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Noise-induced synchrony of two-neuron motifs with asymmetric noise and uneven coupling 具有不对称噪声和不均匀耦合的双神经元图案的噪声诱导同步性
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-23 DOI: 10.3389/fncom.2024.1347748
Gurpreet Jagdev, Na Yu
Synchronous dynamics play a pivotal role in various cognitive processes. Previous studies extensively investigate noise-induced synchrony in coupled neural oscillators, with a focus on scenarios featuring uniform noise and equal coupling strengths between neurons. However, real-world or experimental settings frequently exhibit heterogeneity, including deviations from uniformity in coupling and noise patterns. This study investigates noise-induced synchrony in a pair of coupled excitable neurons operating in a heterogeneous environment, where both noise intensity and coupling strength can vary independently. Each neuron is an excitable oscillator, represented by the normal form of Hopf bifurcation (HB). In the absence of stimulus, these neurons remain quiescent but can be triggered by perturbations, such as noise. Typically, noise and coupling exert opposing influences on neural dynamics, with noise diminishing coherence and coupling promoting synchrony. Our results illustrate the ability of asymmetric noise to induce synchronization in such coupled neural oscillators, with synchronization becoming increasingly pronounced as the system approaches the excitation threshold (i.e., HB). Additionally, we find that uneven coupling strengths and noise asymmetries are factors that can promote in-phase synchrony. Notably, we identify an optimal synchronization state when the absolute difference in coupling strengths is maximized, regardless of the specific coupling strengths chosen. Furthermore, we establish a robust relationship between coupling asymmetry and the noise intensity required to maximize synchronization. Specifically, when one oscillator (receiver neuron) receives a strong input from the other oscillator (source neuron) and the source neuron receives significantly weaker or no input from the receiver neuron, synchrony is maximized when the noise applied to the receiver neuron is much weaker than that applied to the source neuron. These findings reveal the significant connection between uneven coupling and asymmetric noise in coupled neuronal oscillators, shedding light on the enhanced propensity for in-phase synchronization in two-neuron motifs with one-way connections compared to those with two-way connections. This research contributes to a deeper understanding of the functional roles of network motifs that may serve within neuronal dynamics.
同步动力学在各种认知过程中起着举足轻重的作用。以往的研究广泛研究了噪声在耦合神经振荡器中诱导的同步性,重点关注神经元之间具有均匀噪声和相等耦合强度的情况。然而,现实世界或实验环境经常表现出异质性,包括耦合和噪声模式偏离均匀性。本研究调查了在异质环境中运行的一对耦合可兴奋神经元的噪声诱导同步性,在这种环境中,噪声强度和耦合强度都可以独立变化。每个神经元都是一个可兴奋振荡器,以霍普夫分岔(HB)的正常形式表示。在没有刺激的情况下,这些神经元会保持静态,但会被噪声等扰动触发。通常情况下,噪声和耦合会对神经动态产生相反的影响,噪声会降低一致性,而耦合则会促进同步性。我们的研究结果表明,非对称噪声能够在这种耦合神经振荡器中诱导同步,当系统接近兴奋阈值(即 HB)时,同步会变得越来越明显。此外,我们发现不均匀的耦合强度和噪声不对称也是促进同相同步的因素。值得注意的是,当耦合强度的绝对差值达到最大时,无论选择的具体耦合强度如何,我们都能识别出最佳同步状态。此外,我们还在耦合不对称性和最大化同步所需的噪声强度之间建立了稳健的关系。具体来说,当一个振荡器(接收神经元)从另一个振荡器(源神经元)接收强输入,而源神经元从接收神经元接收的输入明显较弱或没有输入时,当应用于接收神经元的噪声比应用于源神经元的噪声弱得多时,同步性达到最大。这些发现揭示了耦合神经元振荡器中不均匀耦合与不对称噪声之间的重要联系,阐明了单向连接的双神经元图案与双向连接的双神经元图案相比,具有更强的同相同步倾向。这项研究有助于更深入地了解网络图案在神经元动力学中的功能作用。
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引用次数: 0
Editorial: Bioinformatics for modern neuroscience. 社论:现代神经科学的生物信息学。
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-22 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1385658
Georgios N Dimitrakopoulos, Mathieu Di Miceli
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引用次数: 0
Artificial intelligence approaches for early detection of neurocognitive disorders among older adults 早期发现老年人神经认知障碍的人工智能方法
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-16 DOI: 10.3389/fncom.2024.1307305
Khalid AlHarkan, Nahid Sultana, Noura Al Mulhim, Assim AlAbdulqader, Noor Alsafwani, Marwah Barnawi, Khulud Alasqah, Anhar Bazuhair, Zainab Alhalwah, Dina Bokhamseen, Sumayh S Aljameel, Sultan Alamri, Yousef Alqurashi, Kholoud Alghamdi
IntroductionDementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately.MethodsQuantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient’s cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients’ cognitive function based on gender and developed the predicting models for males and females separately.ResultsFifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time.ConclusionThe results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies.
