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Menopause weakens the brain's structural integrity and increases its susceptibility to a range of degenerative and mental illnesses. 17β estradiol (17βE2) exhibits potent neuroprotective properties. Exogenous estrogen supplementation provides neuroprotection, but the findings presented by the Million Women Study (MWS) and the Women's Health Initiative (WHI), as well as the increased risk of endometrial cancer, breast cancer and venous thromboembolism associated with estrogen use, have cast doubt on its clinical use for neurological disorders. Thus, the objective of our review article is to compile all in vitro and in vivo studies conducted till date demonstrating the neuroprotective potential of nonfeminizing estrogens. This objective has been achieved by gathering various research and review manuscripts from different records such as PubMed, Embase, Scopus, Google Scholar, Web of Science and OVID, using different terms like 'estrogen deficiency, 17β estradiol, non‐feminising estrogens, and brain disorder'. However, recent evidence has revealed the contribution of numerous non‐estrogen receptor‐dependent pathways in neuroprotective effects of estrogen. In conclusion, synthetic nonfeminizing estrogens that have little or no ER binding but are equally powerful (and in some cases more potent) in delivering neuroprotection are emerging as viable and potential alternatives. [ABSTRACT FROM AUTHOR]

Estimates of brain FNs derived from fMRI data play a crucial role in studying brain function. Both static and dynamic FN estimation methods have been proposed. In static FN analyses, hypothesis-driven methods rely on prior knowledge, whereas data-driven methods are constrained by model assumption. Dynamic FN analyses encompass window-based and windowless methods, both of which encounter challenges in effective state extraction. Future work will be necessary to enhance the reproducibility and replicability of FN analyses, and to elucidate the shared and unique capabilities of the various methods. Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements. [ABSTRACT FROM AUTHOR]

BACKGROUND: Globally, about 1.5 billion people suffer from brain disorders, which contribute to the highest number of the world's paediatric disease burden. Nigeria accounts for 7% of the global paediatric population of brain disorder-induced disabilities. Magnetic Resonance Imaging (MRI) is the recommended modality for brain imaging. However, the lack of MRI data relevant to paediatric brain disorders in South-South Nigeria necessitated the present study, which sought to evaluate the MRI spectrum of paediatric brain disorders. OBJECTIVES: To assess the most prevalent brain disorder in paediatrics, the most susceptible age group and the sex. MATERIALS AND METHOD: A cohort of 120 paediatrics aged 0 to 18 years in selected three tertiary hospitals were sampled from January 2019 to January 2023. Brain disorders, age, sex, and MRI images were selected from the hospitals' archives. Microsoft Excel version 16.37 was used for data entry, while the Statistical Package for Social Science (SPSS) version 26.0 was used for statistical analysis. The significance level was set at p < 0.05. RESULTS: A total of 73 males and 47 females were recruited in the study. Results showed epilepsy 28 (23.35%) as the highest disorder, others were hydrocephalus 20 (16.7%), meningitis and space-occupying lesions 16 (13.3%), cerebellar tumours, and cerebral infarcts 8 (6.7%). No significant difference in brain pathology between sex (p = 0.988: > 0.05) and no statistical association (p = 0.075: > 0.05) between the brain disorders and age groups was recorded. CONCLUSION: The study highlights the importance of MRI in diagnosing a wide range of brain disorders. Epilepsy was recorded to be the most common paediatric brain disorder and there were no differences between brain disorders and sex or age groups. [ABSTRACT FROM AUTHOR]

BACKGROUND: Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT: The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients. [ABSTRACT FROM AUTHOR]

