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Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q-network (DQN) could effectively distinguish AD patients using local metrics of resting-state functional magnetic resonance imaging (rs-fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO/2. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre-trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (p < 0.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

A growing body of evidence indicates the existence of abnormal local and long-range functional connection patterns in patients with alcohol use disorder (AUD). However, it has yet to be established whether AUD is associated with abnormal interhemispheric and intrahemispheric functional connection patterns. In the present study, we analysed resting-state functional magnetic resonance imaging data from 55 individuals with AUD and 32 healthy nonalcohol users. For each subject, whole-brain functional connectivity density (FCD) was decomposed into ipsilateral and contralateral parts. Correlation analysis was performed between abnormal FCD and a range of clinical measurements in the AUD group. Compared with healthy controls, the AUD group exhibited a reduced global FCD in the anterior and middle cingulate gyri, prefrontal cortex and thalamus, along with an enhanced global FCD in the temporal, parietal and occipital cortices. Abnormal interhemispheric and intrahemispheric FCD patterns were also detected in the AUD group. Furthermore, abnormal global, contralateral and ipsilateral FCD data were correlated with the mean amount of pure alcohol and the severity of alcohol addiction in the AUD group. Collectively, our findings indicate that global, interhemispheric and intrahemispheric FCD may represent a robust method to detect abnormal functional connection patterns in AUD; this may help us to identify the neural substrates and therapeutic targets of AUD., (© 2024 The Authors. Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct., (© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

In this paper, an additive margin network with adaptive feature recalibration, AMNets, for handling a wide range of visual tasks, is proposed. The new AMNets consists of three improvements on convolutional neural networks: (1) squeeze‐and‐excitation mechanism was embedded into the the residual block of single layer to improve the performance of the classification, (2) softmax loss was replaced by the additive margin softmax loss to minimize intra‐class variance and maximize inter‐class variance, and (3) Adam optimizer was exploited to update iteratively the network parameters. Experiments on the Brain Stroke CT Image Dataset show that our additive margin network is quite effective to improve state‐of‐the‐art algorithms. Network algorithms have been extensively used in medical image field to pursuit a more accuracy of disease recognition. This paper proposes AMNets, an additive margin network with adaptive feature recalibration for handling a wide range of visual tasks in medicine. [ABSTRACT FROM AUTHOR]

The March 2024 issue of JCPP Advances features two neuroimaging studies that investigate links between early environmental risk factors for mental health problems, brain development and psychopathology in children and young adults. The papers provide new insights into how adverse environments and negative experiences in childhood increase risk for depression and mental health problems, and how this may or may not be mediated, or moderated, by individual differences in the brain. [ABSTRACT FROM AUTHOR]

Liver, an important regulator of metabolic homeostasis, is critical for healthy brain function. In particular, age‐related neurodegenerative diseases seriously reduce the quality of life for the elderly. As population aging progresses rapidly, unraveling the mechanisms that effectively delay aging has become critical. Appropriate exercise is reported to improve aging‐related cognitive impairment. Whereas current studies focused on exploring the effect of exercise on the aging brain itself, ignoring the persistent effects of peripheral organs on the brain through the blood circulation. The aim of this paper is to summarize the communication and aging processes of the liver and brain and to emphasize the metabolic mechanisms of the liver–brain axis about exercise ameliorating aging‐related neurodegenerative diseases. A comprehensive understanding of the potential mechanisms about exercise ameliorating aging is critical for improving adaptation to age‐related brain changes and formulating effective interventions against age‐related cognitive decline. [ABSTRACT FROM AUTHOR]

