Impaired visual-motor functional connectivity in first-episode medication-naïve patients with major depressive disorder.

The perceptual dysfunctions have been fundamental causes of cognitive and emotional problems in patients with major depressive disorder. However, visual system impairment in depression has been underexplored. Here, we explored functional connectivity in a large cohort of first-episode medication-naïve patients with major depressive disorder (n = 190) and compared it with age- and sex-matched healthy controls (n = 190). A recently developed individual-oriented approach was applied to parcellate the cerebral cortex into 92 regions of interest using resting-state functional magnetic resonance imaging data. Significant reductions in functional connectivities were observed between the right lateral occipitotemporal junction within the visual network and 2 regions of interest within the sensorimotor network in patients. The volume of right lateral occipitotemporal junction was also significantly reduced in major depressive disorder patients, indicating that this visual region is anatomically and functionally impaired. Behavioral correlation analysis showed that the reduced functional connectivities were significantly associated with inhibition control in visual-motor processing in patients. Taken together, our data suggest that functional connectivity between visual network and sensorimotor network already shows a significant reduction in the first episode of major depressive disorder, which may interfere with the inhibition control in visual-motor processing. The lateral occipitotemporal junction may be a hub of disconnection and may play a role in the pathophysiology of major depressive disorder.

A glimpse into novel acylations and their emerging role in regulating cancer metastasis.

Metastatic cancer is a major cause of cancer-related mortality; however, the complex regulation process remains to be further elucidated. A large amount of preliminary investigations focus on the role of epigenetic mechanisms in cancer metastasis. Notably, the posttranslational modifications were found to be critically involved in malignancy, thus attracting considerable attention. Beyond acetylation, novel forms of acylation have been recently identified following advances in mass spectrometry, proteomics technologies, and bioinformatics, such as propionylation, butyrylation, malonylation, succinylation, crotonylation, 2-hydroxyisobutyrylation, lactylation, among others. These novel acylations play pivotal roles in regulating different aspects of energy mechanism and mediating signal transduction by covalently modifying histone or nonhistone proteins. Furthermore, these acylations and their modifying enzymes show promise regarding the diagnosis and treatment of tumors, especially tumor metastasis. Here, we comprehensively review the identification and characterization of 11 novel acylations, and the corresponding modifying enzymes, highlighting their significance for tumor metastasis. We also focus on their potential application as clinical therapeutic targets and diagnostic predictors, discussing the current obstacles and future research prospects.

Verbal memory network mapping in individual patients predicts postoperative functional impairments.

Verbal memory decline is a significant concern following temporal lobe surgeries in patients with epilepsy, emphasizing the need for precision presurgical verbal memory mapping to optimize functional outcomes. However, the inter-individual variability in functional networks and brain function-structural dissociations pose challenges when relying solely on group-level atlases or anatomical landmarks for surgical guidance. Here, we aimed to develop and validate a personalized functional mapping technique for verbal memory using precision resting-state functional MRI (rs-fMRI) and neurosurgery. A total of 38 patients with refractory epilepsy scheduled for surgical interventions were enrolled and 28 patients were analyzed in the study. Baseline 30-min rs-fMRI scanning, verbal memory and language assessments were collected for each patient before surgery. Personalized verbal memory networks (PVMN) were delineated based on preoperative rs-fMRI data for each patient. The accuracy of PVMN was assessed by comparing post-operative functional impairments and the overlapping extent between PVMN and surgical lesions. A total of 14 out of 28 patients experienced clinically meaningful declines in verbal memory after surgery. The personalized network and the group-level atlas exhibited 100% and 75.0% accuracy in predicting postoperative verbal memory declines, respectively. Moreover, six patients with extra-temporal lesions that overlapped with PVMN showed selective impairments in verbal memory. Furthermore, the lesioned ratio of the personalized network rather than the group-level atlas was significantly correlated with postoperative declines in verbal memory (personalized networks: r = -0.39, p = .038; group-level atlas: r = -0.19, p = .332). In conclusion, our personalized functional mapping technique, using precision rs-fMRI, offers valuable insights into individual variability in the verbal memory network and holds promise in precision verbal memory network mapping in individuals.

Personalized fMRI Delineates Functional Regions Preserved within Brain Tumors.

