scholarly journals Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Davide Momi ◽  
Recep A. Ozdemir ◽  
Ehsan Tadayon ◽  
Pierre Boucher ◽  
Alberto Di Domenico ◽  
...  
Keyword(s):  
Resting State ◽  
Target Selection ◽  
Structural Connectivity ◽  
Signal Propagation ◽  
Brain Regions ◽  
Local Effect ◽  
High Temporal Resolution ◽  
Target Engagement ◽  
Network Nodes ◽  
A Minor

AbstractCombining Transcranial Magnetic Stimulation (TMS) with electroencephalography (EEG) offers the opportunity to study signal propagation dynamics at high temporal resolution in the human brain. TMS pulse induces a local effect which propagates across cortical networks engaging distant cortical and subcortical sites. However, the degree of propagation supported by the structural compared to functional connectome remains unclear. Clarifying this issue would help tailor TMS interventions to maximize target engagement. The goal of this study was to establish the contribution of functional and structural connectivity in predicting TMSinduced signal propagation after perturbation of two distinct brain networks. For this purpose, 24 healthy individuals underwent two identical TMS-EEG visits where neuronavigated TMS pulses were delivered to nodes of the default mode network (DMN) and the dorsal attention network (DAN). The functional and structural connectivity derived from each individual stimulation spot were characterized via functional magnetic resonance imaging (fMRI) and Diffusion Weighted Imaging (DWI), and signal propagation across these two metrics was compared. Direct comparison between the signal extracted from brain regions either functionally or structurally connected to the stimulation sites, shows a stronger activation over cortical areas connected via white matter pathways, with a minor contribution of functional projections. This pattern was not observed when analyzing spontaneous resting state EEG activity. Overall, results suggest that structural links can predict network-level response to perturbation more accurately than functional connectivity. Additionally, DWI-based estimation of propagation patterns can be used to estimate off-target engagement of other networks and possibly guide target selection to maximize specificity.

scholarly journals Lesion Network Localization of Seizure Freedom following MR-guided  Laser Interstitial Thermal Ablation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Karim Mithani ◽  
Alexandre Boutet ◽  
Jurgen Germann ◽  
Gavin J. B. Elias ◽  
Alexander G. Weil ◽  
...  
Keyword(s):  
Resting State ◽  
Target Selection ◽  
Brain Regions ◽  
Seizure Outcome ◽  
Seizure Freedom ◽  
Anatomical Location ◽  
Treatment Resistant ◽  
Resting State Functional Connectivity ◽  
Network Localization ◽  
Subcortical Regions

AbstractTreatment-resistant epilepsy is a common and debilitating neurological condition, for which neurosurgical cure is possible. Despite undergoing nearly identical ablation procedures however, individuals with treatment-resistant epilepsy frequently exhibit heterogeneous outcomes. We hypothesized that treatment response may be related to the brain regions to which MR-guided laser ablation volumes are functionally connected. To test this, we mapped the resting-state functional connectivity of surgical ablations that either resulted in seizure freedom (N = 11) or did not result in seizure freedom (N = 16) in over 1,000 normative connectomes. There was no difference seizure outcome with respect to the anatomical location of the ablations, and very little overlap between ablation areas was identified using the Dice Index. Ablations that did not result in seizure-freedom were preferentially connected to a number of cortical and subcortical regions, as well as multiple canonical resting-state networks. In contrast, ablations that led to seizure-freedom were more functionally connected to prefrontal cortices. Here, we demonstrate that underlying normative neural circuitry may in part explain heterogenous outcomes following ablation procedures in different brain regions. These findings may ultimately inform target selection for ablative epilepsy surgery based on normative intrinsic connectivity of the targeted volume.

scholarly journals Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Reema Shafi ◽  
Adrian P. Crawley ◽  
Maria Carmela Tartaglia ◽  
Charles H. Tator ◽  
Robin E. Green ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Healthy Controls ◽  
Postconcussion Syndrome ◽  
Resting State Functional Connectivity ◽  
Post Injury ◽  
Network Nodes ◽  
Specific Impact

AbstractConcussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

scholarly journals Multi-Modal Analysis of Resting-State fMRI Data in mTBI Patients and Association With Neuropsychological Outcomes

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Shi ◽  
Jing Teng ◽  
Xianping Du ◽  
Na Li
Keyword(s):  
Correlation Analysis ◽  
Resting State ◽  
Brain Injuries ◽  
Structural Connectivity ◽  
Executive Dysfunction ◽  
Brain Regions ◽  
Acute Stage ◽  
Anterior Cingulate ◽  
Control Group ◽  
Neuropsychological Outcomes

