Human Functional Connectome Research Articles

Investigating the Temporal Patterns within and between Intrinsic Connectivity Networks under Eyes-Open and Eyes-Closed Resting States: A Dynamical Functional Connectivity Study Based on Phase Synchronization.

The brain active patterns were organized differently under resting states of eyes open (EO) and eyes closed (EC). The altered voxel-wise and regional-wise resting state active patterns under EO/EC were found by static analysis. More importantly, dynamical spontaneous functional connectivity has been observed in the resting brain. To the best of our knowledge, the dynamical mechanisms of intrinsic connectivity networks (ICNs) under EO/EC remain largely unexplored. The goals of this paper were twofold: 1) investigating the dynamical intra-ICN and inter-ICN temporal patterns during resting state; 2) analyzing the altered dynamical temporal patterns of ICNs under EO/EC. To this end, a cohort of healthy subjects with scan conditions of EO/EC were recruited from 1000 Functional Connectomes Project. Through Hilbert transform, time-varying phase synchronization (PS) was applied to evaluate the inter-ICN synchrony. Meanwhile, time-varying amplitude was analyzed as dynamical intra-ICN temporal patterns. The results found six micro-states of inter-ICN synchrony. The medial visual network (MVN) showed decreased intra-ICN amplitude during EC relative to EO. The sensory-motor network (SMN) and auditory network (AN) exhibited enhanced intra-ICN amplitude during EC relative to EO. Altered inter-ICN PS was found between certain ICNs. Particularly, the SMN and AN exhibited enhanced PS to other ICNs during EC relative to EO. In addition, the intra-ICN amplitude might influence the inter-ICN synchrony. Moreover, default mode network (DMN) might play an important role in information processing during EO/EC. Together, the dynamical temporal patterns within and between ICNs were altered during different scan conditions of EO/EC. Overall, the dynamical intra-ICN and inter-ICN temporal patterns could benefit resting state fMRI-related research, and could be potential biomarkers for human functional connectome.

Genetic influences on resting-state functional networks: A twin study.

Alterations in resting-state networks (RSNs) are often associated with psychiatric and neurologic disorders. Given this critical linkage, it has been hypothesized that RSNs can potentially be used as endophenotypes for brain diseases. To validate this notion, a critical step is to show that RSNs exhibit heritability. However, the investigation of the genetic basis of RSNs has only been attempted in the default-mode network at the region-of-interest level, while the genetic control on other RSNs has not been determined yet. Here, we examined the genetic and environmental influences on eight well-characterized RSNs using a twin design. Resting-state functional magnetic resonance imaging data in 56 pairs of twins were collected. The genetic and environmental effects on each RSN were estimated by fitting the functional connectivity covariance of each voxel in the RSN to the classic ACE twin model. The data showed that although environmental effects accounted for the majority of variance in wide-spread areas, there were specific brain sites that showed significant genetic control for individual RSNs. These results suggest that part of the human brain functional connectome is shaped by genomic constraints. Importantly, this information can be useful for bridging genetic analysis and network-level assessment of brain disorders.

Voxelwise eigenvector centrality mapping of the human functional connectome reveals an influence of the catechol-O-methyltransferase val158met polymorphism on the default mode and somatomotor network.

Functional connections between brain regions constitute the substrate of the human functional connectome, whose topography has been discussed as an endophenotype for psychiatric disorders. Genetic influences on the entire connectome, however, have been rarely investigated so far. We tested for connectome-wide influences of the val158met (rs4860) polymorphism on the catechol-O-methyltransferase (COMT) gene by applying formal network analysis and eigenvector centrality mapping on the voxel level to resting-state functional magnetic imaging data. This approach finds brain regions that are central in the network by aggregating local and global connectivity patterns, most importantly without the requirement to select regions or networks of interest. The COMT variant linked to high enzyme activity increased network centrality in distributed brain areas that are known to constitute the brain's default mode network. Further results also indicated a COMT influence on areas implicated in the somatomotor network. These findings are in line with the polymorphism's alleged role in cognitive processing and its role in psychotic disorders. The study is the first to demonstrate the influence of a functional and behaviorally relevant genetic variant on connectome-wide functional connectivity and is an important step toward establishing the functional connectome as an endophenotype for psychiatric and behavioral phenotypes.

FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data.

Functional near-infrared spectroscopy (fNIRS), a promising noninvasive imaging technique, has recently become an increasingly popular tool in resting-state brain functional connectivity (FC) studies. However, the corresponding software packages for FC analysis are still lacking. To facilitate fNIRS-based human functional connectome studies, we developed a MATLAB software package called “functional connectivity analysis tool for near-infrared spectroscopy data” (FC-NIRS). This package includes the main functions of fNIRS data preprocessing, quality control, FC calculation, and network analysis. Because this software has a friendly graphical user interface (GUI), FC-NIRS allows researchers to perform data analysis in an easy, flexible, and quick way. Furthermore, FC-NIRS can accomplish batch processing during data processing and analysis, thereby greatly reducing the time cost of addressing a large number of datasets. Extensive experimental results using real human brain imaging confirm the viability of the toolbox. This novel toolbox is expected to substantially facilitate fNIRS-data-based human functional connectome studies.

Positive and negative affective processing exhibit dissociable functional hubs during the viewing of affective pictures.

Recent resting-state functional magnetic resonance imaging (fMRI) studies using graph theory metrics have revealed that the functional network of the human brain possesses small-world characteristics and comprises several functional hub regions. However, it is unclear how the affective functional network is organized in the brain during the processing of affective information. In this study, the fMRI data were collected from 25 healthy college students as they viewed a total of 81 positive, neutral, and negative pictures. The results indicated that affective functional networks exhibit weaker small-worldness properties with higher local efficiency, implying that local connections increase during viewing affective pictures. Moreover, positive and negative emotional processing exhibit dissociable functional hubs, emerging mainly in task-positive regions. These functional hubs, which are the centers of information processing, have nodal betweenness centrality values that are at least 1.5 times larger than the average betweenness centrality of the network. Positive affect scores correlated with the betweenness values of the right orbital frontal cortex (OFC) and the right putamen in the positive emotional network; negative affect scores correlated with the betweenness values of the left OFC and the left amygdala in the negative emotional network. The local efficiencies in the left superior and inferior parietal lobe correlated with subsequent arousal ratings of positive and negative pictures, respectively. These observations provide important evidence for the organizational principles of the human brain functional connectome during the processing of affective information.

Integration of visual and motor functional streams in the human brain

A long-standing difficulty in brain research has been to disentangle how information flows across circuits composed by multiple local and distant cerebral areas. At the large-scale level, several brain imaging methods have contributed to the understanding of those circuits by capturing the covariance or coupling patterns of blood oxygen level-dependent (BOLD) activity between distributed brain regions. The hypothesis is that underlying information processes are closely associated to synchronized brain activity, and therefore to the functional connectivity structure of the human brain. In this study, we have used a recently developed method called stepwise functional connectivity analysis. Our results show that motor and visual connectivity merge in a multimodal integration network that links together perception, action and cognition in the human functional connectome.

Sparse weightings for collapsing inverse solutions to cortical parcellations optimize M/EEG source reconstruction accuracy

Source-reconstructed magneto- and electroencephalography (M/EEG) are promising tools for investigating the human functional connectome. To reduce data, decrease noise, and obtain results directly comparable to magnetic resonance imaging (MRI), M/EEG source data can be collapsed into a cortical parcellation. For most collapsing approaches, however, it remains unclear if collapsed parcel time series accurately represent the coherent source dynamics within each parcel. We introduce a collapse-weighting-operator optimization approach that maximizes parcel fidelity, i.e., the phase correlation between original source dynamics and collapsed parcel time series, and thereby the accuracy with which the source dynamics are retained in forward and inverse modeling. The sparse, optimized weighting operator increased parcel fidelity 57-73% and true positive rate of interaction mapping from 0.33 to 0.84 in comparison to a non-sparse weighting approach. These improvements were robust for variable source topographies and parcellation resolutions. Critically, in real inverse-modeled MEG data, the optimized operator yielded close-to-perfect intra-parcel coherence. Previous suggestions for obtaining parcel time series include averaging all source time series within each anatomical parcel or using exclusively the time series of the voxel with maximum power. These methods are sensitive to signal heterogeneity and outlier sources. The approach advanced here avoids these problems. The optimized operator is suitable for collapsing real source-reconstructed M/EEG data into any cortical parcellation. The enhanced time series reconstruction fidelity yields improved accuracy of subsequent analyses of both local dynamics and large-scale interaction mapping.

