Generating Neuroimmune Assembloids Using Human Induced Pluripotent Stem Cell (iPSC)-Derived Cortical Organoids and Microglia.

The emergence of brain organoids has revolutionized our understanding of neurodevelopment and neurological diseases by providing an in vitro model system that recapitulates key aspects of human brain development. However, conventional organoid protocols often overlook the role of microglia, the resident immune cells of the central nervous system. Microglia dysfunction is implicated in various neurological disorders, highlighting the need for their inclusion in organoid models. Here, we present a novel method for generating neuroimmune assembloids using human-induced pluripotent stem cell (iPSC)-derived cortical organoids and microglia. Building upon our previous work generating myelinating cortical organoids, we extend our methodology to include the integration of microglia, ensuring their long-term survival and maturation within the organoids. We describe two integration methods: one involving direct addition of microglia progenitors to the organoids and an alternative approach where microglia and dissociated neuronal progenitors are aggregated together in a defined ratio. To facilitate downstream analysis, we also describe a dissociation protocol for single-cell RNA sequencing (scRNA-seq) and provide guidance on fixation, cryosectioning, and immunostaining of assembloid structures. Overall, our protocol provides a comprehensive framework for generating neuroimmune assembloids, offering researchers a valuable tool for studying the interactions between neural cell types and immune cells in the context of neurological diseases.

Human Brain In Vitro Model for Pathogen Infection-Related Neurodegeneration Study.

Recent advancements in stem cell biology and tissue engineering have revolutionized the field of neurodegeneration research by enabling the development of sophisticated in vitro human brain models. These models, including 2D monolayer cultures, 3D organoids, organ-on-chips, and bioengineered 3D tissue models, aim to recapitulate the cellular diversity, structural organization, and functional properties of the native human brain. This review highlights how these in vitro brain models have been used to investigate the effects of various pathogens, including viruses, bacteria, fungi, and parasites infection, particularly in the human brain cand their subsequent impacts on neurodegenerative diseases. Traditional studies have demonstrated the susceptibility of different 2D brain cell types to infection, elucidated the mechanisms underlying pathogen-induced neuroinflammation, and identified potential therapeutic targets. Therefore, current methodological improvement brought the technology of 3D models to overcome the challenges of 2D cells, such as the limited cellular diversity, incomplete microenvironment, and lack of morphological structures by highlighting the need for further technological advancements. This review underscored the significance of in vitro human brain cell from 2D monolayer to bioengineered 3D tissue model for elucidating the intricate dynamics for pathogen infection modeling. These in vitro human brain cell enabled researchers to unravel human specific mechanisms underlying various pathogen infections such as SARS-CoV-2 to alter blood-brain-barrier function and Toxoplasma gondii impacting neural cell morphology and its function. Ultimately, these in vitro human brain models hold promise as personalized platforms for development of drug compound, gene therapy, and vaccine. Overall, we discussed the recent progress in in vitro human brain models, their applications in studying pathogen infection-related neurodegeneration, and future directions.

A reusable 3D printed brain-like phantom for benchmarking electrical properties tomography reconstructions.

PURPOSE: In MR electrical properties tomography (MR-EPT), electrical properties (EPs, conductivity and permittivity) are reconstructed from MR measurements. Phantom measurements are important to characterize the performance of MR-EPT reconstruction methods, since they allow knowledge of reference EPs values. To assess reconstruction methods in a more realistic scenario, it is important to test the methods using phantoms with realistic shapes, internal structures, and dielectric properties. In this work, we present a 3D printing procedure for the creation of realistic brain-like phantoms to benchmark MR-EPT reconstructions. METHODS: We created two brain-like geometries with three different compartments using 3D printing. The first geometry was filled once, while the second geometry was filled three times with different saline-gelatin solutions, resulting in a total of four phantoms with different EPs. The saline solutions were characterized using a probe. 3D MR-EPT reconstructions were performed from MR measurements at 3T. The reconstructed conductivity values were compared to reference values of the saline-gelatin solutions. The measured fields were also compared to simulated fields using the same phantom geometry and electrical properties. RESULTS: The measured fields were consistent with simulated fields. Reconstructed conductivity values were consistent with the reference (probe) conductivity values. This indicated the suitability of such phantoms for benchmarking MR-EPT reconstructions. CONCLUSION: We presented a new workflow to 3D print realistic brain-like phantoms in an easy and affordable way. These phantoms are suitable to benchmark MR-EPT reconstructions, but can also be used for benchmarking other quantitative MR methods.

