Synchronization likelihood with explicit time-frequency priors

Resting-state EEG oscillatory dynamics in fragile X syndrome: abnormal functional connectivity and brain network organization

Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS) and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI), a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior) and short-range (frontal-frontal and posterior-posterior) clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

Biological motion coding in the brain: analysis of visually driven EEG functional networks

Herein, we address the time evolution of brain functional networks computed from electroencephalographic activity driven by visual stimuli. We describe how these functional network signatures change in fast scale when confronted with point-light display stimuli depicting biological motion (BM) as opposed to scrambled motion (SM). Whereas global network measures (average path length, average clustering coefficient, and average betweenness) computed as a function of time did not discriminate between BM and SM, local node properties did. Comparing the network local measures of the BM condition with those of the SM condition, we found higher degree and betweenness values in the left frontal (F7) electrode, as well as a higher clustering coefficient in the right occipital (O2) electrode, for the SM condition. Conversely, for the BM condition, we found higher degree values in central parietal (Pz) electrode and a higher clustering coefficient in the left parietal (P3) electrode. These results are discussed in the context of the brain networks involved in encoding BM versus SM.

Drug polyconsumption is associated with increased synchronization of brain electrical-activity at rest and in a counting task

Drug abusers typically consume not just one but several types of drugs, starting from alcohol and marijuana consumption, and then dramatically lapsing into addiction to harder drugs, such as cocaine, heroin, or amphetamine. The brain of drug abusers presents various structural and neurophysiological abnormalities, some of which may predate drug consumption onset. However, how these changes translate into modifications in functional brain connectivity is still poorly understood. To characterize functional connectivity patterns, we recorded Electroencephalogram (EEG) activity from 21 detoxified drug abusers and 20 age-matched control subjects performing a simple counting task and at rest activity. To evaluate the cortical brain connectivity network we applied the Synchronization Likelihood algorithm. The results showed that drug abusers had higher synchronization levels at low frequencies, mainly in the θ band (4-8 Hz) between frontal and posterior cortical regions. During the counting task, patients showed increased synchronization in the β (14-35 Hz), and γ (35-45 Hz) frequency bands, in fronto-posterior and interhemispheric temporal regions. Taken together 'slow-down' at rest and task-related 'over-exertion' could indicate that the brain of drug abusers is suffering from a premature form of ageing. Future studies will clarify whether this condition can be reversed following prolonged periods of abstinence.

Heritability of resting state EEG functional connectivity patterns

We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis - network clustering and average path length - are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27-74%) and cluster coefficient and path length in the alpha and theta band (40-44% and 23-40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.

Interictal network properties in mesial temporal lobe epilepsy: a graph theoretical study from intracerebral recordings

OBJECTIVE

Graph theoretical analysis of functional connectivity data has demonstrated a small-world topology of brain networks. There is increasing evidence that the topology of brain networks is changed in epilepsy. Here we investigated the basal properties of epileptogenic networks by applying graph analysis to intracerebral EEG recordings of patients presenting with drug-resistant partial epilepsies during the interictal period.

METHODS

Interictal EEG activity was recorded in mesial temporal lobe of 11 patients with mesial temporal lobe epilepsy (MTLE group) and compared with a "control" group of 8 patients having neocortical epilepsies (non MTLE group) in whom depth-EEG recordings eventually showed an ictal onset outside the MTL structures. Synchronization likelihood (SL) was calculated between selected intracerebral electrodes contacts to obtain SL-weighted graphs. Mean normalized clustering index, average path length and small world index S were calculated to characterize network organization.

RESULTS

Broadband SL values were higher in the MTLE group. Although a small-world pattern was found in the two groups, normalized clustering index and to a lesser extend average path length were higher in the MTLE group.

CONCLUSIONS

We demonstrated a trend toward a more regular (less random) configuration of interictal epileptogenic networks. In addition S index was found to correlate with epilepsy duration.

SIGNIFICANCE

These topological alterations might be a surrogate marker of human focal epilepsy and disclose some changes over time.

Organization of brain networks governed by long-range connections index autistic traits in the general population

BACKGROUND

The dimensional approach to autism spectrum disorder (ASD) considers ASD as the extreme of a dimension traversing through the entire population. We explored the potential utility of electroencephalography (EEG) functional connectivity as a biomarker. We hypothesized that individual differences in autistic traits of typical subjects would involve a long-range connectivity diminution within the delta band.

