[Show abstract][Hide abstract] ABSTRACT: Recent imaging connectomics studies have demonstrated that the spontaneous human brain functional networks derived from resting-state functional MRI (R-fMRI) include many non-trivial topological properties, such as highly efficient small-world architecture and densely connected hub regions. However, very little is known about dynamic functional connectivity (D-FC) patterns of spontaneous human brain networks during rest and about how these spontaneous brain dynamics are constrained by the underlying structural connectivity. Here, we combined sub-second multiband R-fMRI data with graph-theoretical approaches to comprehensively investigate the dynamic characteristics of the topological organization of human whole-brain functional networks, and then employed diffusion imaging data in the same participants to further explore the associated structural substrates. At the connection level, we found that human whole-brain D-FC patterns spontaneously fluctuated over time, while homotopic D-FC exhibited high connectivity strength and low temporal variability. At the network level, dynamic functional networks exhibited time-varying but evident small-world and assortativity architecture, with several regions (e.g., insula, sensorimotor cortex and medial prefrontal cortex) emerging as functionally persistent hubs (i.e., highly connected regions) while possessing large temporal variability in their degree centrality. Finally, the temporal characteristics (i.e., strength and variability) of the connectional and nodal properties of the dynamic brain networks were significantly associated with their structural counterparts. Collectively, we demonstrate the economical, efficient, and flexible characteristics of dynamic functional coordination in large-scale human brain networks during rest, and highlight their relationship with underlying structural connectivity, which deepens our understandings of spontaneous brain network dynamics in humans.
Frontiers in Human Neuroscience 09/2015; 9:478. DOI:10.3389/fnhum.2015.00478 · 3.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 09/2015; 35(37):12932-46. DOI:10.1523/JNEUROSCI.0415-15.2015 · 6.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The multiband EPI sequence has been developed for the human connectome project to accelerate MRI data acquisition. However, no study has yet investigated the test-retest (TRT) reliability of the graph metrics of white matter (WM) structural brain networks constructed from this new sequence. Here, we employed a multiband diffusion MRI (dMRI) dataset with repeated scanning sessions and constructed both low- and high-resolution WM networks by volume- and surface-based parcellation methods. The reproducibility of network metrics and its dependence on type of construction procedures was assessed by the intra-class correlation coefficient (ICC). We observed conserved topological architecture of WM structural networks constructed from the multiband dMRI data as previous findings from conventional dMRI. For the global network properties, the first order metrics were more reliable than second order metrics. Between two parcellation methods, networks with volume-based parcellation showed better reliability than surface-based parcellation, especially for the global metrics. Between different resolutions, the high-resolution network exhibited higher TRT performance than the low-resolution in terms of the global metrics with a large effect size, whereas the low-resolution performs better in terms of local (region and connection) properties with a relatively low effect size. Moreover, we identified that the association and primary cortices showed higher reproducibility than the paralimbic/limbic regions. The important hub regions and rich-club connections are more reliable than the non-hub regions and connections. Finally, we found WM networks from the multiband dMRI showed higher reproducibility compared with those from the conventional dMRI. Together, our results demonstrated the fair to good reliability of the WM structural brain networks from the multiband EPI sequence, suggesting its potential utility for exploring individual differences and for clinical applications.
Frontiers in Human Neuroscience 02/2015; 9:59. DOI:10.3389/fnhum.2015.00059 · 3.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Alzheimer's disease (AD) is associated not only with regional gray matter damages, but also with abnormalities in functional integration between brain regions. Here, we employed resting-state functional magnetic resonance imaging data and voxel-based graph-theory analysis to systematically investigate intrinsic functional connectivity patterns of whole-brain networks in 32 AD patients and 38 healthy controls (HCs). We found that AD selectively targeted highly connected hub regions (in terms of nodal functional connectivity strength) of brain networks, involving the medial and lateral prefrontal and parietal cortices, insula, and thalamus. This impairment was connectivity distance-dependent (Euclidean), with the most prominent disruptions appearing in the long-range connections (e.g., 100-130 mm). Moreover, AD also disrupted functional connections within the default-mode, salience and executive-control modules, and connections between the salience and executive-control modules. These disruptions of hub connectivity and modular integrity significantly correlated with the patients' cognitive performance. Finally, the nodal connectivity strength in the posteromedial cortex exhibited a highly discriminative power in distinguishing individuals with AD from HCs. Taken together, our results emphasize AD-related degeneration of specific brain hubs, thus providing novel insights into the pathophysiological mechanisms of connectivity dysfunction in AD and suggesting the potential of using network hub connectivity as a diagnostic biomarker.