In this study, we investigated recurrent copy number variations (CNVs) in the 19p12 locus, which are associated with neurodevelopmental disorders. The two genes in this locus, ZNF675 and ZNF681, arose via gene duplication in primates, and their presence in several pathological CNVs in the human population suggests that either or both of these genes are required for normal human brain development. ZNF675 and ZNF681 are members of the Krüppel‐associated box zinc finger (KZNF) protein family, a class of transcriptional repressors important for epigenetic silencing of specific genomic regions. About 170 primate‐specific KZNFs are present in the human genome. Although KZNFs are primarily associated with repressing retrotransposon‐derived DNA, evidence is emerging that they can be co‐opted for other gene regulatory processes. We show that genetic deletion of ZNF675 causes developmental defects in cortical organoids, and our data suggest that part of the observed neurodevelopmental phenotype is mediated by a gene regulatory role of ZNF675 on the promoter of the neurodevelopmental gene Hes family BHLH transcription factor 1 (HES1). We also find evidence for the recently evolved regulation of genes involved in neurological disorders, microcephalin 1 and sestrin 3. We show that ZNF675 interferes with HES1 auto‐inhibition, a process essential for the maintenance of neural progenitors. As a striking example of how some KZNFs have integrated into preexisting gene expression networks, these findings suggest the emergence of ZNF675 has caused a change in the balance of HES1 autoregulation. The association of ZNF675 CNV with human developmental disorders and ZNF675‐mediated regulation of neurodevelopmental genes suggests that it evolved into an important factor for human brain development. [ABSTRACT FROM AUTHOR]

Neuron-specific enolase (NSE) is one of the biomarkers used as an indicator of brain disorder, but since it is also found in blood cell components, there is a concern that a spurious increase in NSE may occur after cardiovascular surgery, where cardiopulmonary bypass (CPB) causes hemolysis. In the present study, we investigated the relationship between the degree of hemolysis and NSE after cardiovascular surgery and the usefulness of immediate postoperative NSE values in the diagnosis of brain disorder. A retrospective study of 198 patients who underwent surgery with CPB in the period from May 2019 to May 2021 was conducted. Postoperative NSE levels and Free hemoglobin (F-Hb) levels were compared in both groups. In addition, to verify the relationship between hemolysis and NSE, we examined the correlation between F-Hb levels and NSE levels. We also examined whether different surgical procedures could produce an association between hemolysis and NSE. Among 198 patients, 20 had postoperative stroke (Group S) and 178 had no postoperative stroke (Group U). There was no significant difference in postoperative NSE levels and F-Hb levels between Group S and Group U (p = 0.264, p = 0.064 respectively). F-Hb and NSE were weakly correlated (r = 0.29. p < 0.01). In conclusion, NSE level immediately after cardiac surgery with CPB is modified by hemolysis rather than brain injury, therefore it would be unreliable as a biomarker of brain disorder. [ABSTRACT FROM AUTHOR]

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions. [ABSTRACT FROM AUTHOR]

In her book "Are Mental Disorders Brain Disorders?" (2022), Anneli Jefferson proposes that brain processes that always realize mental dysfunctions are brain dysfunctions. This paper explores possible interpretations of two underdeveloped aspects of this thesis. First, it argues that "realization" should be interpreted as partial rather than full realization. Second, it argues that the "always" should only quantify over biologically normal situations. Taken together, these changes can account for the fact that some psychological dysfunctions are partially realized by functional mechanisms, for the existence of psychological dysfunctions due to mismatches rather than brain dysfunctions, and for the fact that brain dysfunctions do not cease to be brain dysfunctions just because they can be compensated. [ABSTRACT FROM AUTHOR]

The latest advancements in neuroimaging techniques have contributed to studying the disordered human brain, but still, there is a lack of disease-specific brain reference to localise brain seeds and standardise the comparison across studies. The current study first evaluates the inter-subject consistency across Attention-Deficit/Hyperactivity Disorder (ADHD) in a publicly accessible resting-state functional magnetic resonance imaging (RS-fMRI) ADHD-200 dataset by proposing a new voxels similarity index (VSI) that integrates both inter-atlases flexibility (F') from previous studies with proposed intersubjects stability (S') measure to improve outcomes. Secondly, the study employs a dual-layer clustering-based parcellation strategy inspired by the resultant improved Master Atlas networks to examine the ADHD cerebral cortex. The suggested approach to enhancing the ADHD cortex parcellation uses spectral clustering for global structure identification, followed by hierarchical clustering for local refinements and granularity specification. The final connectivitydriven brain reference achieved an average homogeneity of 0.63, and the enhanced Master Atlas with (S') achieved 0.35 homogeneity, surpassing the original Master Atlas with 0.27. This discovery implies that the final brain reference offers a more accurate and reliable framework to examine the connections and functions of the brain in individuals with ADHD. [ABSTRACT FROM AUTHOR]

