scholarly journals Discordant attributes of structural and functional connectivity in a two-layer multiplex network

2018 ◽  
Author(s):  
Sol Lim ◽  
Filippo Radicchi ◽  
Martijn P van den Heuvel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Resting State Networks ◽  
Structural Failure ◽  
Resonance Imaging ◽  
Multiplex Network ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri

AbstractSeveral studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine whether node strength assortativity within and between the SC and FC layer may confer increased robustness against structural failure. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may explain laterality of some neurological dysfunctions and recovery.

scholarly journals Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

scholarly journals Network alignment and similarity reveal atlas-based topological differences in structural connectomes

2020 ◽  
Author(s):  
Matteo Frigo ◽  
Emilio Cruciani ◽  
David Coudert ◽  
Rachid Deriche ◽  
Emanuele Natale ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
Graph Isomorphism ◽  
Brain Regions ◽  
Jaccard Index ◽  
Brain Atlas ◽  
Resonance Imaging ◽  
Mathematical Properties ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri ◽  
A New Technique

The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available.

scholarly journals Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

2021 ◽  
Vol 15 ◽  
Author(s):  
Parinaz Babaeeghazvini ◽  
Laura M. Rueda-Delgado ◽  
Jolien Gooijers ◽  
Stephan P. Swinnen ◽  
Andreas Daffertshofer
Keyword(s):  
Magnetic Resonance Imaging ◽  
White Matter ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Spectral Power ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
And Diffusion

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

2021 ◽  
Author(s):  
Ajay Peddada ◽  
Kevin Holly ◽  
Tejaswi D Sudhakar ◽  
Christina Ledbetter ◽  
Christopher E. Talbot ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Resting State ◽  
Classification Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Group Differences ◽  
Functional Connections ◽  
White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

scholarly journals Medial Temporal Lobe Contributions to Resting-State Networks

2021 ◽  
Author(s):  
Sara Seoane ◽  
Cristián Modroño ◽  
José Luis González Mora ◽  
Niels Janssen
Keyword(s):  
Temporal Lobe ◽  
Resting State ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Resting State Networks ◽  
Attention Networks ◽  
Default Mode ◽  
Parahippocampal Cortex ◽  
Human Connectome Project ◽  
Dorsal Attention Network

Abstract The medial temporal lobe (MTL) is a set of interconnected brain regions that have been shown to play a central role in behavior as well as in neurological disease. Recent studies using resting-state functional Magnetic Resonance Imaging (rsfMRI) have attempted to understand the MTL in terms of its functional connectivity with the rest of the brain. However, the exact characterization of the whole-brain networks that co-activate with the MTL as well as how the various sub-regions of the MTL are associated with these networks remains poorly understood. Here we attempted to advance these issues by exploiting the high spatial-resolution 7T rsfMRI dataset from the Human Connectome Project with a data-driven analysis approach that relied on Independent Component Analysis (ICA) restricted to the MTL. We found that four different well-known resting-state networks co-activated with a unique configuration of MTL subcomponents. Specifically, we found that different sections of the parahippocampal cortex were involved in the default mode, visual and dorsal attention networks, sections of the hippocampus in the somatomotor and default mode networks, and the lateral entorhinal cortex in the dorsal attention network. We replicated this set of results in a validation sample. These results provide new insight into how the MTL and its subcomponents contribute to known resting-state networks. The participation of the MTL in an expanded range of resting-state networks requires a rethink of its presumed role in behavior and disease.

scholarly journals Altered Functional Connectivity within and between Brain Modules in Absence Epilepsy: A Resting-State Functional Magnetic Resonance Imaging Study

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Cui-Ping Xu ◽  
Shou-Wen Zhang ◽  
Tie Fang ◽  
Ma Manxiu ◽  
Qian Chencan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Regions ◽  
Absence Epilepsy ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Superior Frontal Gyrus

Functional connectivity has been correlated with a patient’s level of consciousness and has been found to be altered in several neuropsychiatric disorders. Absence epilepsy patients, who experience a loss of consciousness, are assumed to suffer from alterations in thalamocortical networks; however, previous studies have not explored the changes at a functional module level. We used resting-state functional magnetic resonance imaging to examine the alteration in functional connectivity that occurs in absence epilepsy patients. By parcellating the brain into 90 brain regions/nodes, we uncovered an altered functional connectivity within and between functional modules. Some brain regions had a greater number of altered connections and therefore behaved as key nodes in the changed network pattern; these regions included the superior frontal gyrus, the amygdala, and the putamen. In particular, the superior frontal gyrus demonstrated both an increased value of connections with other nodes of the frontal default mode network and a decreased value of connections with the limbic system. This divergence is positively correlated with epilepsy duration. These findings provide a new perspective and shed light on how functional connectivity and the balance of within/between module connections may contribute to both the state of consciousness and the development of absence epilepsy.

