NUP85 alleviates lipid metabolism and inflammation by regulating PI3K/AKT signaling pathway in nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is one of the common causes of chronic liver disease in the world. The problem of NAFLD had become increasingly prominent. However, its pathogenesis is still indistinct. As we all know, NAFLD begins with the accumulation of triglyceride (TG), leading to fatty degeneration, inflammation and other liver tissues damage. Notably, structure of nucleoporin 85 (NUP85) is related to lipid metabolism and inflammation of liver diseases. In this study, the results of researches indicated that NUP85 played a critical role in NAFLD. Firstly, the expression level of NUP85 in methionine-choline-deficient (MCD)-induced mice increased distinctly, as well as the levels of fat disorder and inflammation. On the contrary, knockdown of NUP85 had the opposite effects. In vitro, AML-12 cells were stimulated with 2 mm free fatty acids (FFA) for 24 h. Results also proved that NUP85 significantly increased in model group, and increased lipid accumulation and inflammation level. Besides, NUP85 protein could interact with C-C motif chemokine receptor 2 (CCR2). Furthermore, when NUP85 protein expressed at an extremely low level, the expression level of CCR2 protein also decreased, accompanied with an inhibition of phosphorylation of phosphoinositol-3 kinase (PI3K)-protein kinase B (AKT) signaling pathway. What is more, trans isomer (ISRIB), a targeted inhibitor of NUP85, could alleviate NAFLD. In summary, our findings suggested that NUP85 functions as an important regulator in NAFLD through modulation of CCR2.

Platelet RNA sequencing reveals profile of caffeic acid affecting hemostasis in mice.

Background: Caffeic acid (CA) is a naturally occurring phenolic compound with diverse pharmacologic properties. CA plays a crucial role in hemostasis by increasing platelet count. However, the mechanism by which CA regulates platelets to promote hemostasis remains unclear. Objectives: We aim to identify the potential target pathways and genes by which CA regulates platelets to promote hemostasis. Methods: We performed RNA sequencing (RNA-seq) analysis of mouse platelet pools in both the CA-gavaged group and phosphate-buffered saline-gavaged group. Results: The 12,934 expressed transcripts had been annotated after platelet RNA-seq. Compared with the phosphate-buffered saline group, 987 differentially expressed genes (DEGs) were identified, of which 466 were downregulated and 521 were upregulated in CA group. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome gene set enrichment analysis demonstrated that upregulated DEGs were enriched in the pathways of hemostasis, platelet activation, signaling, aggregation, and degranulation. Moreover, Kyoto Encyclopedia of Genes and Genomes and Reactome gene set enrichment analysis revealed that 5 of the 25 cosignificantly upregulated DEGs were essential in CA-mediated platelet regulation to promote hemostasis. Conclusion: Our findings of platelet RNA-seq analysis demonstrate that CA regulates the gene expression of hemostasis and platelet activation-related pathways to increase platelet count and promote hemostasis. It will also provide reference molecular resources for future research on the function and mechanism by which CA regulates platelets to promote hemostasis.

Carnosine regulation of intracellular pH homeostasis promotes lysosome-dependent tumor immunoevasion.

Tumor cells and surrounding immune cells undergo metabolic reprogramming, leading to an acidic tumor microenvironment. However, it is unclear how tumor cells adapt to this acidic stress during tumor progression. Here we show that carnosine, a mobile buffering metabolite that accumulates under hypoxia in tumor cells, regulates intracellular pH homeostasis and drives lysosome-dependent tumor immune evasion. A previously unrecognized isoform of carnosine synthase, CARNS2, promotes carnosine synthesis under hypoxia. Carnosine maintains intracellular pH (pHi) homeostasis by functioning as a mobile proton carrier to accelerate cytosolic H+ mobility and release, which in turn controls lysosomal subcellular distribution, acidification and activity. Furthermore, by maintaining lysosomal activity, carnosine facilitates nuclear transcription factor X-box binding 1 (NFX1) degradation, triggering galectin-9 and T-cell-mediated immune escape and tumorigenesis. These findings indicate an unconventional mechanism for pHi regulation in cancer cells and demonstrate how lysosome contributes to immune evasion, thus providing a basis for development of combined therapeutic strategies against hepatocellular carcinoma that exploit disrupted pHi homeostasis with immune checkpoint blockade.

McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction.

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy.

Deep learning based synthesis of MRI, CT and PET: Review and analysis.

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images.

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.

A Dynamic Weights-Based Wavelet Attention Neural Network for Defect Detection.

