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DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data DeepComBat:一种基于统计、超参数稳健、深度学习的神经成像数据协调方法。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-26 DOI: 10.1002/hbm.26708
Fengling Hu, Alfredo Lucas, Andrew A. Chen, Kyle Coleman, Hannah Horng, Raymond W. S. Ng, Nicholas J. Tustison, Kathryn A. Davis, Haochang Shou, Mingyao Li, Russell T. Shinohara, The Alzheimer's Disease Neuroimaging Initiative

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

使用多种扫描仪或方案获取的神经成像数据越来越多。然而,这些数据在不同批次之间会出现技术伪影,从而引入混杂因素并降低可重复性。当使用复杂的下游模型分析多批次数据时,情况尤其如此,因为复杂的下游模型更有可能捕捉并隐含与批次相关的信息。以前提出的图像协调方法试图消除这些批次效应;然而,在应用这些方法后,批次效应仍可在数据中检测到。我们提出了 DeepComBat,这是一种基于条件变异自动编码器和 ComBat 方法的深度学习协调方法。DeepComBat 结合了统计方法和深度学习方法的优势,以考虑特征之间的多变量关系,同时放宽了以往深度学习协调方法的强假设。因此,DeepComBat 可以执行多变量协调,同时保留数据结构并避免引入合成人工痕迹。我们将这种方法应用于认知老化队列的皮层厚度测量,结果表明,DeepComBat 在消除批次效应的同时保留生物异质性,在质量和数量上都优于现有方法。此外,DeepComBat 还为统计动机的深度学习协调方法提供了一个新的视角。
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引用次数: 0
4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia 与认知有关的静态 4D 动态空间大脑网络在精神分裂症患者中显示出非典型的可变性和耦合性。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-24 DOI: 10.1002/hbm.26773
Krishna Pusuluri, Zening Fu, Robyn Miller, Godfrey Pearlson, Peter Kochunov, Theo G. M. Van Erp, Armin Iraji, Vince D. Calhoun

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

尽管人们对大脑功能网络的动态性越来越感兴趣,但大多数研究都集中在空间静态网络或区域之间随时间变化的关系上。在这里,我们提出了一种方法来研究人类静息状态功能磁共振成像(rsfMRI)数据中的动态空间大脑网络,并评估这些四维网络体积的时间变化。我们的研究结果表明,扫描过程中网络之间存在明显的体积耦合(即同步收缩和增长),我们将其称为动态空间网络连通性(dSNC)。我们发现,这种动态空间大脑网络的几个特征与认知能力有关,这些网络的动态变异性越高,网络对之间的体积耦合越高,认知能力就越强。我们的研究表明,精神分裂症患者与典型对照组患者对这些网络的调节方式不同,从而导致网络的增长或收缩,以及网络内活动焦点的改变。精神分裂症患者还表现出多个网络的空间动态变异性较低,成对网络之间的容积耦合较低,从而证实了动态空间大脑网络在精神分裂症认知障碍中的作用。我们的数据证明了研究大脑网络内部和网络之间通常被忽视的体素变化的重要性。
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引用次数: 0
Compressed representation of brain genetic transcription 大脑基因转录的压缩表示
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-24 DOI: 10.1002/hbm.26795
James K. Ruffle, Henry Watkins, Robert J. Gray, Harpreet Hyare, Michel Thiebaut de Schotten, Parashkev Nachev

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods—PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding—quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

大脑的结构过于复杂,如果不使用压缩表示法将其变化投射到一个紧凑、可浏览的空间,就无法直观地对其进行勘测。对于基因表达等高维数据来说,这项任务尤其具有挑战性,因为解剖和转录模式的共同复杂性要求最大程度的压缩。传统的做法是使用标准的主成分分析(PCA),但其计算的便利性被有限的表达能力所抵消,尤其是在压缩比很大的情况下。利用全脑体素艾伦脑图谱转录数据,我们系统地比较了基于最广泛支持的线性和非线性方法--主成分分析(PCA)、核主成分分析(PCA)、非负矩阵因式分解(NMF)、t-随机邻域嵌入(t-SNE)的压缩表示、统一流形近似和投影(UMAP)以及深度自动编码--对重建保真度、解剖一致性以及信号、微结构和代谢目标的预测效用进行量化,这些数据来自大规模开源 MRI 和 PET 数据。我们的研究表明,深度自动编码器在所有性能指标和目标领域都能产生卓越的表示,支持将其作为表示人脑转录模式的参考标准。
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引用次数: 0
Within-person biological mechanisms of mood variability in childhood and adolescence 童年和青春期情绪变化的人体内生物机制。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-24 DOI: 10.1002/hbm.26766
Yara J. Toenders, Marleen H. M. de Moor, Renske van der Cruijsen, Kayla Green, Michelle Achterberg, Eveline A. Crone

