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Anne J. Manning

Harvard Staff Writer

Researchers publish largest-ever dataset of neural connections

A cubic millimeter of brain tissue may not sound like much. But considering that that tiny square contains 57,000 cells, 230 millimeters of blood vessels, and 150 million synapses, all amounting to 1,400 terabytes of data, Harvard and Google researchers have just accomplished something stupendous.   

Led by Jeff Lichtman, the Jeremy R. Knowles Professor of Molecular and Cellular Biology and newly appointed dean of science , the Harvard team helped create the largest 3D brain reconstruction to date, showing in vivid detail each cell and its web of connections in a piece of temporal cortex about half the size of a rice grain.

Published in Science, the study is the latest development in a nearly 10-year collaboration with scientists at Google Research, combining Lichtman’s electron microscopy imaging with AI algorithms to color-code and reconstruct the extremely complex wiring of mammal brains. The paper’s three first co-authors are former Harvard postdoc Alexander Shapson-Coe, Michał Januszewski of Google Research, and Harvard postdoc Daniel Berger.

The ultimate goal, supported by the National Institutes of Health BRAIN Initiative , is to create a comprehensive, high-resolution map of a mouse’s neural wiring, which would entail about 1,000 times the amount of data the group just produced from the 1-cubic-millimeter fragment of human cortex.  

“The word ‘fragment’ is ironic,” Lichtman said. “A terabyte is, for most people, gigantic, yet a fragment of a human brain — just a minuscule, teeny-weeny little bit of human brain — is still thousands of terabytes.”  

Jeff Lichtman.

Kris Snibbe/Harvard Staff Photographer

The latest map contains never-before-seen details of brain structure, including a rare but powerful set of axons connected by up to 50 synapses. The team also noted oddities in the tissue, such as a small number of axons that formed extensive whorls. Because the sample was taken from a patient with epilepsy, the researchers don’t know whether such formations are pathological or simply rare.

Lichtman’s field is connectomics, which seeks to create comprehensive catalogs of brain structure, down to individual cells. Such completed maps would unlock insights into brain function and disease, about which scientists still know very little.

Google’s state-of-the-art AI algorithms allow for reconstruction and mapping of brain tissue in three dimensions. The team has also developed a suite of publicly available tools researchers can use to examine and annotate the connectome.

“Given the enormous investment put into this project, it was important to present the results in a way that anybody else can now go and benefit from them,” said Google collaborator Viren Jain.

Next the team will tackle the mouse hippocampal formation, which is important to neuroscience for its role in memory and neurological disease.

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Monday, March 7, 2022

Researchers uncover how the human brain separates, stores, and retrieves memories

NIH-funded study identifies brain cells that form boundaries between discrete events.

Researchers have identified two types of cells in our brains that are involved in organizing discrete memories based on when they occurred. This finding improves our understanding of how the human brain forms memories and could have implications in memory disorders such as Alzheimer’s disease. The study was supported by the National Institutes of Health’s  Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and published in Nature Neuroscience .

“This work is transformative in how the researchers studied the way the human brain thinks,” said Jim Gnadt, Ph.D., program director at the National Institute of Neurological Disorders and Stroke and the NIH BRAIN Initiative. “It brings to human neuroscience an approach used previously in non-human primates and rodents by recording directly from neurons that are generating thoughts.”

This study, led by Ueli Rutishauser, Ph.D., professor of neurosurgery, neurology and biomedical sciences at Cedars-Sinai Medical Center in Los Angeles, started with a deceptively simple question: how does our brain form and organize memories? We live our awake lives as one continuous experience, but it is believed based on human behavior studies, that we store these life events as individual, distinct moments. What marks the beginning and end of a memory? This theory is referred to as “event segmentation,” and we know relatively little about how the process works in the human brain.

To study this, Rutishauser and his colleagues worked with 20 patients who were undergoing intracranial recording of brain activity to guide surgery for treatment of their drug-resistant epilepsy. They looked at how the patients’ brain activity was affected when shown film clips containing different types of “cognitive boundaries”—transitions thought to trigger changes in how a memory is stored and that mark the beginning and end of memory “files” in the brain.

The first type, referred to as a “soft boundary,” is a video containing a scene that then cuts to another scene that continues the same story. For example, a baseball game showing a pitch is thrown and, when the batter hits the ball, the camera cuts to a shot of the fielder making a play. In contrast, a “hard boundary” is a cut to a completely different story—imagine if the batted ball were immediately followed by a cut to a commercial.

Jie Zheng, Ph.D., postdoctoral fellow at Children’s Hospital Boston and first author of the study, explained the key difference between the two boundaries.

“Is this a new scene within the same story, or are we watching a completely different story? How much the narrative changes from one clip to the next determines the type of cognitive boundary,” said Zheng.  

The researchers recorded the brain activity of participants as they watched the videos, and they noticed two distinct groups of cells that responded to different types of boundaries by increasing their activity. One group, called “boundary cells” became more active in response to either a soft or hard boundary. A second group, referred to as “event cells” responded only to hard boundaries. This led to the theory that the creation of a new memory occurs when there is a peak in the activity of both boundary and event cells, which is something that only occurs following a hard boundary.

One analogy to how memories might be stored and accessed in the brain is how photos are stored on your phone or computer. Often, photos are automatically grouped into events based on when and where they were taken and then later displayed to you as a key photo from that event. When you tap or click on that photo, you can drill down into that specific event.

“A boundary response can be thought of like creating a new photo event,” said Dr. Rutishauser. “As you build the memory, it’s like new photos are being added to that event. When a hard boundary occurs, that event is closed and a new one begins. Soft boundaries can be thought of to represent new images created within a single event.” 

The researchers next looked at memory retrieval and how this process relates to the firing of boundary and event cells. They theorized that the brain uses boundary peaks as markers for “skimming” over past memories, much in the way the key photos are used to identify events. When the brain finds a firing pattern that looks familiar, it “opens” that event.

Two different memory tests designed to study this theory were used. In the first, the participants were shown a series of still images and were asked whether they were from a scene in the film clips they just watched. Study participants were more likely to remember images that occurred soon after a hard or soft boundary, which is when a new “photo” or “event” would have been created.

The second test involved showing pairs of images taken from film clips that they had just watched. The participants were then asked which of the two images had appeared first. It turned out that they had a much harder time choosing the correct image if the two occurred on different sides of a hard boundary, possibly because they had been placed in different “events.”

These findings provide a look into how the human brain creates, stores, and accesses memories. Because event segmentation is a process that can be affected in people living with memory disorders, these insights could be applied to the development of new therapies.

In the future, Dr. Rutishauser and his team plan to look at two possible avenues to develop therapies related to these findings. First, neurons that use the chemical dopamine, which are most-known for their role in reward mechanisms, may be activated by boundary and event cells, suggesting a possible target to help strengthen the formation of memories.

Second, one of the brain’s normal internal rhythms, known as the theta rhythm, has been connected to learning and memory. If event cells fired in time with that rhythm, the participants had an easier time remembering the order of the images that they were shown. Because deep brain stimulation can affect theta rhythms, this could be another avenue for treating patients with certain memory disorders.

This project was made possible by a multi-institutional consortium through the NIH BRAIN Initiative’s Research on Humans program. Institutions involved in this study were Cedars-Sinai Medical Center, Children’s Hospital Boston (site PI Gabriel Kreiman, Ph.D.), and Toronto Western Hospital (site PI Taufik Valiante, M.D., Ph.D.). The study was funded by the NIH BRAIN Initiative (NS103792, NS117839), the National Science Foundation, and Brain Canada.

The BRAIN Initiative ® is a registered trademark of the U.S. Department of Health and Human Services.

The NIH BRAIN Initiative   is managed by 10 institutes whose missions and current research portfolios complement the goals of The BRAIN Initiative ® : National Center for Complementary and Integrative Health, National Eye Institute, National Institute on Aging, National Institute on Alcohol Abuse and Alcoholism, National Institute of Biomedical Imaging and Bioengineering,  Eunice Kennedy Shriver  National Institute of Child Health and Human Development, National Institute on Drug Abuse, National Institute on Deafness and other Communication Disorders, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke.

NINDS  ( https://www.ninds.nih.gov ) is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

Zheng J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience. March 7, 2022. DOI: 10.1038/s41593-022-01020-w

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What We Know About the Brain Structure–Function Relationship

Karla batista-garcía-ramó.

1 Images Processing Group, Basic Division, International Center for Neurological Restoration (Ciren), La Habana 11300, Cuba

Caridad Ivette Fernández-Verdecia

2 Biomodels Laboratory, Basic Division, International Center for Neurological Restoration (Ciren), La Habana 11300, Cuba; uc.neric.oruen@fettevi

How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

1. Introduction

How the human brain works is still an open question, as is its implication with brain architecture: the non-trivial structure–function relationship. The development of neuroimaging techniques, the improvement on their processing methods and the unfolding of computational neuroscience field have driven brain research focused on the relationship between anatomical and functional interactions. The principal hypothesis is that the anatomic architecture determines, but not strictly, the network dynamics [ 1 , 2 , 3 , 4 ], meaning that part of functional connectivity cannot be explained by considering only anatomical connectivity. This hypothesis is based not only on human studies but also on animal models, with the advantage that they can be correlated with genetic, histological and molecular methodologies. Network theory, computational modeling and complex system analysis have played pivotal roles in elucidating structure–function relationship.

Studies on connectivity patterns, both functional and anatomical, in humans and animals [ 5 , 6 , 7 , 8 , 9 , 10 ], and their relationship with neurodegenerative conditions such as Alzheimer’s and Parkinson’s diseases [ 11 , 12 , 13 , 14 , 15 , 16 ] and multiple sclerosis [ 17 , 18 ] has been published in recent years. The brain is considered the most complex system in the Animal kingdom, with complex space-time patterns, and where the degree of correspondence between structural and functional connectivity depends on spatial resolution and time scales [ 19 ]. These elements in turn are continuously modified based on the sensorial inputs that enter the system and the feedback that emerges from the interaction between the processing of these signals and the emergence of eferences whose result is functional and adaptive remodeling at different organizational levels.

Computational models allow inferring the network dynamics from empirical anatomical connectivity obtained from diffusion weighted imaging. Several authors showed that the similarities between the simulated functional matrix, by implementing different types of computational models, and the empirical functional matrix, obtained from functional neuroimaging techniques, are maximum when the dynamics of the global network operates on a critical point of state transition [ 20 , 21 , 22 ]. This finding is supported by empirical evidence, from functional neuroimaging techniques, that show that the dynamical regime of resting state corresponds to critical point of state transition [ 23 , 24 ]. Critical dynamics are crucial from the functional point of view given that in this critical state the system presents an optimal sensitivity to possible inputs and a maximization of the number of different functional states available. This allows the system to rapidly compute a specific brain function according to the stimulus received. These findings suggest that this is an optimized state for information transmission and for early adaptation to external perturbation.

However, the high specificity of the mechanisms that condition the structure–function relationship of neural systems is today an incompletely answered question. There is evidence of the modulation of this interrelation by activity and behavior in its broadest sense, but how this feedback works in the time-space context of neural systems is still a big question. This involves complex and controversial aspects of the neurosciences and that the methods and methodologies to obtain both structural and functional connectivity are not always rigorously applied. The heterogeneity regarding methodological approaches and polemic points such as the mechanism driving functional connectivity and spatio-temporal scales make the comprehension and interpretation of the structure–function relationship difficult.

There are questions involving structure–function relationship without a definitive answer: How can the functional activation patterns be changed? How do plasticity mechanisms become involved? In what way and under what conditions do different favorable plastic responses, neuroprotective and successful from the adaptive point of view, happen to be aberrant events and therefore an expression of maladaptation? A deeper understanding of structure–function relationship is still difficult today, as is how this relationship is modified in some neuropathologies.

There are reviews of this topic from different perspectives [ 19 , 25 , 26 , 27 , 28 ]. We have organized this review summarizing those concepts related to the structure–function relationship within the framework of neuroimaging; that is, updated definitions of anatomic and functional connectivity, the methods used to extract these measures, and their interpretation under different spatial-temporal scales. The following section deals with the network theory and computational modeling related to brain function. Afterwards, we discuss the main findings on the structure–function relationship under neurodiscapacity conditions. Finally, we expose limitations identified in current studies to be considered in future research on this topic.