Although delivery of a wild-type copy of the mutated gene to cells represents the most effective approach for a monogenic disease, proof-of-concept studies highlight significant efficacy caveats for treatment of brain disorders. Herein, we develop a cross-correction-based strategy to enhance the efficiency of a gene therapy for CDKL5 deficiency disorder, a severe neurodevelopmental disorder caused by CDKL5 gene mutations. We created a gene therapy vector that produces an Igk-TATk-CDKL5 fusion protein that can be secreted via constitutive secretory pathways and, due to the cell-penetration property of the TATk peptide, internalized by cells. We found that, although AAVPHP.B_Igk-TATk-CDKL5 and AAVPHP.B_CDKL5 vectors had similar brain infection efficiency, the AAVPHP.B_Igk-TATk-CDKL5 vector led to higher CDKL5 protein replacement due to secretion and penetration of the TATk-CDKL5 protein into the neighboring cells. Importantly, Cdkl5 KO mice treated with the AAVPHP.B_Igk-TATk-CDKL5 vector showed a behavioral and neuroanatomical improvement in comparison with vehicle or AAVPHP.B_CDKL5 vector-treated Cdkl5 KO mice. In conclusion, we provide the first evidence that a gene therapy based on a cross-correction approach is more effective at compensating Cdkl5-null brain defects than gene therapy based on the expression of the native CDKL5, opening avenues for the development of this innovative approach for other monogenic diseases.

Some of the most fundamental human capabilities, including thought, speech, and movement, may be lost due to brain illnesses. The most prevalent form of dementia, Alzheimer's disease (AD), is caused by a steady decline in brain function and is now incurable. Despite the challenges associated with making a conclusive diagnosis of AD, the field has generally shifted toward making diagnoses justified by patient records and neurological analysis, such as MRI. Reports of studies utilizing machine learning for AD identification have increased in recent years. In this publication, we report the results of our most recent research. It details a deep learning-based, 3D brain MRI-based method for automated AD detection. As a result, deep learning models have become increasingly popular in recent years for analyzing medical images. To aid in detecting Alzheimer's disease at an initial phase, we suggest a novel dual attention-aware Octave convolution-based deep learning network (DACN). The three main parts of DACN are as follows: First, we use Patch Convolutional Neural Network (PCNN) to identify discriminative features within each MRI patch while simultaneously boosting the features of abnormally altered micro-structures in the brain; second, we use an Octave convolution to minimize the spatial redundancy and widen the field of perception of the brain's structure; and third, we use a dual attention aware convolution classifier to dissect the resulting depiction further. An outstanding test accuracy of 99.87% is reached for categorizing dementia phases by employing the suggested method in experiments on a publicly available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The proposed model was more effective, efficient, and reliable than the state-of-the-art models through our comparisons. [ABSTRACT FROM AUTHOR]

Nanobodies (Nbs) hold significant potential in molecular imaging due to their unique characteristics. However, there are challenges to overcome when it comes to brain imaging. To address these obstacles, collaborative efforts and interdisciplinary research are needed. This article aims to raise awareness and encourage collaboration among researchers from various fields to find solutions for effective brain imaging using Nbs. By fostering cooperation and knowledge sharing, we can make progress in overcoming the existing limitations and pave the way for improved molecular imaging techniques in the future. We outline in this article the advantages and challenges associated with utilizing nanobodies for brain imaging. As this field is still in its nascent stages, we encourage scientists to share their discoveries related to blood‐brain barrier‐penetrating Nbs generously. Together, we would like to collectively address the obstacles and pave the way for more effective brain receptor imaging in the near future. [ABSTRACT FROM AUTHOR]

Resting‐state functional magnetic resonance imaging (rs‐fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning‐based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time‐consuming and labor‐intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI‐based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine‐tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi‐level fMRI augmentation strategy to increase the sample size by augmenting blood‐oxygen‐level‐dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large‐scale fMRI datasets, without requiring labeled training data. This model is further fine‐tuned on to‐be‐analyzed fMRI data for downstream disease detection in a task‐oriented learning manner. We evaluate the proposed method on three rs‐fMRI datasets for cross‐site and cross‐dataset learning tasks. Experimental results suggest that the UCGL outperforms several state‐of‐the‐art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs‐fMRI data. [ABSTRACT FROM AUTHOR]