Automatic defect detection plays an important role in industrial production. Deep learning-based defect detection methods have achieved promising results. However, there are still two challenges in the current defect detection methods: 1) high-precision detection of weak defects is limited and 2) it is difficult for current defect detection methods to achieve satisfactory results dealing with strong background noise. This article proposes a dynamic weights-based wavelet attention neural network (DWWA-Net) to address these issues, which can enhance the feature representation of defects and simultaneously denoise the image, thereby improving the detection accuracy of weak defects and defects under strong background noise. First, wavelet neural networks and dynamic wavelet convolution networks (DWCNets) are presented, which can effectively filter background noise and improve model convergence. Second, a multiview attention module is designed, which can direct the network attention toward potential targets, thereby guaranteeing the accuracy for detecting weak defects. Finally, a feature feedback module is proposed, which can enhance the feature information of defects to further improve the weak defect detection accuracy. The DWWA-Net can be used for defect detection in multiple industrial fields. Experiment results illustrate that the proposed method outperforms the state-of-the-art methods (mean precision: GC10-DET: 6.0%; NEU: 4.3%). The code is made in https://github.com/781458112/DWWA.

Effect of Traditional Chinese Medicine Physique Classification and Targeted, Individualized Nursing Intervention on Athletes' Mental Health in Sports

Objective: To investigate the impact of Traditional Chinese Medicine (TCM) physique classification and targeted, individualized nursing intervention on athletes' mental health in the context of sports. Methods: From February 2018 to February 2019, we randomly assigned 154 athletes to two groups (n=77) for this study. The control group received standard sports-related nursing care, while the observation group received personalized nursing interventions based on their TCM physique constitution. We assessed various parameters, including mental health indicators and sports performance metrics, before and after the nursing intervention. The assessment included psychological well-being scores, performance metrics, and the incidence of any adverse effects. Results: After the nursing intervention, athletes in the observation group showed significant improvements in psychological well-being, including lower levels of anxiety and depression (all P<0.05). Additionally, their sports performance metrics, including strength, endurance, and recovery, demonstrated remarkable enhancement compared to the control group (P<0.05). The overall quality of life scores for athletes in the observation group also showed significant improvements, particularly in physiological function, mental health, emotional function, and social function (P<0.05). In terms of adverse effects, the observation group experienced significantly fewer issues compared to the control group (P<0.05). Conclusion: Utilizing TCM physique classification and tailored nursing interventions for athletes can lead to improved mental health outcomes, enhanced sports performance, and an overall better quality of life. This approach holds promise for optimizing athletes' well-being and performance and deserves further consideration and application in the realm of sports and athletes' mental health. (AU)

Co-delivery of a tumor microenvironment-responsive disulfiram prodrug and CuO2 nanoparticles for efficient cancer treatment.

Disulfiram (DSF) has been used as a hangover drug for more than seven decades and was found to have potential in cancer treatment, especially mediated by copper. However, the uncoordinated delivery of disulfiram with copper and the instability of disulfiram limit its further applications. Herein, we synthesize a DSF prodrug using a simple strategy that could be activated in a specific tumor microenvironment. Poly amino acids are used as a platform to bind the DSF prodrug through the B-N interaction and encapsulate CuO2 nanoparticles (NPs), obtaining a functional nanoplatform Cu@P-B. In the acidic tumor microenvironment, the loaded CuO2 NPs will produce Cu2+ and cause oxidative stress in cells. At the same time, the increased reactive oxygen species (ROS) will accelerate the release and activation of the DSF prodrug and further chelate the released Cu2+ to produce the noxious copper diethyldithiocarbamate complex, which causes cell apoptosis effectively. Cytotoxicity tests show that the DSF prodrug could effectively kill cancer cells with only a small amount of Cu2+ (0.18 µg mL-1), inhibiting the migration and invasion of tumor cells. In vitro and in vivo experiments have demonstrated that this functional nanoplatform could kill tumor cells effectively with limited toxic side effects, showing a new perspective in DSF prodrug design and cancer treatment.

Xanthatin induce DDP-resistance lung cancer cells apoptosis through regulation of GLUT1 mediated ROS accumulation.