2. Definitions

2.1. structural connectivity.

Anatomical or structural connectivity (SC) refers to physical synaptic connections between neural elements or the white matter fibers connecting gray matter regions, depending on the spatial scale. Tract tracing technology constitutes an invasive method for estimating the weight of axonal connectivity between a pair of pre-defined brain regions at micro-scale. This technique allows a deep characterization of synaptic termination of connections at cellular level and an assessment of the directionality of white matter projections. Tract-tracing studies have allowed collecting connectomes of the mouse [ 29 , 30 ], rat [ 31 ] and the macaque [ 32 ]. On the other hand, diffusion weighted imaging (DWI), an MRI technique, is a non-invasive method to record in vivo white matter’s architecture [ 33 ]. The present review focuses on this non-invasive technique that corresponds to the macro-scale, but its correspondence with the micro-scale is analyzed below. DWI really provides information about the restrictions on the diffusion of water molecules, which is used to infer the orientation of white matter tracts (for review, see [ 34 ]). The main limitations of SC based on DWI are that the connections are non-directed because tractography algorithms cannot determine the direction of axonal projections, and they cannot differentiate between excitatory or inhibitory connections. These limitations are due to intrinsic features of neuroimaging technique.

Different tractography algorithms and software packages have been developed to estimate the likelihood of the existence of connections between two regions from DWI data. In general, these methods can be classified into two types considering how the white matter tracts are modeled within the voxel: probabilistic tractography based methods [ 35 , 36 , 37 ] and deterministic fiber tracking tractography [ 38 , 39 , 40 , 41 ]. The streamline approach is advantageous to characterize long white matter tracts. However, the probabilistic tractography is more effective in resolving crossing fibers [ 42 ]. In any case, using both methods, it is possible to construct structural networks. Bastiani et al. [ 43 ] argued the advantages and disadvantages of tractography algorithms considering not only deterministic or probabilistic distinction but also whether the model is a single-direction or multiple-direction intra-voxel diffusion model and comparing local versus global tractography.

Several measures are used to quantify anatomical connectivity: the probability connection, the fiber count and fiber’s length. Based on these, the density and strength of connection has been defined. These measures have often been used interchangeably in the study of structure–function relationship. There is no consensus on a measure of anatomic connectivity, hampering a comprehensive understanding of the findings obtained by diverse studies.

2.2. Functional Connectivity

Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and positron emission tomography (PET) are imaging methods of macroscopic brain activity. Each of these techniques is sensitive to different physiological processes; therefore, they have different spatio-temporal resolutions. For instance, the non-invasive fMRI technique has high spatial resolution (~1 mm) and poor temporal resolution (seconds). In contrast, EEG/MEG measures neuronal activity with excellent temporal resolution and low spatial resolution. Functional connectivity (FC) has been defined as statistical dependencies between distinct and distant regions of information processing neuronal populations [ 44 , 45 ] and can be extracted from anyone of these techniques including the so-called “resting-state” fMRI (rs-fMRI) [ 46 , 47 ]. Regarding structure–function relationship, the activity at slow time scale of the rs-fMRI signals seems more constrained by structural configuration than the faster time scales of the EEG. Functional connections estimated at larger time windows strongly overlap with the underlying structural connections, while, for smaller time windows, there can be a structural–functional network discrepancy due to distributed delays between neuronal populations that cause transient phase (de-)synchronization [ 1 , 7 ]. For this reason, this review focuses on the findings based essentially in fMRI, specifically rs-fMRI. There is still much debate about the neuronal basis of rs-fMRI, although it has been accepted that the rs-fMRI signal is due to the intrinsic activity dynamics that reflects, at least in part, aspects of the functional capacity of neural systems [ 48 ]. Various authors established a relationship between time-varying functional resting-state connectivity and the network topology using large-scale models, which is discussed below.

The blood-oxygen-level dependent (BOLD) signal of fMRI is an indirect measure of brain neuronal activity. Although what it really reflects are the differences in blood oxygen level in brain tissue. The methods proposed to study FC from fMRI have been classified in two general categories: model-based methods and data-driven methods. Model-based methods such as general linear model [ 49 ], cross-correlation analysis [ 50 ] and coherence analysis [ 51 ] are based on prior knowledge. In contradistinction to these methods, data-driven methods are independent of prior information; some of these are principal component analysis (PCA), independent component analysis (ICA) and clustering analysis. Each method has own advantages and limitations (for review, see [ 52 ]).

A variety of measures have been used to describe FC: correlation, covariance, coherence, Granger causality, and mutual information. Pearson correlation coefficient between the time series of different regions is the most used functional measure. The interpretation of functional connectivity metrics is a crucial point in the comprehension of the relation between structure and function [ 53 ] because the FC may reflect direct interaction between two regions but also may exist between anatomically unconnected regions, in contradistinction to SC that reflect white matter fibers that physically connect two regions. The FC is dealt with as a purely spatial measure and this static description is a moot point given that the non-stationary of resting-state connectivity turns around the way that functional interactions are examined and simulated.

The definitions and some limitations to be considered before starting any connectivity study have been reviewed, but an important point that has not been mentioned in obtaining the anatomical and functional connectivity matrices is the parcellation scheme. In connectivity matrices, the columns and rows (targets and sources) represent the neural elements i andj, and c ij entries represent neural connections. The number of neural elements depends on the parcellation schemes of the brain into regions utilized (for review, see [ 54 ]). There are different anatomical brain atlases that are used to define the nodes of the connectivity matrices. The most widely used anatomical atlas is the Automated Anatomical Labeling (AAL) that comprises 116 ROIs (nodes) [ 55 ], which uses the parcellation proposed by Hagmann et al. [ 2 ]. The various parcellation schemes differ in the number, shape and location of ROIs which can alter connectivity profiles and obstruct the comparison of results across studies. In the particular case of FC, it is likely that they include signals from different functional regions due to the usually large size of the ROIs derived from most parcellation schemes.

2.3. Spatio-Temporal Scales

The brain has been described as a complex network on multiple spatial scales, from synaptic circuits to whole-brain system to process and integrate information. Essentially, it is split into three levels: macroscopic, mesoscopic or microscopic scale. At macroscale, we handle with brain regions and white matter fibers connected these regions. The microscopic scale is extended to single neurons, the dendrites, axons and synaptic activity. However, how these scales are related is still an open question. Even though this review focuses on empirical connectivity obtained from neuroimaging techniques, which corresponds to the macroscale, a comprehension of structural correspondence and functional interplay between these two scales is imposed. In this sense, a significant correlation between microscale cellular metrics with macro-cale network properties of macaque cortex has been reported. For instance, highly connected regions show more neural complexity in terms of dendritic branching, soma size and spine density compared with lowly connected regions [ 56 ]. A correlation between cytoarchitectonic properties, such as neuron size, and macroscale connectivity, reflecting an association between local and global organizational features of human cortex, has also been found [ 57 ]. Likewise, a relationship between the functional connectivity and the underlying chemoarchitecturehas been shown [ 10 , 58 , 59 ]. Even though there is evidence of the correspondence between micro- and macroscale, it has not been possible to explain the physiological mechanisms of many of the phenomena experimentally observable at macroscale. Thus, a third scale has been defined, the mesoscopic level, which represents the transitional state between the macro- and microscales. Mesoscopic refers to neuronal populations under the assumption that the neurons of a population have similar properties with the aim of explaining macroscopic spatio-temporal dynamics from mesoscopic approximations.

The brain has been studied essentially in two time-scales: short-term (seconds to minutes) and long-term (days to years) time-scales. The structural configuration is relatively stable across time but can change due to the development and neuroplastic processes, such that the SC (at macroscale) remain stable on short-term time scale while neuroplasticity can be observed on long-term time scale. On the other hand, we have already mentioned that FC is not time-invariant; relationships between neural elements can quickly vary across time. FC from fMRI studies corresponds to macroscopic scale due to the spatial resolution that is not enough to directly represent the neuronal dynamic activity at microscopic level.

3. What We Know About Structure–Function Relationship

Neuroimage processing, network theory and computational modeling have played essential roles in the study of structure–function interactions. Empirical studies obtained the structural and functional matrices where nodes represent regions and the edges represent the connections between these regions (connections can be anatomical measurements or functional correlations), and the correlation between them, structural and functional connectivity, is calculated to evaluate the closeness of their relationships. In the cases where the SC derived from the DWI studies and the FC derived from the rs-fMRI of the same subjects have been compared, different correlation coefficients between functional and structural connectivity matrices have been reported in approximately a range of 0.4–0.7. However, these studies use different methodologies, namely different neuroimaging processing methods, different subdivision schemes and different connectivity measures.

Some papers implement computational models to infer the FC from SC obtained from DWI, and compare the inferred FC with empirical FC obtained with functional neuroimaging techniques, e.g., rs-fMRI or EEG. A consistent result of these studies of the structure–function relationship is that resting-state network reflects at least in part the underlying SC. This has also been demonstrated in mice [ 60 ]. This relationship between structural network and network of dynamic couplings has been demonstrated at macroscale [ 3 , 40 ], mesoscale [ 61 ], and microscale [ 62 ]. However, part of the FC is not supported by the underlying SC and this may be due to several factors. For example, the FC between two regions can be given in the absence of a structural connection between these regions, that is, it can be mediated by third areas. On the other hand, another factor that influences the non-total correspondence between SC and FC is related to the dynamic aspects of the FC, that is, the non-stationary nature of FC. A dependence on the SC–FC relationship with the time scale considered in the study has been described [ 1 , 7 ]. Mišić et al. [ 6 ] illustrates that, at network-level, a particular structural configuration supports a different non-overlapping functional configuration, which means that functional network does not necessarily correspond node-to-node to the underlying structural network.

The brain has been studied using network analysis and a common architectural characteristic has been found in both anatomical and functional networks. A full description and interpretation of graph-theoretical approach is beyond the scope of this review (for review, see [ 63 ]). A group of measures that reflect the integration and segregation processes allow us to characterize structural and functional organization of the brain, but interpretation of these metrics depends on the choice of nodes and edges, again the parcellation problem (to define node).

Even when different experimental methodologies and different parcellation schemes were used, there is evidence that the brain has small-world properties, which means a tendency to clustering nodes into modules reflecting an efficient topological integration and economic wiring, for both functional [ 64 , 65 ] and structural networks [ 2 , 40 , 64 , 66 , 67 ]. This “small-world” network property has been associated with optimal communication efficiency and high-speed information transmission due to the coexistence of high clustering and short average distance, which facilitates the integration and spreading of signals [ 68 ]. Despite all this preliminary evidence, the assumptions of the small-worldness have been questioned [ 69 ]. Another feature that has been demonstrated in humans is the rich-club organization [ 70 , 71 ], which means a presence of highly interconnected regions (hubs), beyond what is expected considering only the node degree, which represents the number of connections that link the node to the rest of the network. This rich-club organization has been associated with the shape and route that global processes follow in neural system and that contributes to the integration process. Rich-club structure is essential in cross-linking of functional modules in the human brain [ 72 ]. These two properties, the modular structure and the rich-club organization, seem to be determinants in the dynamic activity of the brain that is affected when one of these properties is destroyed [ 9 ]. Other network measures have been directly related to the brain’s activity. For example, a previous study found that the shortest paths of the structural network were strong predictors for functional connections [ 68 ].Some of these large-scale topological features are conserved in some species. Small-world network organization, the modular structure and central rich club organization have been shown in rat [ 31 ], mouse [ 60 ], cat [ 73 ] and macaque monkey [ 1 , 74 , 75 ]. These studies allow us to conclude that there are similar topological organizational features of neural network architecture among species.