导言痴呆症是全球老龄人口的主要健康问题之一,其临床特征是高级认知功能逐渐下降。本文旨在应用各种人工智能(AI)方法来准确检测轻度认知障碍(MCI)或痴呆症患者。通过卡方检验确定了患者认知功能与各种特征(包括人口统计学和病史)之间的关联。两种广泛使用的人工智能算法--逻辑回归和支持向量机(SVM)被用于检测认知功能衰退。本研究还根据性别评估了患者的认知功能,并分别为男性和女性开发了预测模型。结果54%的患者认知功能正常,34%的患者患有 MCI,12%的患者患有痴呆症。所有已开发模型的预测准确率均大于 71%,显示出良好的预测能力。然而,所开发的 SVM 模型表现最佳,对所有患者的预测准确率为 93.3%,对男性患者的预测准确率为 94.4%,对女性患者的预测准确率为 95.5%。根据所开发的 SVM 模型,前 10 个重要的预测因素是教育程度、就寝时间、服用慢性疼痛药物、糖尿病、中风、性别、慢性疼痛、冠状动脉疾病和起床时间。这项研究还为学者们提供了实质性的方向和支持性的直觉,以加强他们对未来认知衰退研究的关键研究、新兴趋势和新发展的理解。
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引用次数: 0
The connectivity degree controls the difficulty in reservoir design of random boolean networks 连通度控制着随机布尔网络水库设计的难度
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-16 DOI: 10.3389/fncom.2024.1348138
Emmanuel Calvet, Bertrand Reulet, Jean Rouat

Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance b has been shown to be pivotal for driving the dynamics and performance of Echo State Networks (ESN) and, more recently, Random Boolean Network (RBN). However, the relationship between b and other parameters of the network is still poorly understood. This article explores how the interplay of the balance b, the connectivity degree K (i.e., the number of synapses per neuron) and the size of the network (i.e., the number of neurons N) influences the dynamics and performance (memory and prediction) of an RBN reservoir. Our findings reveal that K and b are strongly tied in optimal reservoirs. Reservoirs with high K have two optimal balances, one for globally inhibitory networks (b < 0), and the other one for excitatory networks (b > 0). Both show asymmetric performances about a zero balance. In contrast, for moderate K, the optimal value being K = 4, best reservoirs are obtained when excitation and inhibition almost, but not exactly, balance each other. For almost all K, the influence of the size is such that increasing N leads to better performance, even with very large values of N. Our investigation provides clear directions to generate optimal reservoirs or reservoirs with constraints on size or connectivity.

蓄水池计算(Reservoir Computing,RC)是人工智能领域的一种范式,利用蓄水池固有的动态特性来执行复杂的计算,从而使用循环神经网络(RNN)来处理时间数据。在 RC 领域,兴奋-抑制平衡 b 已被证明是驱动回声状态网络(ESN)以及最近的随机布尔网络(RBN)的动态和性能的关键。然而,人们对 b 与网络其他参数之间的关系仍然知之甚少。本文探讨了平衡 b、连通度 K(即每个神经元的突触数)和网络大小(即神经元数 N)之间的相互作用如何影响 RBN 储库的动态和性能(记忆和预测)。我们的研究结果表明,K 和 b 在最佳蓄水池中密切相关。高 K 值水库有两个最佳平衡点,一个是全局抑制性网络(b <0),另一个是兴奋性网络(b >0)。两者在零平衡时表现不对称。相反,对于中等 K(最佳值为 K = 4),当兴奋和抑制几乎(但不是完全)相互平衡时,就会获得最佳蓄水池。对于几乎所有的 K,大小的影响是增加 N 会带来更好的性能,即使 N 的值非常大。我们的研究为生成最佳水库或对大小或连接性有限制的水库提供了明确的方向。
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引用次数: 0
Neurocomputational mechanisms underlying perception and sentience in the neocortex 新皮层感知和知觉的神经计算机制
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-14 DOI: 10.3389/fncom.2024.1335739
Andrew S. Johnson, William Winlow