Resting state FMRI research in child psychiatric disorders

Concurring with the shift from linking functions to specific brain areas towards studying network integration, resting state FMRI (R-FMRI) has become an important tool for delineating the functional network architecture of the brain. Fueled by straightforward data collection, R-FMRI analysis methods as well as studies reporting on R-FMRI have flourished, and already impact research on child- and adolescent psychiatric disorders. Here, we review R-FMRI analysis techniques and outline current methodological debates. Furthermore, we provide an overview of the main R-FMRI findings related to child- and adolescent psychiatric disorders. R-FMRI research has contributed significantly to our understanding of brain function in child and adolescent psychiatry: existing hypotheses based on task-based FMRI were confirmed and new insights into the brain's functional architecture of disorders were established. However, results were not always consistent. While resting state networks are robust and reproducible, neuroimaging research in psychiatric disorders is especially complicated by tremendous phenotypic heterogeneity. It is imperative that we overcome this heterogeneity when integrating neuroimaging into the diagnostic and treatment process. As R-FMRI allows investigating the richness of the human functional connectome and can be easily collected and aggregated into large-scale datasets, it is clear that R-FMRI can be a powerful tool in our quest to understand psychiatric pathology.

Topological organization of the human brain functional connectome across the lifespan

Human brain function undergoes complex transformations across the lifespan. We employed resting-state functional MRI and graph-theory approaches to systematically chart the lifespan trajectory of the topological organization of human whole-brain functional networks in 126 healthy individuals ranging in age from 7 to 85 years. Brain networks were constructed by computing Pearson's correlations in blood-oxygenation-level-dependent temporal fluctuations among 1024 parcellation units followed by graph-based network analyses. We observed that the human brain functional connectome exhibited highly preserved non-random modular and rich club organization over the entire age range studied. Further quantitative analyses revealed linear decreases in modularity and inverted-U shaped trajectories of local efficiency and rich club architecture. Regionally heterogeneous age effects were mainly located in several hubs (e.g., default network, dorsal attention regions). Finally, we observed inverse trajectories of long- and short-distance functional connections, indicating that the reorganization of connectivity concentrates and distributes the brain's functional networks. Our results demonstrate topological changes in the whole-brain functional connectome across nearly the entire human lifespan, providing insights into the neural substrates underlying individual variations in behavior and cognition. These results have important implications for disease connectomics because they provide a baseline for evaluating network impairments in age-related neuropsychiatric disorders.

Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions

We introduce here phase transfer entropy (Phase TE) as a measure of directed connectivity among neuronal oscillations. Phase TE quantifies the transfer entropy between phase time-series extracted from neuronal signals by filtering for instance. To validate the measure, we used coupled Neuronal Mass Models to both evaluate the characteristics of Phase TE and compare its performance with that of a real-valued TE implementation. We showed that Phase TE detects the strength and direction of connectivity even in the presence of such amounts of noise and linear mixing that typically characterize MEG and EEG recordings. Phase TE performed well across a wide range of analysis lags and sample sizes. Comparisons between Phase TE and real-valued TE estimates showed that Phase TE is more robust to nuisance parameters and considerably more efficient computationally. In addition, Phase TE accurately untangled bidirectional frequency band specific interaction patterns that confounded real-valued TE. Finally, we found that surrogate data can be used to construct appropriate null-hypothesis distributions and to estimate statistical significance of Phase TE. These results hence suggest that Phase TE is well suited for the estimation of directed phase-based connectivity in large-scale investigations of the human functional connectome.

