V1 内功能网络和观察到的刺激分类

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Marlis Ontivero-Ortega, Jorge Iglesias-Fuster, Jhoanna Perez-Hidalgo, Daniele Marinazzo, Mitchell Valdes-Sosa, Pedro Valdes-Sosa
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引用次数: 0

摘要

引言 以前的研究表明,V1 内神经活动的共同波动(用 fMRI 测量)带有关于观察到的刺激的信息,可能反映了各种认知机制。本研究通过使用不同的 fMRI 预处理方法来探索形成这种信息的神经来源。可以通过使用受试者间相关性来强调所有个体对刺激的共同反应,也可以通过在计算相关性之前用血液动力学反应函数(HRFs)对 fMRI 进行解卷积来淡化这种反应。方法在此,我们采用上述相关类型,对 V1 内相关矩阵进行多变量模式分析,以预测观察到的 Navon 字母的 "水平 "或 "形状"。我们评估了受试者间预测特定属性组合的准确性,并尝试对刺激属性进行受试者内交叉分类(即在一个特征发生变化的情况下预测另一个特征)。成功分类者的权重图被投射到视野中。结果所有主体间分类器都能准确预测特定观察刺激物的 "水平 "和 "形状"。然而,无论预处理方案如何,只有刺激物的 "水平"(Level)能成功实现被试间交叉分类,而 "形状"(Shape)则不能。成功进行 "水平 "分类的权重图在受试者间相关性和解卷积相关性之间存在差异。后者显示了视野链接强度的不对称性,与已知的知觉不对称性相对应。对眼球 fMRI 信号的事后测量没有发现不同刺激条件下的注视差异,对照实验(衍生模拟)也表明眼球运动不能解释 V1 拓扑中与刺激相关的变化。去卷积增强了特定受试者的活动,突出了全局刺激的半球间联系。对 V1 内网络的进一步探索有望深入了解注意力和知觉组织的神经基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intra-V1 functional networks and classification of observed stimuli
IntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity.MethodsHere, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation.ResultsAll inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology.DiscussionOur findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization.
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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