Neural Interactions Research Articles

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.

Neural functions in cancer: Data analyses and database construction.

Recent studies have revealed that neural functions are involved in possibly every aspect of a cancer development, serving as bridges connecting microenvironmental stressors, activities of intracellular subsystems, and cell survival. Elucidation of the functional roles played by the neural system could provide the missing links in developing a systems-level understanding of cancer biology. However, the existing information is highly fragmented and scattered across the literature and internet databases, making it difficult for cancer researchers to use. We have conducted computational analyses of transcriptomic data of cancer tissues in TCGA and tissues of healthy organs in GTEx, aiming to demonstrate how the functional roles by the neural genes could be derived and what non-neural functions they are associated with, across different stages of 26 cancer types. Several novel discoveries are made, including i) the expressions of certain neural genes can predict the prognosis of a cancer patient; ii) cancer metastasis tends to involve specific neural functions; iii) cancers of low survival rates involve more neural interactions than those with high survival rates; iv) more malignant cancers involve more complex neural functions; and v) neural functions are probably induced to alleviate stresses and help the associated cancer cells to survive. A database, called NGC, is developed for organizing such derived neural functions and associations, along with gene expressions and functional annotations collected from public databases, aiming to provide an integrated and publicly available information resource to enable cancer researchers to take full advantage of the relevant information in their research, facilitated by tools provided by NGC.

Pre-stimulus network responses affect information coding in neural variability quenching

Neural responding variability to the same stimulus typically decreases after a stimulus presented. During neural variability quenching, the pre-stimulus neural activities interact with the post-stimulus neural responses. However, whether these interactions have influences on information coding remains unclear. In this paper, we construct a two-layer k-winner-take-all (k-WTA) spiking network which simulates primary visual cortical neural responses through probabilistic inference. Generating the phenomenon of neural variability quenching, the network could reflect interactions between pre- and post-stimulus neural responses consistent with experimental observations. During neural variability quenching, pre-stimulus neural responding variability and complexity are considered as factors for the post-stimulus neural responses. Simulations to given stimuli are classified with each varying factor, respectively. Neural responding dimensionality measures the capacity of information coding to given stimuli. Over classified simulations, both of two factors could modify interactions between pre- and post-stimulus neural responses, leading to different neural responding dimensionalities. During neural variability quenching, the temporal structure of stimuli performs as another factor which also could modify neural interactions and induce the varying neural responding dimension. Our model provides the possible interpretation to how the pre-stimulus neural responses participate in neural variability quenching and affect the information coding.

The dynamical analysis of non-uniform neocortical network model in up-down state oscillations

The brain is the most complex organ of the human body and consists of many excitatory and inhibitory neurons. The neural interactions are through synaptic connections. Such connections are the pathway of information; therefore, the neurons can exhibit various dynamical behaviors based on the active synapses. This paper employs a non-uniform neuronal network model of the brain neocortex to investigate the synchronization of up-down state oscillations. The dynamical analysis is performed using the bifurcation diagrams, Lyapunov exponents’ spectra, phase portraits, and time series as a function of different coupling parameters. The results revealed that the studied model exhibits chaotic behavior in a wide range of the variation of the coupling strengths. Furthermore, when excitatory–excitatory neuronal coupling strength is assumed as the control parameter, some instantaneous periodic windows were detected within the chaotic regions, which is a rare phenomenon of neural dynamics.

The effects of distractors on brightness perception based on a spiking network

Visual perception can be modified by the surrounding context. Particularly, experimental observations have demonstrated that visual perception and primary visual cortical responses could be modified by properties of surrounding distractors. However, the underlying mechanism remains unclear. To simulate primary visual cortical activities in this paper, we design a k-winner-take-all (k-WTA) spiking network whose responses are generated through probabilistic inference. In simulations, images with the same target and various surrounding distractors perform as stimuli. Distractors are designed with multiple varying properties, including the luminance, the sizes and the distances to the target. Simulations for each varying property are performed with other properties fixed. Each property could modify second-layer neural responses and interactions in the network. To the same target in the designed images, the modified network responses could simulate distinguishing brightness perception consistent with experimental observations. Our model provides a possible explanation of how the surrounding distractors modify primary visual cortical responses to induce various brightness perception of the given target.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for low grade glioma.

