Network Organization Of Human Brain Research Articles

Dysfunction of human brain network hierarchy in Parkinson's disease patients with freezing of gait

IntroductionHierarchy has been identified as a principle underlying the organization of human brain networks. In Parkinson's disease with freezing of gait (PD-FOG), it remains unclear whether and how the network hierarchy is disrupted. Additionally, the associations between changes in the brain network hierarchy of PD patients with FOG and clinical scales remain unclear. The aim of this study was to explore alterations in the network hierarchy of PD-FOG and their clinical relevance. MethodsIn this study, the brain network hierarchy of each group was described through a connectome gradient analysis among 31 PD-FOG, 50 PD patients without FOG (PD-NFOG), and 38 healthy controls (HC). Changes in the network hierarchy were assessed by comparing different gradient values of each network between the PD-FOG, PD-NFOG and HC groups. We further examined the relationship between dynamically changing network gradient values and clinical scales. ResultsFor the second gradient, Salience/ventral attention network-A (SalVentAttnA) network gradient of PD-FOG group was significantly lower than that of PD-NFOG, while both PD subgroups had a Default mode network-C gradient that was significantly lower than that of the HC group. In the third gradient, somatomotor network-A gradient of PD-FOG patients was significantly lower than the PD-NFOG group. Moreover, reduced SalVentAttnA network gradient values were associated with more severe gaits, fall risk, and frozen gait in PD-FOG patients. ConclusionsThe brain network hierarchy in PD-FOG is disturbed, this dysfunction is related to the severity of frozen gait. This study provides novel evidence for the neural mechanisms of FOG.

Multi-modal and multi-subject modular organization of human brain networks

The human brain is a complex network of anatomically interconnected brain areas. Spontaneous neural activity is constrained by this architecture, giving rise to patterns of statistical dependencies between the activity of remote neural elements. The non-trivial relationship between structural and functional connectivity poses many unsolved challenges about cognition, disease, development, learning and aging. While numerous studies have focused on statistical relationships between edge weights in anatomical and functional networks, less is known about dependencies between their modules and communities. In this work, we investigate and characterize the relationship between anatomical and functional modular organization of the human brain, developing a novel multi-layer framework that expands the classical concept of multi-layer modularity. By simultaneously mapping anatomical and functional networks estimated from different subjects into communities, this approach allows us to carry out a multi-subject and multi-modal analysis of the brain’s modular organization. Here, we investigate the relationship between anatomical and functional modules during resting state, finding unique and shared structures. The proposed framework constitutes a methodological advance in the context of multi-layer network analysis and paves the way to further investigate the relationship between structural and functional network organization in clinical cohorts, during cognitively demanding tasks, and in developmental or lifespan studies.

Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment.

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

Altered Weibull degree distribution in resting‐state functional brain networks in Alzheimer’s disease

AbstractBackgroundThe topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is disrupted in Alzheimer’s disease(AD) and its prodromal stage, mild cognitive impairment(MCI). We investigated on the fitting of nodal degree distribution of the whole‐brain functional networks and 7 subnetworks in healthy controls(HC) and individuals with MCI and AD.MethodWe included 40 HCs, 34 MCI and 34 early‐stage AD subjects who were recruited from a memory clinic at the Peking University Sixth Hospital. Resting‐state fMRI scans underwent standard preprocessing and were spatially normalized to the standard space. For each subject, we built whole‐brain FC matrices by computing Pearson’s correlations among brain voxels, followed by a threshold process(r from 0.4 to 0.6). Seven pre‐defined subnetworks were obtained from previous studies, including visual, sensorimotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode (DMN). For a given subnetwork, degree distributions were calculated within and between subnetworks, respectively. Maximum likelihood estimation was used to determine model parameters. The goodness of fit of each fitting was evaluated by bootstrapping and the Kolmogorov‐Smirnov test. Group differences in the estimated parameters were tested by ANCOVAs, controlled for age, gender and education.ResultIn the whole‐brain networks and all subnetworks, the connectivity degree distributions were fitted better by a Weibull distribution(f(x)∼x^(β‐1)e^(‐λx^β)) than power law or alternative models. Compared with the HC group, the MCI group showed lower Weibull β parameter (shape factor) in both the whole‐brain and subnetworks, especially in visual, somatomotor and DMN across all threshold(ps&lt;0.05), some of which were distributed in within‐network(somatomotor), between‐networks(dorsal attention and limbic), or both(visual, ventral attention, frontoparietal and DMN). In the AD group, the β parameters were lower in the frontalparietal(p&lt;0.05) and DMN(p&lt;0.05, except for r=0.6) than those in the HC.ConclusionWe show that the Weibull model fits human brain functional networks better than a power law model, and this network structure is disrupted in AD and MCI. Such a short‐tailed model may capture intrinsic network structure of the human brain in health and disease.

