A Comparison of PTSD and Normal Adults Using Whole Brain Network Science Based Analyses of Resting State fMRI

Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

10.1101/344473 ◽

2018 ◽

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Temporal Structure ◽

Structural Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Network ◽

Dynamical Analysis ◽

Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in 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 between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. 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 gauge more temporal structure rather than spatial coordination 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.

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Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

10.1101/506576 ◽

2018 ◽

Author(s):

Caroline Garcia Forlim ◽

Leonie Klock ◽

Johanna Baechle ◽

Laura Stoll ◽

Patrick Giemsa ◽

...

Keyword(s):

Resting State ◽

Local Level ◽

Resting State Fmri ◽

Brain Network ◽

Graph Analysis ◽

Resting State Functional Connectivity ◽

Whole Brain ◽

Schizophrenic Patients ◽

Brain Circuitry ◽

Behavioral Impairments

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology. In this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41 controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology. Using NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain. Our results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

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Whole-Brain Network Connectivity Underlying the Human Speech Articulation as Emerged Integrating Direct Electric Stimulation, Resting State fMRI and Tractography

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2018.00405 ◽

2018 ◽

Vol 12 ◽

Cited By ~ 10

Author(s):

Domenico Zacà ◽

Francesco Corsini ◽

Umberto Rozzanigo ◽

Monica Dallabona ◽

Paolo Avesani ◽

...

Keyword(s):

Resting State ◽

Electric Stimulation ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Whole Brain ◽

Human Speech ◽

Speech Articulation ◽

Brain Network Connectivity

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

Network Neuroscience ◽

10.1162/netn_a_00070 ◽

2019 ◽

Vol 3 (2) ◽

pp. 405-426 ◽

Cited By ~ 3

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.

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GP.04 Network connectivity following a single unprovoked seizure using 7 Tesla resting-state fMRI

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2017.63 ◽

2017 ◽

Vol 44 (S2) ◽

pp. S8-S8

Author(s):

KM Ikeda ◽

SM Mirsattari ◽

AR Khan ◽

I Johnsrude ◽

JG Burneo ◽

...

Keyword(s):

Functional Mri ◽

Resting State ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Clustering Coefficient ◽

7 Tesla ◽

Unprovoked Seizure ◽

Whole Brain ◽

Network Function

Background: Predicting epilepsy following a first seizure is difficult. Network abnormalities are observed in patients with epilepsy using resting-state functional MRI (rs-fMRI), which worsen with duration of epilepsy. We use rs-fMRI to identify network abnormalities in patients after a first seizure that can be used as a biomarker to predict development of epilepsy. Methods: Patients after a single, unprovoked seizure and age/sex matched healthy controls underwent 7 Tesla structural and resting-state functional MRI. Data were analyzed using graph theory measures. Patients were followed for development of epilepsy. Results: Nine patients and nine control subjects were analyzed. There were no differences in baseline characteristics. No patients developed epilepsy (average follow-up 3 months). No differences between groups occurred on a whole-brain network level. At a 20% threshold, significant differences occurred in the default mode network (DMN). Patients demonstrated an increased local efficiency (p=0.02) and clustering coefficient (p=0.04), and decreased path length (p=0.02) and betweenness centrality (p=0.02). Conclusions: No whole-brain network changes occur after a single unprovoked seizure. No patient has developed epilepsy suggesting this group does not have network alterations after a single seizure. In the DMN, the alterations noted indicate increased segregation of network function.

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