Brain Organoid and Systems Neuroscience Journal最新文献

Impact of unilateral maximal dentate activation on nitrergic neuron distribution in the hippocampus, entorhinal cortex, gdala and habenula 单侧最大齿状体激活对海马、内嗅皮层、杏仁核和缰核中氮能神经元分布的影响

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-12-14 DOI: 10.1016/j.bosn.2025.100038

Cristiano Bombardi , Roberto Colangeli , Maurizio Casarrubea , Philippe De Deurwaerdère , Vincenzo Crunelli , Giuseppe Di Giovanni

Mesial temporal lobe epilepsy (mTLE) is the most common form of epilepsy involving the hippocampus. While hippocampal circuits have been extensively studied, increasing evidence suggests that extrahippocampal structures, including the amygdala and habenula, may contribute to seizure propagation and epileptogenesis. Among modulators of excitability, nitric oxide (NO) is a key regulator, although its role in mTLE remains controversial, with both pro- and anticonvulsant effects reported.

We used maximal dentate activation (MDA) in the dentate gyrus, elicited by repeated unilateral perforant path stimulation in rats, a well-established model for studying early mechanisms of epileptogenesis in mTLE. Repeated stimulation progressively shortened MDA onset latency and prolonged seizure duration, reflecting enhanced network excitability. To assess acute nitrergic alterations, an exploratory neuronal NO synthase (nNOS) and NADPH-diaphorase histochemical study wase performed in the hippocampus, entorhinal cortex, amygdala and habenula to determine whether repeated seizures could induce early changes in nitrergic neuronal expression.

Histochemical analysis revealed region- and layer-specific changes in nitrergic neurons after MDA. In CA1 and CA3, reductions were observed in the stratum oriens, accompanied by an increased density in the CA3 pyramidal layer. The subiculum exhibited a depletion driven by a decrease of nitrergic neurons in the hemisphere contralateral to the stimulation, whereas the entorhinal cortex, basolateral amygdala and medial habenula were largely unaffected. In the lateral habenula, MDA did not alter the overall mean of the density of nitrergic neurons, but it increased it in the stimulated hemisphere and decreased it in the contralateral one.

This preliminary study reveals adaptations that may indicate a sensitivity of nitrergic neurons to paroxysmal dentate activity and provide a basis for further exploring the potential involvement of NO-related pathways in limbic network responses and TLE-relevant mechanisms and treatments.

内侧颞叶癫痫（mTLE）是最常见的癫痫形式，涉及海马。虽然海马体回路已被广泛研究，但越来越多的证据表明，海马体外结构，包括杏仁核和缰核，可能有助于癫痫发作的传播和发生。在兴奋性调节剂中，一氧化氮（NO）是一个关键的调节剂，尽管其在mTLE中的作用仍有争议，有报道称其有促惊厥和抗惊厥作用。我们在大鼠的齿状回中使用了最大齿状激活（MDA），通过反复的单侧穿通通路刺激引起，这是研究mTLE癫痫发生早期机制的一个成熟模型。反复刺激逐渐缩短MDA发作潜伏期，延长癫痫发作持续时间，反映了网络兴奋性增强。为了评估急性氮能改变，我们在海马、嗅内皮层、杏仁核和缰核中进行了探索性神经元NO合成酶（nNOS）和NADPH-diaphorase组织化学研究，以确定反复发作是否会引起氮能神经元表达的早期变化。组织化学分析显示MDA后氮能神经元的区域和层特异性变化。在CA1和CA3中，在取向层中观察到减少，同时在CA3金字塔层中密度增加。在刺激的对侧半球，由于氮能神经元的减少，耻骨下表现出耗竭，而内嗅皮层、杏仁核基底外侧和内侧缰核基本未受影响。在侧缰中，丙二醛并未改变氮能神经元密度的总体平均值，但在受刺激的半球中增加了氮能神经元密度，而在对侧半球中降低了氮能神经元密度。这项初步研究揭示了氮能神经元对突发性齿状活动的敏感性，为进一步探索no相关通路参与边缘网络反应和tle相关机制和治疗提供了基础。