scholarly journals New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

2018 ◽  
Author(s):  
Paolo Finotteli ◽  
Caroline Garcia Forlim ◽  
Paolo Dulio ◽  
Leonie Klock ◽  
Alessia Pini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cortical Network ◽  
Future Research ◽  
Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

scholarly journals Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

2020 ◽  
Author(s):  
Behnaz Yousefi ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Whole Brain ◽  
Functional Connections ◽  
Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

scholarly journals Functional Connectivity in Pediatric Sickle Cell Disease

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2049-2049
Author(s):  
SaRah R. McNeely ◽  
Xirui Hou ◽  
Alicia D. Cannon ◽  
Zixuan Lin ◽  
Sophie M. Lanzkron ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sickle Cell ◽  
Resting State ◽  
Test Scores ◽  
Research Funding ◽  
Brain Regions ◽  
Resting State Networks ◽  
And Control ◽  
The Relationship ◽  
Privately Held

Abstract Children with sickle cell disease (SCD) have a high risk of developing cerebrovascular complications, such as stroke and silent cerebral infarction (SCI). SCI is associated with increased risk of future infarction as well as neurocognitive deficits related to brain injury location and size; however, neurocognitive impairment may occur in the absence of neuroimaging abnormalities. Resting state functional magnetic resonance imaging (RS-fMRI) measures blood oxygen level dependent (BOLD) signal during rest to evaluate functional connectivity between brain regions. Functional connectivity is the temporal correlation between the BOLD signal in spatially distant brain regions, which reflects synchronous activity. For this study, we hypothesized that participants with SCI would have lower functional connectivity than participants without SCI and that specific resting state networks would be associated with specific cognitive tests in SCD and control participants. We recruited 26 participants from the local pediatric hematology and SCD clinics. Children with SCD were included in the study if they had a SCD diagnosis confirmed by laboratory studies and no known prior history of overt stroke or seizure. We obtained clinical history, laboratory tests, neuropsychological testing scores, and RS-fMRI scans in 21 participants with SCD and 5 control participants without SCD, 2 of who had sickle cell trait. Each participant received a resting state functional connectivity scan using a 3T MR scanner. Participants were asked to remain still, stay awake, and keep their eyes open during the resting state scan. The MRI study protocol included a BOLD scan and a T1-weighted magnetization-prepared rapid gradient-echo sequence (MPRAGE) with a scan duration of 8 minutes. We performed standard image pre-processing steps, including realignment, normalization to Montreal Neurologic Institute (MNI) standard brain space via MPRAGE image, spatial smoothing, and slice timing correction. Table 1 shows the characteristics of the study participants. Eight participants with SCD had SCI diagnosed as an incidental finding during the study. The average connectivity within 7 resting state networks (control, default mode, dorsal attention, limbic, salience ventral attention, somato-motor, and visual networks) was compared between all (both SCD and control) participants with SCI and without SCI (Table 2). Participants with SCI had significantly lower functional connectivity in the control network (p = 0.0231, 95% CI: 0.073- 0.144) in comparison to participants without SCI. We also analyzed the relationship between 4 clinical variables and functional connectivity within each resting state network for all of the participants, with and without SCD. After adjusting for age and sex, there was a significant association between 3 resting state networks (control, salience ventral attention, and visual networks) and both hemoglobin and hematocrit (Table 3). There was a significant association between functional connectivity in the visual network and hemoglobin when adjusting for age and sex among just the participants with SCD (p = 0.045, 95% CI: 0.001-0.082). We analyzed the relationship between functional connectivity within each resting state network and neuropsychological test scores and found multiple significant associations between control, default mode, dorsal attention, salience ventral attention, and visual networks and attention/executive functioning test scores for all participants as well as just participants with SCD. Our findings suggest that children with SCD and SCI have decreased functional connectivity in the control network in comparison to children with and without SCD without SCI, which may indicate abnormalities in brain regions underlying executive dysfunction. Our data also established a relationship between the degree of anemia and functional connectivity, showing increased functional connectivity in the control, salience ventral attention, and visual network in participants with higher hemoglobin and hematocrit levels. Neuropsychological data shows that select test scores are associated with changes in functional connectivity in resting state networks primarily involved with attention and executive functioning. This research supports the utility of RS-fMRI as an adjunct analysis for investigating neurocognitive abnormalities in pediatric SCD. Figure 1 Figure 1. Disclosures Lanzkron: Novartis: Research Funding; Imara: Research Funding; CSL Behring: Research Funding; Bluebird Bio: Consultancy; Shire: Research Funding; Novo Nordisk: Consultancy; Pfizer: Current holder of individual stocks in a privately-held company; Teva: Current holder of individual stocks in a privately-held company; GBT: Research Funding. Mirro: NOUS Imaging: Current Employment, Current holder of stock options in a privately-held company. Fields: Global Blood Therapeutics: Consultancy; Proclara Biosciences: Current equity holder in publicly-traded company. Lance: Novartis: Other: participated in research advisory board in 2020.

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