Mood variability, the day-to-day fluctuation in mood, differs between individuals and develops during adolescence. Because adolescents show higher mood variability and average mood than children and adults, puberty might be a potential biological mechanism underlying this increase. The goal of this preregistered developmental study was to examine the neural and hormonal underpinnings of adolescent-specific within-person changes in mood variability, with a specific focus on testosterone, cortisol, pubertal status, and resting-state functional brain connectivity. Data from two longitudinal cohorts were used: the L-CID twin study (aged 7–13, N at the first timepoint = 258) and the accelerated Leiden Self-Concept study (SC; aged 11–21, N at the first timepoint = 138). In both studies resting-state functional magnetic resonance imaging (rs-fMRI) data was collected, as well as daily mood. Additionally, in the SC study self-reported puberty testosterone and cortisol were collected. Random intercept cross-lagged panel models (RI-CLPM) were used to study the within-person relations between these biological measures and mood variability and average mood. Mood variability and average mood peaked in adolescence and testosterone levels and self-reported puberty also showed an increase. Connectivity between prefrontal cortex (dlPFC and vmPFC) and subcortical regions (caudate, amygdala) decreased across development. Moreover, higher testosterone predicted average negative mood at the next time point, but not vice versa. Further, stronger vmPFC–amygdala functional connectivity predicted decreases in mood variability. Here, we show that brain connectivity during development is an important within-person biological mechanism of the development of mood in adolescents.

Practitioner Points

  • Mood variability peaks in adolescence.
  • Within-person changes in testosterone predict within-person changes in mood.
  • Within-person changes in vmPFC–amygdala connectivity predict within-person changes in mood variability.
情绪多变性是指每天的情绪波动,因人而异,并在青春期发展。与儿童和成人相比,青少年的情绪变异性和平均情绪更高,因此青春期可能是导致情绪变异性和平均情绪增加的潜在生物机制。这项预先登记的发育研究的目的是研究青少年特定的人体内情绪变异性变化的神经和激素基础,重点是睾酮、皮质醇、青春期状态和静息状态大脑功能连接。研究使用了两个纵向队列的数据:L-CID 双生子研究(7-13 岁,第一个时间点的 N = 258)和莱顿自我概念加速研究(SC;11-21 岁,第一个时间点的 N = 138)。这两项研究都收集了静息态功能磁共振成像(rs-fMRI)数据以及日常情绪。此外,SC 研究还收集了自我报告的青春期睾酮和皮质醇。随机截距交叉滞后面板模型(RI-CLPM)用于研究这些生物测量指标与情绪变异性和平均情绪之间的人际关系。情绪变异性和平均情绪在青春期达到顶峰,睾酮水平和自我报告的青春期也出现了增长。前额叶皮层(dlPFC 和 vmPFC)与皮层下区域(尾状核、杏仁核)之间的连接性在整个发育过程中都有所下降。此外,较高的睾酮可预测下一个时间点的平均消极情绪,但反之亦然。此外,较强的前脑皮质-杏仁核功能连通性可预测情绪变异性的下降。在此,我们表明,发育过程中的大脑连通性是青少年情绪发展的一个重要的人内生物学机制。实践点:情绪变化在青春期达到高峰。人体内睾酮的变化可预测人体内情绪的变化。前脑皮质-杏仁核连通性的人内变化可预测情绪变异性的人内变化。
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引用次数: 0
Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth 非符号量级处理过程中的前顶叶和显著性网络同步化可预测青少年的大脑年龄和数学成绩。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-24 DOI: 10.1002/hbm.26777
Chan-Tat Ng, Po-Hsien Huang, Yi-Cheng Cho, Pei-Hong Lee, Yi-Chang Liu, Ting-Ting Chang

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7–30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth.