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引用次数: 0

Understanding the contribution of nucleus accumbens D1- and D2-neurons for rewarding and aversive behaviors 了解伏隔核D1和d2神经元对奖励和厌恶行为的贡献

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-11-28 DOI: 10.1016/j.bosn.2025.11.001

Catarina Carreira , Ana Verónica Domingues , Carina Soares-Cunha , Ana João Rodrigues

The nucleus accumbens (NAc) plays a central role in reward and aversion, strongly modulating motivated behavior. Classical models posited a functional dichotomy in which dopamine receptor D1-medium spiny neurons (MSNs) mediate reward-related behaviors, while D2-MSNs are more relevant for aversive behaviors. However, more recent evidence, including from our project, challenges this framework, demonstrating that both NAc D1- and D2-MSNs can trigger positive and negative reinforcement, depending on circuit context and temporal dynamics. Our project aims to understand the exact contribution of NAc D1- and D2-MSNs in rewarding and aversive behaviors, by enabling cell-type-specific and population-level monitoring of MSN dynamics during behaviorally relevant paradigms including Pavlovian conditioning and reinforcement learning, in combination with neurotransmitter release. This approach is complemented with optogenetic, genetic and pharmacological manipulations of each neuronal population to establish causal links between MSN activity patterns and behavior. This integrated methodology revealed that D1- and D2-MSNs exhibit coordinated (but also distinct) temporal patterns of activity during appetitive and aversive learning. These findings reshape our understanding of NAc function in motivated behavior, and have translational relevance for disorders that present NAc dysfunction and motivational deficits, namely addiction and depression. By bridging advanced circuit dissection with behaviorally meaningful readouts, this project advances the field’s capacity to map, manipulate, and interpret NAc neural activity to understand adaptive and maladaptive motivated behavior.

伏隔核（NAc）在奖励和厌恶中起着核心作用，强烈调节动机行为。经典模型假设了一种功能二分法，即多巴胺受体d1 -介质棘神经元（MSNs）介导奖励相关行为，而d2 -介质棘神经元（MSNs）与厌恶行为更相关。然而，最近的证据，包括我们的项目，挑战了这一框架，表明NAc D1-和d2 - msn都可以触发正强化和负强化，这取决于电路环境和时间动态。我们的项目旨在了解NAc D1-和d2 -MSN在奖励和厌恶行为中的确切贡献，通过在行为相关范式（包括巴甫洛夫条件反射和强化学习）中实现细胞类型特异性和群体水平的MSN动态监测，并结合神经递质释放。该方法与每个神经元群体的光遗传学、遗传学和药理学操作相辅相成，以建立MSN活动模式和行为之间的因果关系。这种综合方法揭示了D1-和d2 - msn在食欲和厌恶学习期间表现出协调（但也不同）的活动时间模式。这些发现重塑了我们对NAc在动机行为中的功能的理解，并对表现NAc功能障碍和动机缺陷的疾病，即成瘾和抑郁具有翻译相关性。通过将先进的电路解剖与行为有意义的读数相结合，该项目提高了该领域绘制、操纵和解释NAc神经活动的能力，以了解适应和不适应的动机行为。

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引用次数: 0

Immuno-Golgi method - Integrating immunohistochemistry and Golgi staining to characterize newborn neurons morphology 免疫-高尔基法-结合免疫组织化学和高尔基染色表征新生神经元形态

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-10-16 DOI: 10.1016/j.bosn.2025.10.001

Leandro Rodrigues-Freitas , Luísa Pinto

Visualizing neuronal dendritic architecture while simultaneously identifying specific neuronal phenotype is challenging. Here, we present a detailed description of the Immuno-Golgi technique which combines bromodeoxyuridine (BrdU) immunohistochemistry with Golgi impregnation. This straightforward and non-invasive approach enables the three-dimensional visualization and quantitative analysis of dendritic arborization and spine morphology in newborn neurons within the adult rodent brain.