Functional brain hubs and their test–retest reliability: A multiband resting-state functional MRI study

Resting-state functional MRI (R-fMRI) has emerged as a promising neuroimaging technique used to identify global hubs of the human brain functional connectome. However, most R-fMRI studies on functional hubs mainly utilize traditional R-fMRI data with relatively low sampling rates (e.g., repetition time [TR]=2s). R-fMRI data scanned with higher sampling rates are important for the characterization of reliable functional connectomes because they can provide temporally complementary information about functional integration among brain regions and simultaneously reduce the effects of high frequency physiological noise. Here, we employed a publicly available multiband R-fMRI dataset with a sub-second sampling rate (TR=645ms) to identify global hubs in the human voxel-wise functional networks, and further examined their test–retest (TRT) reliability over scanning time. We showed that the functional hubs of human brain networks were mainly located at the default-mode regions (e.g., medial prefrontal and parietal cortex as well as the lateral parietal and temporal cortex) and the sensorimotor and visual cortex. These hub regions were highly anatomically distance-dependent, where short-range and long-range hubs were primarily located at the primary cortex and the multimodal association cortex, respectively. We found that most functional hubs exhibited fair to good TRT reliability using intraclass correlation coefficients. Interestingly, our analysis suggested that a 6-minute scan duration was able to reliably detect these functional hubs. Further comparison analysis revealed that these results were approximately consistent with those obtained using traditional R-fMRI scans of the same subjects with TR=2500ms, but several regions (e.g., lateral frontal cortex, paracentral lobule and anterior temporal lobe) exhibited different TRT reliability. Finally, we showed that several regions (including the medial/lateral prefrontal cortex and lateral temporal cortex) were identified as brain hubs in a high frequency band (0.2–0.3Hz), which is beyond the frequency scope of traditional R-fMRI scans. Our results demonstrated the validity of multiband R-fMRI data to reliably detect functional hubs in the voxel-wise whole-brain networks, which motivated the acquisition of high temporal resolution R-fMRI data for the studies of human brain functional connectomes in healthy and diseased conditions.

Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain

Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level-dependent and arterial-spin-labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection-distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS-rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.

The human functional connectome: Its methodology, developmental trajectory and behavioral association

人脑内部功能连接的全体称为功能连接组,其功能复杂性表现在:(ⅰ)结构到功能的产生机制;(ⅱ)脑功能到心理行为的产生机制(心脑关联);(ⅲ)整个生命历程中的功能发展轨线.一直以来,神经科学家致力于揭示人脑功能在不同生命历程阶段的特征及其深层机制.上述问题的研究能够极大地改善对人脑功能的理解,改进长期以来必须依赖于动物脑功能模型的状况,为重大神经精神疾病的脑功能的病理生理机制研究提供正常参考(常模),进而有助于及时、准确地对其进行诊断、预警、干预和后期评价.但限于实验技术和计算方法,与脑功能有关的上述问题因极具挑战性而尚未被系统地展开研究;静息态功能磁共振技术的出现正在改变人脑功能的研究现状.本文综述基于静息态功能磁共振技术研究人脑功能连接组的计算方法及其重测信度、功能发展轨线应用、心理行为关联.

Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test-retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo's TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface).

Network Centrality in the Human Functional Connectome

The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel insights into connectivity within the whole-brain functional network (i.e., the functional connectome). We first assemble and visualize the voxel-wise (4 mm) functional connectome as a functional network. We then demonstrate that each centrality measure captures different aspects of connectivity, highlighting the importance of considering both global and local connectivity properties of the functional connectome. Beyond "detecting functional hubs," we treat centrality as measures of functional connectivity within the brain connectome and demonstrate their reliability and phenotypic correlates (i.e., age and sex). Specifically, our analyses reveal age-related decreases in degree centrality, but not eigenvector centrality, within precuneus and posterior cingulate regions. This implies that while local or (direct) connectivity decreases with age, connections with hub-like regions within the brain remain stable with age at a global level. In sum, these findings demonstrate the nonredundancy of various centrality measures and raise questions regarding their underlying physiological mechanisms that may be relevant to the study of neurodegenerative and psychiatric disorders.

Driving and Driven Architectures of Directed Small-World Human Brain Functional Networks

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.