Functional Implications of the Prosomeric Brain Model.

Brain models present a viewpoint on the fundamental structural components of the brain and their mutual organization, generally relative to a particular concept of the brain axis. A model may be based on adult brain structure or on developmental morphogenetic aspects. Brain models usually have functional implications, depending on which functional properties derive from the postulated organization. This essay examines the present scenario about brain models, emphasizing the contrast between columnar or other longitudinal models and transverse subdivisional neuromeric models. In each case, the main functional implications and apparent problems are explored and commented. Particular attention is given to the modern molecularly based 'prosomeric model', which postulates a set of 20 transverse prosomeres as the developmental units that serve to construct all the cerebral parts and the particular typology of many different neuronal populations within the forebrain and the hindbrain, plus a number of additional spinal cord units. These metameric developmental units (serially repeated, but with unique molecular profiles) confer to this model remarkable functional properties based mainly on its multiplicity and modularity. Many important brain functions can be decomposed into subfunctions attended to by combined sets of neuronal elements derived from different neuromeres. Each neuromere may participate in multiple functions. Most aspects related to creation of precise order in neural connections (axonal navigation and synaptogenesis) and function is due to the influence of neuromeric anteroposterior and dorsoventral positional information. Research on neuromeric functionality aspects is increasing significantly in recent times.

A database for MR-based electrical properties tomography with in silico brain data-ADEPT.

PURPOSE: Several reconstruction methods for MR-based electrical properties tomography (EPT) have been developed. However, the lack of common data makes it difficult to objectively compare their performances. This is, however, a necessary precursor for standardizing and introducing this technique in the clinical setting. To enable objective comparison of the performances of reconstruction methods and provide common data for their training and testing, we created ADEPT, a database of simulated data for brain MR-EPT reconstructions. METHODS: ADEPT is a database containing in silico data for brain EPT reconstructions. This database was created from 25 different brain models, with and without tumors. Rigid geometric augmentations were applied, and different electrical properties were assigned to white matter, gray matter, CSF, and tumors to generate 120 different brain models. These models were used as input for finite-difference time-domain simulations in Sim4Life, used to compute the electromagnetic fields needed for MR-EPT reconstructions. RESULTS: Electromagnetic fields from 84 healthy and 36 tumor brain models were simulated. The simulated fields relevant for MR-EPT reconstructions (transmit and receive RF fields and transceive phase) and their ground-truth electrical properties are made publicly available through ADEPT. Additionally, nonattainable fields such as the total magnetic field and the electric field are available upon request. CONCLUSION: ADEPT will serve as reference database for objective comparisons of reconstruction methods and will be a first step toward standardization of MR-EPT reconstructions. Furthermore, it provides a large amount of data that can be exploited to train data-driven methods. It can be accessed from  https://doi.org/10.34894/V0HBJ8.

The promise of genetic screens in human in vitro brain models.

Advances of in vitro culture models have allowed unprecedented insights into human neurobiology. At the same time genetic screening has matured into a robust and accessible experimental strategy allowing for the simultaneous study of many genes in parallel. The combination of both technologies is a newly emerging tool for neuroscientists, opening the door to identifying causal cell- and tissue-specific developmental and disease mechanisms. However, with complex experimental genetic screening set-ups new challenges in data interpretation and experimental scope arise that require a deep understanding of the benefits and challenges of individual approaches. In this review, we summarize the literature that applies genetic screening to in vitro brain models, compare experimental strengths and weaknesses and point towards future directions of these promising approaches.