可视化神经元树突结构，同时确定特定的神经元表型是具有挑战性的。在这里，我们提出了一个详细的描述免疫高尔基技术结合溴脱氧尿苷（BrdU）免疫组织化学与高尔基浸渍。这种直接和非侵入性的方法可以对成年啮齿动物大脑中新生神经元的树突树突和脊柱形态进行三维可视化和定量分析。

引用次数: 0

Human cerebral organoids maintain integrity and viability after transport through mail 人类大脑类器官在通过邮件运输后保持完整性和活力

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-08-07 DOI: 10.1016/j.bosn.2025.08.001

Natalie Smith , Anthony J.A. Baker , Michaelann S. Tartis , Zane R. Lybrand

Human cerebral organoids are stem-cell derived three-dimensional (3D) tissue cultures used to advance our understanding of human neurodevelopment processes and neurological disorders. This complex model system can be utilized in interdisciplinary studies that do not include organoid biologists such as engineering, computer science, and pharmacology. This study introduces a reliable method of transportation of live brain organoids to further collaboration amongst disciplines. Using immunohistochemistry and a papain viability assay, we compared the cellular integrity and viability of organoids transported through overnight mail (“flight”) and organoids remaining in the lab (“ground”). In addition, we performed multi-electrode array (MEA) recordings at multiple time points following transport to assess the preservation of spontaneous electrophysiological activity. Our shipping method produced comparable results between flight and ground organoids in viability and cell death staining as well as morphological characteristics of glial cells and neurons. These findings provide a method in transporting organoids between labs to encourage collaboration between brain organoid researchers and non-life scientists.

人类大脑类器官是干细胞衍生的三维（3D）组织培养物，用于促进我们对人类神经发育过程和神经系统疾病的理解。这种复杂的模型系统可以用于不包括类器官生物学家的跨学科研究，如工程学、计算机科学和药理学。本研究介绍了一种可靠的运输活体脑类器官的方法，以进一步促进学科间的合作。利用免疫组织化学和木瓜蛋白酶活力测定，我们比较了通过隔夜邮件（“飞行”）运输的类器官和留在实验室（“地面”）的类器官的细胞完整性和活力。此外，我们在运输后的多个时间点进行了多电极阵列（MEA）记录，以评估自发电生理活动的保存情况。我们的运输方法在飞行和地面类器官的活力和细胞死亡染色以及胶质细胞和神经元的形态特征方面产生了可比较的结果。这些发现提供了一种在实验室之间运输类器官的方法，以鼓励类脑器官研究人员和非生命科学家之间的合作。

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引用次数: 0

PigBET: A 2.5D deep learning segmentation framework for multimodal and longitudinal domestic pig MRI utilizing ImageNet pre-trained encoders PigBET：利用ImageNet预训练编码器的多模态和纵向国产猪MRI的2.5D深度学习分割框架

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-07-05 DOI: 10.1016/j.bosn.2025.07.001

Zimu Li , Loretta T. Sutkus , Joanne E. Fil , Pradeep Senthil , Fan Lam , Brad P. Sutton , Ryan N. Dilger

The domestic pig model is increasingly utilized in developmental neuroscience studies due to its anatomical and functional similarities to the human brain. Neuroimaging has become an important tool for studying pig brains. Skull Stripping is a critical but labor-intensive task for processing pig magnetic resonance imaging (MRI) data, presenting a significant hurdle in longitudinal and multimodal neuroimaging studies utilizing pig as a biomedical model. To address this, we developed PigBET, a deep learning-based skull stripping optimized for domestic pig. We stacked adjacent slices into three channels, capturing additional spatial context along the third axis. This strategy enables the use of pre-trained encoders, designed for natural images, for a 3D segmentation task. To further improve accuracy, we trained separate models for the axial, coronal, and sagittal planes, and combined their predictions using majority voting. This ensemble strategy enhances robustness and reduces orientation-specific errors. Training utilized a large quantity of 4-week-old pig magnetization-prepared rapid gradient-echo (MPRAGE) data, with transfer learning applied to 1-week-old, 8-week-old, and 18-week-old pig MPRAGE and 4-week-old pig dMRI data. PigBET achieved high segmentation accuracy on the primary test set with a mean Dice coefficient of 0.981 ± 0.004, IoU of 0.962 ± 0.008, and Hausdorff Distance (HD) of 3.4 ± 1.28 voxels. With transfer learning, it maintained robust performance across modalities and ages: Dice = 0.975–0.982, IoU = 0.952–0.965, HD = 1.7–3.5 voxels. When benchmarked against other methods, PigBET outperformed another U-Net approach without pre-trained encoders that previously showed capability of skull stripping pig data as well as a standard registration-based method combining FLIRT and ANTs. These results demonstrate that PigBET is robust and efficient and accommodates pig data from various ages and different MRI contrasts. This tool significantly advances the efficiency of MRI data processing for large-scale pig neuroimaging studies, making it a valuable resource for the biomedical field.

