Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

Kabbara, Aya; Eid, Hassan; Falou, Wassim El; Khalil, Mohamad; Wendling, Fabrice; Hassan, Mahmoud

doi:10.1088/1741-2552/aaaa76

Cited by 74 publications

(53 citation statements)

References 98 publications

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“…It is based on the inverse of the shortest path length, which is an indicator of the ease with which each node can reach other nodes within the network using a path that is composed of only a few edges. Hence, the global efficiency is an indicator of the degree to which a network can share information between distributed regions (Kabbara et al, 2018) Centrality. Betweenness centrality (BC) is a measure of centrality.…”

Section: Intra-brain Metricsmentioning

confidence: 99%

Emotional valence modulates the topology of the parent-infant inter-brain network

Santamaría¹,

Noreika²,

Georgieva³

et al. 2019

Preprint

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Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual-brain connectivity approach to assess how emotional valence modulates the parent-infant neural network.Fifteen mothers modelled positive and negative emotions toward pairs of objects during social interaction with their infants (aged 10.3 months) whilst their neural activity was concurrently measured using dual-EEG. Intra-brain and inter-brain network connectivity in the 6-9 Hz (infant Alpha) range was computed during maternal expression of positive and negative emotions using directed (partial directed coherence) and non-directed (phaselocking value) connectivity metrics. Graph theoretical metrics were used to quantify differences in network topology as a function of emotional valence. Inter-brain network indices (Density, Strength and Divisibility) consistently revealed that the integration of parents' and childrens' neural processes was significantly stronger during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics indicated that mother-to-infant directional influences were stronger during the expression of positive than negative emotions. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in interpersonal network topology. (200/200 words) Keywords:EEG hyperscanning, network connectivity, graph theory, emotional expression, mother-infant interaction atypical patterns of EEG asymmetry, commonly showing higher right frontal EEG activity than controls (Gotlib et al., 1998). Recent research has also started to examine intraindividual network topology during emotion processing using graph-theoretic measures. For example, a recent study with adults showed that EEG graph-theoretic features performed better than traditionally used EEG features (such as spectral power and asymmetry) on the automatic classification of affective neural states (Gupta et al. , 2016).Behavioral and neuroimaging studies into early development suggest that the neural architecture for the detection and prioritized processing of emotional expressions, such as fear, emerges sometime during the first year of life (Hoehl, 2013; Hoehl et al.

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Section: Intra-brain Metricsmentioning

confidence: 99%

Emotional valence modulates the topology of the parent-infant inter-brain network

Santamaría¹,

Noreika²,

Georgieva³

et al. 2019

Preprint

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“…Recently, a study showed that there is no potential bias of the use of a template MRI as compared to individual MRI coregistration (Douw et al, 2018). In this context, a considerable number of EEG/MEG connectivity studies have used the template-based method due to the unavailability of native MRIs Kabbara et al, 2018;Lopez et al, 2014). However, we are aware that the use of subject-specific MRI is more recommended in clinical studies.…”

Section: Methodological Considerationsmentioning

confidence: 99%

“…Electrode impedances were kept below 20 kOhm. EEG signals are frequently contaminated by several sources of artifacts, which were addressed using the same preprocessing steps as described in several previous studies dealing with EEG resting-state data (Kabbara et al, 2017;Kabbara et al, 2018;Rizkallah et al, 2018). Briefly, bad channels (signals that are either completely flat or contaminated by movement artifacts) were identified by visual inspection, complemented by the power spectral density.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Dynamic brain networks were reconstructed using the "EEG source connectivity" method , combined with a sliding window approach as detailed in (Kabbara et al, 2017;Kabbara et al, 2018;Rizkallah et al, 2018). "EEG source connectivity" involves two main steps: i) solving the EEG inverse problem in order to estimate the cortical sources and reconstruct their temporal dynamics, and ii) measuring the functional connectivity between the reconstructed time-series.…”

Section: Dynamic Brain Network Constructionmentioning

confidence: 99%

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Brain network dynamics correlate with personality traits

Kabbara

Paban

Weill

et al. 2019

Preprint

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Identifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personalityrelated differences in functional connectivity has been obtained through the stationary analysis, which do not capture the complex dynamical properties of brain networks. In this study, we aimed to evaluate the feasibility of dynamic network measures to predict personality traits. Using the EEGsource connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from EEG data acquired from 45 subjects during resting state. Then, several dynamic functional connectivity metrics were evaluated. Results showed a negative correlation between neuroticism and the dynamic variability of temporal lobe regions in terms of strength. In addition, a positive correlation was found between agreeableness and centrality variability of the posterior cingulate, which is known as a key hub in resting state networks. Results also revealed that extraversion is positively correlated with the dynamics of the superior parietal region. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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“…To compute the dynamic functional connectivity, the steps adopted here are the same used in many previous studies Kabbara et al, 2018Kabbara et al, , 2017. First, EEG data were co-registered with a template MRI through identification of the same anatomical landmarks (left and right pre-auricular points and nasion).…”

Section: Real Datamentioning

confidence: 99%

Detecting M/EEG modular brain states in rest and task

Kabbara

Mohamad

O'Neill

et al. 2019

Preprint

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The human brain is a dynamic networked system that continually reconfigures its connectivity patterns over time. Thus, developing approaches able to adequately detect fast brain dynamics is critical. Of particular interest are the methods that analyze the modular structure of brain networks, i.e. the presence of clusters of regions that are densely inter-connected. In this paper, we propose a novel framework to identify fast modular states that dynamically fluctuate over time during rest and task. We validated our method using MEG data recorded during a finger movement task, identifying modular states linking somatosensory and primary motor regions.The algorithm was also validated on dense-EEG data recorded during picture naming task, revealing the sub-second transition between several modular states which relate to visual processing, semantic processing and language. Next, we validated our method on a dataset of resting state dense-EEG signals recorded from 124 patients with Parkinson's disease and different cognitive phenotypes. Results disclosed brain modular states that differentiate cognitively intact patients, patients with moderate cognitive deficits and patients with severe cognitive deficits. Our new approach tracks the brain modular states, in healthy subjects and patients, on an adequate task-specific timescale. KeywordsBrain network dynamics, functional connectivity, M/EEG source-space networks.Arenas, 2005; M. Guimerà and Amaral, 2005). between 0.2 and 0.5.

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Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

Abstract: These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Cited by 74 publications

References 98 publications

Emotional valence modulates the topology of the parent-infant inter-brain network

Emotional valence modulates the topology of the parent-infant inter-brain network

Brain network dynamics correlate with personality traits

Detecting M/EEG modular brain states in rest and task

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