Patient-specific modeling for guided rehabilitation of stroke patients: the BrainX3 use-case.

BrainX3 is an interactive neuroinformatics platform that has been thoughtfully designed to support neuroscientists and clinicians with the visualization, analysis, and simulation of human neuroimaging, electrophysiological data, and brain models. The platform is intended to facilitate research and clinical use cases, with a focus on personalized medicine diagnostics, prognostics, and intervention decisions. BrainX3 is designed to provide an intuitive user experience and is equipped to handle different data types and 3D visualizations. To enhance patient-based analysis, and in keeping with the principles of personalized medicine, we propose a framework that can assist clinicians in identifying lesions and making patient-specific intervention decisions. To this end, we are developing an AI-based model for lesion identification, along with a mapping of tract information. By leveraging the patient's lesion information, we can gain valuable insights into the structural damage caused by the lesion. Furthermore, constraining whole-brain models with patient-specific disconnection masks can allow for the detection of mesoscale excitatory-inhibitory imbalances that cause disruptions in macroscale network properties. Finally, such information has the potential to guide neuromodulation approaches, assisting in the choice of candidate targets for stimulation techniques such as Transcranial Ultrasound Stimulation (TUS), which modulate E-I balance, potentiating cortical reorganization and the restoration of the dynamics and functionality disrupted due to the lesion.

Sensorization of microfluidic brain-on-a-chip devices: Towards a new generation of integrated drug screening systems.

Brain-on-a-chip (BoC) devices show typical characteristics of brain complexity, including the presence of different cell types, separation in different compartments, tissue-like three-dimensionality, and inclusion of the extracellular matrix components. Moreover, the incorporation of a vascular system mimicking the blood-brain barrier (BBB) makes BoC particularly attractive, since they can be exploited to test the brain delivery of different drugs and nanoformulations. In this review, we introduce the main innovations in BoC and BBB-on-a-chip models, especially focusing sensorization: electrical, electrochemical, and optical biosensors permit the real-time monitoring of different biological phenomena and markers, such as the release of growth factors, the expression of specific receptors/biomarkers, the activation of immune cells, cell viability, cell-cell interactions, and BBB crossing of drugs and nanoparticles. The recent improvements in signal amplification, miniaturization, and multiplication of the sensors are discussed in an effort to highlight their benefits versus limitations and delineate future challenges in this field.

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub.

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.

3D printing-based full-scale human brain for diverse applications.

Surgery is the most frequent treatment for patients with brain tumors. The construction of full-scale human brain models, which is still challenging to realize via current manufacturing techniques, can effectively train surgeons before brain tumor surgeries. This paper aims to develop a set of three-dimensional (3D) printing approaches to fabricate customized full-scale human brain models for surgery training as well as specialized brain patches for wound healing after surgery. First, a brain patch designed to fit a wound's shape and size can be easily printed in and collected from a stimuli-responsive yield-stress support bath. Then, an inverse 3D printing strategy, called "peeling-boiled-eggs," is proposed to fabricate full-scale human brain models. In this strategy, the contour layer of a brain model is printed using a sacrificial ink to envelop the target brain core within a photocurable yield-stress support bath. After crosslinking the contour layer, the as-printed model can be harvested from the bath to photo crosslink the brain core, which can be eventually released by liquefying the contour layer. Both the brain patch and full-scale human brain model are successfully printed to mimic the scenario of wound healing after removing a brain tumor, validating the effectiveness of the proposed 3D printing approaches.

Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset.

Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57-67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54-62 % accuracy). Feature importance maps derived from MLP -our best-performing model- revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions.

Human Brain Project Partnering Projects Meeting: Status Quo and Outlook.