OBJECTIVE: Accumulating evidence from invasive cortical stimulation mapping and noninvasive neuroimaging studies indicates that brain function may be preserved within brain tumors. However, a noninvasive approach to accurately and comprehensively delineate individual-specific functional networks in the whole brain, especially in brain tissues within and surrounding tumors, is still lacking. The purpose of the study is to develop a clinically useful technique that can map functional regions within tumoral brains. METHODS: We developed an individual-specific functional network parcellation approach using resting state functional magnetic resonance imaging (rsfMRI) that effectively captured functional networks within and nearby tumors in 20 patients. We examined the accuracy of the functional maps using invasive cortical stimulation and task response. RESULTS: We found that approximately 33.2% of the tumoral mass appeared to be functionally active and demonstrated robust functional connectivity with non-tumoral brain regions. Functional networks nearby tumors were validated by invasive cortical stimulation mapping. Intratumoral sensorimotor networks mapped by our technique could be distinguished by their distinct cortico-cerebellar connectivity patterns and were consistent with hand movement evoked fMRI task activations. Furthermore, in some patients, cognitive networks that were detected in the tumor mass showed long-distance and distributed functional connectivity. INTERPRETATION: Our noninvasive approach to mapping individual-specific functional networks using rsfMRI represents a promising new tool for identifying regions with preserved functional connectivity within and surrounding brain tumors, and could be used as a complement to presurgical planning for patients undergoing tumor resection surgery. ANN NEUROL 2022;91:353-366.

Recent advances in targeted delivery of non-coding RNA-based therapeutics for atherosclerosis.

Cardiovascular disease (CVD) has overtaken infectious illnesses as the leading cause of mortality and disability worldwide. The pathology that underpins CVD is atherosclerosis, characterized by chronic inflammation caused by the accumulation of plaques in the arteries. As our knowledge about the microenvironment of blood vessel walls deepens, there is an opportunity to fine-tune treatments to target the mechanisms driving atherosclerosis more directly. The application of non-coding RNAs (ncRNAs) as biomarkers or intervention targets is increasing. Although these ncRNAs play an important role in driving atherosclerosis and vascular dysfunction, the cellular and extracellular environments pose a challenge for targeted transmission and therapeutic regulation of ncRNAs. Specificity, delivery, and tolerance have hampered the clinical translation of ncRNA-based therapeutics. Nanomedicine is an emerging field that uses nanotechnology for targeted drug delivery and advanced imaging. Recently, nanoscale carriers have shown promising results and have introduced new possibilities for nucleic acid targeted drug delivery, particularly for atherosclerosis. In this review, we discuss the latest developments in nanoparticles to aid ncRNA-based drug development, particularly miRNA, and we analyze the current challenges in ncRNA targeted delivery. In particular, we highlight the emergence of various kinds of nanotherapeutic approaches based on ncRNAs, which can improve treatment options for atherosclerosis.

Neuregulin-1 regulates the conversion of M1/M2 microglia phenotype via ErbB4-dependent inhibition of the NF-κB pathway.

BACKGROUND: The inflammatory response caused by microglia in the central nervous system plays an important role in Alzheimer's disease. Neuregulin-1 (NRG1) is a member of the neuregulin family and has been demonstrated to have anti-inflammatory properties. The relationship between NRG1, microglia phenotype and neuroinflammation remains unclear. MATERIALS AND METHODS: BV2 cells were used to examine the mechanism of NRG1 in regulating microglia polarization. Neuronal apoptosis, inflammatory factors TNF-α and iNOS, microglia polarization, ErbB4 and NF-κB p65 expression were assessed. RESULTS: We found that exogenous NRG1 treatment or overexpression improved microglial activity and reduced the secretion of the inflammatory factors TNF-α and iNOS in vitro. The expression of Bax in SH-SY5Y neuron cells incubated with medium collected from the NRG1 treatment group decreased. Additionally, our study showed that NRG1 treatment reduced the levels of the M1 microglia markers CD120 and iNOS and increased the levels of the M2 microglia markers CD206 and Arg-1. Furthermore, we observed that NRG1 treatment attenuated Aß-induced NF-κB activation and promoted the expression of p-ErbB4 and that knockdown of ErbB4 abrogated the effects of NRG1 on NF-κB, Bax levels and M2 microglial polarization. CONCLUSION: NRG1 inhibits the release of inflammatory factors in microglia and regulates the switching of the M1/M2 microglia phenotype, most likely via ErbB4-dependent inhibition of the NF-κB pathway.

Dissociable Auditory Cortico-Cerebellar Pathways in the Human Brain Estimated by Intrinsic Functional Connectivity.