As mentioned before, the relationship between SC and FC does not exhibit a simple one-to-one mapping, thus modular structure helps clarify this relationship. Diez et al. [ 76 ] proposed a brain partition based on structural–functional modules. They hypothesized that there is a brain partition common to both structure and function networks. They found a strong correspondence between brain structure and resting state dynamics by implementing a standard hierarchical agglomerative clustering algorithm. Their work confirmed that both rs-FC and SC networks display a high modularity, and that there is an excellent matching between functional and structural modules.

In addition to the graph-based approaches that have helped to clarify structure–function relationship, important contributions have also been made by computational modeling. In the last decade, many mathematical and computational models have been proposed that can generate detailed neuronal dynamics to make inferences about brain functionality and its complex behavior. These models have contributed to bridge the gap between anatomy and brain dynamics by making inferences to what extent the anatomical configuration predicts neural dynamics, for instance by comparing simulated functional connectivity with empirical one. A crucial point of computational models is the trade-off between complexity and realism. In general, the large-scale brain network models that incorporate realistic anatomical connectivity to simulate neuronal population activity in each region allow a balance between biophysical realism and model complexity owing to its low-dimensionality. These models are based on mesoscopic approximations: neural-mass and mean-field reduction [ 77 ], and a group of neurons (neuronal population) that shares the same physiological properties at a given physical location is only influenced by the mean activity (activity modeled by a set of dynamical equations) of another population. These neural populations can be coupled together according to empirical measures. These mesoscopic approximations allow a close up image to comprehend the underlying physiological mechanisms (microscale) that give rise to spatial-temporal macroscopic dynamics in the healthy and diseased brain. In this context, several works have utilized computational models to generate neural dynamics from the empirical structural connectivity matrix [ 1 , 3 , 7 , 20 , 21 , 22 , 73 , 78 , 79 , 80 ]. The general methodology followed by many of these articles consist of: (1) obtain structural and functional connectivity matrices from DWI and rs-fMRI, respectively; (2) simulate activity from structural connectivity matrix using a computational model; (3)estimate BOLD signal from the simulated neural activity; and (4) compare empirical versus simulated FC. Following this general methodology, all these works have elucidated and reinforced key points about the relationship between resting state networks and the underlying anatomical connectivity. First, there is a tendency towards high FC between regions strongly anatomically connected, as well as between regions without direct structural connections. Second, there is a dependence on this relationship of the time scales: st a slow time scale (minutes), the FC reflects part of the underlying structural network, snd not the FC fluctuations that take places on shorter time scales. Third, it is suggested that these non-stationarities could explain the part of FC not supported by the underlying SC. Fourth, the best fit between simulated (using SC matrix) and empirical (measured experimentally using fMRI) FC is when the network state is at the edge of a dynamical bifurcation point. This last finding reflects that there is a functionally significant dynamic repertoire that is inherent of the structural configuration. That the brain network operates at the edge of instability is functionally relevant because it indicates the existence of a set of available brain states that can be activated and stabilized quickly and easily when necessary, for example before a given task or for a given function.

The structure–function relationship has not only been studied in humans, similar results have also been obtained in animal studies. An advantage of animal models is that they allow studies at the cellular and molecular level, on either a micro-or a sub-microscale. A correspondence between anatomical and functional connectivity in somatosensory cortex has been demonstrated for anesthetized monkeys [ 28 ] and in mice [ 5 , 60 ]. Díaz-Parra et al. [ 8 ] simulated FC using rat connectome derived from tract tracing experiments to compare with empirical FC obtaining from rs-fMRI obtaining a Pearson correlation of 0.53, which intensifies the hypothesis that anatomical topology shapes, at least in part, functional networks. They also found that functional modules are enriched in densely connected anatomical motifs.

4. Structure–Function Relationship in Neurological Disorders

The objective of this section is to cite some examples where the structure–function relationship has helped to elucidate more about neural mechanisms; for example, in the normal aging process and in some pathologies, particularly those that have been described as network disorders such as epilepsy, schizophrenia and autism.

Considering what has been said up to now, the correspondence between SC and FC would be a better biomarker (greater sensitivity) of brain disorders than those biomarkers based only on imaging modalities. Why some functions are preserved after similar structural brain damage in some patients and not in others is an open question. In this framework, inter-individual variability in the evolution, behavior and neurological profile among patients with the same pathology has been associated with different structure–function relationship between patients. That is, more than diseases, there are patients and, therefore, the personalized medicine forms the basis of the future clinical-therapeutic approach.

Aging is associated with changes in brain morphology as well as with a decline in cognitive performance; in fact, several authors reported anatomical and functional impairment in different brain regions. Persson et al. [ 81 ] reported anatomical and functional differences associated with cognitive decline between two groups with different levels of episodic memory performance over time. They found a reduction in anatomical connectivity in the anterior part of the corpus callosum and an increase in the recruitment of frontal regions in the group with a declining memory performance. In fact, they found a negative correlation between anatomical measurement and the increment of the activation pattern (fMRI), in the form of an increase in the number of activated regions during a cognitive task performance. They argued that this result suggests that the additional activation pattern during task performance could be a compensatory mechanism for the structural disruption associated with memory decline. On the other hand, Nakagawa et al. [ 27 ] demonstrated, based on computational modeling, that the decrease in structural connectivity in aged people led to lower complexity of BOLD signals. The complexity allows quantifying temporal patterns of BOLD signal; higher complexity of brain activity leads to richer and more integrated information in the network. They argued that it could reflect changes in processing speed which would explain the cognitive performance decline distinctive of aging. Although the results may seem contradictory, they are not, since the first study is based on the activation pattern during the execution of a certain task, while the second is based on “resting-state” fMRI, in the absence of an explicit task. These results seem to indicate that deterioration in the structural network during aging causes a decline to information processing so that the system needs higher overall activation to cognitive performance. These two results are examples of how the structure–function relationship can be used as biomarkers, in this case of aging.

4.2. Epilepsy

Studies from epileptic patients have reported alterations in both structural and functional connectivity as well as a negative correlation between both parameters. Liao et al. reported a decrease in both FC and SC in mesial temporal lobe epilepsy (TLE) patients when compared with controls [ 82 ]. Zhang and coworkers corroborated these findings in patients suffering idiopathic generalized epilepsy (IGE) [ 83 ]. Furthermore, the correlation between SC and FC shows a tendency to decrease in TLE patients vs. controls [ 84 ] similar to that described in IGE patients [ 84 ]. Nevertheless, all these studies referred to an interesting finding: the SC–FC correlation increased during the epileptic crisis. It could be suggesting that the SC–FC correlation is a possible biomarker for epilepsy monitoring.

A network topology study in children with frontal lobe epilepsy compared to age-matched controls revealed a relationship between functional networks impairment with an increment of the cluster coefficient and path length of the epileptic patients group. This finding represents the existence of networks with tightly clustered modules but with limited inter-modular connectivity. Because this study did not find significant differences between groups for structural networks, it has been suggested that the functional alteration precedes structural changes [ 85 ].

4.3. Schizophrenia

Schizophrenia has been described as a disorder of brain connectivity [ 86 , 87 ]. Numerous topological disturbances have been reported on structural and functional networks [ 86 , 87 , 88 ]. Skudlarski et al. [ 86 ] found a diminished SC accompanied by either decrement or increment of FC in different regions (hypo- and hyper-connectivity) when comparing connectivity maps of schizophrenic patient group with control group. Some of these functional abnormalities are supported by anatomical changes; the complex nature of the structure–function interaction is also evident in schizophrenia [ 88 ]. Lastly, Stephan et al. [ 89 ] reviewed computational models based on neuroimaging data and focused on schizophrenia as a spectrum disease. In cases such as schizophrenia, where a spectrum is described, individual analyses are imposed for each patient (for review, see [ 87 ]).

4.4. Autism

Autism spectrum disorder (ASD) has also been characterized as a network disorder with abnormal anatomical and functional connectivity. One of the reported findings is a decrease in inter-hemispheric FC [ 90 ] that seems to be related to reduced inter-hemispheric anatomic connectivity [ 91 , 92 ]. However, an SC increased in young children [ 93 ] and adolescents [ 94 ] with ASD has also been reported. Thus, there are questions such as: Is connectivity disorganization part of the primary pathogenesis in ASD? Are there differences in connectivity patterns between distinct syndromes of ASD? Basic and clinical studies will shed light on these issues.

5. Limitations

In this review, the most relevant methodologies in neuroimage processing, network topology analysis and computational modeling used to study of structure–function relationship are discussed. However, the images acquisition and processing per se offer inherent limitations of the methodology. For instance, when structural connectivity is based on DTI, it can ignore long-distance and fiber-cross connections and does not provide information about the directionality of the connections [ 2 , 42 ]. In the case of fMRI, it is important to note some aspects: neuronal activity is not directly a measure of the BOLD signal of each voxel; it is an integration of a variety of neuronal activities and the increase of excitatory or inhibitory synaptic activity can cause an increase of metabolic activity. Such limitations, properties of neuroimaging techniques, may lead to imprecise brain network representations, affecting the analysis of network properties such as the study of the structure–function relationship.

Regarding the methodology followed for image processing, there are two points that limit the interpretation of the results as well as make comparison between the different studies difficult: parcellation schemes and connectivity measures. The definition of nodes and edges is a critical step to obtain connectivity matrices; in fact, the limitations on the application of graph theory and the validity in its interpretation depend on graph representation itself: nodes and edges. Until now, there is no universally accepted parcellation scheme: the diverse anatomical brain atlases exhibit remarkable differences in the number, shape, and location of ROIs. The results of anatomical and functional connectivity depend on the algorithm and parcellation scheme considered. This dependency of the results of anatomical/functional connectivity on parcellation schemes and the non-standardization of connectivity metrics cause inconsistencies between studies. Taylor et al. referred to the use of a high-resolution parcellation scheme (50,000 nodes), focusing the analysis on modularity within and between brain areas [ 95 ]. They described a modular architecture within brain areas that could be the structural mechanism to implement different functions.

In addition, the variety of connectivity measures for both structure and function make comparison across studies difficult. Huang and Ding argued that node weight, a measure that considers the number and length of fibers and the ROI size, and conditional Granger causality are more appropriate measures to link functional and structural connectivity [ 53 ].

In summary, the extent to which the different methodologies and procedures (connectivity measure and graph construction) faithfully reproduce the biological phenomenon itself is something that requires additional studies to establish guidelines in this regard.

In the case of computational modeling, models with many variables make parameter estimation very complex. In addition, it is not clear to what extent the values of some parameters affect how well models predict FC. Some models do not differentiate neural regions and are based on certain global parameters, coupling weight, transmission speed and noise, which determine the global network activity. All these elements must to be carefully considered when a computational model is used to study SC–FC relationship.

6. Conclusions

From surveying the current literature, what is clear, despite all methodological limitations, is that resting-state functional connectivity is constrained by the large-scale brain anatomical configuration, but other factors also influence it. Multi-modal neuroimaging techniques and improved methodologies are needed to obtain structural and functional connectivity matrices. Another consistent finding is that simulations of intrinsic neural activity based on anatomical connectivity can reproduce much of the patterns observed in empirical functional connectivity. Computational modeling will continue to be an increasingly essential scientific endeavor, playing an increasing role in elucidating multi-scale relationship sand inferring disorder mechanisms.

Clearly, more research is imperative to deepen the understanding of the structure–function relationship. Longitudinal investigations can be fruitful in future works to evaluate neuroplasticity processes. In addition, the wide variability among patients with the neurological disorders imposes personalized individual treatment as health-care options.

Author Contributions

K.B.G.R. wrote the paper. C.I.F.V. participated in the writing and revision of the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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A Blood Test Accurately Diagnosed Alzheimer’s 90% of the Time, Study Finds

It was much more accurate than primary care doctors using cognitive tests and CT scans. The findings could speed the quest for an affordable and accessible way to diagnose patients with memory problems.

By Pam Belluck

Scientists have made another major stride toward the long-sought goal of diagnosing Alzheimer’s disease with a simple blood test . On Sunday, a team of researchers reported that a blood test was significantly more accurate than doctors’ interpretation of cognitive tests and CT scans in signaling the condition.