METHODS

Resting-state EEG functional connectivity was measured for 74 neurotypical subjects. All participants also provided a questionnaire (Social Responsiveness Scale, SRS) that was completed by an informant who knows the participant in social settings. We conducted multivariate regression between the SRS score and functional connectivity in all EEG frequency bands. We explored modulations of network graph metrics characterizing the optimality of a network using the SRS score.

RESULTS

Our results show a decay in functional connectivity mainly within the delta and theta bands (the lower part of the EEG spectrum) associated with an increasing number of autistic traits. When inspecting the impact of autistic traits on the global organization of the functional network, we found that the optimal properties of the network are inversely related to the number of autistic traits, suggesting that the autistic dimension, throughout the entire population, modulates the efficiency of functional brain networks.

CONCLUSIONS

EEG functional connectivity at low frequencies and its associated network properties may be associated with some autistic traits in the general population.

Local polymorphic delta activity in cortical lesions causes global decreases in functional connectivity

Increasing evidence from neuroimaging and modeling studies suggests that local lesions can give rise to global network changes in the human brain. These changes are often attributed to the disconnection of the lesioned areas. However, damaged brain areas may still be active, although the activity is altered. Here, we hypothesize that empirically observed global decreases in functional connectivity in patients with brain lesions can be explained by specific alterations of local neural activity that are the result of damaged tissue. We simulated local polymorphic delta activity (PDA), which typically characterizes EEG/MEG recordings of patients with cerebral lesions, in a realistic model of human brain activity. 78 neural masses were coupled according to the human structural brain network. Lesions were created by altering the parameters of individual neural masses in order to create PDA (i.e. simulating acute focal brain damage); combining this PDA with weakening of structural connections (i.e. simulating brain tumors), and fully deleting structural connections (i.e. simulating a full resection). Not only structural disconnection but also PDA in itself caused a global decrease in functional connectivity, similar to the observed alterations in MEG recordings of patients with PDA due to brain lesions. Interestingly, connectivity between regions that were not lesioned directly also changed. The impact of PDA depended on the network characteristics of the lesioned region in the structural connectome. This study shows for the first time that locally disturbed neural activity, i.e. PDA, may explain altered functional connectivity between remote areas, even when structural connections are unaffected. We suggest that focal brain lesions and the corresponding altered neural activity should be considered in the framework of the full functionally interacting brain network, implying that the impact of lesions reaches far beyond focal damage.

Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis

Disruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity.

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Global synchronization of BD patients was reduced in the alpha-band at resting.

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De-synchronized connectivity was localized in fronto-centro-parietal connections.

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Global topology of BD had decreased network clustering and increased path length.

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BD showed the less efficient network processing.

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Network characteristics of BD patients were associated with depression severity.

Synchronization dynamics and evidence for a repertoire of network states in resting EEG

Intrinsically driven neural activity generated at rest exhibits complex spatiotemporal dynamics characterized by patterns of synchronization across distant brain regions. Mounting evidence suggests that these patterns exhibit fluctuations and nonstationarity at multiple time scales. Resting-state electroencephalographic (EEG) recordings were examined in 12 young adults for changes in synchronization patterns on a fast time scale in the range of tens to hundreds of milliseconds. Results revealed that EEG dynamics continuously underwent rapid transitions between intermittently stable states. Numerous approximate recurrences of states were observed within single recording epochs, across different epochs separated by longer times, and between participants. For broadband (4-30 Hz) data, a majority of states could be grouped into three families, suggesting the existence of a limited repertoire of core states that is continually revisited and shared across participants. Our results document the existence of fast synchronization dynamics iterating amongst a small set of core networks in the resting brain, complementing earlier findings of nonstationary dynamics in electromagnetic recordings and transient EEG microstates.

External drive to inhibitory cells induces alternating episodes of high- and low-amplitude oscillations

Electrical oscillations in neuronal network activity are ubiquitous in the brain and have been associated with cognition and behavior. Intriguingly, the amplitude of ongoing oscillations, such as measured in EEG recordings, fluctuates irregularly, with episodes of high amplitude alternating with episodes of low amplitude. Despite the widespread occurrence of amplitude fluctuations in many frequency bands and brain regions, the mechanisms by which they are generated are poorly understood. Here, we show that irregular transitions between sub-second episodes of high- and low-amplitude oscillations in the alpha/beta frequency band occur in a generic neuronal network model consisting of interconnected inhibitory and excitatory cells that are externally driven by sustained cholinergic input and trains of action potentials that activate excitatory synapses. In the model, we identify the action potential drive onto inhibitory cells, which represents input from other brain areas and is shown to desynchronize network activity, to be crucial for the emergence of amplitude fluctuations. We show that the duration distributions of high-amplitude episodes in the model match those observed in rat prefrontal cortex for oscillations induced by the cholinergic agonist carbachol. Furthermore, the mean duration of high-amplitude episodes varies in a bell-shaped manner with carbachol concentration, just as in mouse hippocampus. Our results suggest that amplitude fluctuations are a general property of oscillatory neuronal networks that can arise through background input from areas external to the network.

