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Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application. To address these issues, we developed a VAE-GAN framework that combined a VAE (variational auto-encoder) with a GAN (generative adversarial net) for functional brain network identification and fMRI augmentation. As a generative model, the VAE-GAN models the distribution of fMRI so that it enables the extraction of more generalized features, and thus relieve the overfitting issue. The VAE-GAN is easier to train on fMRI than a standard GAN since it uses latent variables from VAE to generate fake data rather than relying on random noise that is used in a GAN, and it can generate higher quality of fake data than VAE since the discriminator can promote the training of the generator. In other words, the VAE-GAN inherits the advantages of VAE and GAN and avoids their limitations in modeling of fMRI data. Extensive experiments on task fMRI datasets from HCP have proved the effectiveness and superiority of the proposed VAE-GAN framework for identifying both temporal features and functional brain networks compared with existing models, and the quality of fake data is higher than those from VAE and GAN. The results on resting state fMRI of Attention Deficit Hyperactivity Disorder (ADHD)-200 dataset further demonstrated that the fake data generated by the VAE-GAN can help improve the performance of brain network modeling and ADHD classification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In human society, the choice of transportation mode between two cities is largely influenced by the distance between the regions. Similarly, when neurons communicate with each other within the cerebral cortex, do they establish their connections based on their physical distance? In this study, we employed a data-driven approach to explore the relationships between fiber length and corresponding geodesic distance between the fiber's two endpoints on brain surface. Diffusion-MRI-derived fiber streamlines were used to represent extra-cortical axonal connections between neurons or cortical regions, while geodesic paths between cortical points were employed to simulate intra-cortical connections. The results demonstrated that the geodesic distance between two cortical regions connected by a fiber streamline was greater than the fiber length most of the time, indicating that cortical regions tend to choose the shortest path for connection; whether it be an intra-cortical or extra-cortical route, especially when intra-cortical routes within cortical regions are longer than potential extrinsic fiber routes, there is an increased probability to establish fiber routes to connect the both regions. These findings were validated in a group of human brains and may provide insights into the underlying mechanisms of neuronal growth, connection, and wiring., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Objective: To understand brain cognition and disorders, modeling the mapping between mind and brain has been of great interest to the neuroscience community. The key is the brain representation, including functional brain networks (FBN) and their corresponding temporal features. Recently, it has been proven that deep learning models have superb representation power on functional magnetic resonance imaging (fMRI) over traditional machine learning methods. However, due to the lack of high-quality data and labels, deep learning models tend to suffer from overfitting in the training process., Methods: In this work, we applied a recurrent Wasserstein generative adversarial net (RWGAN) to learn brain representation from volumetric fMRI data. Generative adversarial net (GAN) is widely used in natural image generation and is able to capture the distribution of the input data, which enables the extraction of generalized features from fMRI and thus relieves the overfitting issue. The recurrent layers in RWGAN are designed to better model the local temporal features of the fMRI time series. The discriminator of RWGAN works as a deep feature extractor. With LASSO regression, the RWGAN model can decompose the fMRI data into temporal features and spatial features (FBNs). Furthermore, the generator of RWGAN can generate high-quality new data for fMRI augmentation., Results: The experimental results on seven tasks from the HCP dataset showed that the RWGAN can learn meaningful and interpretable temporal features and FBNs, compared to HCP task designs and general linear model (GLM) derived networks. Besides, the results on different training datasets showed that the RWGAN performed better on small datasets than other deep learning models. Moreover, we used the generator of RWGAN to yield fake subjects. The result showed that the fake data can also be used to learn meaningful representation compared to those learned from real data., Conclusions: To our best knowledge, this work is among the earliest attempts of applying generative deep learning for modeling fMRI data. The proposed RWGAN offers a novel methodology for learning brain representation from fMRI, and it can generate high-quality fake data for the potential use of fMRI data augmentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

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There is a need to investigate the role of nuclear factor kappa B in the regulation of cyclooxygenase-2 expression in the epileptic rat brain and cultured hippocampal neurons. Immunofluorescence and polymerase chain reaction was used to detect the expression of nuclear factor kappa B and cyclooxygenase-2. In cultured hippocampal neurons and rat brain: the control group compared with the normal group, nuclear factor kappa B expression in the hippocampal dentate gyrus, cerebral cortex, the piriform cortex brain regions were significantly increased ( P < 0.01). This is accompanied by a significant increase in cyclooxygenase-2 protein and mRNA expressions in the hippocampus ( P < 0.01). In the experimental group compared to the control group, the nuclear factor-kappa B expression in the hippocampal dentate gyrus, cerebral cortex, piriform cortex, and other brain regions was significantly lower ( P < 0.01), with the accompanying decrease in cyclooxygenase-2 protein and mRNA expression ( P < 0.01) in the hippocampus. In conclusion, κB-decoy can inhibit nuclear factor kappa B activation in epileptic rat brain and cyclooxygenase-2 overexpression., Competing Interests: The authors declare no conflict of interest., (© 2020 Xu et al. Published by IMR press.)

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain's signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain's signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority., Competing Interests: None of the authors has conflict of interest to declare.

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Modeling the human brain as a network has been widely considered as a powerful approach to investigating the brain's structural and functional systems. However, many previous approaches focused on a single scale of brain network and the multi-scale nature of brain networks has been rarely explored yet. This paper put forward a novel framework to construct multi-scale common networks of brains via multi-scale spectral clustering of fiber connections among DICCCOLs. Specifically, the recently developed and publicly released DICCCOLs provide the nodal structural and functional correspondence across individuals, and thus the employed multi-scale spectral clustering algorithm divided the DICCCOL landmarks and their connections into sub-networks with correspondences on multiple scales. Experimental results showed the promise of the constructed multi-scale networks in applications of structural and functional connectivity mapping. As an application example, these multi-scale networks are used to guide the identification of multi-scale common fiber bundles across individuals and to facilitate the bundle's functional role analysis, which could enable other tract-based and network-based analyses in the future.