Metabolic Brain Network Analysis of FDG-PET in Alzheimer's Disease Using Kernel-Based Persistent Features.

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer’s disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks.

The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.

Multi-Scale Factor Analysis of High-Dimensional Functional Connectivity in Brain Networks

In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.

Quantifying person-level brain network functioning to facilitate clinical translation

Although advances in neuroimaging have yielded insights into the intrinsic organization of human brain networks and their relevance to psychiatric and neurological disorders, there has been no translation of these insights into clinical practice. One necessary step toward clinical translation is identifying a summary metric of network function that is reproducible, reliable, and has known normative data, analogous to normed neuropsychological tests. Our aim was therefore to establish the proof of principle for such a metric, focusing on the default mode network (DMN). We compared three candidate summary metrics: global clustering coefficient, characteristic path length, and average connectivity. Across three samples totaling 322 healthy, mostly Caucasian adults, average connectivity performed best, with good internal consistency (Cronbach’s α=0.69–0.70) and adequate eight-week test–retest reliability (intra-class coefficient=0.62 in a subsample N=65). We therefore present normative data for average connectivity of the DMN and its sub-networks. These proof of principle results are an important first step for the translation of neuroimaging to clinical practice. Ultimately, a normed summary metric will allow a single patient’s DMN function to be quantified and interpreted relative to normative peers.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Functional hypergraph uncovers novel covariant structures over neurodevelopment.

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large‐scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting‐state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously‐described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core–periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core–periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823–3835, 2017. © 2017 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks

Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50–79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg–Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

Different topological organization of human brain functional networks with eyes open versus eyes closed

Opening and closing the eyes are fundamental behaviors for directing attention to the external versus internal world. However, it remains unclear whether the states of eyes-open (EO) relative to eyes-closed (EC) are associated with different topological organizations of functional neural networks for exteroceptive and interoceptive processing (processing the external world and internal state, respectively). Here, we used resting-state functional magnetic resonance imaging and neural network analysis to investigate the topological properties of functional networks of the human brain when the eyes were open versus closed. The brain networks exhibited higher cliquishness and local efficiency, but lower global efficiency during the EO state compared to the EC state. These properties suggest an increase in specialized information processing along with a decrease in integrated information processing in EO (vs. EC). More importantly, the “exteroceptive” network, including the attentional system (e.g., superior parietal gyrus and inferior parietal lobule), ocular motor system (e.g., precentral gyrus and superior frontal gyrus), and arousal system (e.g., insula and thalamus), showed higher regional nodal properties (nodal degree, efficiency and betweenness centrality) in EO relative to EC. In contrast, the “interoceptive” network, composed of visual system (e.g., lingual gyrus, fusiform gyrus and cuneus), auditory system (e.g., Heschl's gyurs), somatosensory system (e.g., postcentral gyrus), and part of the default mode network (e.g., angular gyrus and anterior cingulate gyrus), showed significantly higher regional properties in EC vs. EO. In addition, the connections across sensory modalities were altered by volitional eye opening. The synchronicity between the visual system and the motor, somatosensory and auditory systems, characteristic of EC, was attenuated in EO. Further, the connections between the visual system and the attention, arousal and subcortical systems were increased in EO. These results may indicate that EO leads to a suppression of sensory modalities (other than visual) to allocate resources to exteroceptive processing. Our findings suggest that the topological organization of human brain networks dynamically switches corresponding to the information processing modes as we open or close our eyes.

Correction: Revealing Topological Organization of Human Brain Functional Networks with Resting-State Functional Near Infrared Spectroscopy.

[This corrects the article DOI: 10.1371/journal.pone.0045771.].

Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy.

BackgroundThe human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics.ResultsWe utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques.ConclusionsOur results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

15:45 AUDITORY CORTICAL DYSFUNCTION AS A BASIS FOR AUDITORY HALLUCINATIONS

Opening and closing the eyes are fundamental behaviors for directing attention to the external versus internal world. However, it remains unclear whether the states of eyes-open (EO) relative to eyes-closed (EC) are associated with different topological organizations of functional neural networks for exteroceptive and interoceptive processing (processing the external world and internal state, respectively). Here, we used resting-state functional magnetic resonance imaging and neural network analysis to investigate the topological properties of functional networks of the human brain when the eyes were open versus closed. The brain networks exhibited higher cliquishness and local efficiency, but lower global efficiency during the EO state compared to the EC state. These properties suggest an increase in specialized information processing along with a decrease in integrated information processing in EO (vs. EC). More importantly, the “exteroceptive” network, including the attentional system (e.g., superior parietal gyrus and inferior parietal lobule), ocular motor system (e.g., precentral gyrus and superior frontal gyrus), and arousal system (e.g., insula and thalamus), showed higher regional nodal properties (nodal degree, efficiency and betweenness centrality) in EO relative to EC. In contrast, the “interoceptive” network, composed of visual system (e.g., lingual gyrus, fusiform gyrus and cuneus), auditory system (e.g., Heschl's gyurs), somatosensory system (e.g., postcentral gyrus), and part of the default mode network (e.g., angular gyrus and anterior cingulate gyrus), showed significantly higher regional properties in EC vs. EO. In addition, the connections across sensory modalities were altered by volitional eye opening. The synchronicity between the visual system and the motor, somatosensory and auditory systems, characteristic of EC, was attenuated in EO. Further, the connections between the visual system and the attention, arousal and subcortical systems were increased in EO. These results may indicate that EO leads to a suppression of sensory modalities (other than visual) to allocate resources to exteroceptive processing. Our findings suggest that the topological organization of human brain networks dynamically switches corresponding to the information processing modes as we open or close our eyes.

Brain anatomical networks in early human brain development

Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1month, 1year, and 2years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1year and 2years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information.

Cortical anatomical networks identified by structural MRI

Event Abstract Back to Event Cortical anatomical networks identified by structural MRI A C. Evans1*, Y. He1 and Z. Chen1 1 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill Uni., Canada Most imaging studies of brain connectivity have focused upon functional connectivity (Micheloyannis et al. 2006; Stam et al. 2007, Salvador et al. 2005; Achard et al. 2006) These studies demonstrated that functional networks have “small-world” properties, where the minimum path length between any pair of nodes approximates that of a comparable random network but the nodes have greater local interconnectivity (Watts and Strogatz 1998). However, little is known about anatomical connectivity. Connection models of the human brain are usually inferred from primate research (Stephan et al. 2000; Penny et al. 2004). We have developed a methodology for characterizing the regional correlation of MRI-derived cortical thickness, using (i) the CLASP algorithm for the fully-automated extraction of cortical surfaces (MacDonald et al., 2000 ; Kim et al., 2005, Lyttelton et al., 2007), and (ii) a graph theoretical approach to the study of cortical morphology. This technique, GRETNA (Figure 1), examines the correlation among morphological properties of different cortical regions. It employs network theory to assess the clustering (Cp) and mean path length (Lp) among cortical regions. GRETNA was applied to brain MRI data from 124 right-handed normal adults. The resultant brain network contained 45 nodes and 102 edges2 (~7% of all possible pairs). It demonstrated significant short- and long-range correlations of cortical thickness, corresponding to known fibre connections. The pattern of coordinated variation in the thickness was neither regular nor random but “small-world” in nature, characterized by a high Cp and short Lp. GRETNA revealed a modular architecture in the normal structural brain network which reflects known functional neuroanatomy (Chen et al., 2008). Six modules with denser intra-module than inter-module connections (Newman and Girvan, 2004). This segregation into six modules with apparent functional significance suggests that functional organization of human brain networks may have a modular anatomical correlate. Finally, we used GRETNA to investigate the small-world efficiency of cortical networks in 425 relapsing–remitting MS patients (Charil et al., 2007). Using total WM lesion load (TWMLL) as a measure of disease severity, we defined six groups with increasing mean TWMLL. Both local (~Cp) and global (~1/Lp) efficiency showed significantly negative correlation with TWMLL (Fig. 2). Our results suggest that distributed cortical networks exhibit reduced efficiency in MS, consistent with aberrant fibre connection caused by MS lesions in white matter. Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Oral Presentation Topic: Workshop Citation: Evans AC, He Y and Chen Z (2008). Cortical anatomical networks identified by structural MRI. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.152 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: A C Evans, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill Uni., Montréal, Canada, nemoABS01@frontiersin.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers A C Evans Y. He Z. Chen Google A C Evans Y. He Z. Chen Google Scholar A C Evans Y. He Z. Chen PubMed A C Evans Y. He Z. Chen Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.