Rhythmic changes in electrical activity are observed throughout the brain, and arise as a result of reciprocal interactions between excitatory and inhibitory neurons. Synchronized activity of a large number of neurons gives rise to macroscopic oscillations in electrical activity, which can be measured in EEG recordings and are thought to have a key role in learning and memory. Interestingly, the amplitude of ongoing oscillations fluctuates irregularly, with high-amplitude episodes alternating with low-amplitude episodes. Although these amplitude fluctuations occur in many brain regions, the mechanisms by which they are generated are still poorly known. To get insight into potential mechanisms, we investigated whether such fluctuations occur in a computational model of a neuronal network. We show that the model generates amplitude fluctuations that are similar to those observed in experimental data and that external input from other brain areas to the inhibitory cells of the network is essential for their generation. This input can disrupt the synchrony of activity, causing transitions between episodes of high synchrony (high oscillation amplitudes) and episodes of low synchrony (low oscillation amplitudes). Episodes of high synchrony are relevant for brain function because they provide favorable conditions for learning.

Activity dependent degeneration explains hub vulnerability in Alzheimer's disease

Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.

Cognitive dysfunction in early multiple sclerosis: altered centrality derived from resting-state functional connectivity using magneto-encephalography

BACKGROUND

Cognitive dysfunction in multiple sclerosis (MS) is frequent. Insight into underlying mechanisms would help to develop therapeutic strategies.

OBJECTIVE

To explore the relationship of cognitive performance to patterns of nodal centrality derived from magneto-encephalography (MEG).

METHODS

34 early relapsing-remitting MS patients (median EDSS 2.0) and 28 age- and gender-matched healthy controls (HC) had a MEG, a neuropsychological assessment and structural MRI. Resting-state functional connectivity was determined by the synchronization likelihood. Eigenvector Centrality (EC) was used to quantify for each sensor its connectivity and importance within the network. A cognition-score was calculated, and normalized grey and white matter volumes were determined. EC was compared per sensor and frequency band between groups using permutation testing, and related to cognition.

RESULTS

Patients had lower grey and white matter volumes than HC, male patients lower cognitive performance than female patients. In HC, EC distribution showed highest nodal centrality over bi-parietal sensors ("hubs"). In patients, nodal centrality was even higher bi-parietally (theta-band) but markedly lower left temporally (upper alpha- and beta-band). Lower cognitive performance correlated to decreased nodal centrality over left temporal (lower alpha-band) and right temporal (beta-band) sensors, and to increased nodal centrality over right parieto-temporal sensors (beta-band). Network changes were most pronounced in male patients.

CONCLUSIONS

Partial functional disconnection of the temporal regions was associated with cognitive dysfunction in MS; increased centrality in parietal hubs may reflect a shift from temporal to possibly less efficient parietal processing. To better understand patterns and dynamics of these network changes, longitudinal studies are warranted, also addressing the influence of gender.

Brain excitability and connectivity of neuronal assemblies in Alzheimer's disease: from animal models to human findings

The human brain contains about 100 billion neurons forming an intricate network of innumerable connections, which continuously adapt and rewire themselves following inputs from external and internal environments as well as the physiological synaptic, dendritic and axonal sculpture during brain maturation and throughout the life span. Growing evidence supports the idea that Alzheimer's disease (AD) targets selected and functionally connected neuronal networks and, specifically, their synaptic terminals, affecting brain connectivity well before producing neuronal loss and compartmental atrophy. The understanding of the molecular mechanisms underlying the dismantling of neuronal circuits and the implementation of 'clinically oriented' methods to map-out the dynamic interactions amongst neuronal assemblies will enhance early/pre-symptomatic diagnosis and monitoring of disease progression. More important, this will open the avenues to innovative treatments, bridging the gap between molecular mechanisms and the variety of symptoms forming disease phenotype. In the present review a set of evidence supports the idea that altered brain connectivity, exhausted neural plasticity and aberrant neuronal activity are facets of the same coin linked to age-related neurodegenerative dementia of Alzheimer type. Investigating their respective roles in AD pathophysiology will help in translating findings from basic research to clinical applications.

