Controllability Analysis of Functional Brain Networks

Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)

Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel application of multivariate pattern analysis techniques in the context of brain-wide connectome…

持续同调拓宽人类结构连接组中的可控子空间

Persistent homology broadens the controllable subspace in human structural connectomes

该研究提出基于持续拓扑环的驱动节点选择标准，与基于度的标准控制能相近但可控子空间几何更优，能捕捉标量能量遗漏的脑网络控制信息。

Construction and Evaluation of Hierarchical Parcellation of the Brain using fMRI with Prewhitening

Brain atlases are a ubiquitous tool used for analyzing and interpreting brain imaging datasets. Traditionally, brain atlases divided the brain into regions separated by anatomical landmarks. In the last decade, several attempts have been made to parcellate the brain into regions with distinct functional activity using fMRI. To construct a brain atlas using fMRI, data driven algorithms are used to group voxels with similar functional activity together to form regions. Hierarchical clustering is one parcellation method that has been used for functional parcellation of the brain, resulting in par…

Untangling the brain web: from the early days of complex functional networks to the non-linear dynamical directed functional connectivity measures

Already two decades passed since the first applications of graph theory to brain neuroimaging. Since that early description, the characterization of the brain as a very large interacting complex network has evolved in several directions. In this brief review we discuss our contributions to this topic and discuss some perspective for future work.

应用时间感知PC算法从fMRI数据计算阿尔茨海默病的因果功能连接

Application of Time-Aware PC algorithm to compute Causal Functional Connectivity in Alzheimer's Disease from fMRI data

本研究应用Time-aware PC（TPC）算法从三组受试者的静息态fMRI数据推断全脑因果功能连接，识别出阿尔茨海默病导致改变的因果脑连接及受影响脑区，结果与临床文献一致。

Revisiting non-linear functional brain co-activations: directed, dynamic and delayed

The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In previous work we first demonstrated that the relatively stronger BOLD activations contain most of the information relevant to understand functional connectivity and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this work we re…

分析复杂的功能性大脑网络：融合统计学和网络科学来理解大脑

Analyzing complex functional brain networks: fusing statistics and network science to understand the brain

在过去的八年里，复杂的功能性大脑网络分析已经爆炸式发展，由于其深刻的临床意义而获得了牵引力。网络科学（图论的一个跨学科分支）的应用促进了这些分析，并使研究大脑成为一个产生复杂行为的集成系统。虽然统计学领域在推进激活分析和功能神经成像研究中的一些连接性分析方面不可或缺，但它在复杂网络分析中尚未发挥相应的作用。将新的统计方法与基于网络的功能神经图像分析相结合，将产生强大的分析工具，有助于我们理解正常的大脑功能以及各种大脑疾病引起的变化。在这里，我们调查了广泛使用的统计和网络科学工具分析功能磁共振成像网络数据，并讨论了在填补一些剩余的方法差距所面临的挑战。当正确应用和解释时，网络科学和统计方法的融合有机会彻底改变对大脑功能的理解。

Unifying Modular and Core-Periphery Structure in Functional Brain Networks over Development

At rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core-periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core-pe…

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNNs to extract effective representations from brain networks in multiple modalities remains rarely explored. Moreover, as brain networks provide no initial node features, how to design informative node attributes and leverage edge weights for GNNs to learn is left unsolved. To this…

Network biomarkers of schizophrenia by graph theoretical investigations of Brain Functional Networks

Brain Functional Networks (BFNs), graph theoretical models of brain activity data, provide a systems perspective of complex functional connectivity within the brain. Neurological disorders are known to have basis in abnormal functional activities, that could be captured in terms of network markers. Schizophrenia is a pathological condition characterized with altered brain functional state. We created weighted and binary BFN models of schizophrenia patients as well as healthy subjects starting from fMRI data in an effort to search for network biomarkers of the disease. We investigated 45 topolo…

拓扑随机性和边数预测功能脑网络的模块结构

Topological Randomness and Number of Edges Predict Modular Structure in Functional Brain Networks

在最近的一篇论文中，Bassett等人（2011）分析了人类大脑功能网络的静态和动态组织。我们在这里集中在本文中提出的第一个要求，它指出，这种网络的静态模块结构是嵌套的时间。Bassett等人（2011年）认为，这种分级结构强调了“多尺度模块化结构”。然而，在这封信中，我们表明，这样的关系基本上是由在不同的时间尺度上计算的相关系数的随机变化的增加介导的。