The study , published Sunday in the journal JAMA, found that about 90 percent of the time the blood test correctly identified whether patients with memory problems had Alzheimer’s. Dementia specialists using standard methods that did not include expensive PET scans or invasive spinal taps were accurate 73 percent of the time, while primary care doctors using those methods got it right only 61 percent of the time.

“Not too long ago measuring pathology in the brain of a living human was considered just impossible,” said Dr. Jason Karlawish, a co-director of the Penn Memory Center at the University of Pennsylvania who was not involved in the research. “This study adds to the revolution that has occurred in our ability to measure what’s going on in the brain of living humans.”

The results, presented Sunday at the Alzheimer’s Association International Conference in Philadelphia, are the latest milestone in the search for affordable and accessible ways to diagnose Alzheimer’s, a disease that afflicts nearly seven million Americans and over 32 million people worldwide. Medical experts say the findings bring the field closer to a day when people might receive routine blood tests for cognitive impairment as part of primary care checkups, similar to the way they receive cholesterol tests.

“Now, we screen people with mammograms and PSA or prostate exams and other things to look for very early signs of cancer,” said Dr. Adam Boxer, a neurologist at the University of California, San Francisco, who was not involved in the study. “And I think we’re going to be doing the same thing for Alzheimer’s disease and hopefully other forms of neurodegeneration.”

In recent years, several blood tests have been developed for Alzheimer’s. They are currently used mostly to screen participants in clinical trials and by some specialists like Dr. Boxer to help pinpoint if a patient’s dementia is caused by Alzheimer’s or another condition.

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Morning Rundown: A glorious farewell to the Paris Olympics, U.S. rushes firepower to the Middle East, and a reckoning for Florida condo owners

Ozempic-like weight loss drugs may protect against Alzheimer's

A weight loss drug similar to Ozempic appeared to slow cognitive decline in patients with mild Alzheimer’s disease, new research presented Tuesday at the Alzheimer’s Association International Conference in Philadelphia finds.

The findings, which have not yet been published in a peer-reviewed journal, add to the growing evidence that GLP-1 agonists — a class of medications that includes the popular diabetes and weight loss drugs Ozempic and Wegovy , from Novo Nordisk, and Mounjaro and Zepbound , from Eli Lilly — may also protect the brain.

“What we’ve shown is that these GLP-1s have great potential to be a treatment for Alzheimer’s disease,” said Dr. Paul Edison, a professor of neuroscience at Imperial College London who presented the findings Tuesday. “As a class of drugs, this holds great promise.”

Early evidence has hinted at GLP-1 drugs’ brain-boosting potential. Semaglutide, the active ingredient in Ozempic and Wegovy, has been shown in studies to cut the risk of dementia in patients with Type 2 diabetes. Type 2 diabetes is a known risk factor for the disease.

Novo Nordisk is running two phase 3 clinical trials that will compare semaglutide to a placebo in more than 3,000 patients with mild cognitive impairment or early-stage Alzheimer’s disease. The trial results are expected to be released sometime in 2025. In a statement, a Novo Nordisk spokesperson said there is “an urgent need for treatments that can slow the progression of Alzheimer’s disease.”

The new research presented by Edison looked at liraglutide, the active ingredient used in two of Novo Nordisk’s older GLP-1 drugs , Saxenda, a weight loss drug, and Victoza, a diabetes drug. The midstage clinical trial included about 200 people in the United Kingdom who got daily injections of either liraglutide or a placebo.

After one year, cognitive decline in the patients who got liraglutide had slowed by as much as 18% compared to those who didn’t get the drug, based on the Alzheimer’s Disease Assessment Scale, which tracks the progression of the disease by assessing memory, language, skills, understanding and reasoning abilities.

The drug was also shown to reduce shrinkage in parts of the brain responsible for memory, learning and decision-making by nearly 50%. Shrinkage of the brain, also known as brain atrophy , is often associated with the severity of cognitive decline in people with dementia and Alzheimer’s.

“I think there’s some hope for these drugs,” said Dr. Ronald Petersen, a neurologist at the Mayo Clinic in Rochester, Minnesota, who is not involved with the research. “Their primary use is diabetes and weight loss, but you’ve probably also seen that they may be useful for sleep apnea and heart disease. So all of these effects cumulatively may be very beneficial to the brain.”

The next wave of Alzheimer’s treatments

Nearly 7 million people in the U.S. have Alzheimer’s disease, according to the Alzheimer’s Association . By 2050, the number is projected to almost double to 13 million.

The disease has no known cure.

Within the past two years, the Food and Drug Administration has approved two drugs — Biogen’s Leqembi and Lilly’s Kisunla — that marginally slow the progression of Alzheimer’s by targeting the disease’s hallmark amyloid plaques in the brain . They’re the only drugs on the market available to treat the disease, but they’re pricey and can come with serious side effects, including brain swelling and brain bleeding.

Dr. Maria Carrillo, the chief science officer at the Alzheimer’s Association, said she expects that GLP-1 drugs may be the next advancement in the treatment of the disease, most likely used in combination with the amyloid-fighting drugs.

“I think the first thing out of the gate will be the GLP-1s,” Carrillo said.

If late-stage trials go well, FDA approval could happen as early as next year, she said. But combination treatment of amyloid drugs and GLP-1s is most likely already happening in the real world, Carrillo added, noting that many people with Alzheimer’s disease also have diabetes or obesity and may already be taking a GLP-1 medication.

“Those combinations are already happening,” she said. “They’re just not FDA-approved combinations.”

How might GLP-1s protect the brain?

Dr. Alberto Espay, a neurologist at the University of Cincinnati College of Medicine, said the key questions that remain are how do GLP-1s help protect the brain and by how much.

The fact that they appear to help makes sense: Alzheimer’s, he said, is thought to be a “syndrome of many diseases caused by different biological, toxic or infectious exposures.”

Petersen, of the Mayo Clinic, agreed.

“I think the data are accumulating across the board for a variety of different disorders and mechanisms,” he said. “Any one of these factors, diabetes, obesity, all of these can have deleterious effects on cognitive functions. If you treat those underlying conditions, you make it a beneficial positive effect of cognitive function.”

The existing Alzheimer’s drugs, Leqembi and Kisunla , target only one component of the disease: amyloid plaque in the brain.

The GLP-1 drugs, Petersen said, could work in “nonspecific” ways.

“I don’t think that this class of drugs necessarily will have specific actions on Alzheimer’s disease if you define Alzheimer’s disease by the presence of plaques and tangles of amyloid and tau,” he said. “On the other hand, if these drugs have anti-inflammatory kinds of actions or other cerebrovascular actions, that could be very important.”

Edison said that the liraglutide study found that people who got the drug had reductions in inflammation , insulin resistance and the formation of tau, a protein in the brain that is thought to contribute to Alzheimer’s. They also had improvements in overall brain function.

“If you want to have very effective treatment, what you need is not only targeting amyloid. You have to target other pathological forces, as well,” he said.

Espay said of the liraglutide results: “This looks promising. If this is replicated in a phase 3 trial, it could become the first truly disease-modifying treatment in Alzheimer’s disease.”

Berkeley Lovelace Jr. is a health and medical reporter for NBC News. He covers the Food and Drug Administration, with a special focus on Covid vaccines, prescription drug pricing and health care. He previously covered the biotech and pharmaceutical industry with CNBC.

How does traumatic brain injury progress to Alzheimer’s disease?

An NIH grant to UC Riverside and Indiana University will explore the question

A traumatic brain injury, or TBI, is caused by a contusion to the head that may result in injury to the brain. This type of injury combined with the inherited genetic risk factors can result in the accelerated development of Alzheimer’s disease and related dementia, or ADRD. TBIs range from mild to severe, with the majority being mild. They are especially common in adolescents engaging in contact sports and in the elderly who tend to fall with greater frequency as they age. Regardless of the source, TBI and how it progresses to ADRD is an understudied area of research.

A $3.5 million grant to the University of California, Riverside, and Indiana University, from the National Institute of Neurological Disorders and Stroke at the National Institutes of Health will now examine how TBI at different ages and genetic risk factors leads to ADRD. 

“There’s some literature that suggests that traumatic brain injury can evolve in some cases to Alzheimer’s or accelerate Alzheimer’s-like symptoms,” said Andre Obenaus , a professor of biomedical sciences at UCR’s School of Medicine and principal investigator of the three-year grant. 

“We are interested in the complex interplay between TBI and genetic risk factors, and how these increase the susceptibility for individuals to develop Alzheimer’s disease and related dementia,” said Paul Territo , a professor of medicine at the Indiana University School of Medicine, and co-principal investigator of the proposed work.

Using rodents, Obenaus, Territo, and their teams will study three different time periods in the 24-month lifespan of a mouse: the juvenile age, which is postnatal Day 17; midlife, which is when the mice are 8-9 months old; and late life, which is when the mice are 12 months of age. The researchers will use cognitive behavioral outcomes, clinically relevant medical neuroimaging (PET/CT, MRI), immunopathology changes, and tissue biomarkers to assess disease progression.

“This system will allow us to investigate the interactions of how genetics and TBI in well characterized models across three epochs of lifespan influence progression to ADRD,” Obenaus said.

An expert on TBI, stroke, Alzheimer’s, and epilepsy, Obenaus has worked on TBI for more than two decades. He joined the UCR faculty in March 2024. He is a member of a National Institute of Aging-funded consortium, MODEL-AD , tasked with building better models of Alzheimer’s disease in mice. The consortium is based at UC Irvine. 

Territo, an expert in Alzheimer’s disease, biomarker development, and therapeutics testing, has worked in both the academic and pharmaceutical industries developing rigorous and reproducible systems to evaluate disease progression and therapeutic response. These areas of expertise are extensively leveraged in the MODEL-AD consortium, where his lab characterizes mouse models of ADRD, and then performs therapeutic evaluation of novel drug candidates.   

“We hope to identify early on those individual mice that will go on to have an Alzheimer’s-like phenotype,” Obenaus said. “We don’t expect all the mice to develop Alzheimer’s, but a certain subgroup of mice will. The goal is to identify the combinations of risk genes and timing of TBI that modulate fluid and imaging biomarkers involved so that early intervention is possible to prevent, or delay, progression of Alzheimer’s.”

Medical imaging is the only non-invasive means to assess both TBI and ADRD. When combined with fluid biomarkers, which are biological molecules found in body fluids and tissues, detection of abnormal processes or disease progression is possible. Obenaus explained that considerable research has been done on TBI, but scientists still do not fully understand its long-term progression and the biomarkers involved. Territo underscored the combined strength of linking the readouts of medical imaging, immunopathology, and fluid biomarkers into a comprehensive model, which will provide significant improvements in predictive validity in both TBI and ADRD.

“We now have sufficient technical expertise in the field to address this research problem, allowing us to better define TBI and its role in initiating Alzheimer’s disease,” Obenaus said. “In addition, over the past two decades there has been a wealth of research identifying the two main proteins, tau and amyloid, thought to interfere with the communication between brain cells, and leading to ADRD.” 

Obenaus and Territo said the data from the research project will be aggregated and made freely available to other researchers through the NIH Open Science framework.

They will be joined in the research project by Adam Godzik and Devin Binder , who are professors of biomedical sciences in the UCR School of Medicine. A research team at the Uniformed Services University of the Health Sciences in Maryland, led by Dr. Denes Agoston , will work on the project as well. Also joining the team is Talin Babikian , a neuropsychologist at UCLA, who has extensive experience in TBI and its progression. Graduate students and postdoctoral researchers at UCR and Indiana University also will work on the research project.

Research reported in this news release was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number RF1NS138032. The content does not necessarily represent the official views of the National Institutes of Health.