由于其与人脑的解剖和功能相似，家猪模型越来越多地用于发育神经科学研究。神经影像学已成为研究猪脑的重要工具。颅骨剥离是处理猪磁共振成像（MRI）数据的一项关键但劳动密集型的任务，是利用猪作为生物医学模型进行纵向和多模态神经成像研究的一个重大障碍。为了解决这个问题，我们开发了PigBET，这是一种基于深度学习的颅骨剥离技术，针对家猪进行了优化。我们将相邻的切片堆叠成三个通道，沿着第三个轴捕获额外的空间背景。这种策略可以使用预先训练的编码器，为自然图像设计，用于3D分割任务。为了进一步提高准确性，我们为轴面、冠状面和矢状面训练了单独的模型，并使用多数投票将它们的预测结合起来。这种集成策略增强了鲁棒性并减少了特定于方向的错误。训练使用了大量4周龄猪磁化制备的快速梯度回波（MPRAGE）数据，并将迁移学习应用于1周龄、8周龄和18周龄猪的MPRAGE和4周龄猪的dMRI数据。PigBET在主测试集上取得了较高的分割精度，平均Dice系数为0.981 ± 0.004，IoU为0.962 ± 0.008,Hausdorff Distance （HD）为3.4 ± 1.28体素。通过迁移学习，它在模式和年龄上保持了稳健的性能：Dice = 0.975-0.982,IoU = 0.952-0.965,HD = 1.7-3.5体素。当与其他方法进行基准测试时，PigBET优于另一种没有预先训练编码器的U-Net方法，该方法先前显示了猪颅骨剥离数据的能力，以及结合了FLIRT和ANTs的标准基于配准的方法。这些结果表明，PigBET是稳健和高效的，可以适应不同年龄和不同MRI对比的猪数据。该工具显著提高了大规模猪神经成像研究的MRI数据处理效率，使其成为生物医学领域的宝贵资源。

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引用次数: 0

Functional connectivity signatures in fMRI-derived connectome for the diagnosis of autism spectrum disorder 功能连接特征在fmri衍生的连接组诊断自闭症谱系障碍

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-07-01 DOI: 10.1016/j.bosn.2025.06.004

S.M. Shayez Karim, R.S. Rathore

Rearrangement of synaptic connectivity is believed to be involved in shaping neuronal connections during brain developmental in early childhood, which determine mental health and behaviour of an individual. We here compare two types of the functional connectivity patterns: i) changes of healthy infants (3.7–32.6 months) to adults (25–35 yrs), reflecting normal developmental pattern, and ii) changes of healthy infants to autism spectrum disorder (ASD) patients (9.3–35.2 yrs), which encapsulate both developmental and ASD-specific pathological pattern. Using graph-based network parameters in the fMRI-derived connectome, we quantified changes and calculated average and median percent differences of various brain lobes. The connectome-to-connectome comparison suggests that synaptic rewiring is primarily concentrated in intra-thalamic and inter-lobe connections with the thalamus, and the ASD patients were characterised by significant thalamo-cortical hyperconnectivity. The average percent difference of the functional connectivity between ASD patients and adults for thalamus were as high as 82.58 % (degree), 72.8 % (betweenness centrality) 17.41 % (clustering coefficient) and 10.77/15.57 % (global/local efficiency). Regression models were built for normal brain development using functional connection data of healthy infants, child, adolescents and adults for each brain lobe. These regression curves suggest linearly increasing trends of functional connections from infant to adult in all brain lobes except in thalamus. Functional connections of ASD data are significantly different from this trends and characterized by significant overconnectivity. The distinct functional network signatures have the potential to serve as diagnostic markers of ASD. Towards this end, a method has been developed. The method can distinguish ASD subjects from their typically developing peers and healthy individuals with reasonable accuracy and specificity.

