Source Connectivity Analysis Can Assess Recovery of Acute Mild Traumatic Brain Injury Patients

Li, Lianyang; Arakaki, Xianghong; Harrington, Michael G.; Zouridakis, George

doi:10.1109/embc.2018.8513045

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…In a series of follow-up studies, we (Antonakakis et al, 2016(Antonakakis et al, , 2017a showed less dense connectivity networks in mTBI patients, which was in line with the findings of other groups (Rapp et al, 2015), as well as higher synchronization among mTBI richclub hubs (Antonakakis et al, 2017b). More recently, Li et al (2018) revealed a denser causality network for mTBI patients, whereas Kaltiainen et al (2018) showed that aberrant theta-band activity could provide an early objective sign of brain abnormality after mTBI.…”

Section: Introductionsupporting

confidence: 85%

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Antonakakis

Dimitriadis

Zervakis

et al. 2020

Front. Comput. Neurosci.

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Section: Introductionsupporting

confidence: 85%

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Antonakakis

Dimitriadis

Zervakis

et al. 2020

Front. Comput. Neurosci.

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“…The potential functional implications of RC organization of MEG intrinsic coupling modes, considering its role in network integration and its vulnerability in various disorders like mTBI, seem to deserve further investigation for diagnostic and clinical purposes. Furthermore, our approach is suitable for accessing the recovery process following mTBI using resting state MEG (Zouridakis et al, 2016 ; Li et al, 2017 ) and focusing not only on the strength of the couplings but also on the dominant type of interactions (Bharath et al, 2015 ; Losoi et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

“…Given our previous use of connectivity analysis to study mTBI using Granger causality (Zouridakis et al, 2012 ), phase synchronization (Dimitriadis et al, 2015b ), and cross-frequency coupling (Antonakakis et al, 2015 , 2016 ) of spontaneous MEG, as well as brain activation patterns of both EEG and MEG at the sensor (Li et al, 2015 ) and source (Zouridakis et al, 2016 ; Li et al, 2017 ) levels, an obvious question is whether the possible presence of an RC organization could provide some complementary features to the SW organization that is typically seen in mTBI FCGs. Thus, in the present study, we hypothesize that exploring brain connectivity network models derived from spontaneous MEG activity using estimators for both intra and cross-frequency couplings (Buzsáki and Watson, 2012 ) would help identify meaningful network topological features in compromised mTBI brain networks that could be used as guideline biomarkers for validating the recovery from mTBI (Bharath et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study

Antonakakis

Dimitriadis

Zervakis

et al. 2017

Front. Hum. Neurosci.

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Functional brain connectivity networks exhibit “small-world” characteristics and some of these networks follow a “rich-club” organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an “attack strategy” to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the “true” underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hyper-synchronization among rich-club hubs compared to controls in the δ band and the δ-γ1, θ-γ1, and β-γ2 frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ1 frequencies, and underrepresented in left occipital regions in the δ-β, δ-γ1, θ-β, and β-γ2 frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery from mTBI. Furthermore, the proposed approach might be used as a validation tool to assess patient recovery.

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“…At 6-month follow-up, however, only three of those seven patients had persistently abnormal low-frequency MEG activity. Li et al (2018) similarly reported attenuation of MEG abnormalities over time in patients with mild TBI [ 24 ]. Using resting-state MEG signal source analysis and Granger causality to determine in-going and out-going connections between brain regions, they compared connectivity networks between 13 patients with mild TBI and eight matched controls.…”

Section: Detection Of Tbimentioning

confidence: 99%

Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review

Peitz¹,

Wilde

Grandhi

2021

Medical Sciences

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Magnetoencephalography (MEG) is a functional brain imaging technique with high temporal resolution compared with techniques that rely on metabolic coupling. MEG has an important role in traumatic brain injury (TBI) research, especially in mild TBI, which may not have detectable features in conventional, anatomical imaging techniques. This review addresses the original research articles to date that have reported on the use of MEG in TBI. Specifically, the included studies have demonstrated the utility of MEG in the detection of TBI, characterization of brain connectivity abnormalities associated with TBI, correlation of brain signals with post-concussive symptoms, differentiation of TBI from post-traumatic stress disorder, and monitoring the response to TBI treatments. Although presently the utility of MEG is mostly limited to research in TBI, a clinical role for MEG in TBI may become evident with further investigation.

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Source Connectivity Analysis Can Assess Recovery of Acute Mild Traumatic Brain Injury Patients

Cited by 8 publications

References 27 publications

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study

Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review

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