As a common primary intracranial tumor, the diagnosis and therapy of low-grade glioma (LGG) remains a pivotal barrier. Cuproptosis, a new way induces cell death, has attracted worldwide attention. However, the relationship between cuproptosis and LGG remains unknown. Our study is all about finding out if there are any genes related to coproptosis that can be used to predict the outcome of LGG. RNA data and clinical information were selected from Cancer Genome Atlas (TCGA) datasets and the Genotype-Tissue Expression (GTEx), 5 lncRNAs (GAS5.AS1, MYLK.AS1, AC142472.1, AC011346.1, AL359643.3) were identified by Cox univariate and multivariate regression, as well as LASSO Cox regression. In the training and test sets, a dual validation of the predictive signature comprised of these 5 lncRNAs was undertaken. The findings demonstrate that the risk model is able to predict the survival regression of LGG patients and has a good performance in either the KM curve approach or the ROC curve. GO, GSEA and KEGG were carried out to explore the possible molecular processes that affecting the prognosis of LGG. The characteristics of immune microenvironment were investigated by using CIBERSORT, ESTIMATE and ssGSEA. We identified five lncRNAs related with cuproptosis that were closely associated with the prognosis of LGG and used these five lncRNAs to develop a risk model. Using this risk model, LGG patients were then divided into high-risk and low-risk groups. The two patient groups had significantly distinct survival characteristics. Analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differential genes of the two patient groups were primarily concentrated in neural active ligand-receptor interaction and cytokine-cytokine receptor interaction. The ssGSEA score determined the information related to immune infiltration, and the two groups were differentially expressed in immune subpopulations such as T cells and B cells as well. Our study discovered 5 cuproptosis-related lncRNAs which contribute to predicting patients' survival of LGG and provide ideas for the exploration of new targets for LGG in the future.

Climacteric symptoms, age, and sense of coherence are associated with sexual function scores in women after menopause.

Postmenopausal sexual function presupposes the integration of hormonal, neural, and vascular interactions and is subject to optimal crosstalk among psychological, interpersonal, cultural, and environmental factors. Sense of coherence (SOC) reflects a person's ability to cope with stressors and may influence the occurrence of menopausal symptoms and sexual dysfunction. To investigate the association of severity of climacteric symptoms, cardiometabolic risk factors, and SOC with sexual function in postmenopausal women. Overall 281 sexually active postmenopausal women without significant psychopathology or cardiovascular disease attending the Menopause Unit of Aretaieion Hospital were evaluated by the Female Sexual Function Index (FSFI), Greene Climacteric Scale, Beck Depression Scale, and Sense of Coherence Scale. Hormonal and biochemical parameters and cardiometabolic risk factors were evaluated. FSFI scores <26.5 were considered pathologic. Total and subdomain scores of sexual response were determined. Pathologic FSFI scores were found in 79.7% of the sample. Linear models of multivariable regression analysis showed that FSFI scores were associated with (1) Beck scores (b = -0.200; 95% CI, -0.472 to -0.073, P = .001), vasomotor symptom severity (b = -0.324; 95% CI, -0.985 to 0.051; P < .001), and age and (2) SOC (b = 0.150, 95% CI, 0.036-0.331; P = .008), vasomotor symptom severity (b = -0.361; 95% CI, -0.743 to 0.245; P < .001), and age. Both models were adjusted for menopausal age, diabetes mellitus, hypertension, type of menopause, and menopausal hormone therapy intake. SOC was associated with Beck depression scores (β = -0.487, P < .001; Greene Climacteric Scale total scores, β= -0.199, P < .001). FSFI score <26.5 vs >26.5 was associated with SOC (odds ratio,0.982; 95% CI, 0.563 to 1.947; P = .006) and moderate to severe vasomotor symptom severity (odds ratio,2.476; 95% CI, 1.478 to 3.120; P = .009) independent of age, diabetes mellitus, hypertension, menopausal hormone therapy intake, type of menopause, or Beck depression classification. The results indicate the importance of psychometric assessment of postmenopausal women when presenting with scores of low sexual function. The severity of vasomotor symptoms should also be addressed in any case. This is the first study investigating the relationship between SOC and sexuality in menopause in a carefully selected homogenous population. Limitations included the cross-sectional design and the fact that sexual distress was not assessed. Pathologic FSFI scores were highly prevalent in this sample of postmenopausal women. FSFI is associated positively with age and severity of vasomotor symptoms and negatively with SOC.

Lexicality-Modulated Influence of Auditory Cortex on Subthalamic Nucleus During Motor Planning for Speech.