突触连接的重排被认为参与了儿童早期大脑发育过程中神经元连接的形成，而神经元连接的形成决定了个体的心理健康和行为。我们在这里比较了两种类型的功能连接模式：i)健康婴儿（3.7-32.6个月）到成人（25-35 年）的变化，反映了正常的发育模式；ii)健康婴儿到自闭症谱系障碍（ASD）患者（9.3-35.2 年）的变化，包含了发育和ASD特异性病理模式。使用基于图的网络参数在fmri衍生的连接组，我们量化变化和计算平均和中位数百分比差异的各个脑叶。连接组对连接组的比较表明，突触重新布线主要集中在丘脑内和与丘脑的叶间连接上，ASD患者的特征是显著的丘脑-皮质过度连接。ASD患者与成人丘脑功能连通性的平均百分比差异高达82.58 %（度）、72.8 %（中间中心性）、17.41 %（聚类系数）和10.77/15.57 %（整体/局部效率）。利用健康婴儿、儿童、青少年和成人各脑叶的功能连接数据，建立正常脑发育的回归模型。这些回归曲线表明，从婴儿到成人，除丘脑外，所有脑叶的功能连接呈线性增加趋势。ASD数据的功能连接明显不同于这一趋势，并以明显的过度连接为特征。不同的功能网络特征有可能作为ASD的诊断标记。为此，开发了一种方法。该方法能够将ASD受试者与正常发育的同龄人和健康个体区分开来，具有合理的准确性和特异性。

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引用次数: 0

PIWI-interacting RNAs in neurological and neuropsychiatric disorders and potential for clinical development 神经和神经精神疾病中的piwi相互作用rna及其临床发展潜力

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-06-14 DOI: 10.1016/j.bosn.2025.06.003

Silvia Beatini , Letizia La Rosa , Lidia Giantomasi, Davide De Pietri Tonelli

Neural diseases, including neurological and neuropsychiatric disorders, represent a significant and growing public health burden. While the understanding of the genetic origins of these diseases has advanced considerably in recent years, the underlying causal mechanisms of many sporadic cases remain poorly understood. Recent research highlights the role of noncoding RNAs, such as PIWI-interacting RNAs (piRNAs) and PIWI proteins, in regulating gene expression and maintaining genome stability within neural cells. The piRNA pathway is emerging as a significant regulator of chromatin accessibility and transcriptional homeostasis in nervous system development and plasticity. Concomitantly, a disruption of the piRNA pathway has been associated with neuronal dysfunction and age-associated neuropathologies, underscoring the urgent need to investigate their mechanistic roles in neuronal homeostasis and disease pathogenesis. This review focuses on the emerging functions of the piRNA pathway beyond the gonads, with particular attention given to its potential roles in mammalian nervous system physiology, disease mechanisms, and opportunities for diagnostic and therapeutic development.

神经疾病，包括神经和神经精神疾病，是一个重大的和日益增长的公共卫生负担。虽然近年来对这些疾病的遗传起源的了解有了很大进展，但对许多散发病例的潜在因果机制仍然知之甚少。最近的研究强调了非编码rna，如PIWI相互作用rna （piRNAs）和PIWI蛋白，在调节基因表达和维持神经细胞内基因组稳定性中的作用。piRNA通路是神经系统发育和可塑性中染色质可及性和转录稳态的重要调节因子。同时，piRNA通路的破坏与神经元功能障碍和年龄相关的神经病变有关，这表明迫切需要研究它们在神经元稳态和疾病发病机制中的机制作用。这篇综述的重点是piRNA通路在性腺之外的新功能，特别关注其在哺乳动物神经系统生理学、疾病机制以及诊断和治疗发展中的潜在作用。