The latest advancements in neuroimaging techniques have contributedto studying the disordered human brain, but still, there is a lack of disease-specific brain reference to localise brain seeds and standardise the comparison across studies. The current study first evaluates the inter-subject consistency across Attention-Deficit/ Hyperactivity Disorder (ADHD) in a publicly accessible resting-state functional magnetic resonance imaging (RS-fMRI) ADHD-200 dataset by proposing a new voxels similarity index (VSI) that integrates both inter-atlases flexibility (F`) from previous studies with proposed intersubjects stability (S`) measure to improve outcomes. Secondly, the study employs a dual-layer clustering-based parcellation strategy inspired by the resultant improved Master Atlas networks to examine the ADHD cerebral cortex. The suggested approach to enhancing the ADHD cortex parcellation uses spectral clustering for global structure identification, followed by hierarchical clustering for local refinements and granularity specification. The final connectivity-driven brain reference achieved an average homogeneity of 0.63, and the enhanced Master Atlas with (S’) achieved 0.35 homogeneity, surpassing the original Master Atlas with 0.27. This discovery implies that the final brain reference offers a more accurate and reliable framework to examine the connections and functions of the brain in individuals with ADHD.

Recent advances in single-cell transposase-accessible chromatin using a sequencing assay (scATAC-seq) allow cellular heterogeneity dissection and regulatory landscape reconstruction with an unprecedented resolution. However, compared to bulk-sequencing, its ultra-high missingness remarkably reduces usable reads in each cell type, resulting in broader, fuzzier peak boundary definitions and limiting our ability to pinpoint functional regions and interpret variant impacts precisely. We propose a weakly supervised learning method, scEpiLock, to directly identify core functional regions from coarse peak labels and quantify variant impacts in a cell-type-specific manner. First, scEpiLock uses a multi-label classifier to predict chromatin accessibility via a deep convolutional neural network. Then, its weakly supervised object detection module further refines the peak boundary definition using gradient-weighted class activation mapping (Grad-CAM). Finally, scEpiLock provides cell-type-specific variant impacts within a given peak region. We applied scEpiLock to various scATAC-seq datasets and found that it achieves an area under receiver operating characteristic curve (AUC) of ~0.9 and an area under precision recall (AUPR) above 0.7. Besides, scEpiLock's object detection condenses coarse peaks to only ⅓ of their original size while still reporting higher conservation scores. In addition, we applied scEpiLock on brain scATAC-seq data and reported several genome-wide association studies (GWAS) variants disrupting regulatory elements around known risk genes for Alzheimer's disease, demonstrating its potential to provide cell-type-specific biological insights in disease studies.

Intracranial compliance (ICC) holds significant potential in neuromonitoring, serving as a diagnostic tool and contributing to the evaluation of treatment outcomes. Despite its comprehensive concept, which allows consideration of changes in both volume and intracranial pressure (ICP), ICC monitoring has not yet established itself as a standard component of medical care, unlike ICP monitoring. This review highlighted that the first challenge is the assessment of ICC values, because of the invasive nature of direct measurement, the time-consuming aspect of non-invasive calculation through computer simulations, and the inability to quantify ICC values in estimation methods. Addressing these challenges is crucial, and the development of a rapid, non-invasive computer simulation method could alleviate obstacles in quantifying ICC. Additionally, this review indicated the second challenge in the clinical application of ICC, which involves the dynamic and time-dependent nature of ICC. This was considered by introducing the concept of time elapsed (TE) in measuring the changes in volume or ICP in the ICC equation (volume change/ICP change). The choice of TE, whether short or long, directly influences the ICC values that must be considered in the clinical application of the ICC. Compensatory responses of the brain exhibit non-monotonic and variable changes in long TE assessments for certain disorders, contrasting with the mono-exponential pattern observed in short TE assessments. Furthermore, the recovery behavior of the brain undergoes changes during the treatment process of various brain disorders when exposed to short and long TE conditions. The review also highlighted differences in ICC values across brain disorders with various strain rates and loading durations on the brain, further emphasizing the dynamic nature of ICC for clinical application. The insight provided in this review may prove valuable to professionals in neurocritical care, neurology, and neurosurgery for standardizing ICC monitoring in practical application related to the diagnosis and evaluation of treatment outcomes in brain disorders. [ABSTRACT FROM AUTHOR]