As the European Flagship Human Brain Project (HBP) ends in September 2023, a meeting dedicated to the Partnering Projects (PPs), a collective of independent research groups that partnered with the HBP, was held on September 4-7, 2022. The purpose of this meeting was to allow these groups to present their results, reflect on their collaboration with the HBP and discuss future interactions with the European Research Infrastructure (RI) EBRAINS that has emerged from the HBP. In this report, we share the tour-de-force that the Partnering Projects that were present in the meeting have made in furthering knowledge concerning various aspects of Brain Research with the HBP. We describe briefly major achievements of the HBP Partnering Projects in terms of a systems-level understanding of the functional architecture of the brain and its possible emulation in artificial systems. We then recapitulate open discussions with EBRAINS representatives about the evolution of EBRAINS as a sustainable Research Infrastructure for the Partnering Projects after the HBP, and also for the wider scientific community.

Advanced 3D Models of Human Brain Tissue Using Neural Cell Lines: State-of-the-Art and Future Prospects.

Human-relevant three-dimensional (3D) models of cerebral tissue can be invaluable tools to boost our understanding of the cellular mechanisms underlying brain pathophysiology. Nowadays, the accessibility, isolation and harvesting of human neural cells represents a bottleneck for obtaining reproducible and accurate models and gaining insights in the fields of oncology, neurodegenerative diseases and toxicology. In this scenario, given their low cost, ease of culture and reproducibility, neural cell lines constitute a key tool for developing usable and reliable models of the human brain. Here, we review the most recent advances in 3D constructs laden with neural cell lines, highlighting their advantages and limitations and their possible future applications.

The current and future contribution of neuroimaging to the understanding of disorders of consciousness.

Patients with disorders of consciousness (DoC) represent a group of severely brain-injured patients with varying capacities for consciousness in terms of both wakefulness and awareness. The current state-of-the-art for assessing these patients is through standardised behavioural examinations, but inaccuracies are commonplace. Neuroimaging and electrophysiological techniques have revealed vast insights into the relationships between neural alterations, andcognitive and behavioural features of consciousness in patients with DoC. This has led to the establishment of neuroimaging paradigms for the clinical assessment of DoC patients. Here, we review selected neuroimaging findings on the DoC population, outlining key findings of the dysfunction underlying DoC and presenting the current clinical utility of neuroimaging tools. We discuss that whilst individual brain areas play instrumental roles in generating and supporting consciousness, activation of these areas alone is not sufficient for conscious experience. Instead, for consciousness to arise, we need preserved thalamo-cortical circuits, in addition to sufficient connectivity between distinctly differentiated brain networks, underlined by connectivity both within, and between such brain networks. Finally, we present recent advances and future perspectives in computational methodologies applied to DoC, supporting the notion that progress in the science of DoC will be driven by a symbiosis of these data-driven analyses, and theory-driven research. Both perspectives will work in tandem to provide mechanistic insights contextualised within theoretical frameworks which ultimately inform the practice of clinical neurology.

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation.

Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholine α4ß2 correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.

Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling.

Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.

Perceptions of selected undergraduate medical students in the Philippines on the effectiveness of the combined use of plastinated and formalin-preserved brains in neuroanatomy education: A cross-sectional study