Practitioner Points

  • This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks.
  • Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks.
  • Leveraging advanced machine learning methods, we accurately p
对人类认知至关重要的大脑功能回路的发展和完善是一个从童年到成年的持续过程。越来越多的研究侧重于绘制这些不断演变的配置图,目的是找出功能障碍和非典型发育的标志物。在人类认知系统中,非符号的大小表征是个人未来成功学习数学和取得成就的基础。利用非符号量级处理过程中基于任务的额顶叶(FPN)和显著性网络(SN)特征以及机器学习算法,我们开发了一个框架,为7-30岁的参与者构建大脑年龄预测模型。我们的研究揭示了 FPN 和 SN 网络内部和之间同步的不同发展特征。具体来说,我们观察到在整个年龄跨度内,FPN 连接性呈线性增长,而 SN 连接性则呈下降趋势。我们发现 FPN 和 SN 之间的连通性呈非线性 U 型轨迹,这表明与儿童和成人队列相比,青少年的 FPN-SN 同步性降低了。利用梯度提升(Gradient Boosting)机器学习算法和独立训练数据集的嵌套五重分层交叉验证,我们证明了 FPN 和 SN 节点的功能连接测量可预测年代年龄,实际年龄和预测年龄之间的相关系数为 0.727,平均绝对误差为 2.944。值得注意的是,FPN内部的连通性是对年龄预测最有帮助的特征。重要的是,更成熟的大脑年龄估计与更好的算术表现相关。我们的发现揭示了支持幅度表征的神经网络中发生的错综复杂的发展变化。我们强调脑年龄估计是了解认知发展及其与青少年关键发育期数学能力关系的有效工具。实践点:本研究调查了大脑结构在儿童期、青春期和成年期的长期变化,重点是任务状态的顶叶前部和显著性网络。研究发现了不同的发育途径:额顶叶的同步性在整个发育过程中不断加强,而显著性网络的连通性则随着年龄的增长而减弱。此外,青少年在这些网络之间的连通性上表现出独特的下降。利用先进的机器学习方法,我们根据这些脑回路准确预测了个体的年龄,而更成熟的估计脑年龄与更好的数学技能相关。
{"title":"Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth","authors":"Chan-Tat Ng,&nbsp;Po-Hsien Huang,&nbsp;Yi-Cheng Cho,&nbsp;Pei-Hong Lee,&nbsp;Yi-Chang Liu,&nbsp;Ting-Ting Chang","doi":"10.1002/hbm.26777","DOIUrl":"10.1002/hbm.26777","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7–30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Practitioner Points</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks.</li>\u0000 \u0000 <li>Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks.</li>\u0000 \u0000 <li>Leveraging advanced machine learning methods, we accurately p","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11267564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Large-scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents 神经发育过程中大脑结构不对称的大规模分析:4265 名儿童和青少年的年龄与性别关系。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-24 DOI: 10.1002/hbm.26754
F. Kurth, D. Schijven, O. A. van den Heuvel, M. Hoogman, D. van Rooij, D. J. Stein, J. K. Buitelaar, S. Bölte, G. Auzias, A. Kushki, G. Venkatasubramanian, K. Rubia, S. Bollmann, J. Isaksson, F. Jaspers-Fayer, R. Marsh, M. C. Batistuzzo, P. D. Arnold, R. A. Bressan, S. E. Stewart, P. Gruner, L. Sorensen, P. M. Pan, T. J. Silk, R. C. Gur, A. I. Cubillo, J. Haavik, R. L. O'Gorman Tuura, C. A. Hartman, R. Calvo, J. McGrath, S. Calderoni, A. Jackowski, K. C. Chantiluke, T. D. Satterthwaite, G. F. Busatto, J. T. Nigg, R. E. Gur, A. Retico, M. Tosetti, L. Gallagher, P. R. Szeszko, J. Neufeld, A. E. Ortiz, C. Ghisleni, L. Lazaro, P. J. Hoekstra, E. Anagnostou, L. Hoekstra, B. Simpson, J. K. Plessen, C. Deruelle, N. Soreni, A. James, J. Narayanaswamy, J. Y. Reddy, J. Fitzgerald, M. A. Bellgrove, G. A. Salum, J. Janssen, F. Muratori, M. Vila, M. Garcia Giral, S. H. Ameis, P. Bosco, K. Lundin Remnélius, C. Huyser, J. C. Pariente, M. Jalbrzikowski, P. G. Rosa, K. M. O'Hearn, S. Ehrlich, J. Mollon, A. Zugman, A. Christakou, C. Arango, S. E. Fisher, X. Kong, B. Franke, S. E. Medland, S. I. Thomopoulos, N. Jahanshad, D. C. Glahn, P. M. Thompson, C. Francks, E. Luders

Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1–18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.

