Emergence of the ZNF675 Gene During Primate Evolution-Influenced Human Neurodevelopment Through Changing HES1 Autoregulation.

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.

Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis.

BACKGROUND AND OBJECTIVE: Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future. METHODS: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study. RESULTS: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer's, autism, and Parkinson's disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis. CONCLUSION: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis.

A survey of brain functional network extraction methods using fMRI data.

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.

Pathogenic variants in autism gene KATNAL2 cause hydrocephalus and disrupt neuronal connectivity by impairing ciliary microtubule dynamics.

Enlargement of the cerebrospinal fluid (CSF)-filled brain ventricles (cerebral ventriculomegaly), the cardinal feature of congenital hydrocephalus (CH), is increasingly recognized among patients with autism spectrum disorders (ASD). KATNAL2, a member of Katanin family microtubule-severing ATPases, is a known ASD risk gene, but its roles in human brain development remain unclear. Here, we show that nonsense truncation of Katnal2 (Katnal2Δ17) in mice results in classic ciliopathy phenotypes, including impaired spermatogenesis and cerebral ventriculomegaly. In both humans and mice, KATNAL2 is highly expressed in ciliated radial glia of the fetal ventricular-subventricular zone as well as in their postnatal ependymal and neuronal progeny. The ventriculomegaly observed in Katnal2Δ17 mice is associated with disrupted primary cilia and ependymal planar cell polarity that results in impaired cilia-generated CSF flow. Further, prefrontal pyramidal neurons in ventriculomegalic Katnal2Δ17 mice exhibit decreased excitatory drive and reduced high-frequency firing. Consistent with these findings in mice, we identified rare, damaging heterozygous germline variants in KATNAL2 in five unrelated patients with neurosurgically treated CH and comorbid ASD or other neurodevelopmental disorders. Mice engineered with the orthologous ASD-associated KATNAL2 F244L missense variant recapitulated the ventriculomegaly found in human patients. Together, these data suggest KATNAL2 pathogenic variants alter intraventricular CSF homeostasis and parenchymal neuronal connectivity by disrupting microtubule dynamics in fetal radial glia and their postnatal ependymal and neuronal descendants. The results identify a molecular mechanism underlying the development of ventriculomegaly in a genetic subset of patients with ASD and may explain persistence of neurodevelopmental phenotypes in some patients with CH despite neurosurgical CSF shunting.

Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN).

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.

Virtual brain twins: from basic neuroscience to clinical use.

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.

Why Intracranial Compliance Is Not Utilized as a Common Practical Tool in Clinical Practice.

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.

Site-specifically radiolabeled nanobodies for imaging blood-brain barrier penetration and targeting in the brain.

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.

Epilepsy awareness days, weeks, and months: Their roles in the fight against epilepsy and the intersectoral global action plan on epilepsy and other neurological disorders.

OBJECTIVES: This research sought to find out the epilepsy awareness days around the world and understand the nature and role of the days in the fight against epilepsy in relation to the Intersectoral Global Action Plan (IGAP) on epilepsy and other neurological disorders (2022-2031). METHODS: We conducted a review of journal articles. The databases that we searched were ProQuest Central, EBSCOhost Academic Search Complete, EBSCO Medline, PubMed Central, Wiley Online, Directory of Open Access Journals (DOAJ), African Journals Online (AJOL), and Google Scholar. We limited our search to papers of relevance to our subject published between January 2000 and January 2023. We searched 'epilepsy awareness day, week, or month'. From the databases, 13 articles met our inclusion criteria. We augmented our results with a search on Google of articles about epilepsy awareness day, week, or month. We also searched directly on the websites of epilepsy organizations. RESULTS: We found that epilepsy awareness days fall into these categories: global awareness days (n = 2), awareness months (n = 4), regional awareness weeks (n = 5), and regional awareness days (n = 1). Our search for national awareness days (n = 7) was not comprehensive, and this could be an area for future research. The literature shows that epilepsy awareness days could play a role in (1) reducing knowledge and treatment gaps, (2) increasing participation, (3) unlocking resources, and (4) necessitating policy change and increasing networking. The major role of these dedicated days in the IGAP is to accelerate awareness and advocacy for policy change and improved interventions. CONCLUSIONS: Epilepsy awareness days are bringing stakeholders together already, and IGAP initiatives could tap into this achievement to accelerate awareness in a cost effective, contextual and collaborative manner. This could be achieved by adopting themes that relate more directly to the IGAP goals. Another important strategy is to motivate countries that do not have national epilepsy days or regions that do not have a regional awareness days, to consider doing one within the confines of resources.