[Show abstract][Hide abstract] ABSTRACT: Objective
The pathophysiology of chronic schizophrenia may reflect long term brain changes related to the disorder. The effect of chronicity on intrinsic functional connectivity patterns in schizophrenia without the potentially confounding effect of antipsychotic medications, however, remains largely unknown.
We collected resting-state fMRI data in 21 minimally treated chronic schizophrenia patients and 20 healthy controls. We computed regional functional connectivity strength for each voxel in the brain, and further divided regional functional connectivity strength into short-range regional functional connectivity strength and long-range regional functional connectivity strength. General linear models were used to detect between-group differences in these regional functional connectivity strength metrics and to further systematically investigate the relationship between these differences and clinical/behavioral variables in the patients.
Compared to healthy controls, the minimally treated chronic schizophrenia patients showed an overall reduced regional functional connectivity strength especially in bilateral sensorimotor cortex, right lateral prefrontal cortex, left insula and right lingual gyrus, and these regional functional connectivity strength decreases mainly resulted from disruption of short-range regional functional connectivity strength. The minimally treated chronic schizophrenia patients also showed reduced long-range regional functional connectivity strength in the bilateral posterior cingulate cortex/precuneus, and increased long-range regional functional connectivity strength in the right lateral prefrontal cortex and lingual gyrus. Notably, disrupted short-range regional functional connectivity strength mainly correlated with duration of illness and negative symptoms, whereas disrupted long-range regional functional connectivity strength correlated with neurocognitive performance. All of the results were corrected using Monte-Carlo simulation.
This exploratory study demonstrates a disruption of intrinsic functional connectivity without long-term exposure to antipsychotic medications in chronic schizophrenia. Furthermore, this disruption was connection–distance dependent, thus raising the possibility for differential neural pathways in neurocognitive impairment and psychiatric symptoms in schizophrenia.
Schizophrenia Research 07/2014; 156(2-3). DOI:10.1016/j.schres.2014.03.033 · 3.92 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Relating the brain's structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.
The Neuroscientist 06/2014; 21(3). DOI:10.1177/1073858414537560 · 6.84 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Alzheimer's disease (AD) is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.
[Show abstract][Hide abstract] ABSTRACT: Recent research on Alzheimer's disease (AD) has shown that the altered structure and function of the inferior parietal lobule (IPL) provides a promising indicator of AD. However, little is known about the functional connectivity of the IPL subregions in AD subjects. In this study, we collected resting-state functional magnetic resonance imaging data from 32 AD patients and 38 healthy controls. We defined seven subregions of the IPL according to probabilistic cytoarchitectonic atlases and mapped the whole-brain resting-state functional connectivity for each subregion. Using hierarchical clustering analysis, we identified three distinct functional connectivity patterns of the IPL subregions: the anterior IPL connected with the sensorimotor network (SMN) and salience network (SN); the central IPL had connectivity with the executive-control network (ECN); and the posterior IPL exhibited connections with the default-mode network (DMN). Compared with the controls, the AD patients demonstrated distinct disruptive patterns of the IPL subregional connectivity with these different networks (SMN, SN, ECN and DMN), which suggests the impairment of the functional integration in the IPL. Notably, we also observed that the IPL subregions showed increased connectivity with the posterior part of the DMN in AD patients, which potentially indicates a compensatory mechanism. Finally, these abnormal IPL functional connectivity changes were closely associated with cognitive performance. Collectively, we show that the subregions of the IPL present distinct functional connectivity patterns with various functional networks that are differentially impaired in AD patients. Our results also suggest that functional disconnection and compensation in the IPL may coexist in AD.
