Spontaneous brain activity observed with functional magnetic resonance imaging as a potential biomarker in neuropsychiatric disorders

As functional magnetic resonance imaging (fMRI) studies have yielded increasing amounts of information about the brain’s spontaneous activity, they have revealed fMRI’s potential to locate changes in brain hemodynamics that are associated with neuropsychiatric disorders. In this paper, we review studies that support the notion that changes in brain spontaneous activity observed by fMRI can be used as potential biomarkers for diagnosis and treatment evaluation in neuropsychiatric disorders. We first review the methods used to study spontaneous activity from the perspectives of (1) the properties of local spontaneous activity, (2) the spatial pattern of spontaneous activity, and (3) the topological properties of brain networks. We also summarize the major findings associated with major neuropsychiatric disorders obtained using these methods. Then we review the pilot studies that have used spontaneous activity to discriminate patients from normal controls. Finally, we discuss current challenges and potential research directions to further elucidate the clinical use of spontaneous brain activity in neuropsychiatric disorders.     

基于fMRI的脑功能整合数据分析方法综述

脑功能成像在人脑信息处理和认知活动的神经关联中发挥了不可轻视的作用.从大脑功能整合出发,可以将脑功能成像数据分析方法分为探测大脑功能整合的功能连接和有效连接两方面,功能连接探究空间远离的两个脑区之间的连接,有效连接研究一个脑区对另一个脑区作用的大小.根据这两个概念,相应地可以将功能磁共振数据分析方法分为两大类.本文着重...     

Increased functional connectivity with puberty in the mentalising network involved in social emotion processing

This article is part of a Special Issue “Puberty and Adolescence”.     

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

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城市生态网络功能性连接辨识方法

城市生态网络是景观生态学应用领域研究的热点和重点之一,识别、评估生境之间的连接是构建生态网络的关键环节。在总结已有连接辨识方法的基础上,提出采用最小费用模型和图论分析相结合的方法,探讨功能性连接的辨识和优先恢复途径。以新西兰基督城为案例,分别利用景观发展强度指数建立阻力面,新西兰鸡毛松(Dacrycarpus dacrydioides)种子最大传播距离作为连接阈值来模拟、评价网络连接。结果表明:在1200 m距离阈值下,共有408条连接,其重要性分为10类。其中Richmond—Petrie公园,Hansons—Auburn保护地,Centaurus公园—King George保护地是整个生态网络的关键连接;若去除,景观整体连接度将下降31.73%。此外,研究发现连接重要值与两端的源面积之和没有显著相关性,即面积大的源斑块之间的连接不一定对网络构建起关键作用,这一结论还有待进一步证明。针对缺少动物迁移资料的城市环境,改进最小费用模型和网络连接分析的部分参数;可操作性与实用性强,对中国城市区域生态恢复建设、栖息地选择具有借鉴意义。     

Dysregulated daily rhythmicity of neuronal resting-state networks in MCI patients

In young healthy participants, the degree of daily rhythmicity largely varies across different neuronal resting-state networks (RSNs), while it is to date unknown whether this temporal pattern of activity is conserved in healthy and pathological aging. Twelve healthy elderly (mean age?=?65.1?±?5.7 years) and 12 patients with amnestic mild cognitive impairment (aMCI; mean age?=?69.6?±?6.2 years) underwent four resting-state functional magnetic resonance imaging scans at fixed 2.5?h intervals throughout a day. Time courses of a RSN were extracted by a connectivity strength and a spatial extent approach performed individually for each participant. Highly rhythmic RSNs included a sensorimotor, a cerebellar and a visual network in healthy elderly; the least rhythmic RSNs in this group included a network associated with executive control and an orbitofrontal network. The degree of daily rhythmicity in aMCI patients was reduced and dysregulated. For healthy elderly, the findings are in accordance with results reported for young healthy participants suggesting a comparable distribution of daily rhythmicity across RSNs during healthy aging. In contrast, the reduction and dysregulation of daily rhythmicity observed in aMCI patients is presumably indicative of underlying neurodegenerative processes in this group.     

Algebraic connectivity may explain the evolution of gene regulatory networks

Gene expression is a result of the interplay between the structure, type, kinetics, and specificity of gene regulatory interactions, whose diversity gives rise to the variety of life forms. As the dynamic behavior of gene regulatory networks depends on their structure, here we attempt to determine structural reasons which, despite the similarities in global network properties, may explain the large differences in organismal complexity. We demonstrate that the algebraic connectivity, the smallest non-trivial eigenvalue of the Laplacian, of the directed gene regulatory networks decreases with the increase of organismal complexity, and may therefore explain the difference between the variety of analyzed regulatory networks. In addition, our results point out that, for the species considered in this study, evolution favours decreasing concentration of strategically positioned feed forward loops, so that the network as a whole can increase the specificity towards changing environments. Moreover, contrary to the existing results, we show that the average degree, the length of the longest cascade, and the average cascade length of gene regulatory networks cannot recover the evolutionary relationships between organisms. Whereas the dynamical properties of special subnetworks are relatively well understood, there is still limited knowledge about the evolutionary reasons for the already identified design principles pertaining to these special subnetworks, underlying the global quantitative features of gene regulatory networks of different organisms. The behavior of the algebraic connectivity, which we show valid on gene regulatory networks extracted from curated databases, can serve as an additional evolutionary principle of organism-specific regulatory networks.     