Background and Objective@#Neuroanatomy is both terrifying to learn and problematic to teach, and the different methods of neuroanatomical education have their own strengths and weaknesses. In this cross-sectional study, we evaluated the perception of undergraduate medical students towards the combined use of plastinated and formalinpreserved brain specimen in their neuroanatomy course. @*Methods@#A bridging program was designed for students whose medical education was interrupted by the COVID-19 pandemic in order to reinforce the knowledge and understanding of anatomy that they acquired in a virtual environment. A total of 175 first year medical students participated in this learning activity, which included seven stations in neuroanatomy spread across two hours, and covered the anatomy of the circle of Willis, brainstem, cranial nerves, spinal cord, internal cerebrum, and external cerebrum. To evaluate short-term learning, the students were asked to take a quiz containing 10 multiple-choice questions before and after the learning activity. In addition, the students also answered a survey containing 11 Likert-type questions asking about their perception of the learningactivity. @*Results@#Following the learning activity, mean test scores increased from 4.73 (SD 1.74) to 5.32 (SD 1.52; mean difference 0.59, p = 0.008). Majority of the students (mean 81%, range 43-93%) had a neutral or positive perception of plastinated brain specimen, and on factor analysis, plastinated brain specimen were found to be both practical and useful for learning neuroanatomy. However, the participants perceived plastinated brain specimen alone to be insufficient for learning neuroanatomy, and a multimodal approach to learning neuroanatomy is ideal. @*Conclusion@#Plastinated brain specimens were found to be an effective complement to formalin-preserved brain, and these should be used by medical schools when designing neuroanatomy learning activities for their students.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models.

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require ad hoc programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework. Thanks to its inherent characteristics, the HHNB facilitates the data-driven model building workflow and its reproducibility, hence fostering a collaborative approach to brain modeling.

Modeling Schizophrenia In Vitro: Challenges and Insights on Studying Brain Cells.

One of the challenges in studying neuropsychiatric disorders is the difficulty in accessing brain tissue from living patients. Schizophrenia is a chronic mental illness that affects 1% of the population worldwide, and its development stems from genetic and environmental factors. In order to better understand the pathophysiology underlying schizophrenia, the development of efficient in vitro methods to model this disorder has been required. In addition to several in vitro models, induced pluripotent stem cells (iPSCs) arose as a powerful tool, enabling access to the genetic background of the donor. Moreover, genetic modification of these cells can improve studies of specific dysfunctions observed in the pathophysiology of several neuropsychiatric disorders, not only schizophrenia. Here, we summarize which in vitro models are currently available and their applications in schizophrenia research, describing their advantages and limitations. These technologies in the cell culture field hold great potential to contribute to a better understanding of the pathophysiology of schizophrenia in an integrated manner, in addition to testing potential therapeutic interventions based on the genetic background of the patient.

Effect of 3D Synthetic Microscaffold Nichoid on the Morphology of Cultured Hippocampal Neurons and Astrocytes.

The human brain is the most complex organ in biology. This complexity is due to the number and the intricate connections of brain cells and has so far limited the development of in vitro models for basic and applied brain research. We decided to create a new, reliable, and cost-effective in vitro system based on the Nichoid, a 3D microscaffold microfabricated by two-photon laser polymerization technology. We investigated whether these 3D microscaffold devices can create an environment allowing the manipulation, monitoring, and functional assessment of a mixed population of brain cells in vitro. With this aim, we set up a new model of hippocampal neurons and astrocytes co-cultured in the Nichoid microscaffold to generate brain micro-tissues of 30 µm thickness. After 21 days in culture, we morphologically characterized the 3D spatial organization of the hippocampal astrocytes and neurons within the microscaffold, and we compared our observations to those made using the classical 2D co-culture system. We found that the co-cultured cells colonized the entire volume of the 3D devices. Using confocal microscopy, we observed that within this period the different cell types had become well-differentiated. This was further elaborated with the use of drebrin, PSD-95, and synaptophysin antibodies that labeled the majority of neurons, both in the 2D as well as in the 3D co-cultures. Using scanning electron microscopy, we found that neurons in the 3D co-culture displayed a significantly larger amount of dendritic protrusions compared to neurons in the 2D co-culture. This latter observation indicates that neurons growing in a 3D environment may be more prone to form connections than those co-cultured in a 2D condition. Our results show that the Nichoid can be used as a 3D device to investigate the structure and morphology of neurons and astrocytes in vitro. In the future, this model can be used as a tool to study brain cell interactions in the discovery of important mechanisms governing neuronal plasticity and to determine the factors that form the basis of different human brain diseases. This system may potentially be further used for drug screening in the context of various brain diseases.