Chemoresistance to cisplatin (DDP) therapy is a major obstacle that needs to be overcome in treating lung cancer patients. Xanthatin has been reported to exhibit an antitumor effect on various cancers, but the function of xanthatin in DDP-resistance lung cancer remains unclear. The study aimed to explore the effect and mechanisms of xanthatin on proliferation, apoptosis, and migration in DDP-resistance lung cancer cells. In the present study, xanthatin suppresses the expression of glucose transporter 1 (GLUT1), attenuates the pentose phosphate pathway (PPP), and causes ROS accumulation and apoptosis, thereby mitigating the antioxidative capacity in DDP-resistance cells. Previous studies have shown that GLUT1 is associated with resistance to platinum drugs. We found that GLUT1 was significantly increased in the DDP-resistant lung cancer cell line compared to the parental cell line, and xanthatin significantly downregulated GLUT1 expression in DDP-resistant lung cancer cells. Notably, overexpression of GLUT1 significantly reduced the production of ROS and increased cellular NADPH/NADP+ and GSH/GSSG ratios. Thus, these results suggest that xanthatin induces DDP-resistance lung cancer cells apoptosis through regulation of GLUT1-mediated ROS accumulation. These findings might provide a possible strategy for the clinical treatment of DDP-resistant lung cancer.

Secreted protease PRSS35 suppresses hepatocellular carcinoma by disabling CXCL2-mediated neutrophil extracellular traps.

Hepatocytes function largely through the secretion of proteins that regulate cell proliferation, metabolism, and intercellular communications. During the progression of hepatocellular carcinoma (HCC), the hepatocyte secretome changes dynamically as both a consequence and a causative factor in tumorigenesis, although the full scope of secreted protein function in this process remains unclear. Here, we show that the secreted pseudo serine protease PRSS35 functions as a tumor suppressor in HCC. Mechanistically, we demonstrate that active PRSS35 is processed via cleavage by proprotein convertases. Active PRSS35 then suppresses protein levels of CXCL2 through targeted cleavage of tandem lysine (KK) recognition motif. Consequently, CXCL2 degradation attenuates neutrophil recruitment to tumors and formation of neutrophil extracellular traps, ultimately suppressing HCC progression. These findings expand our understanding of the hepatocyte secretome's role in cancer development while providing a basis for the clinical translation of PRRS35 as a therapeutic target or diagnostic biomarker.

Iron-siRNA Nanohybrids for Enhanced Chemodynamic Therapy via Ferritin Heavy Chain Downregulation.

Ferrous iron (Fe2+ ) has more potent hydroxyl radical (⋅OH)-generating ability than other Fenton-type metal ions, making Fe-based nanomaterials attractive for chemodynamic therapy (CDT). However, because Fe2+ can be converted by ferritin heavy chain (FHC) to nontoxic ferric form and then sequestered in ferritin, therapeutic outcomes of Fe-mediated CDT agents are still far from satisfactory. Here we report the synthesis of siRNA-embedded Fe0 nanoparticles (Fe0 -siRNA NPs) for self-reinforcing CDT via FHC downregulation. Upon internalization by cancer cells, pH-responsive Fe0 -siRNA NPs are degraded to release Fe2+ and FHC siRNA in acidic endo/lysosomes with the aid of oxygen (O2 ). The accompanied O2 depletion causes an intracellular pH decrease, which further promotes the degradation of Fe0 -siRNA NPs. In addition to initiating chemodynamic process, Fe2+ -catalyzed ⋅OH generation facilitates endo/lysosomal escape of siRNA by disrupting the membranes, enabling FHC downregulation-enhanced CDT.

Recent advances of Phosphodiesterase 4B in cancer.

INTRODUCTION: Phosphodiesterase 4B (PDE4B) is a crucial enzyme in the phosphodiesterases (PDEs), acting as a regulator of cyclic adenosine monophosphate (cAMP). It is involved in cancer process through PDE4B/cAMP signaling pathway. Cancer occurs and develops with the regulation of PDE4B in the body, suggesting that PDE4B is a promising therapeutic target. AREAS COVERED: This review covereed the function and mechanism of PDE4B in cancer. We summarized the possible clinical applications of PDE4B, and highlighted the possible ways to develop clinical applications of PDE4B inhibitors. We also discussed some common PDEs inhibitors, and expected the development of combined targeting PDE4B and other PDEs drugs in the future. EXPERT OPINION: The existing research and clinical data can strongly prove the role of PDE4B in cancer. PDE4B inhibition can effectively increase cell apoptosis, inhibit cell proliferation, transformation, migration, etc., indicating that PDE4B inhibition can effectively inhibit the development of cancer. Other PDEs may antagonize or coordinate this effect. As for the further study on the relationship between PDE4B and other PDEs in cancer, it is still a challenge to develop multi-targeted PDEs inhibitors.

Frontostriatothalamic effective connectivity and dopaminergic function in the psychosis continuum.

Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signalling or impaired top-down cortical regulation. We used spectral dynamic causal modelling of resting-state functional MRI to characterize the effective connectivity of dorsal and ventral FST circuits in a sample of 46 antipsychotic-naïve first-episode psychosis patients and 23 controls and an independent sample of 36 patients with established schizophrenia and 100 controls. We also investigated the association between FST effective connectivity and striatal 18F-DOPA uptake in an independent healthy cohort of 33 individuals who underwent concurrent functional MRI and PET. Using a posterior probability threshold of 0.95, we found that midbrain and thalamic connectivity were implicated as dysfunctional across both patient groups. Dysconnectivity in first-episode psychosis patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in established schizophrenia patients. In the healthy 18F-DOPA cohort, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits to those associated with psychotic symptom severity in patients. Overall, our findings indicate that subcortical dysconnectivity is evident in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signalling are closely related to striatal dopamine synthesis capacity, which is a robust marker for psychosis.

Research progress on the role of cholesterol in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is often diagnosed at advanced stages with no effective treatment options. Mechanistically, it is a complex biological process. Recently, the main cause of its incidence is changing from viral to non-viral. It has been shown that high cholesterol levels can cause the further transformation of non-alcoholic fatty liver disease (NAFLD) to HCC, but some investigations have found that serum cholesterol levels are negatively correlated with morbidity and mortality. Conflicting experimental results and epidemiological investigations illustrate the complex mechanisms of HCC. Cholesterol is essential for the survival of the body and tumors, although research on the function of cholesterol in tumors is evolving, the use of lowering cholesterol drugs in treating HCC remains limited. In this review, the cholesterol-involved mechanisms that cause the development of HCC or reduce the mortality and the latest progress in the use of cholesterol in the treatment of HCC and prospects for prevention and diagnosis have been summarized.

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

Influence of CYP3A5, IL-10 polymorphisms and metabolism rate on tacrolimus exposure in renal post-transplant recipients.

Aim: To investigate the influence of CYP3A5 and IL-10 polymorphisms on tarcolimus metabolism and renal function for renal transplantation recipients at a stable period. Methods: CYP3A5 and IL-10 polymorphisms, together with other clinical factors, were collected for 149 renal transplantation patients at postoperative stable period. Statistics analysis was performed to explore key factors affecting tarcolimus metabolism. Results: CYP3A5 6986A >G and IL-10 -819C >T significantly impacted tacrolimus metabolism (p < 0.001). CYP3A5 6986A >G G allele and IL-10 -819C >T T allele were associated with poorer tacrolimus metabolic capability. Patients with various tacrolimus metabolism rates presented little difference in renal functions at stable period. Conclusion: Genotyping of CYP3A5 and IL-10 might benefit the precision dosage of tacrolimus for renal transplantation recipients.

RNA sequencing uncover crucial genes mediating progression of large-artery atherosclerotic and small-artery occlusion ischemic stroke.

PURPOSE: The goal of our study is to uncover the pathogenesis of large-artery atherosclerotic ischemic stroke (LAAIS) and small-artery occlusion ischemic stroke (SAOIS) and analyze their difference using RNA sequencing. METHODS: RNA sequencing was used to filtrate differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in LAAIS and SAOIS. Specific DEmRNAs and DElncRNAs in LAAIS and SAOIS were further found. Functional annotation and DElncRNA-DEmRNA co-expression network were built to reveal biological function of DEmRNAs. RESULTS: A total of 832 DEmRNAs and 96 DElncRNAs were identified in LAAIS vs normal controls. 587 DEmRNAs and 105 DElncRNAs were identified in SAOIS vs normal controls. In LAAIS vs SAOIS, 636 DEmRNAs and 112 DElncRNAs were identified. Among which, 571 DEmRNAs and 61 DElncRNAs were LAAIS specific DEmRNAs and DElncRNAs, respectively. 325 DEmRNAs and 66 DElncRNAs were respectively SAOIS specific DEmRNAs and DElncRNAs. We also obtained 3086 LAAIS specific DElncRNA-DEmRNA co-expression pairs and 661 SAOIS specific DElncRNA-DEmRNA co-expression pairs. Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in both LAAIS specific DEmRNAs and DEmRNAs in LAAIS specific DElncRNA-DEmRNA co-expression network. ECM-receptor interaction, hypertrophic cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in both SAOIS specific DEmRNAs and DEmRNAs in SAOIS specific DElncRNA-DEmRNA co-expression network. CONCLUSION: This finding may help to understand the mechanisms of LAAIS and SAOIS and offer novel clues for finding specific biomarkers for LAAIS and SAOIS.

Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis.

OBJECTIVE: Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. METHODS: We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. RESULTS: The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. INTERPRETATION: Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.