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• Published: 06 August 2024

Research progress of brain organoids in the field of diabetes

• Ying Su 1 , 2   na1 ,
• Aimei Liu 1   na1 ,
• Hongguang Chen 1 ,
• Qingjie Chen 1 ,
• Bo Zhao 1 , 2 ,
• Runze Gao 1 , 2 ,
• Kangwei Zhang 2 ,
• Tie Peng 3 ,
• Zhenwang Zhang 1 ,
• Changhan Ouyang 1 , 2 &
• Dan Zhu 1  

Molecular Brain volume  17 , Article number:  53 ( 2024 ) Cite this article

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Human embryonic stem cells and human induced pluripotent stem cells may be used to create 3D tissues called brain organoids. They duplicate the physiological and pathological characteristics of human brain tissue more faithfully in terms of both structure and function, and they more precisely resemble the morphology and cellular structure of the human embryonic brain. This makes them valuable models for both drug screening and in vitro studies on the development of the human brain and associated disorders. The technical breakthroughs enabled by brain organoids have a significant impact on the research of different brain regions, brain development and sickness, the connections between the brain and other tissues and organs, and brain evolution. This article discusses the development of brain organoids, their use in diabetes research, and their progress.

Introduction

Diabetes mellitus is a metabolic disorder that affects over 400 million people globally. It is characterized by hyperglycemia and may have a number of serious side effects, including early death [ 1 ].Beta-cell dysfunction is a consequence of type 1 diabetes (T1D), an autoimmune disorder. Type 2 diabetes (T2D), the most common form of adult diabetes, is characterized by peripheral insulin resistance and significantly incorrect insulin production [ 2 ]. Furthermore, rare monogenic diabetes mellitus is becoming more prevalent. These disorders, which include neonatal diabetes (ND) and mature-onset diabetes of the young (MODY), are caused by mutations in a single gene that is necessary for the development or function of pancreatic beta cells [ 2 , 3 ]. Regretfully, there is still a great deal to learn about effective treatment options and the genesis of diabetes.

The term “organoids” refers to the technique of using stem cells cultured in vitro in a specific three-dimensional environment to create tissues with an architecture and functions similar to the original organ. The ability of stem cells to form complex tissue architectures and self-organize is necessary for the development of organoids. These self-organizing structures may include portions that reflect different brain areas; they are commonly referred to as “brain organoids” because of how frequently human brain regions are seen throughout the body. Cerebral organoids may have structural characteristics that are distinct to a particular region of the brain.

The novel technology known as organoids makes new models for the study of developmental biology and disease conceivable [ 4 ]. Human neurons are now widely used in in vitro systems that enable a wide range of phenotypic and mechanistic studies due to the recent and rapid advancements in stem cell technology, such as the ability to differentiate pluripotent stem cells (PSCs) and reprogramme somatic cells into induced pluripotent stem cells (iPSCs) [ 5 , 6 ]. Modeling neuropsychiatric and neurological diseases using organoids derived from induced pluripotent stem cells (iPSCs) might be useful for drug discovery (Fig.  1 ). These efforts have recently led to the development of three-dimensional (3D) brain organoids, which are being used as experimental models to study the pathophysiology of disease and normal organogenesis [ 4 , 7 ]. These organoids imitate the developing nervous system.

Brain organoid generation and therapeutic potential. Brain organoids can be generated from patient induced pluripotent stem cells (iPSCs) derived from adult fibroblasts and can be used to simulate human neurological disorders. Drug screening may be one of the potential applications for predicting drug efficacy before treatment

Brain organoids have significant promise for a variety of applications, including as drug screening, gene editing, modeling nervous system illnesses, exploring the evolutionary rules governing brain development, and modifying the brain’s evolutionary processes. There are several options for treating diabetes thanks to the recent and ongoing advancements in brain organoid technology. The composition, development, and use of brain organoids in diabetes are discussed in this article.

Construction and development of brain organoids

Research progress of brain organoid construction methods.

Though not all 3D systems for neuronal culture could be regarded as brain organoids, methods to stimulate neuronal differentiation from RGCs into 3D neuronal structures have been pursued since the early 1990s. Brain organoids may replicate the brain’s developmental process and reflect the physiological, pathological, and pharmacological characteristics of the brain. They also have many anatomical and cellular similarities with the real brain [ 8 , 9 ].

The creation of brain organoid technology is based on early research into the two-dimensional induction of neuroectodermal cells and the three-dimensional differentiation of embryoid bodies (EBs). Researchers also started looking at EB differentiation approaches because of the relatively basic cell types in two-dimensional (2D) culture systems, the stark disparities between cell interactions and actual tissue, and the challenge of directly examining human brain tissue. Advances in stem cell technology have made it possible for researchers to employ human induced pluripotent stem cells (hiPSCs) to construct brain-like tissues and organs from a 3D viewpoint. Researchers have also started to investigate neural cell differentiation procedures of PSCs [ 10 , 11 ].

The creation of innovative procedures for brain organoid formation was made possible by the initial groundbreaking studies of differentiation employing 2D monolayer cultures [ 12 , 13 , 14 , 15 , 16 ] and the groundbreaking work on 3D cultures by the Sasai and Kleber group [ 17 ]. Generally speaking, there are two primary methods for creating brain organoids: VSC self-assembly and external sensor inputs. Using neural guiding molecules and extracellular arrays, the research groups of Sasai and Knoblich conducted experiments on 3D brain organoid culture systems [ 11 , 13 ]. Key features of the fetal brain are mimicked by human brain organoids; nevertheless, the inability to create 3D brain structures that accurately represent late fetal development is due to the drawbacks of existing organoid techniques, including intrinsic hypoxia and cell death [ 18 ]. The development of a technique by Gordon et al. to create three-dimensional organoids of the human cerebral cortex with characteristics of the neonatal period is astounding [ 19 ] (Fig.  2 ).

Advances in methodology of brain organoid generation. ( A ) A simple method using a minimum of medium and extracellular matrix to create self-organized brain organoids. ( B ) Synthetic materials promote organoid maturation. ( C ) Organoids with mixed systems, such as neuromuscular organoids, make it possible to study the interaction between organs. ( D ) Microfluidics develop vasculature in organoids

Brain organoids have progressed from non-directional whole brain organoids to several brain organoids with distinct regional features, including cortex [ 20 , 21 ], midbrain [ 22 , 23 ], hippocampus [ 24 ], cerebellum [ 25 ], and spinal cord [ 26 ]. Specifically, realized brain organoid vascularity and regional brain organoids.

Currently, brain organoid cultures are classified into two categories based on whether or not targeted differentiation is carried out. First, stem cell differentiation produces organoid structures that, when grown in cultures, produce multi-brain organoids without the need for external morphogenetic agents. The second approach involves timing the addition of exogenous morphogenetic and neurotrophic substances to cultivate organoids in certain brain areas in accordance with the regulatory systems of the human brain development process (Fig.  3 ).

Unguided and guided approaches for making brain organoids. Unguided methods take use of hPSCs’ inherent signaling and self-organization abilities to allow them to naturally differentiate into tissues that resemble the growing brain. The resultant brain organoids often have diverse tissues that mimic various parts of the brain. Directed techniques use growth factors and tiny chemicals to create spheres that symbolize a particular tissue type. Organoid techniques specific to brain regions include the early usage of modular variables to influence the destiny of stem cells. Later phases of differentiation subsequently eliminate these components. Moreover, alignment techniques may be used to create two or more spheres or organoids that symbolize the identities of various brain areas. These can then be combined to create “class assemblies” that simulate the interactions of various brain regions

The non-oriented brain organoids

Serum-free culture was primarily used to create neuroectoderm during the early stages of brain organoid differentiation [ 27 ], while matrigel and bioreactors were used to accomplish long-term neuronal differentiation culture [ 11 ], among other methods. The SFEBq culture method, for example, successfully differentiates embryoid bodies of mouse embryonic stem cells (mESCs) into telencephalic tissue by simultaneously adding inducers for neuronal differentiation (e.g., Wnt antagonists and Nodal antagonists). Sasai’s team [ 28 ] produced a serum-free liquid culture of embryoid body-like aggregates with rapid reaggregation in 2005. By blocking the Notch pathway, the team used SFEBq to create human and mouse embryonic stem cells (ESCs) that resemble the retina in terms of composition and cell structure, so mimicking the retina’s developing process to some degree. He established the foundation for the creation of brain organoids [ 18 , 29 ].

Using the SFEBq technique, Lancaster et al. used hiPSCs for the first time in 2013 to promote differentiation into entire brain organoids [ 11 ] (Fig.  2 ). They stimulated and directed the differentiation of hiPSC embryoid structures with endoderm, mesoderm, and ectoderm. After that, they went through neuroectoderm and neuroepithelium to produce structures that resembled the early embryo’s cerebral cortex, which may have represented the early embryo’s development of the human brain.

Cells linked to the hippocampus, retina, forebrain, midbrain, hindbrain, and other areas with apico-basal polarity are seen in whole-brain organoids [ 11 ]. Additional research has shown that the integration of tissue engineering and three-dimensional culture may enhance the tissue structure of brain organoids and boost the repeatability of differentiation [ 30 ]. Researchers used microfilaments of poly (lactide-co-glycolic acid) (PLGA) fibers as scaffolding to generate microfilament-engineered brain organoids (MEOs) in 2017 [ 31 ]. The brain organoid scaffolds made from microfilms are part of a system that advances cortical development and encourages the production of neuronal ectoderms [ 31 ]. He used air-liquid interface culture of whole brain organoids (ALI-COR) in 2019; this method improves axonal development and neuron survival in whole brain organoids [ 32 ].

To put it briefly, the unguided brain organoids created in this work mimic the cellular makeup and anatomical structure of the brain in vivo, can model the brain’s developmental process, and can represent the physiological and pathological characteristics of the brain. These capabilities open up new avenues for research on brain function, disease simulation, drug discovery, and other related topics.

Region-specific brain organoids

Organoids made out of the whole brain exhibit unique heterogeneity [ 33 ]. Brain organoids were developed to mimic specific functional properties of individual brain regions in order to better understand the functions of various brain regions and their interregulation, as well as to look into patterns of neuronal development and the onset and progression of diseases in particular brain regions. Organoids of the brain were created to mimic the properties of several brain areas, including the cerebellum, midbrain, and forebrain (Table  1 ).

The optic nerve, hippocampus, thalamus, hypothalamus, and other brain areas are included in the forebrain. In 2011, Sasai et al. caused PSCs to spontaneously generate upper hemisphere vesicles [ 18 ], inhibiting the WNT pathway and concurrently activating the WNT pathway to establish a proximal-distal axis. The distal portion folds inward to create the optic cup structure, while the proximal section includes retinal features including inner nuclear layer (INL) and retinal ganglion cells (RGCs). The research produced organoids called optic cups that contained several kinds of retinal cells. After that, the scientists constructed a 3D culture system that resembled cortical growth [ 21 , 22 ], setting the stage for the production of organoids of the brain that are particular to a certain area. 2015 Sasai and colleagues optimized cortical organoids and produced hippocampal organoids by inhibiting SMAD pathways and activating WNT and BMP pathways [ 25 ]. In 2016, Qian et al. created a hypothalamus organoid by inhibiting the SMAD route and activating the WNT and SHH signaling pathways [ 34 ]. In order to create neuroectodermal organization and increase BMP7, Xiang et al. blocked the SMAD pathway in 2019 [ 35 ]. This resulted in the development of thalamic organoids.

In the pathophysiology and therapy of Parkinson’s disease (PD), the midbrain—which regulates information transmission, motor control, and sensory processing between the forebrain and spinal cord—has drawn a lot of interest. Jo et al. produced midbrain organoids in 2016 by adding SHH/FGF8 to induce ceiling structure [ 24 ], activating the WNT pathway, and inhibiting the SMADs pathway. By identifying dopamine production, they opened up a new avenue for research on Parkinson’s and other illnesses. Furthermore, in 2015, MONZEL et al. stimulated neuroepithelial stem cells to produce organoids in the midbrain using SHH inhibitors and GSK3 inhibitors [ 23 ].

The brain’s motor control center, the cerebellum, has the capacity to develop into distinct neuronal groupings. To form cerebellar organoids, Muguruma et al. created a boundary structure between the midbrain and cerebellum by blocking the SMADs pathway, adding FGF2 and insulin to promote caudation of cerebellar organoids, and then adding FGF19 and SDF1 to induce cells to promote neuroepithelial formation in the cerebellar lamina [ 26 ]. Because of the diverse cell types and fragile structure of the cerebellum area, the cerebellar organoid culture system still needs a long-term culture system.