Early dysfunction of functional connectivity in healthy elderly with subjective memory complaints

It is still an open question whether subjective memory complaints (SMC) can actually be considered to be clinically relevant predictors for the development of an objective memory impairment and even dementia. There is growing evidence that suggests that SMC are associated with an increased risk of dementia and with the presence of biological correlates of early Alzheimer's disease. In this paper, in order to shed some light on this issue, we try to discern whether subjects with SMC showed a different profile of functional connectivity compared with subjects with mild cognitive impairment (MCI) and healthy elderly subjects. In the present study, we compare the degree of synchronization of brain signals recorded with magnetoencephalography between three groups of subjects (56 in total): 19 with MCI, 12 with SMC and 25 healthy controls during a memory task. Synchronization likelihood, an index based on the theory of nonlinear dynamical systems, was used to measure functional connectivity. Briefly, results show that subjects with SMC have a very similar pattern of connectivity to control group, but on average, they present a lower synchronization value. These results could indicate that SMC are representing an initial stage with a hypo-synchronization (in comparison with the control group) where the brain system is still not compensating for the failing memory networks, but behaving as controls when compared with the MCI subjects.

Noninvasively Decoding the Contents of Visual Working Memory in the Human Prefrontal Cortex within High-gamma Oscillatory Patterns

The temporal maintenance and subsequent retrieval of information that no longer exists in the environment is called working memory. It is believed that this type of memory is controlled by the persistent activity of neuronal populations, including the prefrontal, temporal, and parietal cortex. For a long time, it has been controversially discussed whether, in working memory, the PFC stores past sensory events or, instead, its activation is an extramnemonic source of top–down control over posterior regions. Recent animal studies suggest that specific information about the contents of working memory can be decoded from population activity in prefrontal areas. However, it has not been shown whether the contents of working memory during the delay periods can be decoded from EEG recordings in the human brain. We show that by analyzing the nonlinear dynamics of EEG oscillatory patterns it is possible to noninvasively decode with high accuracy, during encoding and maintenance periods, the contents of visual working memory information within high-gamma oscillations in the human PFC. These results are thus in favor of an active storage function of the human PFC in working memory; this, without ruling out the role of PFC in top–down processes. The ability to noninvasively decode the contents of working memory is promising in applications such as brain computer interfaces, together with computation of value function during planning and decision making processes.

Dominant component analysis of electrophysiological connectivity networks

Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying it to simulated connectivity matrices as well as to a clinical study using connectivity matrices derived from resting state magnetoencephalography (MEG) data in a population of subjects diagnosed with autism spectrum disorder (ASD).

Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease

The relation between pathology and cognitive dysfunction in dementia is still poorly understood, although disturbed communication between different brain regions is almost certainly involved. In this study we combine magneto-encephalography (MEG) and network analysis to investigate the role of functional sub-networks (modules) in the brain with regard to cognitive failure in Alzheimer's disease. Whole-head resting-state (MEG) was performed in 18 Alzheimer patients (age 67 ± 9, 6 females, MMSE 23 ± 5) and 18 healthy controls (age 66 ± 9, 11 females, MMSE 29 ± 1). We constructed functional brain networks based on interregional synchronization measurements, and performed graph theoretical analysis with a focus on modular organization. The overall modular strength and the number of modules changed significantly in Alzheimer patients. The parietal cortex was the most highly connected network area, but showed the strongest intramodular losses. Nonetheless, weakening of intermodular connectivity was even more outspoken, and more strongly related to cognitive impairment. The results of this study demonstrate that particularly the loss of communication between different functional brain regions reflects cognitive decline in Alzheimer's disease. These findings imply the relevance of regarding dementia as a functional network disorder.

Does sleep restore the topology of functional brain networks?

Previous studies have shown that healthy anatomical as well as functional brain networks have small-world properties and become less optimal with brain disease. During sleep, the functional brain network becomes more small-world-like. Here we test the hypothesis that the functional brain network during wakefulness becomes less optimal after sleep deprivation (SD). Electroencephalography (EEG) was recorded five times a day after a night of SD and after a night of normal sleep in eight young healthy subjects, both during eyes-closed and eyes-open resting state. Overall synchronization was determined with the synchronization likelihood (SL) and the phase lag index (PLI). From these coupling strength matrices the normalized clustering coefficient C (a measurement of local clustering) and path length L (a measurement of global integration) were computed. Both measures were normalized by dividing them by their corresponding C-s and L-s values of random control networks. SD reduced alpha band C/C-s and L/L-s and theta band C/C-s during eyes-closed resting state. In contrast, SD increased gamma-band C/C-s and L/L-s during eyes-open resting state. Functional relevance of these changes in network properties was suggested by their association with sleep deprivation-induced performance deficits on a sustained attention simple reaction time task. The findings indicate that SD results in a more random network of alpha-coupling and a more ordered network of gamma-coupling. The present study shows that SD induces frequency-specific changes in the functional network topology of the brain, supporting the idea that sleep plays a role in the maintenance of an optimal functional network.