只有少数研究对儿童和青少年时期大脑两个半球的结构差异进行了评估。然而,现有的研究结果缺乏一致性,或局限于特定的大脑区域、特定的大脑特征或相对狭窄的年龄范围。在这里,我们研究了迄今为止最大的儿科样本之一(n = 4265)中大脑不对称与年龄和性别之间的关系,这些样本的年龄在 1-18 岁之间,在参加 ENIGMA(通过元分析增强神经成像遗传学)联盟的 69 个研究机构进行了扫描。我们的研究发现,儿童时期大脑已经存在明显的不对称性,但其程度和方向取决于所检查的大脑区域和所使用的形态测量方法(皮质体积或厚度、区域表面积或皮质下体积)。在年龄的影响方面,有些不对称会随着时间的推移而减弱,而另一些则会增强,有时甚至会逆转方向。在性别差异方面,女性表现出显著不对称性的区域总数多于男性,而男性表现出显著不对称性的测量结果总数多于女性(因为我们在每个皮层区域获得了不止一次测量结果)。男性显著不对称的程度也更大。然而,年龄效应和性别差异的效应大小都很小。综上所述,这些研究结果表明,大脑不对称是大脑固有的组织模式,在生命早期就会表现出来。总体而言,大脑不对称性似乎在整个童年和青少年时期相对稳定,但在男性和女性中存在一些不同的影响。
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引用次数: 0
Multisensory integration of speech and gestures in a naturalistic paradigm 在自然范式中对语音和手势进行多感官整合。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-23 DOI: 10.1002/hbm.26797
Magdalena Matyjek, Sotaro Kita, Mireia Torralba Cuello, Salvador Soto Faraco

Speech comprehension is crucial for human social interaction, relying on the integration of auditory and visual cues across various levels of representation. While research has extensively studied multisensory integration (MSI) using idealised, well-controlled stimuli, there is a need to understand this process in response to complex, naturalistic stimuli encountered in everyday life. This study investigated behavioural and neural MSI in neurotypical adults experiencing audio-visual speech within a naturalistic, social context. Our novel paradigm incorporated a broader social situational context, complete words, and speech-supporting iconic gestures, allowing for context-based pragmatics and semantic priors. We investigated MSI in the presence of unimodal (auditory or visual) or complementary, bimodal speech signals. During audio-visual speech trials, compared to unimodal trials, participants more accurately recognised spoken words and showed a more pronounced suppression of alpha power—an indicator of heightened integration load. Importantly, on the neural level, these effects surpassed mere summation of unimodal responses, suggesting non-linear MSI mechanisms. Overall, our findings demonstrate that typically developing adults integrate audio-visual speech and gesture information to facilitate speech comprehension in noisy environments, highlighting the importance of studying MSI in ecologically valid contexts.

语音理解对于人类的社会交往至关重要,它依赖于不同表征层次的听觉和视觉线索的整合。虽然研究人员已经利用理想化、控制良好的刺激对多感官整合(MSI)进行了广泛研究,但仍有必要了解这一过程对日常生活中遇到的复杂、自然刺激的反应。本研究调查了神经畸形成年人在自然的社会环境中体验视听语言时的行为和神经多感觉统合。我们的新颖范式包含了更广泛的社会情境、完整的单词和支持语音的标志性手势,允许基于语境的语用和语义先验。我们研究了单模态(听觉或视觉)或互补双模态语音信号下的 MSI。与单模态试验相比,在视听语音试验中,参与者能更准确地识别口语单词,并表现出更明显的阿尔法功率抑制--这是整合负荷增加的指标。重要的是,在神经层面上,这些效应超越了单纯的单模态反应总和,表明存在非线性 MSI 机制。总之,我们的研究结果表明,发育正常的成年人会整合视听语音和手势信息,以促进在嘈杂环境中的语音理解,这凸显了在生态有效的环境中研究 MSI 的重要性。
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引用次数: 0
Automated diffusion-weighted image analysis along the perivascular space index reveals glymphatic dysfunction in association with brain parenchymal lesions 沿血管周围空间指数进行的自动弥散加权成像分析揭示了与脑实质病变相关的甘液功能障碍。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-22 DOI: 10.1002/hbm.26790
Wen-Xin Li, Zi-Yue Liu, Fei-Fei Zhai, Fei Han, Ming-Li Li, Li-Xin Zhou, Jun Ni, Ming Yao, Shu-Yang Zhang, Li-Ying Cui, Zheng-Yu Jin, Yi-Cheng Zhu