Kleine–Levin Syndrome is a rare neurological disorder with onset typically during adolescence that is characterized by recurrent episodes of hypersomnia, behavioral changes, and cognitive abnormalities, in the absence of structural changes in neuroimaging. As for many functional brain disorders, the exact disease mechanism in Kleine-Levin Syndrome is presently unknown, preventing the development of specific treatment approaches or protective measures. Here we review the pathophysiology and genetics of this functional brain disorder and then present a specific working hypothesis. A neurodevelopmental mechanism has been suspected based on associations with obstetric complications. Recent studies have focused on genetic factors whereby the first genome-wide association study (GWAS) in Kleine–Levin Syndrome has defined a linkage at the TRANK1 locus. A Gene x Environment interaction model involving obstetric complications was proposed based on concepts developed for other functional brain disorders. To stimulate future research, we here performed annotations of the genes under consideration for Kleine–Levin Syndrome in relation to factors expected to be associated with obstetric complications. Annotations used data-mining of gene/protein lists related to for hypoxia, ischemia, and vascular factors and targeted literature searches. Tentative links for TRANK1, four additional genes in the TRANK1 locus, and LMOD3-LMO2 are described. Protein interaction data for TRANK1 indicate links to CBX2, CBX4, and KDM3A, that in turn can be tied to hypoxia. Taken together, the neurological sleep disorder, Kleine-Levin Syndrome, shows genetic and mechanistic overlap with well analyzed brain disorders such as schizophrenia, autism spectrum disorder and ADHD in which polygenic predisposition interacts with external events during brain development, including obstetric complications. Gene x Environment interaction (G x E) model for the sleep disorder, Kleine-Levin Syndrome (KLS). This hypothesis-generating study proposes that perturbations during neurodevelopment affect the formation of neural circuits for sleep regulation in KLS. Thereby, genetic factors linked to KLS interact ("x") with obstetric complications as environmental factors. A subset of genes may mediate between the risk for KLS and hypoxia, ischemia, or vascular changes during obstetric complications. [ABSTRACT FROM AUTHOR]

Objectives: Phenobarbital (PB) q12h is the most common treatment recommendation for cats with recurrent epileptic seizures. Medicating cats may be challenging and result in decreased quality of life for both cat and owner. The aim of this retrospective study was to evaluate treatment with oral PB q24h in cats with presumptive idiopathic epilepsy. Methods: Nine cats with presumptive idiopathic epilepsy, receiving oral PB q24h, were included in a retrospective descriptive study. Results: Seizure remission was achieved in 88% (8/9) of the cats and good seizure control in 12% (1/9) of the cats, treated with a mean dose of oral PB of 2.6 mg/kg q24h (range 1.4–3.8 mg/kg). No cats required an increase of their PB frequency at any time during a mean follow-up period of 3.5 years (range 1.1–8.0 years). No cats displayed side effects or issues with compliance at the last recorded follow-up. Conclusions and relevance: Once-a-day administration of PB for feline epilepsy was safe and resulted in satisfactory seizure control for the nine cats included in this study. The results of this study justify exploring this topic further in larger prospective studies. [ABSTRACT FROM AUTHOR]

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the detection of brain disorders such as autism spectrum disorder based on various machine/deep learning techniques. Learning-based methods typically rely on functional connectivity networks (FCNs) derived from blood-oxygen-level-dependent time series of rs-fMRI data to capture interactions between brain regions-of-interest (ROIs). Graph neural networks have been recently used to extract fMRI features from graph-structured FCNs, but cannot effectively characterize spatiotemporal dynamics of FCNs, e.g., the functional connectivity of brain ROIs is dynamically changing in a short period of time. Also, many studies usually focus on single-scale topology of FCN, thereby ignoring the potential complementary topological information of FCN at different spatial resolutions. To this end, in this paper, we propose a multi-scale dynamic graph learning (MDGL) framework to capture multi-scale spatiotemporal dynamic representations of rs-fMRI data for automated brain disorder diagnosis. The MDGL framework consists of three major components: 1) multi-scale dynamic FCN construction using multiple brain atlases to model multi-scale topological information, 2) multi-scale dynamic graph representation learning to capture spatiotemporal information conveyed in fMRI data, and 3) multi-scale feature fusion and classification. Experimental results on two datasets show that MDGL outperforms several state-of-the-art methods.