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引用次数: 0

The yin and yang of neuronal mitophagy: Physiological functions and pathological implications in neurodevelopmental disorders 神经元自噬的阴阳：神经发育障碍的生理功能和病理意义

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-06-09 DOI: 10.1016/j.bosn.2025.06.001

Christina Ploumi, Konstantinos Palikaras

Maintenance of mitochondrial function is crucial, especially for the energy-demanding and metabolically active neuronal cells. Owing to their post-mitotic nature, neurons are highly vulnerable to mitochondrial dysfunction and therefore rely heavily on mitochondrial quality control mechanisms to mitigate damage and sustain cellular and organismal homeostasis. Mitophagy, the selective autophagic degradation of mitochondria, is the prominent mitochondrial turnover mechanism, required for supporting proper neuronal development and function. Impairment or deregulation of mitophagy pathways has been associated with a wide spectrum of neurological disorders. While mitophagy generally exerts neuroprotective effects, its excessive or uncontrolled activation can be detrimental, potentially leading to neuronal death. This review summarizes the primary pathways involved in neuronal mitophagy and explores their relevance to the etiology and pathophysiology of common neurodevelopmental disorders.

线粒体功能的维持是至关重要的，特别是对能量需求和代谢活跃的神经元细胞。由于其有丝分裂后的性质，神经元非常容易受到线粒体功能障碍的影响，因此严重依赖线粒体质量控制机制来减轻损伤并维持细胞和生物体的稳态。线粒体自噬（Mitophagy）是线粒体的选择性自噬降解，是支持正常神经元发育和功能所需的重要线粒体更新机制。线粒体自噬途径的损伤或失调与广泛的神经系统疾病有关。虽然线粒体自噬通常具有神经保护作用，但其过度或不受控制的激活可能是有害的，可能导致神经元死亡。本文综述了参与神经元自噬的主要途径，并探讨了它们与常见神经发育障碍的病因学和病理生理学的相关性。

引用次数: 0

Genetically encoded tools for Cell–Cell interactions underlying brain connectivity 大脑连接下细胞-细胞相互作用的基因编码工具

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-06-06 DOI: 10.1016/j.bosn.2025.06.002

Natsumi Hoshino , Takeshi Yagi , Takeharu Nagai , Takashi Kanadome

Brain development and function depend on complex yet organized neural networks and efficient communication between the neurons that form these networks. To understand the role of neural networks and communication between neurons, it is important to visualize interactions between neurons. In this review, we provide a comprehensive overview of visualization tools for cell–cell interactions, especially among neurons. We first introduce tools based on fluorescent protein technology that directly visualize cell–cell interactions, categorized into three different mechanisms: split fluorescent protein (split FP), dimerization-dependent fluorescent protein (ddFP), and Förster resonance energy transfer (FRET). Each type offers distinct advantages in terms of temporal resolution, reversibility, and spatial specificity, making them suitable for different experimental contexts within neural networks. Next, we introduce tools that indirectly visualize cell–cell interactions, based on a reporter system. Finally, we present several tools based on other mechanisms. By systematically comparing the performance and applicability of each tool, we provide a strategic framework for selecting appropriate tools based on experimental goals, ranging from short-lived synaptic interactions to long-term network connectivity studies.

大脑的发育和功能依赖于复杂而有组织的神经网络以及构成这些网络的神经元之间的有效交流。为了理解神经网络的作用和神经元之间的交流，可视化神经元之间的相互作用是很重要的。在这篇综述中，我们提供了一个全面的概述可视化工具的细胞-细胞相互作用，特别是神经元之间。我们首先介绍了基于荧光蛋白技术的工具，直接可视化细胞-细胞相互作用，分为三种不同的机制：分裂荧光蛋白（分裂FP），二聚化依赖性荧光蛋白（ddFP）和Förster共振能量转移（FRET）。每种类型在时间分辨率、可逆性和空间特异性方面都具有独特的优势，使它们适用于神经网络中的不同实验环境。接下来，我们将介绍基于报告系统的间接可视化细胞-细胞相互作用的工具。最后，我们介绍了几种基于其他机制的工具。通过系统地比较每个工具的性能和适用性，我们提供了一个基于实验目标选择适当工具的战略框架，从短期突触相互作用到长期网络连接研究。