Effects of visual context upon functional connectivity during observation of biological motions

The aim of this study was to examine brain responses, in particular functional connectivity, to different visual stimuli depicting familiar biological motions. Ten subjects actively observed familiar biological motions embedded in point-light and video displays. Electroencephalograms were recorded from 64 electrodes. Activity was considered in three frequency bands (4-8 Hz, 8-10 Hz, and 10-13 Hz) using a non-linear measure of functional connectivity. In the 4-8 Hz and 8-10 Hz frequency bands, functional connectivity for the SMA was greater during the observation of biological motions presented in a point-light display compared to the observation of motions presented in a video display. The reverse was observed for the 4-8 Hz frequency band for the left temporal area. Explanations related to: (i) the task demands (i.e., attention and mental effort), (ii) the role(s) of theta and alpha oscillations in cognitive processes, and (iii) the function(s) of cortical areas are discussed. It has been suggested that attention was required to process human biological motions under unfamiliar viewing conditions such as point-light display.

Functional connectivity changes in multiple sclerosis patients: a graph analytical study of MEG resting state data

Multiple sclerosis (MS) is characterized by extensive damage in the central nervous system. Within this field, there is a strong need for more advanced, functional imaging measures, as abnormalities measured with structural imaging insufficiently explain clinicocognitive decline in MS. In this study we investigated functional connectivity changes in MS using resting-state magnetoencephalography (MEG). Data from 34 MS patients and 28 age and gender-matched controls was assessed using synchronization likelihood (SL) as a measure of functional interaction strength between brain regions, and graph analysis to characterize topological patterns of connectivity changes. Cognition was assessed using extensive neuropsychological evaluation. Structural measures included brain and lesion volumes, using MRI. Results show SL increases in MS patients in theta, lower alpha and beta bands, with decreases in the upper alpha band. Graph analysis revealed a more regular topology in the lower alpha band in patients, indicated by an increased path length (λ) and clustering coefficient (γ). Attention and working memory domains were impaired, with decreased brain volumes. A stepwise linear regression model using clinical, MRI and MEG parameters as predictors revealed that only increases in lower alpha band γ predicted impaired cognition. Cognitive impairments and related altered connectivity patterns were found to be especially predominant in male patients. These results show specific functional changes in MS as measured with MEG. Only changes in network topology were related to poorer cognitive outcome. This indicates the value of graph analysis beyond traditional structural and functional measures, with possible implications for diagnostic and/or prognostic purposes in MS.

Alteration and reorganization of functional networks: a new perspective in brain injury study

Plasticity is the mechanism underlying the brain’s potential capability to compensate injury. Recently several studies have shown how functional connections among the brain areas are severely altered by brain injury and plasticity leading to a reorganization of the networks. This new approach studies the impact of brain injury by means of alteration of functional interactions. The concept of functional connectivity refers to the statistical interdependencies between physiological time series simultaneously recorded in various areas of the brain and it could be an essential tool for brain functional studies, being its deviation from healthy reference an indicator for damage. In this article, we review studies investigating functional connectivity changes after brain injury and subsequent recovery, providing an accessible introduction to common mathematical methods to infer functional connectivity, exploring their capabilities, future perspectives, and clinical uses in brain injury studies.

Gender-related differences in functional connectivity in multiple sclerosis

Background: Gender effects are strong in multiple sclerosis (MS), with male patients showing a worse clinical outcome than female patients. Functional reorganization of neural activity may contribute to limit disability, and possible gender differences in this process may have important clinical implications.

Objectives: The aim of this study was to explore gender-related changes in functional connectivity and network efficiency in MS patients. Additionally, we explored the association of functional changes with cognitive function.

Methods: Sixty subjects were included in the study, matched for age, education level and intelligence quotient (IQ). Male and female patients were matched for disability, disease duration and white matter lesion load. Two cognitive domains often impaired in MS, i.e. visuospatial memory and information processing speed, were evaluated in all subjects. Functional connectivity between brain regions and network efficiency was explored using resting-state functional magnetic resonance imaging and graph analysis. Differences in cognitive and functional characteristics between groups, and correlations with cognitive performance, were examined.