Once-a-day oral treatment with phenobarbital in cats with presumptive idiopathic epilepsy.

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.

Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection.

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.

Sleep Disorder Kleine-Levin Syndrome (KLS) Joins the List of Polygenic Brain Disorders Associated with Obstetric Complications.

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.

Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity.

Excitatory and inhibitory neurons are fundamental components of the brain, and healthy neural circuits are well balanced between excitation and inhibition (E/I balance). However, it is not clear how an E/I imbalance affects the self-organization of the network structure and function in general. In this study, we examined how locally altered E/I balance affects neural dynamics such as the connectivity by activity-dependent formation, the complexity (multiscale entropy) of neural activity, and information transmission. In our simulation, a spiking neural network model was used with the spike-timing dependent plasticity rule to explore the above neural dynamics. We controlled the number of inhibitory neurons and the inhibitory synaptic weights in a single neuron group out of multiple neuron groups. The results showed that a locally increased E/I ratio strengthens excitatory connections, reduces the complexity of neural activity, and decreases information transmission between neuron groups in response to an external input. Finally, we argued the relationship between our results and excessive connections and low complexity of brain activity in the neuropsychiatric brain disorders.

An Update on Parkinson's Disease and its Neurodegenerative Counterparts.

INTRODUCTION: Neurodegenerative disorders are a group of diseases that cause nerve cell degeneration in the brain, resulting in a variety of symptoms and are not treatable with drugs. Parkinson's disease (PD), prion disease, motor neuron disease (MND), Huntington's disease (HD), spinal cerebral dyskinesia (SCA), spinal muscle atrophy (SMA), multiple system atrophy, Alzheimer's disease (AD), spinocerebellar ataxia (SCA) (ALS), pantothenate kinase-related neurodegeneration, and TDP-43 protein disorder are examples of neurodegenerative diseases. Dementia is caused by the loss of brain and spinal cord nerve cells in neurodegenerative diseases. BACKGROUND: Even though environmental and genetic predispositions have also been involved in the process, redox metal abuse plays a crucial role in neurodegeneration since the preponderance of symptoms originates from abnormal metal metabolism. METHOD: Hence, this review investigates several neurodegenerative diseases that may occur symptoms similar to Parkinson's disease to understand the differences and similarities between Parkinson's disease and other neurodegenerative disorders based on reviewing previously published papers. RESULTS: Based on the findings, the aggregation of alpha-synuclein occurs in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. Other neurodegenerative diseases occur with different protein aggregation or mutations. CONCLUSION: We can conclude that Parkinson's disease, Multiple system atrophy, and Dementia with Lewy bodies are closely related. Therefore, researchers must distinguish among the three diseases to avoid misdiagnosis of Multiple System Atrophy and Dementia with Lewy bodies with Parkinson's disease symptoms.

Lipid mediated brain disorders: A perspective.

The brain, one of the most resilient organs of the body is highly enriched in lipid content, suggesting the essential role of lipids in brain physiological activities. Lipids constitute an important structural part of the brain and act as a rich source of metabolic energy. Besides, lipids in their bioactive form (known as bioactive lipids) play an essential signaling and regulatory role, facilitating neurogenesis, synaptogenesis, and cell-cell communication. Brain lipid metabolism is thus a tightly regulated process. Any alteration/dysregulation of lipid metabolism greatly impact brain health and activity. Moreover, since central nervous system (CNS) is the most metabolically active system and lacks an efficient antioxidative defence system, it acts as a hub for the production of reactive oxygen species (ROS) and subsequent lipid peroxidation. These peroxidation events are reported during pathological changes such as neuronal tissue injury and inflammation. Present review is a modest attempt to gain insights into the role of dysregulated bioactive lipid levels and lipid oxidation status in the pathogenesis and progression of neurodegenerative disorders. This may open up new avenues exploiting lipids as the therapeutic targets for improving brain health, and treatment of nervous system disorders.

Neuron-specific enolase levels immediately following cardiovascular surgery is modulated by hemolysis due to cardiopulmonary bypass, making it unsuitable as a brain damage biomarker.

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.

Oleanolic acid and ursolic acid: therapeutic potential in neurodegenerative diseases, neuropsychiatric diseases and other brain disorders.

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.