Brain disorders such as neurodegenerative diseases and neuropsychiatric diseases have become serious threatens to human health and quality of life. Oleanolic acid (OA) and ursolic acid (UA) are pentacyclic triterpenoid isomers widely distributed in various plant foods and Chinese herbal medicines. Accumulating evidence indicates that OA and UA exhibit neuroprotective effects on multiple brain disorders. Therefore, this paper reviews researches of OA and UA on neurodegenerative diseases, neuropsychiatric diseases and other brain disorders including ischemic stroke, epilepsy, etc, as well as the potential underlying molecular mechanisms. [ABSTRACT FROM AUTHOR]

This document is an editorial published in Frontiers in Human Neuroscience. It discusses the shared cellular and molecular mechanisms of neurodegenerative and neuropsychiatric disorders, such as anxiety and depression, in terms of oxidative stress and neuroinflammation. The editorial emphasizes the importance of identifying modifiable risk factors for these disorders and implementing long-term brain health improvement strategies. It also highlights various risk factors, including lifestyle choices, social and economic inequality, maternal infection, poor dental health, and air pollution, that contribute to the development of brain disorders. The editorial concludes by stating that there is still much to learn about preventing and understanding these disorders. [Extracted from the article]

The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson's disease (PD) and 96% for Alzheimer's disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain. [ABSTRACT FROM AUTHOR]

Intracranial compliance (ICC) holds significant potential in neuromonitoring, serving as a diagnostic tool and contributing to the evaluation of treatment outcomes. Despite its comprehensive concept, which allows consideration of changes in both volume and intracranial pressure (ICP), ICC monitoring has not yet established itself as a standard component of medical care, unlike ICP monitoring. This review highlighted that the first challenge is the assessment of ICC values, because of the invasive nature of direct measurement, the time-consuming aspect of non-invasive calculation through computer simulations, and the inability to quantify ICC values in estimation methods. Addressing these challenges is crucial, and the development of a rapid, non-invasive computer simulation method could alleviate obstacles in quantifying ICC. Additionally, this review indicated the second challenge in the clinical application of ICC, which involves the dynamic and time-dependent nature of ICC. This was considered by introducing the concept of time elapsed (TE) in measuring the changes in volume or ICP in the ICC equation (volume change/ICP change). The choice of TE, whether short or long, directly influences the ICC values that must be considered in the clinical application of the ICC. Compensatory responses of the brain exhibit non-monotonic and variable changes in long TE assessments for certain disorders, contrasting with the mono-exponential pattern observed in short TE assessments. Furthermore, the recovery behavior of the brain undergoes changes during the treatment process of various brain disorders when exposed to short and long TE conditions. The review also highlighted differences in ICC values across brain disorders with various strain rates and loading durations on the brain, further emphasizing the dynamic nature of ICC for clinical application. The insight provided in this review may prove valuable to professionals in neurocritical care, neurology, and neurosurgery for standardizing ICC monitoring in practical application related to the diagnosis and evaluation of treatment outcomes in brain disorders.

Feelings, conduct, thought and comprehension is interlaced and initiates in the human cerebrum, it together assists with creating character of a person. Presently a day's neuroimaging strategies is extremely famous to analyse bury and intra mental confusion or mind problem dependent on conduct. Mental turmoil impacts our mind and controls our mind-set, discernment, feelings and insight. Mental turmoil influences the social and individual existence of a person. Neuroimaging strategies, can plan neural movement reactions and anticipate how human brain functions, gains from the climate and acts appropriately. Passionate prosperity is similarly significant as actual wellbeing. Mental confusion influences human mental status just as conduct and strength. Numerous scientists are working in this space yet at the same time numerous inquiries are unanswered. This paper audited the thoughts of intellectual neuroscience utilizing practical attractive reverberation imaging (fMRI), EEG and MEG that uncovers the mysteries of the human cerebrum. Subsequent to investigating current examinations and difficulties the substance of writing audit dependent on state of mind problem, sadness and uneasiness, Emotions, mental issues to discover the cerebrum and social reactions. At long last, the conversation gives a comprehensive rundown that assists scientists with comprehension about the substances of neuroimaging in cerebrum problem. [ABSTRACT FROM AUTHOR]