Results: Male patients showed worse performance on cognitive tests than female and male controls, while female patients were cognitively normal. Decreases in functional connectivity and network efficiency, observed in male patients, correlated with reduced visuospatial memory ( r = −0.6 and r = −0.5, respectively). In the control group, no cognitive differences were found between genders, despite differences in functional connectivity between healthy men and women.

Conclusions: Functional connectivity differences were found in male patients only and were related to impaired visuospatial memory. These results underline the importance of gender in MS and require further investigation in larger and longitudinal studies.

Decreased cortical connectivity and information flow in type 1 diabetes

OBJECTIVE

To investigate the effect of type 1 diabetes on EEG connectivity and information flow and study the relationship between these parameters and electrophysiological, neuropsychological and clinical variables.

METHODS

Connectivity was assessed using several measures (phase coherence, phase lag index, synchronization likelihood and phase slope index) on 119 patients and 61 healthy controls over several frequency bands (between 0.5 and 45 Hz). Data was further correlated to EEG power, event related potentials, neuropsychological function and demographic variables.

RESULTS

Multivariate test on the connectivity data showed a difference between patients and controls both with mastoid reference (p<0.01) and current source density estimates (p<0.04). Connectivity and information flow correlated with EEG power but not with event related potentials or neuropsychological function.

CONCLUSIONS

Connectivity and information flow are decreased in diabetes. These variables assess other functions of the brain than captured by the present cognitive tests. Several tests need to be performed in order to monitor the effect of diabetes on brain function.

SIGNIFICANCE

The decrease in connectivity and cortical information flow are EEG abnormalities that add to the previously described EEG and ERP abnormalities described for type 1 diabetes.

Network analysis of resting state EEG in the developing young brain: structure comes with maturation

During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths. We used graph theoretical concepts to examine changes in functional brain networks during normal development in young children. Resting-state eyes-closed electroencephalography (EEG) was recorded (14 channels) from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) was calculated in three different frequency bands and between each pair of electrodes to obtain SL-weighted graphs. Mean normalized clustering index, average path length and weight dispersion were calculated to characterize network organization. Repeated measures analysis of variance tested for time and gender effects. For all frequency bands mean SL decreased from 5 to 7 years. Clustering coefficient increased in the alpha band. Path length increased in all frequency bands. Mean normalized weight dispersion decreased in beta band. Girls showed higher synchronization for all frequency bands and a higher mean clustering in alpha and beta bands. The overall decrease in functional connectivity (SL) might reflect pruning of unused synapses and preservation of strong connections resulting in more cost-effective networks. Accordingly, we found increases in average clustering and path length and decreased weight dispersion indicating that normal brain maturation is characterized by a shift from random to more organized small-world functional networks. This developmental process is influenced by gender differences early in development.

Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder

Synchronization as a measure of quantification of similarities in dynamic systems is an important concept in many scientific fields such as nonlinear science, neuroscience, cardiology, ecology, and economics. When interdependencies and connections of coupled dynamic systems are not directly accessible and measurable such as those of the neurons of the brain, quantification of similarities between their time series outputs is the best possible way to detect the existent interdependencies among them. In recent years, Synchronization Likelihood (SL) has been used as one of the most suitable algorithms in highly nonlinear and non-stationary systems. In this method, the likelihood of patterns is measured statistically, and then it is determined which patterns of the time series are similar to each other considering a threshold. But the degree of similarities is not considered in the decision. In this paper, a new measure of synchronization, fuzzy SL, is presented using the theory of fuzzy logic and Gaussian membership functions. The new fuzzy SL is compared with the conventional SL using both a standard problem from the chaos literature and a complicated real life neurological diagnostic problem, that is, the EEG-based diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD). The results of ANOVA analysis indicate the interdependencies measured by the fuzzy SL are more reliable than the conventional SL for discriminating ADHD patients from healthy individuals.

Influences of Instructions and Expertise on the Mechanisms Involved During a Working Memory Task

The purpose of this study was to examine the effects of instructions and expertise upon cortical mechanisms during a working memory task. Ten professional pianists and ten musically naïve subjects were instructed to retain for a short period of time, sequential finger movements viewed previously with the aim of either replicating them or recognizing them at a later stage. The results showed that in the 20–30 Hz frequency band and in musically naïve subjects, functional connectivity was greater within the occipital, parietal, central, frontal, right, and left temporal areas when the subjects were invited to remember the observed movement in order to replicate it compared to the recognition condition in which they had to recognize it. In professional pianists, incomplete connectivity equivalence was detected between the two conditions. In addition, under the condition for replica, functional connectivity in musically naïve subjects was greater in the central area compared to professional pianists. Explanations related to the: (i) level of expertise, (ii) nature of operations involved during the retention period, and (iii) task demand are discussed.