The basis for computation in the brain is the quantum threshold of “soliton,” which accompanies the ion changes of the action potential, and the refractory membrane at convergences. Here, we provide a logical explanation from the action potential to a neuronal model of the coding and computation of the retina. We also explain how the visual cortex operates through quantum-phase processing. In the small-world network, parallel frequencies collide into definable patterns of distinct objects. Elsewhere, we have shown how many sensory cells are meanly sampled from a single neuron and that convergences of neurons are common. We also demonstrate, using the threshold and refractory period of a quantum-phase pulse, that action potentials diffract across a neural network due to the annulment of parallel collisions in the phase ternary computation (PTC). Thus, PTC applied to neuron convergences results in a collective mean sampled frequency and is the only mathematical solution within the constraints of the brain neural networks (BNN). In the retina and other sensory areas, we discuss how this information is initially coded and then understood in terms of network abstracts within the lateral geniculate nucleus (LGN) and visual cortex. First, by defining neural patterning within a neural network, and then in terms of contextual networks, we demonstrate that the output of frequencies from the visual cortex contains information amounting to abstract representations of objects in increasing detail. We show that nerve tracts from the LGN provide time synchronization to the neocortex (defined as the location of the combination of connections of the visual cortex, motor cortex, auditory cortex, etc.). The full image is therefore combined in the neocortex with other sensory modalities so that it receives information about the object from the eye and all the abstracts that make up the object. Spatial patterns in the visual cortex are formed from individual patterns illuminating the retina, and memory is encoded by reverberatory loops of computational action potentials (CAPs). We demonstrate that a similar process of PTC may take place in the cochlea and associated ganglia, as well as ascending information from the spinal cord, and that this function should be considered universal where convergences of neurons occur.

大脑计算的基础是 "孤子 "的量子阈值,它伴随着动作电位的离子变化和会聚时的折射膜。在这里,我们提供了从动作电位到视网膜编码和计算的神经元模型的逻辑解释。我们还解释了视觉皮层如何通过量子相位处理进行运作。在 "小世界 "网络中,平行频率会碰撞成不同物体的可定义模式。在其他地方,我们已经展示了如何从单个神经元平均采样许多感觉细胞,以及神经元的汇聚是常见的。我们还利用量子相位脉冲的阈值和折射周期证明,由于相位三元计算(PTC)中的平行碰撞无效,动作电位在神经网络中扩散。因此,将相位三元计算应用于神经元汇聚会产生集体平均采样频率,是脑神经网络(BNN)限制条件下的唯一数学解决方案。在视网膜和其他感觉区域,我们将讨论如何对这些信息进行初步编码,然后通过外侧膝状核(LGN)和视觉皮层内的网络抽象来理解这些信息。首先,通过定义神经网络内的神经模式,然后从上下文网络的角度,我们证明了视觉皮层的频率输出包含的信息相当于物体的抽象表征,而且越来越详细。我们表明,来自 LGN 的神经束为新皮层(定义为视觉皮层、运动皮层、听觉皮层等连接组合的位置)提供了时间同步。因此,完整的图像在新皮层中与其他感觉模式相结合,从而接收来自眼睛的物体信息以及构成物体的所有抽象信息。视觉皮层中的空间模式是由照亮视网膜的单个模式形成的,而记忆则是由计算动作电位(CAP)的混响回路编码的。我们证明,耳蜗和相关神经节中也可能发生类似的 PTC 过程,脊髓中的信息也是如此。
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引用次数: 0
Colorectal image analysis for polyp diagnosis 用于息肉诊断的大肠图像分析
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-09 DOI: 10.3389/fncom.2024.1356447
Peng-Cheng Zhu, Jing-Jing Wan, Wei Shao, Xian-Chun Meng, Bo-Lun Chen
Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.