In addition to neurons, microgila also play a pivotal role in the brain’s functionality. Currently, the lack of microglia with the ability to reshape neuronal networks and phagocytose apoptotic cells and debris is a major shortcoming of the midbrain organoid system. Moreover, modeling of diabetes-related neurological complications is not possible in the absence of microglia. By co-culturating hiPSCs-derived mesodermal progenitor cells (Brachyury + ) with neurospheres, Worsdorfer et al. renewably generated vascularized neuroorganoids that included vasculoid structures (CD31 + ) and microglia-like cells [ 36 ]. This study provided a model for studying angiogenesis and neurodevelopment, but did not investigate the function of microglia in the organoids. In the another study, Fagerlund et al. reported that hiPSCs-derived eythro-myeloid progenitors (CD41 + ) migrated into human brain organoids [ 37 ]. Differentiated into microglia-like cells, and interacted with synaptic material. Whole-cell patch-clamp and multi-electrode array recording showed that microglia within organoids promoted the maturation of neural networks. A recent study that co-cultured human midbrain organoids with hiPSCs-derived macrophage progenitor cells also reported that microglia integration let to increased nerve maturity and function [ 38 ]. Whole-cell patch-clamp and multi-electrode array recordings showed that lower action potential generation thresholds and shorter peak-to-peak intervals were observed in midbrain organoids with microglia integration, suggesting that microglia integration improve neural maturation.

The methodologies for constructing region-specific brain organoids are also examined. The development of these organoids continues to advance, integrating developmental inducers, biomaterials, and bioreactor systems. It is anticipated that more precise realization of region-specific brain organoids can be achieved. The aim is to replicate the maturation processes of various brain regions and establish relevant disease models. These may be used to research the control of neurodevelopment and the beginning of illness in certain brain areas. To more accurately mimic the physiological structure of the brain, however, further work has to be done on the precision of the generated brain areas and the repeatability of the cells.

Brain organoid fusion

Although brain organoids may be utilized to model many interacting brain areas, their sizes and spatial configurations are very varied and unpredictable. Researchers have attempted to mimic the structure and environment of the genuine human brain by integrating several brain organoid areas in an effort to create a more realistic brain organoid design that can replicate the development of different brain regions and model disorders. 2017, Team Pasca performed neural induction of PSC by SFEBq to induce the formation of dorsal and ventral telencephalic organoids by regulating WNT and SHH signalling [ 39 ], spontaneously fused ventral and dorsal telencephalic organoids and observed irregular migration of interneurons in the cortical tissue. Interneurons in fused organoids from Timothy syndrome patients migrated abnormally. Bagley et al. fused the ventral telencephalon and whole brain organoids to form brain organoids fused to the dorsoventral axis [ 40 ]. Based on this, Xiang et al. studied the migration of CXC chemokine receptor 4 (CXCR4) dependent interneurons from ventral to dorsal migration [ 41 ]. Medial ganglionic neurite (MGE) organoids were constructed, and then fused with cortical organoids to examine CXC chemokine receptor (CXCR4) dependent interneurons.

To better imitate the mutual projection of the thalamus and cortex, XIANG et al. produced thalamocortical fusion organoids by physically combining thalamic and cortical organoids [ 35 ]. Studies of neurological conditions including schizophrenia and autism spectrum disorders may be conducted using the biaxial projection between the thalamus and cortex, which replicates synaptic connections in the body. Subsequently, MIURA et al. employing fusion of striatal and cortical organoids, showed that cortical neurons project axons to striatal organoids and make synaptic connections with neutral invertebrate neurons, showing enhanced electrical features and calcium activity [ 42 ]. By combining the two kinds of organoids, functional integration was accomplished in these four investigations. In an in vitro three-dimensional culture media, they replicated the tangential movement of human endoneurons [ 39 , 40 , 41 , 42 ].

Brain organoid fusion methods provide a potent platform for investigating the relationships between various brain regions/tissues, including the impact of tissue growth centers on brain organoid development and the investigation of cell-cell interactions in vitro. Nevertheless, in order to create functioning circuits and provide helpful instruments for researching brain function, current fusion organoid manufacturing techniques need to be further refined to represent particular brain space projections and physiological reactions.

Vascularization of brain organoids

Organoids in the brain still differ significantly from the genuine human brain. Lack of a circulatory system is one of the main obstacles. Gas penetration, nutrition delivery, neuron differentiation, and other processes are all impacted by vascular function [ 43 , 44 ]. Necrotic regions will form in the organoid center as a result of inadequate oxygen and nutrient penetration, which will interfere with the proper growth of brain organoids and the neuronal migration route [ 45 ]. Thus, the development of a vascular network is a critical requirement for the optimization of brain organoids. As of right now, there are primarily two methods for vascularizing brain organoids: creating blood vessels in the organoids by in vivo transplantation and creating blood vessels in vitro.

In 2018, researchers transplanted brain organoids into the cerebral cortex of NOD-SCID immunodeficient mice, and the blood vessels of mice infiltrated into the implanted brain organoids within 14 days after transplantation; Compared with non-vascularized brain organoids in vitro, the in vivo development environment improves cell maturation and survival in brain organoids [ 46 ]. For the purpose of achieving the functional link between human axons and neurons in the mouse brain, brain organoids that have been transplanted may produce a lot of new neurons and live for over 200 days [ 46 ]. To create human-mouse vascular tissue linkages in the grafts, human vascularized organoids (vOrganoids) co-cultured with human umbilical vein endothelial cells were inserted into the mouse S1 cortex. Compared to non-vascularized brain organoids, vascularized brain organoid transplantation increases blood vessel development and cell survival [ 47 ]. As a result, a series of transplantation experiments have demonstrated the importance of vascularization in the maturation of brain organoids.

In terms of in vitro vascularization, in 2019, Cakir et al. co-differentiated human embryonic stem cells expressing ETV2 (ETS variant 2) with wild-type embryonic stem cells to achieve directional induction of vascular endothelial cell differentiation in cortical organoids, based on which vascularization cortical organoids were constructed [ 48 ]. vascularized human cortical organoids (vhCOs) form perfusable blood vessels; Compared with control cortical organoids, the cell survival rate in vascularized cortical organoids was significantly improved [ 48 ]. In addition, by co-culturing with venous endothelial cells, researchers were able to establish vascularized brain organoids in vitro, in which venous endothelial cells can form well-developed reticular or tubular vascular systems, as confirmed by single-cell RNA sequencing. This vascularized brain organoid system has similar molecular properties and cell types to the human fetal telencephalon [ 47 ]. The integration of brain organoids and vascular system under in vitro culture will help to improve the phenomenon of central necrosis during the long-term cultivation of brain organoids.

Application of brain organoid research technology in the field of diabetes

As a novel in vitro cultivation technology, brain organoids may not only mimic early brain development in vitro, but also help to understanding brain development and developmental paths. In addition, brain organoids provide novel tools for neurological illness modeling, in vitro drug screening, gene therapy, and the simulation of human brain development (Fig.  4 ).

Applications of brain organoid research. As part of a regenerative medicine therapy, pluripotent stem cells (PSCs) from brain organoids may be implanted to brain injury areas to heal damaged tissue or utilized to investigate brain illnesses. To simulate vascular and infectious disorders and investigate their interactions with organoid cells, non-CNS derived entities including microglia, blood arteries, and viruses may be incorporated into brain organoids. The genesis of disorders affecting the nervous system may be studied using brain organoids obtained from patients or genetically engineered using CRISPR-Cas9 to carry disease-associated genetic abnormalities

The uses of brain organoids for illness [ 49 ], drug development [ 50 ], evolution [ 51 ], and brain development [ 40 ] may be further expanded by combining gene editing methods with brain organoids to generate various sorts of mutations. Brain organoids may also be used in conjunction with single-cell sequencing technology, which is crucial for understanding how the brain develops and figuring out how diseases are caused. Recent years have seen significant developments in the field of diabetes, offering fresh perspectives on the management and prevention of diabetes-related disorders, thanks to the quick growth of brain organoid research technologies.

An organoid brain model associated with KCNJ11 p.V59M was used to examine the pathogenic mechanism of neonatal diabetes

The brain organoid approach is a good model for studying neuronal differentiation and the consequences of genetic variety on brain development and disease gene expression [ 52 , 53 , 54 ]. Neonatal diabetes (NDM) has been studied through the direct effects of P. Vir59met (V59M) in the KCNJ11 gene on precursor neurons and neuronal cells. Gokhan et al. generated brain organoids from martyrs or hiPSCs with the KCNJ11 V59M mutant allele to isolate confounding effects associated with NDM [ 55 ].

The pancreas and the brain express the KCNJ11 gene, which genes for Kir6.2, a crucial subunit of the ATP-sensitive potassium channel (KATP). NDM may result from the acquisition of a functional mutation in heterozygosity in the KCNJ11 gene. A dominant heterozygous mutation in the KCNJ111 gene causes NDM, a monogenic illness that affects around 30% of the population. These mutations often cause the KATP channel to be permanently activated, which keeps the cell persistently hyperpolarized. Due to the altered functionality of these KATP channels, the beta cells within the islets are incapable of secreting insulin, Consequently, this results in elevated blood sugar levels [ 56 ]. Neural stem cells in V59M organisms often do not develop and move, according to data. As a result, there are abnormalities in the development and function of brain circuits. This lowers neurogenesis. Tolbutamide (Tol), a KATP channel blocker, may be used as a medication to treat mutant organoids, which can partly correct the molecular flaws brought on by the cell membrane’s hyperpolarization. In brain tissue taken from HIPSC patients, this work offers the first concrete proof that the mutant KCNJ11 channel results in neurological impairment.

New drug therapies for people with inherited diseases can typically be found thanks to advancements in personalised medicine platforms that use stem cell-derived tissue [ 57 , 58 ]. Additionally, pathology linked to mutations in the KCNJ11 gene for neonatal diabetes can be detected thanks to hiPSC-derived brain organoid platforms. Using a brain organoid platform, confounding effects associated with neonatal diabetes were differentiated from the direct impact of V59M mutations on neurocytes and neurons. It may be inferred from this that the development of brain organoids offers valuable insights into the fundamental principles of cellular and neurophysiological events linked to intricate metabolic disorders.

Treatment of diabetic retinopathy with cerebral organoids containing optic vesicles

In recent years, various 3D brain organoids including hiPSC-derived neuroretinal discs have presented new chances to research retinal illnesses [ 39 , 59 , 60 ].

By altering the growth conditions to transform iPSCs into neural tissue, Elke et al. were able to successfully create bilaterally symmetric visual slices in brain organoids in 2021 [ 61 ]. The researchers began the induction culture with a decreased cell density. After that, during the neuroectodermal growth phase, retinol acetate (RA) was given to the media at various concentrations. Staining structures, most likely the original “eyes,” emerged in the tissue cultivated with retinol acetate after about 30 days in culture (Fig.  5 ). Immunofluorescence labeling indicated considerable expression of the eye-associated marker genes RAX, Pax6 and FOXG1 in the stained areas of these organoids.

Schematic showing steps of OVB-organoid generation from iPSCs. The results of the research demonstrated the use of IPSC-derived human brain organoids in the generation of bilateral forebrain-connected OV, cellular diversity, and complexity reduction. The brain organoids start to gather OV at day 30, and during the next 60 days, they manifest as distinct structures

Subsequent examination revealed that FOXG1 expression was gradient in the SOX2-positive invagination zone, suggesting that the visual and forebrain regions in these organoids were distinct, in line with the eye’s maturation process throughout human embryonic development. After a growth period of 50–60 days, the rudimentary “eyes” transformed into one or two mature, visible optic nerve cell structures (Fig.  5 ), and such organoids are designated optic vesicle cell-brain organoids (OVB organoids). This work is the first to functionally connect brain organoids with retinal structure. It helps to explore the interplay between “brain and eye” during embryonic development and offers a strong tool for the pathophysiology and treatment of diabetic retinal disorders.

Based on this, scientists have cultivated exosomes from tissues resembling OVB, which are involved in both retinal development and retinal disorders including diabetic retinopathy [ 62 ].