The cerebellum, a structure historically associated with motor control, has more recently been implicated in several higher-order auditory-cognitive functions. However, the exact functional pathways that mediate cerebellar influences on auditory cortex (AC) remain unclear. Here, we sought to identify auditory cortico-cerebellar pathways based on intrinsic functional connectivity magnetic resonance imaging. In contrast to previous connectivity studies that principally consider the AC as a single functionally homogenous unit, we mapped the cerebellar connectivity across different parts of the AC. Our results reveal that auditory subareas demonstrating different levels of interindividual functional variability are functionally coupled with distinct cerebellar regions. Moreover, auditory and sensorimotor areas show divergent cortico-cerebellar connectivity patterns, although sensorimotor areas proximal to the AC are often functionally grouped with the AC in previous connectivity-based network analyses. Lastly, we found that the AC can be functionally segmented into highly similar subareas based on either cortico-cerebellar or cortico-cortical functional connectivity, suggesting the existence of multiple parallel auditory cortico-cerebellar circuits that involve different subareas of the AC. Overall, the present study revealed multiple auditory cortico-cerebellar pathways and provided a fine-grained map of AC subareas, indicative of the critical role of the cerebellum in auditory processing and multisensory integration.

Inhibition of PTEN Attenuates Endoplasmic Reticulum Stress and Apoptosis via Activation of PI3K/AKT Pathway in Alzheimer's Disease.

Amyloid plaques and neurofibrillary tangles are pathologic hallmarks of Alzheimer's disease (AD). Endoplasmic reticulum (ER) stress has been implicated in the loss of neurons in AD. The phosphatase and tensin homolog deleted on chromosome ten (PTEN) plays an important role in regulating neuronal survival processes. However, the direct effects of the PTEN on ER stress and apoptosis in AD have not been elucidated. In this study, we demonstrate that the expression of PTEN and ER stress related proteins, GRP78 and CHOP, increased in APP/PS1 transgenic AD mice compared with WT mice. A PTEN inhibitor, dipotassium bisperoxo-(5-hydroxypyridine-2-carboxyl)-oxovanadate (bpv) could decrease apoptosis, induce AKT phosphorylation and inhibit the ER stress response proteins in hippocampus in APP/PS1 transgenic AD model mice. Furthermore, treatment with the specific PI3K inhibitor, LY294002, significantly blocked the anti-apoptotic effects of bpv in AD mice. The expression in GRP78, CHOP and apoptosis levels by bpv was reversed after PI3K inhibitor treatment. Taken together, our results indicate that the neuroprotective role of bpv involves the suppression of ER stress via the activation of the PI3K/AKT signalling pathways in APP/PS1 transgenic AD model mice.

Induction of Tca8113 tumor cell apoptosis by icotinib is associated with reactive oxygen species mediated p38-MAPK activation.

Icotinib, a selective EGFR tyrosine kinase inhibitor (EGFR-TKI), has been shown to exhibit anti-tumor activity against several tumor cell lines. However, the exact molecular mechanism of icotinib's anti-tumor effect remains unknown. This study aims to examine the zytotoxic effect of icotinib on Tca8113 cells and its potential molecular mechanism. Icotinib significantly resulted in dose-dependent cell death as determined by MTT assay, accompanied by increased levels of Bax and DNA fragmentation. Icotinib could also induce Reactive Oxygen Species (ROS) generation. Further studies confirmed that scavenging of reactive oxygen species by N-acetyl-L-cysteine (NAC), and pharmacological inhibition of MAPK reversed icotinib-induced apoptosis in Tca8113 cells. Our data provide evidence that icotinib induces apoptosis, possibly via ROS-mediated MAPK pathway in Tca8113 cells.

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks.

Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using electroencephalogram (EEG) signals. However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance. In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individual-specific information, leading to the poor cross-subject transfer performance. In response to these limitations, we propose a cross-scale Transformer and triple-view attention based domain-rectified transfer learning (CST-TVA-DRTL) for the RSVP classification. Specially, we first develop a cross-scale Transformer (CST) to extract multi-scale temporal features and exploit the dependencies of different scales features. Then, a triple-view attention (TVA) is designed to capture spectral features from triple views of multi-channel time-frequency images. Finally, a domain-rectified transfer learning (DRTL) framework is proposed to simultaneously obtain transferable domain-invariant representations and untransferable domain-specific representations, then utilize domain-specific information to rectify domain-invariant representations to adapt to target data. Experimental results on two public RSVP datasets suggests that our CST-TVA-DRTL outperforms the state-of-the-art methods in the RSVP classification task. The source code of our model is publicly available in https://github.com/ljbuaa/CST_TVA_DRTL.

Deep Fusion of Multi-Template Using Spatio-Temporal Weighted Multi-Hypergraph Convolutional Networks for Brain Disease Analysis.

Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI) can only reflect the relationship between pairwise brain regions. Thus, the hyper-connectivity network (HCN) has been widely used to reveal high-order interactions among multiple brain regions. However, existing HCN models are essentially spatial HCN, which reflect the spatial relevance of multiple brain regions, but ignore the temporal correlation among multiple time points. Furthermore, the majority of HCN construction and learning frameworks are limited to using a single template, while the multi-template carries richer information. To address these issues, we first employ multiple templates to parcellate the rs-fMRI into different brain regions. Then, based on the multi-template data, we propose a spatio-temporal weighted HCN (STW-HCN) to capture more comprehensive high-order temporal and spatial properties of brain activity. Next, a novel deep fusion model of multi-template called spatio-temporal weighted multi-hypergraph convolutional network (STW-MHGCN) is proposed to fuse the STW-HCN of multiple templates, which extracts the deep interrelation information between different templates. Finally, we evaluate our method on the ADNI-2 and ABIDE-I datasets for mild cognitive impairment (MCI) and autism spectrum disorder (ASD) analysis. Experimental results demonstrate that the proposed method is superior to the state-of-the-art approaches in MCI and ASD classification, and the abnormal spatio-temporal hyper-edges discovered by our method have significant significance for the brain abnormalities analysis of MCI and ASD.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Role of succinylation modification in central nervous system diseases.

Diseases of the central nervous system (CNS), including stroke, brain tumors, and neurodegenerative diseases, have a serious impact on human health worldwide, especially in elderly patients. The brain, which is one of the body's most metabolically dynamic organs, lacks fuel stores and therefore requires a continuous supply of energy substrates. Metabolic abnormalities are closely associated with the pathogenesis of CNS disorders. Post-translational modifications (PTMs) are essential regulatory mechanisms that affect the functions of almost all proteins. Succinylation, a broad-spectrum dynamic PTM, primarily occurs in mitochondria and plays a crucial regulatory role in various diseases. In addition to directly affecting various metabolic cycle pathways, succinylation serves as an efficient and rapid biological regulatory mechanism that establishes a connection between metabolism and proteins, thereby influencing cellular functions in CNS diseases. This review offers a comprehensive analysis of succinylation and its implications in the pathological mechanisms of CNS diseases. The objective is to outline novel strategies and targets for the prevention and treatment of CNS conditions.

The construction of Z-scheme heterojunction ZnIn2S4@CuO with enhanced charge transfer capability and its mechanism study for the visible light degradation of tetracycline.

In this study, copper oxide (CuO) was prepared by the microwave-assisted hydrothermal technique subsequently, CuO was grown in situ onto different rare metal compounds to prepare Z-scheme heterojunctions to improve the degradation efficiency of tetracycline (TC) in water environments. Various characterization proved the successful synthesis of all composite materials, and the formation of tight heterojunction interfaces, among which, the core-shell structure ZnIn2S4@CuO exhibited excellent photocatalytic degradation capability. Research results indicated that the degradation efficiency of ZnIn2S4@CuO for TC (50 mg/L) in the water environment reached 95.8 %, and the degradation rate is 2.41 times and 12.93 times that of CuO and ZnIn2S4 alone, respectively, the reason is because of the introduction of ZnIn2S4, Z-scheme heterojunction structures and internal electric field (IEF) is constructed and formed to extend the visible light response range of photocatalysts to improve electron-hole separation efficiency, and enhance charge transfer. In addition, ZnIn2S4@CuO-2 exhibited good stability and reproducibility, with no significant loss of activity after five cycles. Finally, the precise locations of free radical attack on TC were investigated by the combined use of high-resolution mass spectrometry (HR-MC) and frontier electron densities (FEDs), and a reasonable degradation pathway was provided. The results of this research provide a new and viable approach to overcome the limitations of conventional photocatalytic materials in terms of limited visible light absorption range and fast carrier recombination rates, which offers promising prospects for a wide range of applications in the field of wastewater purification.

Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same feature representations from source domain to different target domains, resulting in poor transfer performance. To address this issue, we propose a novel subject-specific EEG recognition method named deep multiview module adaption transfer (DMV-MAT) network. First, we design a universal deep multiview (DMV) network to generate different types of discriminative features from multiple perspectives, which improves the generalization performance by extensive feature sets. Second, module adaption transfer (MAT) is designed to evaluate each module by the feature distributions of source and target samples, which can generate an optimal weight sharing strategy for each target subject and promote the model to learn domain-invariant and domain-specific features simultaneously. We conduct extensive experiments in two EEG recognition tasks, i.e., motor imagery (MI) and seizure prediction, on four datasets. Experimental results demonstrate that the proposed method achieves promising performance compared with the state-of-the-art methods, indicating a feasible solution for subject-specific EEG recognition tasks. Implementation codes are available at https://github.com/YangLibuaa/DMV-MAT.