Brain glymphatic dysfunction is critical in neurodegenerative processes. While animal studies have provided substantial insights, understandings in humans remains limited. Recent attention has focused on the non-invasive evaluation of brain glymphatic function. However, its association with brain parenchymal lesions in large-scale population remains under-investigated. In this cross-sectional analysis of 1030 participants (57.14 ± 9.34 years, 37.18% males) from the Shunyi cohort, we developed an automated pipeline to calculate diffusion-weighted image analysis along the perivascular space (ALPS), with a lower ALPS value indicating worse glymphatic function. The automated ALPS showed high consistency with the manual calculation of this index (ICC = 0.81, 95% CI: 0.662–0.898). We found that those with older age and male sex had lower automated ALPS values (β = −0.051, SE = 0.004, p < .001, per 10 years, and β = −0.036, SE = 0.008, p < .001, respectively). White matter hyperintensity (β = −2.458, SE = 0.175, p < .001) and presence of lacunes (OR = 0.004, 95% CI < 0.002–0.016, p < .001) were significantly correlated with decreased ALPS. The brain parenchymal and hippocampal fractions were significantly associated with decreased ALPS (β = 0.067, SE = 0.007, p < .001 and β = 0.040, SE = 0.014, p = .006, respectively) independent of white matter hyperintensity. Our research implies that the automated ALPS index is potentially a valuable imaging marker for the glymphatic system, deepening our understanding of glymphatic dysfunction.

脑 glymphatic 功能障碍在神经退行性过程中至关重要。虽然动物研究提供了大量的见解,但对人类的了解仍然有限。最近,人们将注意力集中在对大脑甘液功能的非侵入性评估上。然而,在大规模人群中,其与脑实质病变的关系仍未得到充分研究。在对顺义队列的 1030 名参与者(57.14 ± 9.34 岁,37.18% 为男性)进行的横断面分析中,我们开发了一个自动管道来计算沿血管周围空间的弥散加权图像分析(ALPS),ALPS 值越低,表示脑 glymphatic 功能越差。自动 ALPS 与人工计算该指数的结果具有很高的一致性(ICC = 0.81,95% CI:0.662-0.898)。我们发现,年龄较大和性别为男性的人的自动 ALPS 值较低(β = -0.051,SE = 0.004,p
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引用次数: 0
Sensitivity and specificity of the action observation network to kinematics, target object, and gesture meaning 动作观察网络对运动学、目标对象和手势含义的敏感性和特异性。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-22 DOI: 10.1002/hbm.26762
Francesca Simonelli, Giacomo Handjaras, Francesca Benuzzi, Giulio Bernardi, Andrea Leo, Davide Duzzi, Luca Cecchetti, Paolo F. Nichelli, Carlo A. Porro, Pietro Pietrini, Emiliano Ricciardi, Fausta Lui

Hierarchical models have been proposed to explain how the brain encodes actions, whereby different areas represent different features, such as gesture kinematics, target object, action goal, and meaning. The visual processing of action-related information is distributed over a well-known network of brain regions spanning separate anatomical areas, attuned to specific stimulus properties, and referred to as action observation network (AON). To determine the brain organization of these features, we measured representational geometries during the observation of a large set of transitive and intransitive gestures in two independent functional magnetic resonance imaging experiments. We provided evidence for a partial dissociation between kinematics, object characteristics, and action meaning in the occipito-parietal, ventro-temporal, and lateral occipito-temporal cortex, respectively. Importantly, most of the AON showed low specificity to all the explored features, and representational spaces sharing similar information content were spread across the cortex without being anatomically adjacent. Overall, our results support the notion that the AON relies on overlapping and distributed coding and may act as a unique representational space instead of mapping features in a modular and segregated manner.