The primary symptom of diabetic microangiopathy, which causes distinct alterations in the retinal lesions, is diabetic retinopathy. Furthermore, retinal ischemia-reperfusion damage (IRI) is a significant factor in the development of DR [ 62 ]. Directly after retinal ischemia are transient hypoxia and nutritional depletion. Reperfusion produces excessive reactive oxygen species (ROS), which leads to oxidative stress and an exaggerated inflammatory response [ 63 ]. The significance of mesenchymal stem cells (MSCs) in vitro exosome secretion in the eye has gained more interest since studies have shown that intravitreal injection of LV-derived exosomes may alleviate diabetic retinopathy (DR) [ 64 ]. Moissiev et al. established the impact of exosomes on retinal ischemia by intravitreally injecting hypoxia-grown exosomes containing angiogenic active components and control saline into mice models of oxygen-induced retinopathy (OIR) [ 65 ]. Exosome therapy decreased and avoided retinal thinning in comparison to the control group.

Moreover, Liu et al. discovered that exosomes may transport endogenous miR-579 and circulating RNA cPWWP2A, and that these effects on retinal vascular function in diabetes patients are mediated by differences in the expression levels of these two molecules [ 66 ]. 148 Adipose tissue mesenchymal stem cell exosomes carrying miR-192 or miR-222 have been shown by Safwat et al. to be able to suppress angiogenesis and inflammatory responses in the DR [ 67 ].

The potential to cure diabetic retinopathy has increased with the discovery of OVB organoids. OVB organoids are a significant tool for disease modeling, high-throughput drug screening as an alternative to animal models. An affordable substitute for animal models in illness research and medication screening are in vitro models. Exosomes produced from brain organoids seem to offer tremendous promise for medication delivery and the treatment of ocular illnesses, according to recent research on the therapeutic effects of exosomes in ocular diseases. Furthermore, a great deal of obstacles still need to be addressed, such as the absence of microglia and blood arteries, which are essential for preserving the long-term survival of organoids and accurately replicating the retina. In addition, novel biotechnologies including 3D bioprinting, oxygen delivery systems, and retina-on-a-chip are being created to meet these difficulties.

Building novel and emerging organoid culture systems to simulate organoid co-culture microenviroment in diabetes disease modeling

The properties and uses of brain organoid systems can currently be further increased when paired with additional engineering methods. Organ-on-a-chip technology offers a critical platform for the facile manipulation of the microenvironment and nutrient supply, and it constitutes a pivotal method for the concurrent culture of diverse cell and tissue types within organoid systems [ 68 ]. Furthermore, organ chips mitigate the challenge of co-culturing distinct organs of the same type in a singular medium to a considerable extent. The ability to replicate the interplay of several organs in an in vitro setting is extremely important, particularly for complicated metabolic illnesses that impact multiple tissues, like diabetes mellitus. A microfluidic in vitro model is employed for simulating neural tube development. For instance, an organ-on-a-chip configuration may utilize soluble factor-infused microchannels as entry and convergence zones, facilitating the establishment of a consistent morphogenetic gradient via diffusive processes within a central culture chamber. This system was able to replicate the Sonic Hedgehog (SHH) signaling gradients and the bone morphogenetic protein (BMP) gradients along the dorsoventral axis of the neural tube, thereby facilitating the induction of neural tube development models [ 69 ].

In 3D cultured organoids, material diffusion and transport are not enough to meet the growing metabolic demand, so it is difficult to ensure long-term growth and maturity. The establishment of functional vascular system is a necessary condition for the continuous healthy cultivation of brain organoids. Capabilities of perfusable blood vessels can be emulated through the utilization of organ-on-a-chip technology [ 70 , 71 , 72 ]. In addition, microchips or micro-bioreactors have also been constructed for brain organoids, such as in microfluidic systems that reduce necrotic areas in midbrain organoids by ensuring the supply of media through continuous laminar flow [ 73 ], which utilizes forced convection and media mixing to enhance nutrient supply. Therefore, organ chips is also an important solution to solve the difficulties faced in the process of organoid culture [ 68 ].

Summary and prospect

Recent developments in the field of organoid technology have yielded substantial improvements in our comprehension of the principles behind human brain development and the etiology of neurological disorders. By combining various technologies, scientists have made significant progress in understanding the evolutionary laws of brain development and the mechanisms that regulate brain development. These models, which include non-targeted, regional, and combined brain organoids, hold promise for modeling nervous system diseases, drug testing, gene editing, and other areas.

Nevertheless, the present technology for producing brain organoids has not been created yet and has significant limitations because to the constraints of culture and induction approaches. The size of organoids, the maturation of neurons, and the subsequent generation of more complete cell types are all limited by the conditions of in vitro culture. Additionally, because brain organoids lack complete neuronal circuits and functional zoning, it is difficult to predict internal structures such as oligodendrocytes and astrocytes. Finally, the application of brain organoids is limited because they lack essential cell types like immune cells, which cannot form complex neuronal circuits. Thirdly, brain organoids’ metabolic properties vary greatly from those of the real brain. Fourthly, the growing conditions of brain organoids and the chemical combinations introduced vary due to the heterogeneity of iPSC cells in various labs, resulting in wildly disparate brain organoid models. Fifth, the cell composition, morphological features, and differentiation efficiency of the various batches of brain organoids vary as well [ 8 ].

The use of brain organoids in diabetes therapy is still in its infancy. In 2021, Gokhan et al. separated the direct effects of the V59M mutation on neuroprecursors and neurons from the disruptive effects linked to neonatal diabetes using the brain organoid platform [ 55 ]. Electrophysiological investigations have revealed that mutant brain organoids may create functioning neural networks whose excitability is reduced under baseline circumstances, even if mutant KCNJ11 channel activity hinders the formation of neuronal precursor cells. Furthermore, raising extracellular potassium levels erased the difference between the amount of mutant brain organoids and spontaneous active control. According to the findings, in human samples, mutant KCNJ11 channel activity does not control network excitability or circuit development. The method by which brain organoids may more effectively regulate the cellular and neurophysiological processes that accompany complicated metabolic disorders is supported by the findings of this research. This is a novel approach to the treatment of diabetic neuropathy using brain organoids in research.

Furthermore, new therapeutic approaches for diabetic retinopathy have been made possible by the discovery of OVB organoids as well as complicated brain and retinal multiorganoids. CPWWWP2A cyclin RNA was found in exosomes produced from OVB organoids by Liu et al. [ 66 ]. This finding may indirectly affect retinal vascular function in diabetes patients. Exosomes rich in miR-192 or miR-222 produced from mesenchymal stem cells were discovered by Safwat et al. [ 67 ]. These miRNAs may suppress angiogenesis and the inflammatory response in DR.

In addition, it is challenging to guarantee long-term viability and maturity in 3D cultivated organoids because material diffusion and transport are insufficient to fulfill the increasing metabolic requirement. For the proper culture of brain organoids to continue, a functioning circulatory system must be established. Organoid chips can be used to replicate blood arteries with the capacity to perfuse [ 70 , 71 , 72 ]. Microfluidic systems that minimize necrotic areas in midbrain organoids by guaranteeing the supply of media through continuous laminar flow [ 73 ] a technique that makes use of forced convection and media mixing to enhance nutrient supply have also been developed for brain organoids, as have microchips or micro-bioreactors. Organ chips are therefore a significant way to address the challenges associated with organoid cultivation [ 68 ]. Conversely, the microbiome exerts influences on neurodevelopment and the functionality of the central nervous system, which employing co-culture techniques with microbial entities or their by-products within tissue cultures may elucidate these intricate interactions, and the amalgamation of microbiota and immune elements within an organ-on-a-chip platform could also enhance its fidelity as a model [ 74 ]. Moreover, the deployment of neural tissue proliferation molecules aligns with the utilization of dorsal forebrain organoids (FGF2) and epidermal growth factor (EGF), as well as ventral forebrain organoids [ 75 ]. Upon identification, growth factors were incorporated into the media for both dorsal and ventral forebrain organoids to facilitate neural differentiation and maturation. For purpose of generating oligodendrocytes containing forebrain organoids, additional insulin-like growth factor (IGF), platelet-derived growth factor AA (PDGF-AA), hepatocyte growth factor (HGF) was used during differentiation and maturation. These promising culture conditions and using growth factors will improve the long-term viability and maturation of organoids [ 76 , 77 ].

The convergence of bioengineering with the organoid domain has facilitated the advancement of automation and miniaturization in detection processes, alongside the capacity for real-time acquisition of biological data during culturing. Bioengineered hydrogels have been developed and evaluated for their efficacy in supporting the three-dimensional cultivation of organoids, albeit they typically exhibit less promotional impact on growth compared to hydrogels derived from the extracellular matrix. A hydrogel constitutes a three-dimensional matrix of insoluble water-containing polymers. The dimensions of the micropores are capable of accurately replicating the size of organoids [ 78 ]. Furthermore, the system can be engineered to capture cells or their contents post-experimentation. This facilitates the examination of interactions between various organoids, with the intent to mirror physiological interactions observed in organs.

As to scale up organoid production, automation and miniaturization techniques are born at the right time, expect for the microfluidic systems, recently, an emerging frontier is combining organoids with artificial intelligence (AI) systems, known as “organoid intelligence” [ 79 , 80 ]. The goal is to use stem cell-derived organoids not only as models, but also as active components integrated into biological hybrid systems that demonstrate cognition and learning. Osteogenesis imperfecta provides an unprecedented opportunity to elucidate neurophysiological mechanisms and advance the pharmacology and toxicology related to the brain. By summarizing the composition of human brain cells [ 81 ] and adding structure, OI systems allow direct experimental access to investigate the processes underlying neural signals and network activity. OI also has the potential to inspire new directions in neuromorphic engineering and proceed unprecedented biological computing capabilities [ 74 ]. Since the study elucidates how brain organoids exhibit learning and information processing based on dynamic neuronal signals, these findings could guide the development of hardware and scale up organoid production.

The creation and use of brain organoids will remain a crucial topic for the life sciences in the future due to the quick development of new technologies and the increased interest in brain organoids in recent years. These developments have created new opportunities for a better understanding of human brain development, function, evolution, and disease. Although brain organoids can simulate the cellular, molecular and functional characteristics of brain development, due to the challenges associated with maintaining long-term healthy culture, brain organoids created in vitro are mostly limited in their ability to describe embryonic brain properties during their brief culture period. The significant variability in organoid generation and differentiation is a concern. Despite there are a large number of standardizing protocols to improve reproducibility across different laboratories about brain organoids, such as promising culture systems, growth factors and small molecule [ 75 ], its research in the field of diabetes remains to be further explored. More research is still needed to build brain organoids with more sophisticated and developed neural networks. Among them, it is anticipated that the creation of functionally vascularized brain organoids will enable the long-term growth of brain organoids. In summary, brain organoids represent a novel technology that has garnered significant attention and quick growth in recent years, presenting both potential and obstacles for research. It is anticipated that as this technology advances, it will offer a crucial resource for comprehending the human brain and examining a wide range of biological and medical issues.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Not applicable.

This study was supported by the Natural Science Foundation of Hubei Province (Grant Number 2024AFB1025 to Dan Zhu), and Hubei University of Science and Technology “Medical Research Special Fund” (Grant Number 2022YKY18 to Dan Zhu), and Foundation of Hubei University of Science and Technology “Doctoral Initiation Fund” (Grant Number BK202414 to Dan Zhu), and Foundation of Hubei University of Science and Technology “Horizontal Research Projects” (Grant Number 2023HX121 to Dan Zhu).

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Ying Su, Aimei Liu, Hongguang Chen and Qingjie Chen contributed equally to this work.