Cognition is related to resting-state small-world network topology: an magnetoencephalographic study

Brain networks and cognition have recently begun to attract attention: studies suggest that more efficiently wired resting-state brain networks are indeed correlated with better cognitive performance. "Small-world" brain networks combine local segregation with global integration, hereby subserving information processing. Furthermore, recent studies implicate that gender effects may be present in both network dynamics and its correlations with cognition. This study reports on the relation between resting-state functional brain topology with overall and domain-specific cognitive performance in healthy participants and possible gender differences herein. Healthy participants underwent neuropsychological tests, of which individual scores were converted to z-scores. Network analysis was performed on resting-state, eyes-closed magnetoencephalography (MEG) data, after determining functional connectivity between each pair of sensors. The clustering coefficient (local specialization), average path length (overall integration and efficiency) and "small-world index" (i.e. ratio between clustering and path length) were calculated in six frequency bands. 14 male and 14 female participants were included. Better total cognitive performance was related to increased local connectivity in the theta band, higher clustering coefficient (in delta and theta bands) and higher small-worldness (in theta and lower gamma bands). Women showed less clustering and shorter path length in the delta band. There were no significant correlations between network topology and cognitive functioning in females. In contrast, higher cognitive scores in men were associated with increased theta band clustering and small-worldness. These results provide further evidence for the value of functional brain network topology for cognitive functioning and suggest that gender is an important factor in this respect.

The lesioned brain: still a small-world?

The intra-arterial amobarbital procedure (IAP or Wada test) is used to determine language lateralization and contralateral memory functioning in patients eligible for neurosurgery because of pharmaco-resistant epilepsy. During unilateral sedation, functioning of the contralateral hemisphere is assessed by means of neuropsychological tests. We use the IAP as a reversible model for the effect of lesions on brain network topology. Three artifact-free epochs (4096 samples) were selected from each electroencephalogram record before and after amobarbital injection. Functional connectivity was assessed by means of the synchronization likelihood. The resulting functional connectivity matrices were constructed for all six epochs per patient in four frequency bands, and weighted network analysis was performed. The clustering coefficient, average path length, small-world index, and edge weight correlation were calculated. Recordings of 33 patients were available. Network topology changed significantly after amobarbital injection: clustering decreased in all frequency bands, while path length decreased in the theta and lower alpha band, indicating a shift toward a more random network topology. Likewise, the edge weight correlation decreased after injection of amobarbital in the theta and beta bands. Network characteristics after injection of amobarbital were correlated with memory score: higher theta band small-world index and increased upper alpha path length were related to better memory score. The whole-brain network topology in patients eligible for epilepsy surgery becomes more random and less optimally organized after selective sedation of one hemisphere, as has been reported in studies with brain tumor patients. Furthermore, memory functioning after injection seems related to network topology, indicating that functional performance is related to topological network properties of the brain.

Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity

Background

Local network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data.

Methodology/Principal Findings

18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions.

Conclusions/Significance

We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.

Modulating functional connectivity patterns and topological functional organization of the human brain with transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that alters cortical excitability and activity in a polarity-dependent way. Stimulation for few minutes has been shown to induce plastic alterations of cortical excitability and to improve cognitive performance. These effects might be caused by stimulation-induced alterations of functional cortical network connectivity. We aimed to investigate the impact of tDCS on cortical network function through functional connectivity and graph theoretical analysis. Single recordings in healthy volunteers with 62 electroencephalography channels were acquired before and after 10 min of facilitatory anodal tDCS over the primary motor cortex (M1), combined with inhibitory cathodal tDCS of the contralateral frontopolar cortex, in resting state and during voluntary hand movements. Correlation matrices containing all 62 pairwise electrode combinations were calculated with the synchronization likelihood (SL) method and thresholded to construct undirected graphs for the θ, α, β, low-γ and high-γ frequency bands. SL matrices and undirected graphs were compared before and after tDCS. Functional connectivity patterns significantly increased within premotor, motor, and sensorimotor areas of the stimulated hemisphere during motor activity in the 60-90 Hz frequency range. Additionally, tDCS-induced significant intrahemispheric and interhemispheric connectivity changes in all the studied frequency bands. In summary, we show for the first time evidence for tDCS-induced changes in brain synchronization and topological functional organization.