人们提出了分层模型来解释大脑如何对动作进行编码,即不同的区域代表不同的特征,如手势运动学、目标对象、动作目标和意义。对动作相关信息的视觉处理分布在一个众所周知的大脑区域网络中,该网络跨越不同的解剖区域,与特定的刺激属性相适应,被称为动作观察网络(AON)。为了确定这些特征的大脑组织结构,我们在两个独立的功能磁共振成像实验中测量了观察大量传递性和非传递性手势时的表征几何图形。我们提供的证据表明,运动学、物体特征和动作意义在枕顶叶皮层、文颞叶皮层和枕颞叶外侧皮层分别存在部分分离。重要的是,大多数 AON 对所有被探索的特征都表现出较低的特异性,而且具有相似信息内容的表征空间分布在皮层中,在解剖学上并不相邻。总之,我们的研究结果支持这样一种观点,即AON依赖于重叠和分布式编码,并可能作为一个独特的表征空间,而不是以模块化和分离的方式映射特征。
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引用次数: 0
Electrophysiological correlation of auditory selective spatial attention in the “cocktail party” situation 鸡尾酒会 "情境中听觉选择性空间注意的电生理学相关性。
IF 3.5 2区 医学 Q1 NEUROIMAGING Pub Date : 2024-07-22 DOI: 10.1002/hbm.26793
Hongxing Liu, Yanru Bai, Qi Zheng, Jihan Liu, Jianing Zhu, Guangjian Ni

The auditory system can selectively attend to the target source in complex environments, the phenomenon known as the “cocktail party” effect. However, the spatiotemporal dynamics of electrophysiological activity associated with auditory selective spatial attention (ASSA) remain largely unexplored. In this study, single-source and multiple-source paradigms were designed to simulate different auditory environments, and microstate analysis was introduced to reveal the electrophysiological correlates of ASSA. Furthermore, cortical source analysis was employed to reveal the neural activity regions of these microstates. The results showed that five microstates could explain the spatiotemporal dynamics of ASSA, ranging from MS1 to MS5. Notably, MS2 and MS3 showed significantly lower partial properties in multiple-source situations than in single-source situations, whereas MS4 had shorter durations and MS5 longer durations in multiple-source situations than in single-source situations. MS1 had insignificant differences between the two situations. Cortical source analysis showed that the activation regions of these microstates initially transferred from the right temporal cortex to the temporal–parietal cortex, and subsequently to the dorsofrontal cortex. Moreover, the neural activity of the single-source situations was greater than that of the multiple-source situations in MS2 and MS3, correlating with the N1 and P2 components, with the greatest differences observed in the superior temporal gyrus and inferior parietal lobule. These findings suggest that these specific microstates and their associated activation regions may serve as promising substrates for decoding ASSA in complex environments.

听觉系统可以在复杂的环境中选择性地注意目标声源,这种现象被称为 "鸡尾酒会 "效应。然而,与听觉选择性空间注意(ASSA)相关的电生理活动的时空动态在很大程度上仍未得到研究。本研究设计了单源和多源范例来模拟不同的听觉环境,并引入微状态分析来揭示 ASSA 的电生理相关性。此外,还采用了皮层源分析来揭示这些微状态的神经活动区域。结果显示,从 MS1 到 MS5,有五种微状态可以解释 ASSA 的时空动态。值得注意的是,MS2 和 MS3 在多源情况下的部分属性明显低于单源情况,而 MS4 在多源情况下的持续时间短于单源情况,MS5 的持续时间长于单源情况。MS1 在两种情况下的差异不明显。皮层源分析表明,这些微状态的激活区域最初从右侧颞叶皮层转移到颞顶叶皮层,随后又转移到额叶皮层。此外,在 MS2 和 MS3 中,单源情况下的神经活动大于多源情况下的神经活动,与 N1 和 P2 成分相关,在颞上回和顶叶下部观察到的差异最大。这些研究结果表明,这些特定的微状态及其相关的激活区域可能是在复杂环境中解码 ASSA 的有前途的基底。
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引用次数: 0
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Human Brain Mapping
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