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Hubei Key Laboratory of Diabetes and Angiopathy, Xianning Medical College, Hubei University of Science and Technology, No.88, Xianning Avenue, Xianan District, Xianning, 437000, Hubei Province, P. R. China

Ying Su, Aimei Liu, Hongguang Chen, Qingjie Chen, Bo Zhao, Runze Gao, Zhenwang Zhang, Changhan Ouyang & Dan Zhu

School of Phamacy, Hubei University of Science and Technology, Xianning, 437000, Hubei Province, P. R. China

Ying Su, Bo Zhao, Runze Gao, Kangwei Zhang & Changhan Ouyang

Hubei University of Science and Technology, Xianning, 437100, P. R. China

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Ying Su, Aimei Liu and Hongguang Chen wrote, edited and revised the manuscript, and confirmed the authenticity of the raw data. Qingjie Chen and Bo Zhao drawed the schematic illustrations. Runze Gao and Tie Peng participated in the collation of article tables. Zhenwang Zhang, Changhan Ouyang and Dan Zhu provided direction and guidance throughout the preparation of this manuscript. All authors read and approved final version of manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Zhenwang Zhang , Changhan Ouyang or Dan Zhu .

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Su, Y., Liu, A., Chen, H. et al. Research progress of brain organoids in the field of diabetes. Mol Brain 17 , 53 (2024). https://doi.org/10.1186/s13041-024-01123-4

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Study reveals ways in which 40Hz sensory stimulation may preserve brain’s ‘white matter’

MIT scientists report that gamma frequency light and sound stimulation preserves myelination in mouse models and reveal molecular mechanisms that may underlie the benefit.

Early-stage trials in Alzheimer’s disease patients and studies in mouse models of the disease have suggested positive impacts on pathology and symptoms from exposure to light and sound presented at the “gamma” band frequency of 40 Hz. A new study zeroes in on how 40Hz sensory stimulation helps to sustain an essential process in which the signal-sending branches of neurons, called axons, are wrapped in a fatty insulation called myelin. Often called the brain’s “white matter,” myelin protects axons and insures better electrical signal transmission in brain circuits.

“Previous publications from our lab have mainly focused on neuronal protection,” said Li-Huei Tsai , Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT and senior author of the new study in Nature Communications . Tsai also lead’s MIT’s Aging Brain Initiative. “But this study shows that it’s not just the gray matter, but also the white matter that’s protected by this method.”

This year Cognito Therapeutics, the spin-off company that licensed MIT’s sensory stimulation technology, published phase II human trial results in the Journal of Alzheimer’s Disease indicating that 40Hz light and sound stimulation significantly slowed the loss of myelin in volunteers with Alzheimer’s. Also this year Tsai’s lab published a study showing that gamma sensory stimulation helped mice withstand neurological effects of chemotherapy medicines, including by preserving myelin. In the new study, members of Tsai’s lab led by former postdoc Daniela Rodrigues Amorim used a common mouse model of myelin loss—a diet with the chemical cuprizone— to explore how sensory stimulation preserves myelination.

Amorim and Tsai’s team found that 40Hz light and sound not only preserved myelination in the brains of cuprizone-exposed mice, it also appeared to protect oligodendrocytes (the cells that myelinate neural axons), sustain the electrical performance of neurons, and preserve a key marker of axon structural integrity. When the team looked into the molecular underpinnings of these benefits, they found clear signs of specific mechanisms including preservation of neural circuit connections called synapses; a reduction in a cause of oligodendrocyte death called “ferroptosis;” reduced inflammation; and an increase in the ability of microglia brain cells to clean up myelin damage so that new myelin could be restored.

“Gamma stimulation promotes a healthy environment,” said Amorim who is now a Marie Curie Fellow at the University of Galway in Ireland. “There are several ways we are seeing different effects.”

The findings suggest that gamma sensory stimulation may help not only Alzheimer’s disease patients but also people battling other diseases involving myelin loss, such as multiple sclerosis, the authors wrote in the study.

Maintaining myelin

To conduct the study, Tsai and Amorim’s team fed some male mice a diet with cuprizone and gave other male mice a normal diet for six weeks. Halfway into that period, when cuprizone is known to begin causing its most acute effects on myelination, they exposed some mice from each group to gamma sensory stimulation for the remaining three weeks. In this way they had four groups: completely unaffected mice, mice that received no cuprizone but did get gamma stimulation, mice that received cuprizone and constant (but not 40Hz) light and sound as a control, and mice that received cuprizone and also gamma stimulation.

After the six weeks elapsed, the scientists measured signs of myelination throughout the brains of the mice in each group. Mice that weren’t fed cuprizone maintained healthy levels, as expected. Mice that were fed cuprizone and didn’t receive 40Hz gamma sensory stimulation showed drastic levels of myelin loss. Cuprizone-fed mice that received 40Hz stimulation retained significantly more myelin, rivaling the health of mice never fed cuprizone by some, but not all, measures.

The researchers also looked at numbers of oligodendrocytes to see if they survived better with sensory stimulation. Several measures revealed that in mice fed cuprizone, oligodendrocytes in the corpus callosum region of the brain (a key point for the transit of neural signals because it connects the brain’s hemispheres) were markedly reduced. But in mice fed cuprizone and also treated with gamma stimulation, the number of cells were much closer to healthy levels.

Electrophysiological tests among neural axons in the corpus callosum showed that gamma sensory stimulation was associated with improved electrical performance in cuprizone-fed mice who received gamma stimulation compared to cuprizone-fed mice left untreated by 40Hz stimulation. And when researchers looked in the anterior cingulate cortex region of the brain, they saw that MAP2, a protein that signals the structural integrity of axons, was much better preserved in mice that received cuprizone and gamma stimulation compared to cuprizone-fed mice who did not.

Molecular mechanisms

A key goal of the study was to identify possible ways in which 40Hz sensory stimulation may protect myelin.

To find out, the researchers conducted a sweeping assessment of protein expression in each mouse group and identified which proteins were differentially expressed based on cuprizone diet and exposure to gamma frequency stimulation. The analysis revealed distinct sets of effects between the cuprizone mice exposed to control stimulation and cuprizone-plus-gamma mice.

A highlight of one set of effects was the increase in MAP2 in gamma-treated cuprizone-fed mice. A highlight of another set was that cuprizone mice who received control stimulation showed a substantial deficit in expression of proteins associated with synapses. The gamma-treated cuprizone-fed mice did not show any significant loss, mirroring results in a 2019 Alzheimer’s 40Hz study that showed synaptic preservation. This result is important, the researchers wrote, because neural circuit activity, which depends on maintaining synapses, is associated with preserving myelin. They confirmed the protein expression results by looking directly at brain tissues.

Another set of protein expression results hinted at another important mechanism: ferroptosis. This phenomenon, in which errant metabolism of iron leads to a lethal buildup of reactive oxygen species in cells, is a known problem for oligodendrocytes in the cuprizone mouse model. Among the signs was an increase in cuprizone-fed, control stimulation mice in expression of the protein HMGB1, which is a marker of ferroptosis-associated damage that triggers an inflammatory response. Gamma stimulation, however, reduced levels of HMGB1.

Looking more deeply at the cellular and molecular response to cuprizone demyelination and the effects of gamma stimulation, the team assessed gene expression using single-cell RNA sequencing technology. They found that astrocytes and microglia became very inflammatory in cuprizone-control mice but gamma stimulation calmed that response. Fewer cells became inflammatory and direct observations of tissue showed that microglia became more proficient at clearing away myelin debris, a key step in effecting repairs.

The team also learned more about how oligodendrocytes in cuprizone-fed mice exposed to 40Hz sensory stimulation managed to survive better. Expression of protective proteins such as HSP70 increased and as did expression of GPX4, a master regulator of processes that constrain ferroptosis.

In addition to Amorim and Tsai, the paper’s other authors are Lorenzo Bozzelli, TaeHyun Kim, Liwang Liu, Oliver Gibson, Cheng-Yi Yang, Mitch Murdock, Fabiola Galiana-Meléndez, Brooke Schatz, Alexis Davison, Md Rezaul Islam, Dong Shin Park, Ravikiran M. Raju, Fatema Abdurrob, Alissa J. Nelson, Jian Min Ren, Vicky  Yang and Matthew P. Stokes.

Fundacion Bancaria la Caixa, The JPB Foundation, The Picower Institute for Learning and Memory, the Carol and Gene Ludwig Family Foundation, Lester A. Gimpelson, Eduardo Eurnekian, The Dolby Family, Kathy and Miguel Octavio, the Marc Haas Foundation, Ben Lenail and Laurie Yoler, and the National Institutes of Health provided funding for the study.

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• 08 August 2024

How the stressed-out brain can weaken the immune system

• Sara Reardon

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The vagus nerve connects the brain region called the amygdala (red; artist’s illustration) to nerves for the Brunner’s glands in the gut. Credit: Sebastian Kaulitzki/Science Photo Library

Stress can make people feel sick , and bacteria in the gut might be to blame, according to a study 1 in mice. The research suggests that a stressed brain directly shuts down specific glands in the gut, affecting gut bacteria and the body’s broader immune system.

The study “is a technical tour de force”, says neuroscientist John Cryan at University College Cork in Ireland, who reviewed the study. Most work on the gut–brain connection has focused on how bacteria affect the brain , so Cryan welcomes research into how psychological states can exert ‘top-down’ control of bacteria. “It’s a really cool part of the puzzle”, he says. The research was published on 8 August in Cell .

Gut–brain chitchat

Researchers have long known that the gut and brain ‘talk’ to each other. Under stress, the brain spurs the release of hormones that can trigger gut conditions such as inflammatory bowel disease . And certain bacteria in the gut can release chemical signals that affect the brain and behaviour.

Your brain could be controlling how sick you get — and how you recover

But the neural communication pathways are less well understood. To find out more, neuroscientist Ivan de Araujo at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, and his colleagues focused on small organs called Brunner’s glands that are found in the walls of the small intestine. Little is known about these glands, other than that they produce mucus and contain numerous neurons.

De Araujo’s team found that removing the Brunner’s glands of mice made the animals more susceptible to infection. It also raised markers of inflammation, a flood of immune chemicals and cells that can damage tissues . The team saw a similar effect in humans: people who’d had tumours removed from the part of the gut containing Brunner’s glands had higher levels of white blood cells — a marker of inflammation — than people who’d had tumours removed from other areas.

Housekeeping bacteria

Closer analysis showed that removing the Brunner’s glands from mice eliminates bacteria in the Lactobacillus genus , which live in the small intestine. In a healthy gastrointestinal tract, Lactobacilli stimulate production of proteins that act as grout between the cells lining the gut, keeping most of the gut’s contents inside while allowing certain nutrients to enter the bloodstream. But when Lactobacilli are gone, the gut becomes ‘leaky’ and “things that shouldn’t cross into the blood do so”, de Araujo says. The immune system attacks these foreign molecules, causing the inflammation and illness seen in mice without Brunner’s glands.

The researchers then examined the glands’ neurons. They found that the neurons connect to fibres in the vagus nerve, a communications pathway between the gut and the brain. These fibres run directly to the brain’s amygdala, which is involved in emotion and stress response.

Placing mice with intact Brunner’s glands under chronic stress had the same effect as removing the glands: Lactobacillus levels fell, and inflammation increased. This suggested that stress had shut down the Brunner’s glands.

Lines of communication

Asya Rolls, a neuroimmunologist at the Technion — Israel Institute of Technology in Haifa, is impressed by the direct line between the brain, Brunner’s glands, bacteria and immune system. “The specificity of the connection is amazing,” she says. But she cautions that the pathways in mice might not be identical to those in humans.

“This paper is pretty inspiring,” says Christoph Thaiss, a microbiologist and neuroscientist at the University of Pennsylvania in Philadelphia. Understanding the specific pathways that connect the brain and gut, he says, could help researchers to study questions such as why some people are more resilient to stress than others.

De Araujo says the study could have implications for treating stress-related disorders such as inflammatory bowel disease. His group is now studying whether chronic stress affects this pathway in infants, who receive their Lactobacillus through breast milk. “We are excited about the idea that these glands are important for normal development and immune function early in life,” de Araujo says.

doi: https://doi.org/10.1038/d41586-024-02557-5

Chang, H. et al. Cell https://doi.org/10.1016/j.cell.2024.07.019 (2024).

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West China School of Medicine/West China Hospital

Welcome Global Talents to West China Hospital/West China School of Medicine of Sichuan University

Top Talents; Leading Talents; Excellent Overseas Young Talents on National level; Overseas Young Talents

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