Speech requires successful information transfer within cortical-basal ganglia loop circuits to produce the desired acoustic output. For this reason, up to 90% of Parkinson's disease patients experience impairments of speech articulation. Deep brain stimulation (DBS) is highly effective in controlling the symptoms of Parkinson's disease, sometimes alongside speech improvement, but subthalamic nucleus (STN) DBS can also lead to decreases in semantic and phonological fluency. This paradox demands better understanding of the interactions between the cortical speech network and the STN, which can be investigated with intracranial EEG recordings collected during DBS implantation surgery. We analyzed the propagation of high-gamma activity between STN, superior temporal gyrus (STG), and ventral sensorimotor cortices during reading aloud via event-related causality, a method that estimates strengths and directionalities of neural activity propagation. We employed a newly developed bivariate smoothing model based on a two-dimensional moving average, which is optimal for reducing random noise while retaining a sharp step response, to ensure precise embedding of statistical significance in the time-frequency space. Sustained and reciprocal neural interactions between STN and ventral sensorimotor cortex were observed. Moreover, high-gamma activity propagated from the STG to the STN prior to speech onset. The strength of this influence was affected by the lexical status of the utterance, with increased activity propagation during word versus pseudoword reading. These unique data suggest a potential role for the STN in the feedforward control of speech.

NR3C1 overexpression regulates the expression and alternative splicing of inflammation-associated genes involved in PTSD

NR3C1-encoding glucocorticoid receptors have dual roles as RNA-binding protein and transcription factor. Recent studies revealed that NR3C1 might play an important role in the pathogenesis of PTSD (Post-traumatic stress disorder). However, its molecular mechanism remained unclear. In the present study, a neuronal cell model was constructed by transfecting a NR3C1-overexpressing plasmid pIRES-hrGFP-1a-NR3C1 or empty vector into HT22 cells. The changes in global transcription levels and alternative splicing events in HT22 cells after NR3C1 overexpression were analyzed by RNA sequencing. Compared with the empty vector control, the expression of inflammatory factors were differentially regulated by NR3C1, including genes involved in chemokine signal pathway, PI3K-Akt signal pathway, cytokine receptor interaction, neural ligand-receptor interaction and so on. In addition, NR3C1 regulated the alternative splicing of many genes involved in immune response, axon formation, stress response and inflammation. This study was the first to perform a transcriptome analysis of differential gene expression and alternative splicing in a NR3C1-overexpressing HT22 cell model. Our results suggested that NR3C1 could manipulate the expression of inflammatory transcription factors and their alternative splicing patterns, subsequently affecting the expression of downstream targets, may be leading to the onset of PTSD. This study will provide new insights in the NR3C1-mediated gene regulation in relation to PTSD.

Multiscale Fuzzy Entropy Analysis of Balance: Evidences of Scale-Dependent Dynamics on Diabetic Patients With and Without Neuropathy.

Postural control is usually assessed by examining the fluctuations of the center of pressure (COP). Balance maintenance is based on sensory feedback and neural interactions, deployed over multiple temporal scales and producing less complex outputs with aging and disease. This paper aims to investigate postural dynamics and complexity on diabetic patients, since diabetic neuropathy (DN) affects the somatosensory system and impairs postural steadiness. A multiscale fuzzy entropy (MSFEn) analysis, over a wide range of temporal scales, was performed on COP timeseries during unperturbed stance in a group of diabetic individuals without neuropathy and two groups of DN patients, with and without symptoms. A parameterization of the MSFEn curve is also proposed. A significant loss of complexity was recognized for the medial-lateral direction in DN groups with respect to non-neuropathic population. For the anterior-posterior direction, symptomatic DN group showed a lowered sway complexity for longer time scales with respect to non neuropathic and asymptomatic patients. The MSFEn approach and the related parameters highlighted that the loss of complexity might be attributed to different factors depending on sway direction, i.e. related to the presence of neuropathy along the medial-lateral axis and to a symptomatic state on the anterior-posterior direction. Results of this study support the use of the MSFEn for gaining insights into balance control mechanisms for diabetic patients, in particular when comparing non neuropathic with neuropathic asymptomatic patients, whose identification by posturographic analysis would be of great value.

Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects.

We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural networks to predict local energy contributions and multiple atom-in-molecule properties that are then used as geometry-dependent parameters for physically-motivated energy terms which account for long-range electrostatics and dispersion. Using high-accuracy ab initio data (small organic molecules/dimers), we trained a first version of the model. Exhibiting accurate gas-phase energy predictions, FENNIX is transferable to the condensed phase. It is able to produce stable Molecular Dynamics simulations, including nuclear quantum effects, for water predicting accurate liquid properties. The extrapolating power of the hybrid physically-driven machine learning FENNIX approach is exemplified by computing: (i) the solvated alanine dipeptide free energy landscape; (ii) the reactive dissociation of small molecules.