Brain Structure and Function 12/2013; 220(2). DOI:10.1007/s00429-013-0681-9 · 5.62 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Neuroimaging studies have demonstrated that patients with Alzheimer's disease (AD) have remarkable focal grey matter loss and hypometabolism in the posteromedial cortex (PMC), which is composed of the precuneus and posterior cingulate cortex, suggesting an important association of the PMC with AD pathophysiology. Studies have also shown that the PMC is a structurally and functionally heterogeneous structure containing various subregions with distinct connectivity profiles. However, whether these PMC subregions show differentially disrupted connectivity patterns in AD remains largely unknown. Here, we addressed this issue by collecting resting-state functional MRI data from 32 AD patients and 38 healthy controls. We automatically identified the PMC subregions using a graph-based module detection algorithm and then mapped the whole-brain functional connectivity pattern of each subregion. The functional connectivity analysis was followed by a hierarchical clustering analysis to classify each subregion. Three distinct spatial connectivity patterns were observed across the PMC subregions: the anterior dorsal zone was functionally connected with the sensorimotor cortex; the posterior dorsal zone was functionally connected with the frontoparietal cortex; and the central and ventral zones were functionally connected with the default-mode regions. Group comparison analysis revealed that all three functional systems were significantly disrupted in the AD patients compared to the controls and these disruptions were positively correlated with the patients' cognitive performance. Collectively, we showed that the subregions of the PMC exhibit differentially disrupted neuronal circuitry in AD patients, which provides new insight into the functional neuroanatomy of the human PMC and the alterations that may be relevant to disease.
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND: Alzheimer's disease disrupts the topological architecture of whole-brain connectivity (i.e., the connectome); however, whether this disruption is present in amnestic mild cognitive impairment (aMCI), the prodromal stage of Alzheimer's disease, remains largely unknown. METHODS: We employed resting-state functional magnetic resonance imaging and graph theory approaches to systematically investigate the topological organization of the functional connectome of 37 patients with aMCI and 47 healthy control subjects. Frequency-dependent brain networks were derived from wavelet-based correlations of both high- and low-resolution parcellation units. RESULTS: In the frequency interval .031-.063 Hz, the aMCI patients showed an overall decreased functional connectivity of their brain connectome compared with control subjects. Further graph theory analyses of this frequency band revealed an increased path length of the connectome in the aMCI group. Moreover, the disease targeted several key nodes predominantly in the default-mode regions and key links primarily in the intramodule connections within the default-mode network and the intermodule connections among different functional systems. Intriguingly, the topological aberrations correlated with the patients' memory performance and differentiated individuals with aMCI from healthy elderly individuals with a sensitivity of 86.5% and a specificity of 85.1%. Finally, we demonstrated a high reproducibility of our findings across different large-scale parcellation schemes and validated the test-retest reliability of our network-based approaches. CONCLUSIONS: This study demonstrates a disruption of whole-brain topological organization of the functional connectome in aMCI. Our finding provides novel insights into the pathophysiological mechanism of aMCI and highlights the potential for using connectome-based metrics as a disease biomarker.
[Show abstract][Hide abstract] ABSTRACT: Increasing attention has recently been directed to the applications of pattern recognition and brain imaging techniques in the effective and accurate diagnosis of Alzheimer's disease (AD). However, most of the existing research focuses on the use of single-modal (e.g., structural or functional MRI) or single-level (e.g., brain local or connectivity metrics) biomarkers for the diagnosis of AD. In this study, we propose a methodological framework, called multi-modal imaging and multi-level characteristics with multi-classifier (M3), to discriminate patients with AD from healthy controls. This approach involved data analysis from two imaging modalities: structural MRI, which was used to measure regional gray matter volume, and resting-state functional MRI, which was used to measure three different levels of functional characteristics, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and regional functional connectivity strength (RFCS). For each metric, we computed the values of ninety regions of interest derived from a prior atlas, which were then further trained using a multi-classifier based on four maximum uncertainty linear discriminant analysis base classifiers. The performance of this method was evaluated using leave-one-out cross-validation. Applying the M3 approach to the dataset containing 16 AD patients and 22 healthy controls led to a classification accuracy of 89.47% with a sensitivity of 87.50% and a specificity of 90.91%. Further analysis revealed that the most discriminative features for classification are predominantly involved in several default-mode (medial frontal gyrus, posterior cingulate gyrus, hippocampus and parahippocampal gyrus), occipital (fusiform gyrus, inferior and middle occipital gyrus) and subcortical (amygdale and pallidum of lenticular nucleus) regions. Thus, the M3 method shows promising classification performance by incorporating information from different imaging modalities and different functional properties, and it has the potential to improve the clinical diagnosis and treatment evaluation of AD.