Depression is a serious public health problem, and adolescence is an 'age of risk' for the onset of Major Depressive Disorder. Recently, we and others have proposed neuroimmune network models that highlight bidirectional communication between the brain and the immune system in both mental and physical health, including depression. These models draw on research indicating that the cellular actors (particularly monocytes) and signaling molecules (particularly cytokines) that orchestrate inflammation in the periphery can directly modulate the structure and function of the brain. In the brain, inflammatory activity heightens sensitivity to threats in the cortico‐amygdala circuit, lowers sensitivity to rewards in the cortico‐striatal circuit, and alters executive control and emotion regulation in the prefrontal cortex. When dysregulated, and particularly under conditions of chronic stress, inflammation can generate feelings of dysphoria, distress, and anhedonia. This is proposed to initiate unhealthy, self‐medicating behaviors (e.g. substance use, poor diet) to manage the dysphoria, which further heighten inflammation. Over time, dysregulation in these brain circuits and the inflammatory response may compound each other to form a positive feedback loop, whereby dysregulation in one organ system exacerbates the other. We and others suggest that this neuroimmune dysregulation is a dynamic joint vulnerability for depression, particularly during adolescence. We have three goals for the present paper. First, we extend neuroimmune network models of mental and physical health to generate a developmental framework of risk for the onset of depression during adolescence. Second, we examine how a neuroimmune network perspective can help explain the high rates of comorbidity between depression and other psychiatric disorders across development, and multimorbidity between depression and stress‐related medical illnesses. Finally, we consider how identifying neuroimmune pathways to depression can facilitate a 'next generation' of behavioral and biological interventions that target neuroimmune signaling to treat, and ideally prevent, depression in youth and adolescents. [ABSTRACT FROM AUTHOR]

Objectives: Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS. Materials and Methods: Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co‐expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism‐related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism‐related genes were identified based on Cytoscape‐CytoNCA and Cytoscape‐MCODE plug‐ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real‐time PCR. Results: Metabolism‐related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510‐hsa‐miR‐330‐3p‐HEATR1 axis may be involved in regulating AIS. Conclusions: The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis. [ABSTRACT FROM AUTHOR]

The current consensus holds that optically‐cleared specimens are unsuitable for Magnetic Resonance Imaging (MRI); exhibiting absence of contrast. Prior studies combined MRI with tissue‐clearing techniques relying on the latter's ability to eliminate lipids, thereby fostering the assumption that lipids constitute the primary source of ex vivo MRI‐contrast. Nevertheless, these findings contradict an extensive body of literature that underscores the contribution of other features to contrast. Furthermore, it remains unknown whether non‐delipidating clearing methods can produce MRI‐compatible specimens or whether MRI‐contrast can be re‐established. These limitations hinder the development of multimodal MRI‐light‐microscopy (LM) imaging approaches. This study assesses the relation between MRI‐contrast, and delipidation in optically‐cleared whole brains following different tissue‐clearing approaches. It is demonstrated that uDISCO and ECi‐brains are MRI‐compatible upon tissue rehydration, despite both methods' substantial delipidating‐nature. It is also demonstrated that, whereas Scale‐clearing preserves most lipids, Scale‐cleared brain lack MRI‐contrast. Furthermore, MRI‐contrast is restored to lipid‐free CLARITY‐brains without introducing lipids. Our results thereby dissociate between the essentiality of lipids to MRI‐contrast. A tight association is found between tissue expansion, hyperhydration and loss of MRI‐contrast. These findings then enabled us to develop a multimodal MRI‐LM‐imaging approach, opening new avenues to bridge between the micro‐ and mesoscale for biomedical research and clinical applications. [ABSTRACT FROM AUTHOR]

Solute carriers (SLCs) regulate transfer of a wide range of molecules across cell membranes using facilitative or secondary active transport. In pregnancy, these transporters, expressed at the placental barrier, are important for delivery of nutrients to the fetus, whilst also limiting entry of potentially harmful substances, such as drugs. In the present study, RNA‐sequencing analysis was used to investigate expression of SLCs in the fetal (embryonic day 19) rat brain, choroid plexus and placenta in untreated control animals and following maternal paracetamol treatment. In the treated group, paracetamol (15 mg/kg) was administered to dams twice daily for 5 days (from embryonic day 15 to 19). In untreated animals, overall expression of SLCs was highest in the placenta. In the paracetamol treatment group, expression of several SLCs was significantly different compared with control animals, with ion, amino acid, neurotransmitter and sugar transporters most affected. The number of SLC transcripts that changed significantly following treatment was the highest in the choroid plexus and lowest in the brain. All SLC transcripts that changed in the placenta following paracetamol treatment were downregulated. These results suggest that administration of paracetamol during pregnancy could potentially disrupt fetal nutrient homeostasis and affect brain development, resulting in major consequences for the neonate and extending into childhood. What is the central question of this study?How does maternal paracetamol treatment affect expression of solute carrier (SLC) transporters in the fetal (embryonic day 19) rat brain, choroid plexus and placenta?What is the main finding and its importance?Paracetamol administered to dams at embryonic days 15–19 resulted in changed expression of a large number of SLC transporters in the placenta and the fetal brain and choroid plexus. In the placenta, all SLC transporters that changed expression were downregulated. In the fetal brain and choroid plexus, expression of SLC transporters was both up‐ and downregulated. These findings might have implications for development of fetuses and offspring of mothers who have taken paracetamol during pregnancy. [ABSTRACT FROM AUTHOR]