Visuomotor Mismatch Responses as a Hallmark of Explaining Away in Causal Inference.

How are visuomotor mismatch responses in primary visual cortex embedded into cortical processing? We here show that mismatch responses can be understood as the result of a cooperation of motor and visual areas to jointly explain optic flow. This cooperation requires that optic flow is not explained redundantly by both areas, meaning that optic flow inputs to V1 that are predictable from motor neurons should be canceled (i.e., explained away). As a result, neurons in V1 represent only external causes of optic flow, which could allow the animal to easily detect movements that are independent of its own locomotion. We implement the proposed model in a spiking neural network, where coding errors are computed in dendrites and synaptic weights are learned with voltage-dependent plasticity rules. We find that both positive and negative mismatch responses arise, providing an alternative to the prevailing idea that visuomotor mismatch responses are linked to dedicated neurons for error computation. These results also provide a new perspective on several other recent observations of cross-modal neural interactions in cortex.

Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders

BackgroundExploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). A cerebellar involvement in autism has long been suspected and recent methodological advances now enable studying cerebellar functioning in a naturalistic setting. Here, we investigated the electrophysiological activity of the cerebro-cerebellar network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3–8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD. MethodsWe combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cerebral-cortical areas, and compared our findings in a sample of participants with ASD (n = 107) and with typical development (TD) (n = 33). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data. ResultsIndividuals with ASD showed a significant increase in theta band power over the cerebellum and the frontal and temporal cortices during social interaction compared to resting state, along with significant coherence increases between the cerebellum and the sensorimotor, frontal and parietal cortices. However, a phase-based connectivity measure did not support a strict activity increase in the cortico-cerebellar functional network. We did not find any significant differences between the ASD and the TD group. ConclusionsThis exploratory study uncovered increases in the theta band activity of participants with ASD during social interaction, pointing at the presence of neural interactions between the cerebellum and cerebral networks associated with social cognition. It also emphasizes the need for complementary functional connectivity measures to capture network-level alterations. Future work will focus on optimizing artifact correction to include more participants with TD and increase the statistical power of group-level contrasts.

Template-based graph registration network for boosting the diagnosis of brain connectivity disorders.

Brain graphs are powerful representations to explore the biological roadmaps of the human brain in its healthy and disordered states. Recently, a few graph neural networks (GNNs) have been designed for brain connectivity synthesis and diagnosis. However, such non-Euclidean deep learning architectures might fail to capture the neural interactions between different brain regions as they are trained without guidance from any prior biological template-i.e., template-free learning. Here we assume that using a population-driven brain connectional template (CBT) that captures well the connectivity patterns fingerprinting a given brain state (e.g., healthy) can better guide the GNN training in its downstream learning task such as classification or regression. To this aim we design a plug-in graph registration network (GRN) that can be coupled with any conventional graph neural network (GNN) so as to boost its learning accuracy and generalizability to unseen samples. Our GRN is a graph generative adversarial network (gGAN), which registers brain graphs to a prior CBT. Next, the registered brain graphs are used to train typical GNN models. Our GRN can be integrated into any GNN working in an end-to-end fashion to boost its prediction accuracy. Our experiments showed that GRN remarkably boosted the prediction accuracy of four conventional GNN models across four neurological datasets.

Neurocomputational mechanisms of young children's observational learning of delayed gratification.

The ability to delay gratification is crucial for a successful and healthy life. An effective way for young children to learn this ability is to observe the action of adult models. However, the underlying neurocomputational mechanism remains unknown. Here, we tested the hypotheses that children employed either the simple imitation strategy or the goal-inference strategy when learning from adult models in a high-uncertainty context. Results of computational modeling indicated that children used the goal-inference strategy regardless of whether the adult model was their mother or a stranger. At the neural level, results showed that successful learning of delayed gratification was associated with enhanced interpersonal neural synchronization (INS) between children and the adult models in the dorsal lateral prefrontal cortex but was not associated with children's own single-brain activity. Moreover, the discounting of future reward's value obtained from computational modeling of the goal-inference strategy was positively correlated with the strength of INS. These findings from our exploratory study suggest that, even for 3-year-olds, the goal-inference strategy is used to learn delayed gratification from adult models, and the learning strategy is associated with neural interaction between the brains of children and adult models.

