Connectivity differences in brain networks

The scenario considered here is one where brain connectivity is represented as a network and an experimenter wishes to assess the evidence for an experimental effect at each of the typically thousands of connections comprising the network. To do this, a univariate model is independently fitted to each connection. It would be unwise to declare significance based on an uncorrected threshold of α=0.05, since the expected number of false positives for a network comprising N=90 nodes and N(N-1)/2=4005 connections would be 200. Control of Type I errors over all connections is therefore necessary. The network-based statistic (NBS) and spatial pairwise clustering (SPC) are two distinct methods that have been used to control family-wise errors when assessing the evidence for an experimental effect with mass univariate testing. The basic principle of the NBS and SPC is the same as supra-threshold voxel clustering. Unlike voxel clustering, where the definition of a voxel cluster is unambiguous, 'clusters' formed among supra-threshold connections can be defined in different ways. The NBS defines clusters using the graph theoretical concept of connected components. SPC on the other hand uses a more stringent pairwise clustering concept. The purpose of this article is to compare the pros and cons of the NBS and SPC, provide some guidelines on their practical use and demonstrate their utility using a case study involving neuroimaging data.     

Weight-conserving characterization of complex functional brain networks

Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.     

功能性近红外光谱应用于脑认知领域的可视化分析

目的：对功能性近红外光谱技术应用于脑认知相关研究的现状、热点及前沿行可视化分析。方法：检索Web of Science（WOS）数据库2012年1月1日～2022年12月31日功能性近红外光谱技术应用于脑认知研究的文献,使用CiteSpace 5.7软件进行可视化分析。结果：最终纳入文献1685篇,文献年发表量呈增长趋势。Ann-Christine Ehlis是发文最多的作者（36篇）,美国是发文量最多的国家（480篇）,德国图宾根大学是发文量最多的机构（47篇）,发文机构间合作较密切。研究热点集中于认知障碍及儿童脑发育障碍等认知疾病的神经机制研究、认知水平测量及认知疾病治疗评价等方面。结论：功能性近红外光谱技术在脑认知领域的应用处于发展阶段,国内机构应继续强化合作,近年研究开始结合多模态检测数据,研究者可进一步将多模态大数据结合人工智能建立各类认知疾病的认知评估、疗效评价等模型,实现脑认知疾病的客观化精准医疗。     

Gender differences in brain networks supporting empathy

Females frequently score higher on standard tests of empathy, social sensitivity, and emotion recognition than do males. It remains to be clarified, however, whether these gender differences are associated with gender specific neural mechanisms of emotional social cognition. We investigated gender differences in an emotion attribution task using functional magnetic resonance imaging. Subjects either focused on their own emotional response to emotion expressing faces (SELF-task) or evaluated the emotional state expressed by the faces (OTHER-task). Behaviorally, females rated SELF-related emotions significantly stronger than males. Across the sexes, SELF- and OTHER-related processing of facial expressions activated a network of medial and lateral prefrontal, temporal, and parietal brain regions involved in emotional perspective taking. During SELF-related processing, females recruited the right inferior frontal cortex and superior temporal sulcus stronger than males. In contrast, there was increased neural activity in the left temporoparietal junction in males (relative to females). When performing the OTHER-task, females showed increased activation of the right inferior frontal cortex while there were no differential activations in males. The data suggest that females recruit areas containing mirror neurons to a higher degree than males during both SELF- and OTHER-related processing in empathic face-to-face interactions. This may underlie facilitated emotional "contagion" in females. Together with the observation that males differentially rely on the left temporoparietal junction (an area mediating the distinction between the SELF and OTHERS) the data suggest that females and males rely on different strategies when assessing their own emotions in response to other people.     

Hyper-connectivity of functional networks for brain disease diagnosis

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.     

Frequency specificity of functional connectivity in brain networks

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have been shown to be associated with electroencephalography (EEG) power fluctuations in multiple brain networks within predefined frequency bands. However, it remains unclear whether frequency-specific characteristics exist in the resting-state fMRI signal. In this study, fMRI signals in five functional brain networks (sensorimotor, 'default mode', visual, amygdala, and hippocampus) were decomposed into various frequency bands within a low-frequency range (0-0.24 Hz). Results show that the correlations in cortical networks concentrate within ultra-low frequencies (0.01-0.06 Hz) while connections within limbic networks distribute over a wider frequency range (0.01-0.14 Hz), suggesting distinct frequency-specific features in the resting-state fMRI signal within these functional networks. Moreover, the connectivity decay rates along the frequency bands are positively correlated with the physical distances between connected brain regions and seed points. This distance-frequency relationship might be attributed to a larger attenuation of synchrony of brain regions separated with longer distance and/or connected with more synaptic steps.     

Reproducibility of graph metrics of human brain functional networks

Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test–retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from γ to low δ in the overall range 1–60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC = 0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency γ- and β-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes.     