Defect-engineering improves the activity of Metal-Organic frameworks for catalyzing hydroboration of Alkynes: A combination of experimental investigation and Density functional theory calculations.

Defect-engineered metal-organic frameworks (DEMOFs) are emerging advanced materials. The construction of DEMOFs is of great significance; however, DEMOF-based catalysis remains unexplored. (E)-vinylboronates, an important building block for asymmetric synthesis, can be synthesized via the hydroboration of alkynes. However, the lack of high-performance catalysts considerably hinders their synthesis. Herein, a series of DEHKUST-1 (HKUST = Hong Kong University of Science and Technology) (Da-f) catalysts with missing occupation of linkers at Cu nodes were designed by partially replacing benzene-1,3,5-tricarboxylate (H3BTC) with defective connectors of pyridine-3,5-dicarboxylate (PYDC) to efficiently promote the hydroboration of alkynes. Results showed that the Dd containing 0.8 doping ratio of PYDC exhibited remarkable catalytic activity than the defect-free HKUST-1. This originated from the improved accessibility for reactants towards the Lewis acid active Cu sites of DEHKUST-1 due to the presence of plenty of rooms next to the Cu sites and enhanced coordination ability in such 'defective' HKUST-1. Dd had high selectivity (>99 %) and yield (>96 %) for (E)-vinylboronates and extensive functional group compatibility for terminal alkynes. Density functional theory (DFT) calculations were performed to elucidate the mechanism of hydroboration. Compared with that of defect-free HKUST-1, the low energy barrier of DEHKUST-1 can be attributed to the lower coordination number of Cu sites and enhanced accessibility of Cu active sites towards reagents.

Personalized brain MRI revealed distinct functional and anatomical disruptions in Creutzfeldt-Jakob disease and Alzheimer's disease.

AIMS: Creutzfeldt-Jakob disease (CJD) is a lethal neurodegenerative disorder, which leads to a rapidly progressive dementia. This study aimed to examine the cortical alterations in CJD, changes in these brain characteristics over time, and the differences between CJD and Alzheimer's disease (AD) that show similar clinical manifestations. METHODS: To obtain reliable, subject-specific functional measures, we acquired 24 min of resting-state fMRI data from each subject. We applied an individual-based approach to characterize the functional brain organization of 10 patients with CJD, 8 matched patients with AD, and 8 normal controls. We measured cortical atrophy as well as disruption in resting-state functional connectivity (rsFC) and then investigated longitudinal brain changes in a subset of CJD patients. RESULTS: CJD was associated with widespread cortical thinning and weakened rsFC. Compared with AD, CJD showed distinct atrophy patterns and greater disruptions in rsFC. Moreover, the longitudinal data demonstrated that the progressive cortical thinning and disruption in rsFC mainly affected the association rather than the primary cortex in CJD. CONCLUSIONS: CJD shows unique anatomical and functional disruptions in the cerebral cortex, distinct from AD. Rapid progression of CJD affects both the cortical thickness and rsFC in the association cortex.

SUGAR: Spherical ultrafast graph attention framework for cortical surface registration.

Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.

Neuregulin1beta1 antagonizes apoptosis via ErbB4-dependent activation of PI3-kinase/Akt in APP/PS1 transgenic mice.

Alzheimer's disease (AD) is characterized by the deposition of beta-amyloid protein (Aß) and extensive neuronal cell death. Apoptosis plays a crucial role in loss of neurons in AD. Neuregulin1 (NRG1) has been found to protect neurons from oxygen glucose deprivation induced apoptosis and hypoxia ischemia induced apoptosis. However, the relationship between NRG1 and apoptosis related protein expression in AD and its mechanism remain uncertain. The present study explores the effects of NRG1 on Aß-induced apoptosis in AD. In this study, extracellular domain of NRG1beta1 (NRG1ß1-ECD) promoted the expression of p-ErbB4 receptor, p-Akt and increased the level of Bcl-2 both in APP/PS1 transgenic mice and in vitro. In primary culture of neurons, the level of Bcl-2 protein decreased significantly after Aß treatment. These changes were inhibited by pretreatment of neurons with NRG1ß1-ECD. A specific inhibitor of PI3-kinase/Akt pathway, wortmannin, significantly abrogated the effects of NRG1ß1-ECD on p-Akt and Bcl-2 levels. Furthermore, the expression of PI3-kinase/Akt by NRG1ß1-ECD was ErbB4-dependent. Our data demonstrated that NRG1ß1-ECD might serve as an obvious neuroprotection in AD, and the possible protective mechanism occurs most likely via ErbB4-dependent activation of PI3-kinase/Akt pathway.