大肠息肉是大肠癌的重要早期表现,对预防大肠癌意义重大。尽管及时发现和人工干预大肠息肉可以降低其癌变几率,但现有方法大多忽视了息肉的不确定性和位置问题,导致检测性能下降。针对这些问题,本文提出了一种通过 PAM-Net 进行息肉诊断的新型大肠图像分析方法。具体来说,我们设计了一个并行注意力模块来加强对大肠息肉图像的分析,以提高息肉的确定性。此外,我们的方法还引入了 GWD 损失,从息肉位置的角度提高了息肉诊断的准确性。大量实验结果表明,与 SOTA 基线相比,所提出的方法非常有效。这项研究提高了息肉检测的准确性,为临床医学中的息肉检测做出了贡献。
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引用次数: 0
Dynamics of antiphase bursting modulated by the inhibitory synaptic and hyperpolarization-activated cation currents 抑制性突触和超极化激活阳离子电流调制的反相猝发动力学
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-09 DOI: 10.3389/fncom.2024.1303925
Linan Guan, Huaguang Gu, Xinjing Zhang
Antiphase bursting related to the rhythmic motor behavior exhibits complex dynamics modulated by the inhibitory synaptic current (Isyn), especially in the presence of the hyperpolarization-activated cation current (Ih). In the present paper, the dynamics of antiphase bursting modulated by the Ih and Isyn is studied in three aspects with a theoretical model. Firstly, the Isyn and the slow Ih with strong strength are the identified to be the necessary conditions for the antiphase bursting. The dependence of the antiphase bursting on the two currents is different for low (escape mode) and high (release mode) threshold voltages (Vth) of the inhibitory synapse. Secondly, more detailed co-regulations of the two currents to induce opposite changes of the bursting period are obtained. For the escape mode, increase of the Ih induces elevated membrane potential of the silence inhibited by a strong Isyn and shortened silence duration to go beyond Vth, resulting in reduced bursting period. For the release mode, increase of the Ih induces elevated tough value of the former part of the burst modulated by a nearly zero Isyn and lengthen burst duration to fall below Vth, resulting in prolonged bursting period. Finally, the fast-slow dynamics of the antiphase bursting are acquired. Using one-and two-parameter bifurcations of the fast subsystem of a single neuron, the burst of the antiphase bursting is related to the stable limit cycle, and the silence modulated by a strong Isyn to the stable equilibrium to a certain extent. The Ih mainly modulates the dynamics within the burst and quiescent state. Furthermore, with the fast subsystem of the coupled neurons, the silence is associated with the unstable equilibrium point. The results present theoretical explanations to the changes in the bursting period and fast-slow dynamics of the antiphase bursting modulated by the Isyn and Ih, which is helpful for understanding the antiphase bursting and modulating rhythmic motor patterns.
与节律性运动行为相关的反相猝发表现出受抑制性突触电流(Isyn)调制的复杂动态,尤其是在存在超极化激活阳离子电流(Ih)的情况下。本文通过一个理论模型,从三个方面研究了受 Ih 和 Isyn 调节的反相猝发动力学。首先,Isyn 和强度较强的慢速 Ih 被认为是反相猝灭的必要条件。在抑制性突触的低阈值电压(逃避模式)和高阈值电压(释放模式)下,反相猝发对这两种电流的依赖性是不同的。其次,我们得到了两种电流诱导猝发期发生相反变化的更详细的共同调节。在逃逸模式下,Ih 的增加会导致被强 Isyn 抑制的沉默膜电位升高,沉默持续时间缩短至 Vth 以上,从而导致猝发期缩短。在释放模式下,Ih 的增加会导致猝发前半部分的韧值升高,并受到几乎为零的 Isyn 的调节,同时猝发持续时间延长,使其低于 Vth,从而导致猝发期延长。最后,获得了反相猝发的快慢动态。利用单个神经元快速子系统的一参数和二参数分岔,反相猝发的猝发与稳定的极限周期有关,而由强 Isyn 调制的沉默与稳定的平衡在一定程度上有关。Ih 主要调节猝发和静息状态内的动态。此外,在耦合神经元的快速子系统中,沉默与不稳定平衡点有关。研究结果从理论上解释了 Isyn 和 Ih 所调制的反相猝发的猝发周期和快慢动态变化,有助于理解反相猝发和调制节律性运动模式。
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引用次数: 0
An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control 对小鼠大脑网络中广泛的癫痫发作位置进行探索性计算分析,以及有效干预和传播控制的潜在策略
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-08 DOI: 10.3389/fncom.2024.1360009
Juliette Courson, Mathias Quoy, Yulia Timofeeva, Thanos Manos

Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

目前已开发出平均场模型来复制癫痫发作动态的关键特征。然而,人们对负责癫痫发作和传播的脑区的确切机制和作用仍然知之甚少。在这项研究中,我们在虚拟大脑框架和 Epileptor 模型中采用计算方法,探索小鼠大脑中致痫区(EZ)的位置和连通性与局灶性癫痫发作(癫痫发作从一个脑区开始,可能会也可能不会保持局部性)的关系,并特别关注因与癫痫发作相关而闻名的海马区。然后,我们设计出限制癫痫发作(防止广泛传播)的计算策略,模拟类似医疗的治疗方法,如组织切除、应用抗癫痫药物或神经刺激来抑制过度兴奋。通过有选择性地移除(阻断)由结构连接组和图网络测量结果提供的特定连接,或局部降低 EZ 区域的外向连接权重,我们证明癫痫发作可以被限制在 EZ 区域周围。我们成功地确定了防止大范围癫痫发作所需的最小连接,重点是最大限度地减少手术或药物干预,同时保留原有的结构连接并最大限度地提高大脑功能。
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引用次数: 0
Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories 开发海马神经假体,促进人类对刺激特征和类别的记忆编码和回忆
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-08 DOI: 10.3389/fncom.2024.1263311
Brent M. Roeder, Xiwei She, Alexander S. Dakos, Bryan Moore, Robert T. Wicks, Mark R. Witcher, Daniel E. Couture, Adrian W. Laxton, Heidi Munger Clary, Gautam Popli, Charles Liu, Brian Lee, Christianne Heck, George Nune, Hui Gong, Susan Shaw, Vasilis Z. Marmarelis, Theodore W. Berger, Sam A. Deadwyler, Dong Song, Robert E. Hampson
ObjectiveHere, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject’s own hippocampal spatiotemporal neural codes for memory.ApproachWe constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task.Main resultsMDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation.SignificanceThese results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.