Functional connectivity networks (FCN) analysis is instructive for the diagnosis of brain diseases, such as mild cognitive impairment (MCI) and major depressive disorder (MDD) at their early stages. As the critical step of FCN analysis, feature representation provides the basis for finding potential biomarkers of brain diseases. In previous studies, different node statistics (e.g. local efficiency and local clustering coefficients) are usually extracted from FCNs as features for the diagnosis/classification task, which can specifically locate disease-related regions on the node level, so as to help us understand the neurodevelopmental roots of brain disorders. However, each node statistic is proposed only considering a kind of specific network property, which has one-sidedness and limitations. As a result, it is incomplete to represent a node with only one statistic. To resolve this issue, we put forward a novel scheme to select multiple node statistics jointly from the estimated FCNs for automated classification, called multiple node statistics feature selection (MNSFS). Specifically, we first extract multiple statistics from the same nodes and assign each kind of statistic into a group. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and statistics in the groups towards a better classification performance. Such a technique enables us to simultaneously locate the discriminative brain regions, as well as the specific statistics associated with these brain regions, making the classification results more interpretable. We conducted our scheme on two public databases for identifying subjects with MCI and MDD from normal controls. Experimental results show that the proposed scheme achieves superior classification accuracy and features interpreted on the benchmark datasets. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an irreversible and progressive disorder where a large number of brain cells and their connections degenerate and die, eventually destroy the memory and other important mental functions that affect memory, thinking, language, judgment, and behavior. Not a single test can effectively determine AD; however, CT and magnetic resonance imaging (MRI) can be used to observe the decrease in size of different areas (mainly temporal and parietal lobes). This paper proposes an integrative deep ensemble learning framework to obtain better predictive performance for AD diagnosis. Unlike DenseNet, we present a multiresolutional ensemble PartialNet tailored to Alzheimer detection using brain MRIs. PartialNet incorporates the properties of identity mappings, diversified depth as well as deep supervision, thus, considers feature reuse that in turn results in better learning. Additionally, the proposed ensemble PartialNet demonstrates better characteristics in terms of vanishing gradient, diminishing forward flow with better training time, and a low number of parameters compared with DenseNet. Experiments performed on benchmark AD neuroimaging initiative data set that showed considerable performance gain (2 + %↑ $\uparrow $) and (1.2 + %↑ $\uparrow $) for multiclass and binary class in AD detection in comparison to state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Long interspersed nuclear element 1 (LINE1, L1) is a retrotransposon comprising ~17% of the human genome. A subset of L1s maintains the potential to mobilize and alter the genomic landscape, consequently contributing to the change in genome integrity and gene expression. L1 retrotransposition occurs in the human brain regardless of disease status. However, in the brain of patients with various brain diseases, the expression level and copy number of L1 are significantly increased. In this review, we briefly introduce the methodologies applied to measure L1 mobility and identify genomic loci where new insertion of L1 occurs in the brain. Then, we present a list of genes disrupted by L1 transposition in the genome of patients with brain disorders. Finally, we discuss the association between genes disrupted by L1 and relative brain disorders. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide has been known to humans for about 300 years and the previous studies emphasize only its toxic side effects. In the last two decennium, researchers have varied their perspectives and insights towards H 2 S biology based on experimental findings. It has been found that H 2 S is an endogenic gaseous signaling molecule in many organisms and plays a crucial role in many systems and diseases. Early reports suggest that H 2 S as a neuromodulator influences calcium levels within the brain cells which ultimately control memory, learning, and cognition. It has also been observed that some complications in the pathogenesis of neurodegenerative diseases are due to anomalies in the biosynthesis and metabolism of H 2 S. This review focuses on the role of H 2 S in the pathophysiology of major neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, Huntington's disease and Vascular dementia. H 2 S was observed to have a protective role in the above-mentioned neurological conditions and the H 2 S donor therapy may help in disease management. The H 2 S gas displays a neuroprotective role and protects against cellular damage thereby declining the neurological conditions. Some studies have revealed that treatment with H 2 S donors has improved neuronal damage, restored memory and cognition in animal models. In this review, we have discussed the role of H 2 S donors as neuroprotective agents with examples of some of the natural and synthetic H 2 S donors, and also briefly enumerated the molecules used to detect H 2 S in neurodegenerative diseases., (Copyright © 2024. Published by Elsevier Inc.)