Competition between functional brain networks mediates behavioral variability

Increased intraindividual variability (IIV) is a hallmark of disorders of attention. Recent work has linked these disorders to abnormalities in a "default mode" network, comprising brain regions routinely deactivated during goal-directed cognitive tasks. Findings from a study of the neural basis of attentional lapses suggest that a competitive relationship between the "task-negative" default mode network and regions of a "task-positive" attentional network is a potential locus of dysfunction in individuals with increased IIV. Resting state studies have shown that this competitive relationship is intrinsically represented in the brain, in the form of a negative correlation or antiphase relationship between spontaneous activity occurring in the two networks. We quantified the negative correlation between these two networks in 26 subjects, during active (Eriksen flanker task) and resting state scans. We hypothesized that the strength of the negative correlation is an index of the degree of regulation of activity in the default mode and task-positive networks and would be positively related to consistent behavioral performance. We found that the strength of the correlation between the two networks varies across individuals. These individual differences appear to be behaviorally relevant, as interindividual variation in the strength of the correlation was significantly related to individual differences in response time variability: the stronger the negative correlation (i.e., the closer to 180 degrees antiphase), the less variable the behavioral performance. This relationship was moderately consistent across resting and task conditions, suggesting that the measure indexes moderately stable individual differences in the integrity of functional brain networks. We discuss the implications of these findings for our understanding of the behavioral significance of spontaneous brain activity, in both healthy and clinical populations.     

Multisensory integration for timing engages different brain networks

How does the brain integrate information from different senses into a unitary percept? What factors influence such multisensory integration? Using a rhythmic behavioral paradigm and functional magnetic resonance imaging, we identified networks of brain regions for perceptions of physically synchronous and asynchronous auditory-visual events. Measures of behavioral performance revealed the existence of three distinct perceptual states. Perception of asynchrony activated a network of the primary sensory, prefrontal, and inferior parietal cortices, perception of synchrony disengaged the inferior parietal cortex and further recruited the superior colliculus, and when no clear percept was established, only the residual areas comprised of prefrontal and sensory areas were active. These results indicate that distinct percepts arise within specific brain sub-networks, the components of which are differentially engaged and disengaged depending on the timing of environmental signals.     

Manifold learning on brain functional networks in aging

We propose a new analysis framework to utilize the full information of brain functional networks for computing the mean of a set of brain functional networks and embedding brain functional networks into a low-dimensional space in which traditional regression and classification analyses can be easily employed. For this, we first represent the brain functional network by a symmetric positive matrix computed using sparse inverse covariance estimation. We then impose a Log-Euclidean Riemannian manifold structure on brain functional networks whose norm gives a convenient and practical way to define a mean. Finally, based on the fact that the computation of linear operations can be done in the tangent space of this Riemannian manifold, we adopt Locally Linear Embedding (LLE) to the Log-Euclidean Riemannian manifold space in order to embed the brain functional networks into a low-dimensional space. We show that the integration of the Log-Euclidean manifold with LLE provides more efficient and succinct representation of the functional network and facilitates regression analysis, such as ridge regression, on the brain functional network to more accurately predict age when compared to that of the Euclidean space of functional networks with LLE. Interestingly, using the Log-Euclidean analysis framework, we demonstrate the integration and segregation of cortical–subcortical networks as well as among the salience, executive, and emotional networks across lifespan.     

Learning effective brain connectivity with dynamic Bayesian networks

We propose to use dynamic Bayesian networks (DBN) to learn the structure of effective brain connectivity from functional MRI data in an exploratory manner. In our previous work, we used Bayesian networks (BN) to learn the functional structure of the brain (Zheng, X., Rajapakse, J.C., 2006. Learning functional structure from fMR images. NeuroImage 31 (4), 1601-1613). However, BN provides a single snapshot of effective connectivity of the entire experiment and therefore is unable to accurately capture the temporal characteristics of connectivity. Dynamic Bayesian networks (DBN) use a Markov chain to model fMRI time-series and thereby determine temporal relationships of interactions among brain regions. Experiments on synthetic fMRI data demonstrate that the performance of DBN is comparable to Granger causality mapping (GCM) in determining the structure of linearly connected networks. Dynamic Bayesian networks render more accurate and informative brain connectivity than earlier methods as connectivity is described in complete statistical sense and temporal characteristics of time-series are explicitly taken into account. The functional structures inferred on two real fMRI datasets are consistent with the previous literature and more accurate than those discovered by BN. Furthermore, we study the effects of hemodynamic noise, scanner noise, inter-scan interval, and the variability of hemodynamic parameters on the derived connectivity.     