Various cognitive disorders have been reported for mild traumatic brain injury (mTBI) patients during the acute stage. This acute stage provides an opportunity for clinicians to optimize treatment protocols, which are based on the evaluation of brain structural connectivity. So far, most brain functional magnetic resonance imaging studies are focused on moderate to severe traumatic brain injuries (TBIs). In this study, we prospectively collected resting state data on 50 mTBI within 3 days of injury and 50 healthy volunteers and analyzed them using Amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), graph theory methods and behavior measure, to explore the dysfunctional brain regions in acute mTBI. In our study, a total of 50 patients suffering <3 days mTBI and 50 healthy subjects were tested in rs-fMRI, as well as under neuropsychological examinations including the Wechsler Intelligence Scale and Stroop Color and Word Test. The correlation analysis was conducted between graph theoretic parameters and neuropsychological results. For the mTBI group, the ReHo of the inferior temporal gyrus and the cerebellum superior are significantly lower than in the control group, and the ALFF of the left insula, the cerebellum inferior, and the middle occipital gyrus were significantly higher than in the control group, which implies the dysfunctionality usually observed in Parkinson's disease. Executive function disorder was significantly correlated with the global efficiencies of the dorsolateral superior frontal gyrus and the anterior cingulate cortex, which is consistent with the literature: the acute mTBI patients demonstrate abnormality in terms of motor speed, association, information processing speed, attention, and short-term memory function. Correlation analysis between the neuropsychological outcomes and the network efficiency for the mTBI group indicates that executive dysfunction might be caused by local brain changes. Our data support the idea that the cerebral internal network has compensatory reactions in response to sudden pathological and neurophysiological changes. In the future, multimode rs-fMRI analysis could be a valuable tool for evaluating dysfunctional brain regions after mTBI.

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 Comparison of functional connectivity between empirical and randomized structural brain networks

2016 ◽  
Author(s):  
Seyma Bayrak ◽  
Philipp Hövel ◽  
Vesna Vuksanovic
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Random Graphs ◽  
Spatial Organization ◽  
Temporal Dynamics ◽  
Structural Connectivity ◽  
Signal Propagation ◽  
Brain Regions ◽  
Neuroimaging Data ◽  
Structural Connections

This study combines experimental and modeling approaches in order to investigate the temporal dynamics of the human brain at rest. The dynamics of the neuronal activity is modeled with FitzHugh-Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon-Windkessel hemodynamic model. The simulations are based on structural connections that are derived from diffusion-weighted magnetic resonance imaging measurements yielding anatomical probabilities between the considered brain regions of interest. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim (i) to investigate the network topology of our neuroimaging data and (ii) how randomization of structural connections influence dynamics on top of it. The network characteristics of the structural connectivity data are compared to density-matched Erdős-Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both real and random (Erdős-Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results supported that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. Moreover, the simulated BOLD activity on real and random graphs are observed to be dissimilar. The difference of the modeled temporal dynamics on the brain and random graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.

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 Dynamic properties of simulated brain network models and empirical resting-state data

2019 ◽  
Vol 3 (2) ◽  
pp. 405-426 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Simulated Data ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.

scholarly journals Comparison of functional connectivity between empirical and randomized structural brain networks

2016 ◽  
Author(s):  
Seyma Bayrak ◽  
Philipp Hövel ◽  
Vesna Vuksanovic
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Random Graphs ◽  
Spatial Organization ◽  
Temporal Dynamics ◽  
Structural Connectivity ◽  
Signal Propagation ◽  
Brain Regions ◽  
Neuroimaging Data ◽  
Structural Connections

This study combines experimental and modeling approaches in order to investigate the temporal dynamics of the human brain at rest. The dynamics of the neuronal activity is modeled with FitzHugh-Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon-Windkessel hemodynamic model. The simulations are based on structural connections that are derived from diffusion-weighted magnetic resonance imaging measurements yielding anatomical probabilities between the considered brain regions of interest. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim (i) to investigate the network topology of our neuroimaging data and (ii) how randomization of structural connections influence dynamics on top of it. The network characteristics of the structural connectivity data are compared to density-matched Erdős-Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both real and random (Erdős-Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results supported that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. Moreover, the simulated BOLD activity on real and random graphs are observed to be dissimilar. The difference of the modeled temporal dynamics on the brain and random graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.

scholarly journals Time scale separation of information processing between sensory and associative regions

2020 ◽  
Author(s):  
P Sorrentino ◽  
G Rabuffo ◽  
R Rucco ◽  
F Baselice ◽  
E Troisi Lopez ◽  
...  
Keyword(s):  
Information Processing ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
High Temporal Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Speed Of Information Processing ◽  
The Brain

AbstractStimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Here we used high temporal-resolution Magnetoencephalography (MEG) signals to estimate the persistence of the information in functional links across the brain. We show that short- and long-lasting retention of the information, entailing different speeds in the update rate, naturally split the brain into two anatomically distinct subnetworks. The “fast updating network” (FUN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the “slow updating network” (SUN) hinges classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belongs to both subnetworks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.Significance statementThe human brain constantly scans the environment in search of relevant incoming stimuli, and appropriately reconfigures its large-scale activation according to environmental requests. The functional organization substanding these bottom-up and top-down processes, however, is not understood. Studying the speed of information processing between brain regions during resting state, we show the existence of two spatially segregated subnetworks processing information at fast- and slow-rates. Notably, these networks involve the regions that typically belong to the perception stream and the associative regions, respectively. Therefore, we provide evidence that, regardless of the presence of a stimulus, the bottom-up and top-down perceptive pathways are inherent to the resting state dynamics.

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.

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