Cross‐scale effects of structural and functional connectivity in pond networks on amphibian distribution in agricultural landscapes

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Quantifying functional connectivity: experimental assessment of boundary permeability for the natterjack toad (Bufo calamita)

Like other pond-breeding amphibians, the natterjack toad (Bufo calamita) typically presents a patchy distribution. Because the species experiences high probabilities of local population extinction, its persistence within landscapes relies on both local and landscape-scale processes [dispersal allowing the (re)colonization of habitat patches]. However, the structure and composition of the matrix surrounding local populations can alter the dispersal rates between populations. As shown previously (Landscape Ecol 19:829–842, 2004), the locomotor performances of individuals at the dispersal stage depend on the nature of the component crossed: some landscape components offer high resistance to movement (high resistance or high viscosity components) whereas others allow high efficiency of movement (low resistance components). We now examine the ability of individuals to discriminate between landscape components and select low-resistance components. Our experimental study investigates the ways in which young natterjack toads choose from among landscape components common to southern Belgium. Toadlets (the dispersal stage) were experimentally confronted with boundaries between surrogates of sandy soils, roads, forests, agricultural fields and intensive pastures. Our results show: 1 the ability of toadlets to react to boundaries between landscape components, 2 differences in permeability among boundaries, and 3 our inability to predict correctly the permeability of the boundaries from the patch-specific resistance assessed previously. Toadlets showed a preference for bare environments and forests, whereas they avoided the use of agricultural environments. This pattern could not be explained in terms of patch-specific resistance only, and is discussed in terms of mortality risks and resource availability in the various landscape components, with particular attention to repercussions on conservation strategies.     

基于空间ICA和时间相关方法的人脑视觉皮层V5区的功能连通性研究

利用功能磁共振成像技术,将空间ICA和时间相关方法相结合来研究不同活动状态下人脑视觉皮层V5区的功能连通性。首先利用空间ICA处理组块视觉运动刺激的数据,定位V5区;然后分别计算静息和连续视觉运动刺激两种稳态下V5区与其它脑区低频振荡的时间相关,检测出该区的功能连通网络。实验结果表明,静息时V5区的功能连通网络更广泛,且与已知的解剖连通一致;当被试接受连续视觉运动刺激时,与V5区连通的脑区网络局限在视觉皮层,此时的网络特定于处理视觉运动这一任务。     

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.     

Classification of group-specific variations in songs within House Wren species using machine learning models

Songbirds have shown variation in vocalizations across different populations and different geographical ranges. Such variations can over time lead to divergence in song characteristics, sometimes referred to as dialects. House Wren (Troglodytes aedon) is one such widely distributed bird species that has shown variation in its song characteristics within different populations. Traditionally, such studies have been conducted using manual approaches for classification. In this work we explore the use of machine learning models that can assist in performing classification of bird songs at a conspecific level. Two machine learning techniques, the random forest and a shallow feed forward neural network, are fed with pre-computed sound features to classify vocal variation in House Wren species across different reported population groups and latitudinal areas. A randomized approach is employed to create balanced subsets of sounds from different locations for repeated classification runs in order to provide a reliable estimate of performance. It is observed that such an automated approach is able to classify variations in songs within House Wren with high accuracy. We were also able to confirm the latitudinal variation of House Wren songs reported in previous studies. Given these results, we believe, such a purely data-driven way of analyzing bird songs in general can provide useful hints to biologists on where to look for interesting patterns in order to understand the evolutionary divergence in song characteristics.     

Synchrony based learning rule of Hopfield like chaotic neural networks with desirable structure

In this paper a new learning rule for the coupling weights tuning of Hopfield like chaotic neural networks is developed in such a way that all neurons behave in a synchronous manner, while the desirable structure of the network is preserved during the learning process. The proposed learning rule is based on sufficient synchronization criteria, on the eigenvalues of the weight matrix belonging to the neural network and the idea of Structured Inverse Eigenvalue Problem. Our developed learning rule not only synchronizes all neuron’s outputs with each other in a desirable topology, but also enables us to enhance the synchronizability of the networks by choosing the appropriate set of weight matrix eigenvalues. Specifically, this method is evaluated by performing simulations on the scale-free topology.     

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.     

Computational structural analysis of protein interactions and networks

Protein interactions have been at the focus of computational biology in recent years. In particular, interest has come from two different communities--structural and systems biology. Here, we will discuss key systems and structural biology methods that have been used for analysis and prediction of protein-protein interactions and the insight these approaches have provided on the nature and organization of protein-protein interactions inside cells.