Disentangling the brain networks supporting affective speech comprehension

Areas involved in social cognition, such as the medial prefrontal cortex (mPFC) and the left temporo-parietal junction (TPJ) appear to be active during the classification of sentences according to emotional criteria (happy, angry or sad, [Beaucousin et al., 2007]). These two regions are frequently co-activated in studies about theory of mind (ToM). To confirm that these regions constitute a coherent network during affective speech comprehension, new event-related functional magnetic resonance imaging data were acquired, using the emotional and grammatical-person sentence classification tasks on a larger sample of 51 participants. The comparison of the emotional and grammatical tasks confirmed the previous findings. Functional connectivity analyses established a clear demarcation between a "Medial" network, including the mPFC and TPJ regions, and a bilateral "Language" network, which gathered inferior frontal and temporal areas. These findings suggest that emotional speech comprehension results from interactions between language, ToM and emotion processing networks. The language network, active during both tasks, would be involved in the extraction of lexical and prosodic emotional cues, while the medial network, active only during the emotional task, would drive the making of inferences about the sentences' emotional content, based on their meanings. The left and right amygdalae displayed a stronger response during the emotional condition, but were seldom correlated with the other regions, and thus formed a third entity. Finally, distinct regions belonging to the Language and Medial networks were found in the left angular gyrus, where these two systems could interface.     

复杂脑网络分析在神经精神疾病中的研究进展

近年来复杂脑网络分析从全脑角度出发,在神经精神疾病的研究中成为热点。复杂脑网络分析有助于促进对神经精神疾病机制的认识并具有提供相关影像学标记的潜在价值,可能为临床对神经精神疾病的诊断和疗效评价提供新的方法。随着多学科的共同进步,复杂脑网络分析将在神经精神疾病的研究中发挥越来越大的作用。本文介绍复杂脑网络所涉及的基本概念,并对与其相关的疾病进行综述。     

Identification of large-scale networks in the brain using fMRI

Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0-0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the "default" network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253-258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain.     

Age-related changes in modular organization of human brain functional networks

Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased.     

Subsampled brain MRI reconstruction by generative adversarial neural networks

A main challenge in magnetic resonance imaging (MRI) is speeding up scan time. Beyond improving patient experience and reducing operational costs, faster scans are essential for time-sensitive imaging, such as fetal, cardiac, or functional MRI, where temporal resolution is important and target movement is unavoidable, yet must be reduced. Current MRI acquisition methods speed up scan time at the expense of lower spatial resolution and costlier hardware. We introduce a practical, software-only framework, based on deep learning, for accelerating MRI acquisition, while maintaining anatomically meaningful imaging. This is accomplished by MRI subsampling followed by estimating the missing k-space samples via generative adversarial neural networks. A generator-discriminator interplay enables the introduction of an adversarial cost in addition to fidelity and image-quality losses used for optimizing the reconstruction.Promising reconstruction results are obtained from feasible sampling patterns of up to a fivefold acceleration of diverse brain MRIs, from a large publicly available dataset of healthy adult scans as well as multimodal acquisitions of multiple sclerosis patients and dynamic contrast-enhanced MRI (DCE-MRI) sequences of stroke and tumor patients. Clinical usability of the reconstructed MRI scans is assessed by performing either lesion or healthy tissue segmentation and comparing the results to those obtained by using the original, fully sampled images. Reconstruction quality and usability of the DCE-MRI sequences is demonstrated by calculating the pharmacokinetic (PK) parameters. The proposed MRI reconstruction approach is shown to outperform state-of-the-art methods for all datasets tested in terms of the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), as well as either the mean squared error (MSE) with respect to the PK parameters, calculated for the fully sampled DCE-MRI sequences, or the segmentation compatibility, measured in terms of Dice scores and Hausdorff distance. The code is available on GitHub.     

Automatic brain labeling via multi-atlas guided fully convolutional networks

Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.     

Similar brain networks for detecting visuo-motor and visuo-proprioceptive synchrony

The ability to recognize feedback from own movement as opposed to the movement of someone else is important for motor control and social interaction. The neural processes involved in feedback recognition are incompletely understood. Two competing hypotheses have been proposed: the stimulus is compared with either (a) the proprioceptive feedback or with (b) the motor command and if they match, then the external stimulus is identified as feedback. Hypothesis (a) predicts that the neural mechanisms or brain areas involved in distinguishing self from other during passive and active movement are similar, whereas hypothesis (b) predicts that they are different. In this fMRI study, healthy subjects saw visual cursor movement that was either synchronous or asynchronous with their active or passive finger movements. The aim was to identify the brain areas where the neural activity depended on whether the visual stimulus was feedback from own movement and to contrast the functional activation maps for active and passive movement. We found activity increases in the right temporoparietal cortex in the condition with asynchronous relative to synchronous visual feedback from both active and passive movements. However, no statistically significant difference was found between these sets of activated areas when the active and passive movement conditions were compared. With a posterior probability of 0.95, no brain voxel had a contrast effect above 0.11% of the whole-brain mean signal. These results do not support the hypothesis that recognition of visual feedback during active and passive movement relies on different brain areas.     

Accurate brain age prediction with lightweight deep neural networks

Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub.