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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.

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.

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.

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.

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