Fiber clustering is an essential step towards brain connectivity modeling and tract-based analysis of white matter integrity via diffusion tensor imaging (DTI) in many clinical neuroscience applications. A variety of methods have been developed to cluster fibers based on various types of features such as geometry, anatomy, connection, or function. However, identification of group-wise consistent fiber bundles that are harmonious across multi-modalities is rarely explored yet. This paper proposes a novel hybrid two-stage approach that incorporates connectional and functional features, and identifies group-wise consistent fiber bundles across subjects. In the first stage, based on our recently developed 358 dense and consistent cortical landmarks, we identified consistent backbone bundles with representative fibers. In the second stage, other remaining fibers are then classified into the existing backbone bundles using their correlations of resting state fMRI signals at the two ends of fibers. Our experimental results show that the proposed methods can achieve group-wise consistent fiber bundles with similar shapes and anatomic profiles, as well as strong functional coherences.

Methylenetetrahydrofolate reductase (MTHFR) deficiency is the most common genetic cause of hyperhomocysteinemia, which is associated with increased risk for cardiovascular disease, stroke and possibly other neurological disorders. Microarray analysis of brain RNA from day 14 Mthfr(-/-) mice revealed several genes with altered expression. Expression changes in inositol 1,4,5-triphosphate receptor, type 1 (Itpr1), proteolipid protein (Plp), neurogenic differentiation factor 1 (Neurod1), S100 calcium binding protein A8 (S100a8), and methylenetetrahydrofolate dehydrogenase (NAD+ dependent), methenyltetrahydrofolate cyclohydrolase (Mthfd2) were confirmed by RT-PCR. We propose that neuronal damage by hyperhomocysteinemia may involve disruption of intracellular calcium.

Enriched cultures of human oligodendrocytes were obtained from fetal brain specimens between 16 and 21 gestational weeks. Brain cells were separated over a Percoll density gradient and collected as two fractions with initial relative densities of approximately 1.035 g/ml and 1.102 g/ml, for fractions 1 and 2, respectively. After separation, 58.3 and 67.7% of the cells in fractions 1 and 2, respectively, were labeled by the antibody O4 that recognizes immature oligodendrocytes. A total of 15.5 and 29.4% of the cells in fractions 1 and 2, respectively, were positive for tubulin-beta(III), a marker for neurons but none of the freshly isolated cells were positive for glial fibrillary acidic protein (GFAP), a protein associated with astrocytes in the central nervous system. When the fractionated cells were cultured on poly-ornithine coated coverslips for 3 days and processed for immunocytochemistry, the percentage of O4+ oligodendrocytes decreased to less than 4% whereas GFAP+ cells increased to 1.8 and 12.4% for fractions 1 and 2 respectively. The percentage of tubulin-betaIII+ cells increased to 46 and 61% in cultures from the two Percoll fractions. This increase is probably due to the decrease in the number of oligodendrocytes. To avoid the loss of oligodendrocytes, cells were cultured as free-floating aggregates in the presence of 20 ng/ml of fibroblast growth factor-2 for 2 weeks. The resultant cultures became enriched for oligodendrocytes as demonstrated by cellular morphology and by positive O4 labeling. The method described here provides a means of obtaining enriched cultures of immature human oligodendrocytes for developmental and transplantation studies., (Copyright 1999 Wiley-Liss, Inc.)

Remyelination of focal areas of the central nervous system (CNS) in animals can be achieved by transplantation of glial cells, yet the source of these cells in humans to similarly treat myelin disorders is limited at present to fetal tissue. Multipotent precursor cells are present in the CNS of adult as well as embryonic and neonatal animals and can differentiate into lineage-restricted progenitors such as oligodendroglial progenitors (OPs). The OPs present in adults have a different phenotype from those seen in earlier life, and their potential role in CNS repair remains unknown. To gain insights into the potential to manipulate the myelinating capacity of these precursor and/or progenitor cells, we generated a homogenous culture of OPs from neural precursor cells isolated from adult rat subependymal tissues. Phenotypic characterization indicated that these OPs resembled neonatal rather than adult OPs and produced robust myelin after transplantation. The ability to generate such cells from the adult brain therefore opens an avenue to explore the potential of these cells for repairing myelin disorders in adulthood.

Rat brain membranes exhibiting stereospecific opiate binding activity were solubilized by sonication and detergent treatment. The active material could be bound to an affinity column containing 6-succinylmorphine but could not be eluted with free agonist. Although two protein peaks could be eluted with NaCl, neither possessed binding activity; however, one of the peaks (A), in combination with an acidic lipid fraction, eluted subsequently from the column, showed stereospecific binding. Opiate ligands of the mu type bound to this protein/lipid mixture with an order of affinities closely correlating with those of the original membrane but one to two orders of magnitude lower; binding of delta, kappa, and sigma prototype opioids was considerably less. The protein/lipid mixture also competed with the membranes for mu ligands. These results suggest that the isolated protein-lipid complex may be a component of the opiate receptor and, specifically, the mu receptor or binding site. However, because of the lower affinities of mu opiates for this complex, it is conceivable that some essential membrane component is still missing. Preliminary analysis of peak A indicates that it contains a broad spectrum of protein bands, but it remains to be seen which of these are essential for activity.