Learning cell identity in immunology, neuroscience, and cancer.

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Neural crest cell-placodal neuron interactions are mediated by Cadherin-7 and N-cadherin during early chick trigeminal ganglion assembly.

Background: Arising at distinct positions in the head, the cranial ganglia are crucial for integrating various sensory inputs. The largest of these ganglia is the trigeminal ganglion, which relays pain, touch and temperature information through its three primary nerve branches to the central nervous system. The trigeminal ganglion and its nerves are composed of derivatives of two critical embryonic cell types, neural crest cells and placode cells, that migrate from different anatomical locations, coalesce together, and differentiate to form trigeminal sensory neurons and supporting glia. While the dual cellular origin of the trigeminal ganglion has been known for over 60 years, molecules expressed by neural crest cells and placode cells that regulate initial ganglion assembly remain obscure. Prior studies revealed the importance of cell surface cadherin proteins during early trigeminal gangliogenesis, with Cadherin-7 and neural cadherin (N-cadherin) expressed in neural crest cells and placode cells, respectively. Although cadherins typically interact in a homophilic ( i.e., like) fashion, the presence of different cadherins expressed in neural crest cells and placode cells raises the question as to whether heterophilic cadherin interactions may also be occurring. Given this, the aim of the study was to understand whether Cadherin-7 and N-cadherin were interacting during initial trigeminal ganglion formation. Methods: To assess potential interactions between Cadherin-7 and N-cadherin, we used biochemistry and innovative imaging assays conducted in vitro and in vivo, including in the forming chick trigeminal ganglion. Results: Our data revealed a physical interaction between Cadherin-7 and N-cadherin. Conclusions: These studies identify a new molecular basis by which neural crest cells and placode cells can aggregate in vivo to build the trigeminal ganglion during embryogenesis.

Intermodulation from Unisensory to Multisensory Perception: A Review.

Previous intermodulation (IM) studies have employed two (or more) temporal modulations of a stimulus, with different local elements of the stimulus being modulated by different frequencies. Brain activities of IM obtained mainly from electroencephalograms (EEG) have been analyzed in the frequency domain. As a powerful tool, IM, which can provide a direct and objective physiological measure of neural interaction, has emerged as a promising method to decipher neural interactions in visual perception, and reveal the underlying different perceptual processing levels. In this review, we summarize the recent applications of IM in visual perception, detail the protocols and types of IM, and extend its utility and potential applications to the multisensory domain. We propose that using IM could prevail in partially revealing the potential hierarchical processing of multisensory information and contribute to a deeper understanding of the underlying brain dynamics.

The dynamics of cortical interactions in visual recognition of object category: living versus nonliving.

Noninvasive brain imaging studies have shown that higher visual processing of objects occurs in neural populations that are separable along broad semantic categories, particularly living versus nonliving objects. However, because of their limited temporal resolution, these studies have not been able to determine whether broad semantic categories are also reflected in the dynamics of neural interactions within cortical networks. We investigated the time course of neural propagation among cortical areas activated during object naming in 12 patients implanted with subdural electrode grids prior to epilepsy surgery, with a special focus on the visual recognition phase of the task. Analysis of event-related causality revealed significantly stronger neural propagation among sites within ventral temporal lobe (VTL) at early latencies, around 250ms, for living objects compared to nonliving objects. Differences in other features, including familiarity, visual complexity, and age of acquisition, did not significantly change the patterns of neural propagation. Our findings suggest that the visual processing of living objects relies on stronger causal interactions among sites within VTL, perhaps reflecting greater integration of visual feature processing. In turn, this may help explain the fragility of naming living objects in neurological diseases affecting VTL.

Modeling Alzheimer's Disease Using Human Brain Organoids.

Alzheimer's disease (AD) is the primary cause of dementia, to date. The urgent need to understand the biological and biochemical processes related to this condition, as well as the demand for reliable in vitro models for drug screening, has led to the development of novel techniques, among which stem cell methods are of utmost relevance for AD research, particularly the development of human brain organoids. Brain organoids are three-dimensional cellular aggregates derived from induced pluripotent stem cells (iPSCs) that recreate different neuralcell interactions and tissue characteristics in culture. Here, we describe the protocol for the generation of brain organoids derived from AD patients and for the analysis of AD-derived pathology. AD organoids can recapitulate beta-amyloid and tau pathological features, making them a promising model for studying the molecular mechanisms underlying disease and for in vitro drug testing.