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.

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.

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.
CONCLUSIONS: 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.

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UNASSIGNED: 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.
UNASSIGNED: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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 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.

Resting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional connectivity patterns caused by brain disorders, thereby facilitating objective quantification of brain pathology. Existing studies typically extract fMRI features using various machine/deep learning methods, but the generated imaging biomarkers are often challenging to interpret. Besides, the brain operates as a modular system with many cognitive/topological modules, where each module contains subsets of densely inter-connected regions-of-interest (ROIs) that are sparsely connected to ROIs in other modules. However, current methods cannot effectively characterize brain modularity. This paper proposes a modularity-constrained dynamic representation learning (MDRL) framework for interpretable brain disorder analysis with rs-fMRI. The MDRL consists of 3 parts: (1) dynamic graph construction, (2) modularity-constrained spatiotemporal graph neural network (MSGNN) for dynamic feature learning, and (3) prediction and biomarker detection. In particular, the MSGNN is designed to learn spatiotemporal dynamic representations of fMRI, constrained by 3 functional modules (i.e., central executive network, salience network, and default mode network). To enhance discriminative ability of learned features, we encourage the MSGNN to reconstruct network topology of input graphs. Experimental results on two public and one private datasets with a total of 1,155 subjects validate that our MDRL outperforms several state-of-the-art methods in fMRI-based brain disorder analysis. The detected fMRI biomarkers have good explainability and can be potentially used to improve clinical diagnosis.

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.

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.

OBJECTIVE: 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.

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.
Nine cats with presumptive idiopathic epilepsy, receiving oral PB q24h, were included in a retrospective descriptive study.
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.
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.