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引用次数: 0

Harnessing artificial intelligence for transforming dementia care: Innovations in early detection and treatment 利用人工智能改变痴呆症护理：早期发现和治疗方面的创新

Brain Organoid and Systems Neuroscience Journal

Pub Date : 2025-05-16 DOI: 10.1016/j.bosn.2025.05.001

Saadeddine Habbal , Maamoon Mian , Musa Imam , Jihane Tahiri , Adam Amor , P. Hemachandra Reddy

Dementia, particularly Alzheimer's Disease, continues to be a significant global health concern, driven by increasing prevalence as the population ages. Early detection and accurate diagnosis are essential for improving patient outcomes and mitigating the associated healthcare burden. Artificial intelligence (AI) has emerged as a powerful tool in dementia care, providing innovative approaches to the early detection, diagnosis, and management of these neurodegenerative conditions. This review examines the role of AI in revolutionizing dementia care by focusing on its application in neuroimaging, biomarker identification, predictive modeling, and therapeutic interventions. This narrative review synthesizes recent literature on AI methodologies, including machine learning, deep learning, and neural networks, for their effectiveness in detecting and managing dementia. Emphasis is placed on AI’s integration of multimodal data, such as neuroimaging, genomics, and clinical records, to enhance diagnostic accuracy and predict disease progression. The review also evaluates AI-driven tools for non-invasive screening, personalized treatment planning, and patient monitoring. Findings indicate that AI significantly improves the accuracy and timeliness of dementia diagnoses, often detecting early-stage disease with greater precision than conventional methods. AI’s capacity to analyze complex datasets enables earlier interventions, which are critical for slowing the progression of AD. In the realm of treatment, AI-driven approaches are optimizing personalized care, predicting patient responses to therapies, and advancing drug discovery. The integration of AI into clinical practice is enhancing real-time decision-making and improving overall disease management. In conclusion, AI holds immense potential to transform the future of dementia care. While challenges such as ethical considerations, data privacy, and the need for widespread clinical validation remain, the benefits of AI in early detection, personalized treatment, and improved patient outcomes are substantial. Continued research and cross-disciplinary collaboration will be vital in fully realizing AI’s capabilities in addressing the global dementia epidemic.

随着人口老龄化，痴呆症，特别是阿尔茨海默病的患病率不断上升，痴呆症仍然是一个重大的全球健康问题。早期发现和准确诊断对于改善患者预后和减轻相关的医疗负担至关重要。人工智能（AI）已成为痴呆症护理的有力工具，为这些神经退行性疾病的早期发现、诊断和管理提供了创新方法。本文通过重点研究人工智能在神经成像、生物标志物识别、预测建模和治疗干预方面的应用，探讨了人工智能在彻底改变痴呆症护理中的作用。这篇叙述性综述综合了最近关于人工智能方法的文献，包括机器学习、深度学习和神经网络，因为它们在检测和管理痴呆症方面的有效性。重点放在人工智能整合多模式数据，如神经影像学、基因组学和临床记录，以提高诊断准确性和预测疾病进展。该综述还评估了用于非侵入性筛查、个性化治疗计划和患者监测的人工智能驱动工具。研究结果表明，人工智能显著提高了痴呆症诊断的准确性和及时性，通常比传统方法更准确地检测出早期疾病。人工智能分析复杂数据集的能力使早期干预成为可能，这对减缓阿尔茨海默病的进展至关重要。在治疗领域，人工智能驱动的方法正在优化个性化护理，预测患者对治疗的反应，并推进药物发现。人工智能与临床实践的融合增强了实时决策，改善了整体疾病管理。总之，人工智能在改变痴呆症护理的未来方面具有巨大的潜力。尽管诸如伦理考虑、数据隐私和广泛临床验证的需求等挑战仍然存在，但人工智能在早期检测、个性化治疗和改善患者预后方面的好处是巨大的。持续的研究和跨学科合作对于充分实现人工智能在应对全球痴呆症流行方面的能力至关重要。

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引用次数: 0