目的在这里,我们首次成功地利用静态神经刺激模式来刺激特定的信息内容。方法我们构建了一个新模型,该模型描述了海马通过神经组合的时空发射对特定记忆项目进行编码的过程,这是成功将目标内容编码到短时记忆的基础。通过计算海马CA3和CA1神经发射的记忆解码模型(MDM),得出了在延迟匹配到样本(DMS)人类短时记忆任务的编码(样本)阶段对CA1和CA3神经元的刺激模式。主要结果 在DMS试验的取样阶段对海马CA1和CA3位置进行MDM电刺激,有助于在延迟识别(DR)任务中对DMS任务中的图像进行记忆,该任务还包括非DMS任务中的对照图像。在所有受试者中,有 22.4% 的患者和类别组合在受刺激试验中表现出显著的成绩变化。成绩的变化既包括记忆成绩的提高,也包括记忆成绩的降低,成绩的提高与降低的比例几乎为 2:1。在接受双侧刺激的记忆受损患者中,超过 37.9% 的患者和类别组合的记忆表现发生了显著变化,记忆表现的提高和降低比例超过 4:1。记忆能力的改变取决于记忆功能是完好还是受损,以及是双侧还是单侧刺激,几乎所有记忆受损的受试者在接受双侧刺激后记忆能力都有所提高。
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引用次数: 0
Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level 利用[18F]-FDG PET对中脑代谢低下进行随机森林分析,在受试者层面识别帕金森病
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-02-07 DOI: 10.3389/fncom.2024.1328699
Marina C. Ruppert-Junck, Gunter Kräling, Andrea Greuel, Marc Tittgemeyer, Lars Timmermann, Alexander Drzezga, Carsten Eggers, David Pedrosa
Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.
目前,帕金森病(PD)的诊断主要依靠专家判断,神经影像学检查只是辅助工具。在最近的一项研究中,我们根据[18F]-氟脱氧葡萄糖([18F]-FDG)正电子发射计算机断层扫描的分组比较,确定了包括部分黑质在内的低代谢中脑群是区分帕金森病患者的最佳代谢特征。一项独立研究表明,黑质代谢在帕金森综合征的诊断工作中具有巨大潜力。在本研究中,我们采用了一种机器学习方法来评估用 [18F]-FDG PET 测量的中脑代谢,将其作为帕金森病的诊断标志物。共有 51 名中期帕金森病患者和 16 名健康对照受试者接受了高分辨率 [18F]-FDG PET 扫描。通过组间比较确定的中脑群的归一化示踪剂更新值从个人扫描中逐个体素提取。提取的摄取值采用随机森林特征分类算法。为了测试模型在区分患者和对照组方面的稳健性,采用了一种经过调整的 "留一弃一 "交叉验证方法。通过计算验证数据集的灵敏度、特异性和模型准确性,以及测试数据集正确分类受试者的百分比,评估了模型在所有运行中的性能。在验证数据集中,基于中脑簇体素摄取值的随机森林特征分类能识别出患者,平均灵敏度为 0.91(最低:0.82,最高:0.94)。在所有 67 次运行中,每个人都被作为测试数据集处理一次,测试数据集被我们的模型正确分类。所应用的特征重要性提取方法在所有运行中都一致识别出了中脑集群中重要性最高的一个体素子集,该子集在空间上与左侧黑质趋于一致。我们的数据表明,[18F]-FDG PET 测量的中脑代谢是一种很有前景的诊断帕金森病的成像工具。鉴于其与帕金森病病理生理学的密切关系和极高的鉴别准确性,这种方法有助于客观化帕金森病的诊断,并能在临床试验中进行更准确的分类,这也适用于前驱期疾病患者。
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Frontiers in Computational Neuroscience
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