, compared with the state-of-the-art Low-Rank (LR) denoising method, the ReLSTM achieves lower relative errors and the Cramér-Rao lower bounds in quantifying some important biomarkers. In summary, ReLSTM can perform high-fidelity spectral denoising of the spectra with the fast acquisition (24 SA), which is valuable to MRS clinical studies.

Multiple psychiatric disorders are characterized by a failure to suppress default-mode network (DMN) activity during tasks and by weaker anti-correlations between DMN and other brain networks at rest. However, the cellular and molecular mechanisms underlying this phenomenon are poorly understood. At the cellular level, neuronal activity is regulated by multiple neurochemical processes including cycling of glutamate and GABA, the major excitatory and inhibitory neurotransmitters in brain. By combining functional MRI and magnetic resonance spectroscopy techniques, it has been shown that the neurotransmitter concentrations in DMN modulate not only functional activity during cognitive tasks, but also the functional connectivity between DMN and other brain networks such as frontoparietal executive control network (CN) at rest in the healthy brain. In the current study, we extend previous research to first episode psychosis (FEP) patients and their relatives. We detected higher glutamate (Glu) levels in the medial prefrontal cortex (MPFC) in FEP compared to healthy controls without a significant difference in GABA. We also observed a significantly lower functional anti-correlated connectivity between critical nodes within the DMN (MPFC) and CN (DLPFC) in FEP. Furthermore, the relationship between MPFC Glu and GABA concentrations and the functional anti-correlation that is seen in healthy people was absent in FEP patients. These findings imply that both the DMN Glu level and the interaction between DMN and CN are affected by the illness, as is the association between neurochemistry and functional connectivity. A better understanding of this observation could provide opportunities for developing novel treatment strategies for psychosis.

Myasthenia Gravis (MG) is a T cell-driven, autoantibody-mediated disease. Here we show that oral Berberine (BBR) ameliorated clinical symptoms of experimental autoimmune myasthenia gravis(EAMG) rat model via decreasing the frequencies of Th1, Th17, Th1/17 cell subsets. JAK-STAT pathway was highlighted by transcriptomic analysis with EAMG mononuclear cells (MNCs). Surface plasmon resonance identified ligand binding interaction between BBR and JAK2, and electrostatic interaction was proposed by molecular dynamic simulation. Reduced phosphorylated JAK1/2/3 and STAT1/3 in MNCs from BBR-fed EAMG rats were demonstrated. These results suggest that BBR might improve EAMG by rebalancing T cell subsets through targeting JAK-STAT pathway.