The buildup of beta-amyloid plaques in the brain results in Alzheimer’s disease (AD), a neurodegenerative condition. A permanent treatment for AD is not yet available. Only a slowing down of its advancement is possible with the current pharmaceutical options. Nevertheless, nanotechnology has proven to be advantageous in medical applications. It has a lot of potential for AD therapy, particularly in diagnosing the condition and providing an alternative course of treatment. In this review, we outline the developments and benefits of nanomedicines in treating AD. Prospective nanomedicines for diagnosing and surveillance therapeutic interventions for AD and other diseases of the central nervous system (CNS) may be clinically accessible, persuading the development of investigation in this field.

The natural aging process as well as many age-related diseases is associated with impaired metabolic adaptation and declined ability to cope with stress. As major causes of disability and morbidity during the aging process, brain disorders, including psychiatric and neurodegenerative disorders, are likely to increase across the globe in the future decades. This narrative review investigates the link among exercise and brain disorders, aging, and inflammatory biomarkers, along with the function of brain-derived neurotrophic factor. For this study, related manuscript from all databases, Google scholar, Scopus, PubMed, and ISI were assessed. Also, in the search process, the keywords of exercise, neurodegeneration, neurotrophin, mitochondrial dysfunction, and aging were used. Mitochondrial abnormality increases neuronal abnormality and brain disease during the aging process. Stress and inflammatory factors caused by lifestyle and aging also increase brain disorders. Evidences suggest that exercise, as a noninvasive treatment strategy, has antioxidant effects and can reduce neuronal lesions. Brain-derived neurotrophic factor expression following the exercise can reduce brain symptoms; however, careful consideration should be given to a number of factors affecting the results. [ABSTRACT FROM AUTHOR]

Dementia is a condition in which cognitive ability deteriorates beyond what can be anticipated with natural ageing. Characteristically it is recurring and deteriorates gradually with time affecting a person's ability to remember, think logically, to move about, to learn, and to speak just to name a few. A decline in a person's ability to control emotions or to be social can result in demotivation which can severely affect the brain's ability to perform optimally. One of the main causes of reliance and disability among older people worldwide is dementia. Often it is misunderstood which results in people not accepting it causing a delay in treatment. In this research, the data imputation process, and an artificial neural network (ANN), will be established to predict the impact of dementia. based on the considered dataset. The scaled conjugate gradient algorithm (SCG) is employed as a training algorithm. Cross-entropy error rates are so minimal, showing an accuracy of 95%, 85.7% and 89.3% for training, validation, and test. The area under receiver operating characteristic (ROC) curve (AUC) is generated for all phases. A Web-based interface is built to get the values and make predictions. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) is a key molecule in the biology of human life. NO is involved in the physiology of organ viability and in the pathophysiology of organ dysfunction, respectively. In this narrative review, we aimed at elucidating the mechanisms behind the role of NO in the respiratory and cardio-cerebrovascular systems, in the presence of a healthy or dysfunctional endothelium. NO is a key player in maintaining multiorgan viability with adequate organ blood perfusion. We report on its physiological endogenous production and effects in the circulation and within the lungs, as well as the pathophysiological implication of its disturbances related to NO depletion and excess. The review covers from preclinical information about endogenous NO produced by nitric oxide synthase (NOS) to the potential therapeutic role of exogenous NO (inhaled nitric oxide, iNO). Moreover, the importance of NO in several clinical conditions in critically ill patients such as hypoxemia, pulmonary hypertension, hemolysis, cerebrovascular events and ischemia–reperfusion syndrome is evaluated in preclinical and clinical settings. Accordingly, the mechanism behind the beneficial iNO treatment in hypoxemia and pulmonary hypertension is investigated. Furthermore, investigating the pathophysiology of brain injury, cardiopulmonary bypass, and red blood cell and artificial hemoglobin transfusion provides a focus on the potential role of NO as a protective molecule in multiorgan dysfunction. Finally, the preclinical toxicology of iNO and the antimicrobial role of NO—including its recent investigation on its role against the Sars-CoV2 infection during the COVID-19 pandemic—are described. Take home message: Nitric oxide (NO) is a key molecule involved in the vascular homeostasis and a key player in maintaining multiorgan viability. The therapeutic role of inhaled NO ranges from cardio-cerebrovascular to respiratory diseases to antimicrobial properties. [ABSTRACT FROM AUTHOR]