Neuron localization is a fundamental step in the neuron morphology reconstruction and quantitative analysis of cell counting and spatial distribution. The recent development of labelling and imaging techniques has resulted in growing demand for automatic neuron localization methods of 3D neuronal microscopy images. However, accurately localizing touching neurons remains a challenge. To address this issue, a novel method utilizing a super‐resolution segmentation network based on 2D Conv‐LSTM and a region growing method are proposed. This network can map and detect individual neurons in higher resolution space, allowing for the separation of closely touching neurons with reduced resource consumption. Subsequently, a region growing method is employed to localize neurons accurately. This method is evaluated using neuronal images generated by TDI‐fMOST. This method achieved neuron localization with an F1 score of 0.91. In comparison, other automatic localization methods achieved F1 scores lower than 0.85. It is also demonstrated that our network has fewer computational requirements compared to 3D neural networks. This method is promising for accurately localizing neurons in large‐scale neuron images. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) is a clear, transparent fluid derived from blood plasma that protects the brain and spinal cord against mechanical shock, provides buoyancy, clears metabolic waste and transports extracellular components to remote sites in the brain. Given its contact with the brain and the spinal cord, CSF is the most informative biofluid for studies of the central nervous system (CNS). In addition to other components, CSF contains extracellular vesicles (EVs) that carry bioactive cargoes (e.g., lipids, nucleic acids, proteins), and that can have biological functions within and beyond the CNS. Thus, CSF EVs likely serve as both mediators of and contributors to communication in the CNS. Accordingly, their potential as biomarkers for CNS diseases has stimulated much excitement for and attention to CSF EV research. However, studies on CSF EVs present unique challenges relative to EV studies in other biofluids, including the invasive nature of CSF collection, limited CSF volumes and the low numbers of EVs in CSF as compared to plasma. Here, the objectives of the International Society for Extracellular Vesicles CSF Task Force are to promote the reproducibility of CSF EV studies by providing current reporting and best practices, and recommendations and reporting guidelines, for CSF EV studies. To accomplish this, we created and distributed a world‐wide survey to ISEV members to assess methods considered 'best practices' for CSF EVs, then performed a detailed literature review for CSF EV publications that was used to curate methods and resources. Based on responses to the survey and curated information from publications, the CSF Task Force herein provides recommendations and reporting guidelines to promote the reproducibility of CSF EV studies in seven domains: (i) CSF Collection, Processing, and Storage; (ii) CSF EV Separation/Concentration; (iii) CSF EV Size and Number Measurements; (iv) CSF EV Protein Studies; (v) CSF EV RNA Studies; (vi) CSF EV Omics Studies and (vii) CSF EV Functional Studies. [ABSTRACT FROM AUTHOR]

Caviomorph rodents are endemic to South America and are one of the most adaptively diverse radiations of rodents today. Although their origin and diversification have been intensively studied, questions still remain about many of the details of where, when and how the group radiated. One area of continuing debate relates to the evolution of their neurosensory system. Modern caviomorphs exhibit a rich brain shape and size diversity. So far the oldest species for which endocranial data are known is dated to the Early Miocene. Here, we describe the virtual brain endocast of the late Oligocene stem chinchillid Incamys bolivianus from Bolivia and provide new hypotheses regarding the ancestral brain of Chinchillidae and Caviomorpha more broadly. Caviomorph rodents, independent from other rodent clades, acquired an expanded neocortex and their common ancestor was probably lissencephalic or had few sulci. Incamys uniquely combines extended neocortical temporal lobes and exposed caudal colliculi, which have roles in audition and vocalization processing. We interpret this morphology as evidence for enhanced auditory acuity, vocalization processing and potentially group‐living in Incamys, which is known in modern members of the Chinchillidae family. No temporal effect was found on relative brain size in South American mammals; however, our sample is limited to available brain endocasts and as such remains small and unevenly distributed taxonomically and temporally. Incamys provides crucial insight into the evolution of the caviomorph brain and shows that we still have much to explore regarding how these small mammals achieved one the most impressive adaptive radiations of the Cenozoic. [ABSTRACT FROM AUTHOR]