Investigating the Role of Alpha and Beta Rhythms in Functional Motor Networks

Athanasiou, Alkinoos; Klados, Manousos A.; Styliadis, Charis; Foroglou, Nicolas; Polyzoidis, Konstantinos S.; Bamidis, Panagiotis D.

doi:10.1016/j.neuroscience.2016.05.044

Cited by 43 publications

(48 citation statements)

References 67 publications

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“…We found reliable evidence (Figure 2 ) that the highest SW and RC distributions were mainly located over temporal areas. The absence of significant differences in the distribution of the CFCG-SW (Figure 2B , upper part) was also reported by a recent study (Athanasiou et al, in press ) using the complementary modalities of EEG/MEG and multivariate functional connectivity. Over-representation in right frontal areas has also been found by previous studies, suggesting that the effects of mTBI are more prevalent in frontal regions which are more vulnerable to brain injury (Eierud et al, 2014 ; Antonakakis et al, 2016 ).…”

Section: Discussionsupporting

confidence: 86%

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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Section: Discussionsupporting

confidence: 86%

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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“…Interestingly, previous studies showed how the QMT could lead to an increase of alpha coherence, suggesting that this result could be a direct effect of the complexity of the training, which require a great combination of mind-body information (Ben-Soussan et al, 2013). More specifically, when looking at alpha functional connectivity, it is noteworthy that previous studies reported an increased clustering, in the context of a model where alpha rhythm elaborates and transmits information on the neurophysiological processes, assuming the role of local neuronal processing (Athanasiou et al, 2012, 2014, 2016). …”

Section: Discussionmentioning

confidence: 99%

“…In particular, alpha oscillation and alpha band functional connectivity could be the target of sensorimotor processes (Neuper et al, 2006; Sabate et al, 2011, 2012; Lopes da Silva, 2013), also as they are closely involved in motor execution, motor imagery and visuomotor accuracy (Rilk et al, 2011; Sabate et al, 2011). In addition, the consistent report related to increased clustering of alpha networks has led to the model suggesting that alpha oscillation could carry elaborate information about the neurophysiological ongoing processes assuming also the role of local neuronal processing (Athanasiou et al, 2012, 2014, 2016). …”

Section: Introductionmentioning

confidence: 96%

Increased Alpha Band Functional Connectivity Following the Quadrato Motor Training: A Longitudinal Study

Lasaponara

Mauro

Carducci

et al. 2017

Front. Hum. Neurosci.

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Quadrato Motor Training (QMT) is a new training paradigm, which was found to increase cognitive flexibility, creativity and spatial cognition. In addition, QMT was reported to enhance inter- and intra-hemispheric alpha coherence as well as Fractional Anisotropy (FA) in a number of white matter pathways including corpus callosum. Taken together, these results seem to suggest that electrophysiological and structural changes induced by QMT may be due to an enhanced interplay and communication of the different brain areas within and between the right and the left hemisphere. In order to test this hypothesis using the exact low-resolution brain electromagnetic tomography (eLORETA), we estimated the current neural density and lagged linear connectivity (LLC) of the alpha band in the resting state electroencephalography (rsEEG) recorded with open (OE) and closed eyes (CE) at three different time points, following 6 and 12 weeks of daily QMT. Significant changes were observed for the functional connectivity. In particular, we found that limbic and fronto-temporal alpha connectivity in the OE condition increased after 6 weeks, while it enhanced at the CE condition in occipital network following 12-weeks of daily training. These findings seem to show that the QMT may have dissociable long-term effects on the functional connectivity depending on the different ways of recording rsEEG. OE recording pointed out a faster onset of Linear Lag Connectivity modulations that tend to decay as quickly, while CE recording showed sensible effect only after the complete 3-months training.

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“…CMA is one of the most successful regions in 0.1-30 Hz band analysis, as shown in Figure 4. The study of Athanasiou et al [25] presented an analysis of the motor execution of α and β band connectivity. Their findings show that CMA has connections with premotor areas.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery

2019

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Brain-computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1-30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.

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Investigating the Role of Alpha and Beta Rhythms in Functional Motor Networks

Cited by 43 publications

References 67 publications

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

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

Increased Alpha Band Functional Connectivity Following the Quadrato Motor Training: A Longitudinal Study

A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery

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