'Functional connectivity' is a sensitive predictor of epilepsy diagnosis after the first seizure

BACKGROUND

Although epilepsy affects almost 1% of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30-50%. Here we investigate whether using 'functional connectivity' can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy.

METHODOLOGY/PRINCIPAL FINDINGS

Patients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. > or = two seizures) were compared to matched non-epilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76% and sensitivity of 62%.

CONCLUSION/SIGNIFICANCE

Theta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy, especially in those patients who do not show IEDs on their first EEG. Our results indicate that epilepsy diagnosis could be improved by using functional connectivity.

Non-linear EEG synchronization during observation: effects of instructions and expertise

The aim of this study was to examine the effects of instructions and expertise upon neuronal changes during observation of sequential finger movements. Professional pianists and musically naïve subjects observed these movements with the aim of either replicating or recognizing them at a later stage. A non-linear measure of functional coupling was used to investigate EEG activity. In the 10-13 Hz frequency band and in musically naïve subjects, functional coupling during observation for replica was greater within central and neighboring areas than during observation for recognition. An opposite pattern was found in the 4-8 Hz frequency band. In the 10-13 Hz band and in areas including the parietal cortex, functional coupling in musically naïve subjects was greater compared to professional pianists under observation for replica. Results are discussed in the light of recent findings from the cognitive and behavioral neuroscience literature.

Detection of ischemic electroencephalography changes during carotid endarterectomy using synchronization likelihood analysis

INTRODUCTION

The intensive care unit is short of a suitable tool for instant detection of cerebral ischemia. Synchronization likelihood (SL) electroencephalography (EEG) analysis has proven to be a promising approach for instant online seizure detection. The aim of this study was to investigate the value of SL analysis in the detection of cerebral ischemia.

PATIENTS AND METHODS

SL analysis was studied using conventional EEG recordings during 143 carotid endarterectomy procedures. We compared EEG data before and after clamping of the carotid artery for 2 different anesthesia protocols (isoflurane or propofol) in patients with and without development of cerebral ischemia, making use of SL analysis. Cerebral ischemia was defined by using EEG variables.

RESULTS

Fifty-eight patients received isoflurane and 85 propofol anesthesia of whom overall 27% developed ischemia. In patients from the isoflurane group who developed ischemia, the mean overall SL decreased [from 0.220 (SD 0.052) before clamping to 0.208 (SD 0.044) after clamping; P=0.06] In patients with ischemia in the propofol group, the mean overall SL remained stable (0.185; P=0.87) Patients from both groups without development of ischemia had increased mean overall SL values after clamping [isoflurane: from 0.210 (SD 0.041) before clamping to 0.219 (SD 0.051) after clamping; P=0.08. Propofol: from 0.188 (SD 0.019) to 0.189 (SD 0.021); P=0.57].

CONCLUSIONS

No significant changes in the mean SL were observed after development of ischemia during either isoflurane or propofol anesthesia. SL analysis does not seem suitable for detection of cerebral ischemia in anesthetized patients.

Endophenotypes in a dynamically connected brain

We examined the longitudinal genetic architecture of three parameters of functional brain connectivity. One parameter described overall connectivity (synchronization likelihood, SL). The two others were derived from graph theory and described local (clustering coefficient, CC) and global (average path length, L) aspects of connectivity. We measured resting state EEG in 1,438 subjects from four age groups of about 16, 18, 25 and 50 years. Developmental curves for SL and L indicate that connectivity is more random at adolescence and old age, and more structured in middle-aged adulthood. Individual variation in SL and L were moderately to highly heritable at each age (SL: 40–82%; L: 29–63%). Genetic factors underlying these phenotypes overlapped. CC was also heritable (25–49%) but showed no systematic overlap with SL and L. SL, CC, and L in the alpha band showed high phenotypic and genetic stability from 16 to 25 years. Heritability for parameters in the beta band was lower, and less stable across ages, but genetic stability was high. We conclude that the connectivity parameters SL, CC, and L in the alpha band show the hallmarks of a good endophenotype for behavior and developmental disorders.

Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD

A multi-paradigm methodology is presented for electroencephalogram (EEG) based diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) through adroit integration of nonlinear science; wavelets, a signal processing technique; and neural networks, a pattern recognition technique. The selected nonlinear features are generalized synchronizations known as synchronization likelihoods (SL), both among all electrodes and among electrode pairs. The methodology consists of three parts: first detecting the more synchronized loci (group 1) and loci with more discriminative deficit connections (group 2). Using SLs among all electrodes, discriminative SLs in certain sub-bands are extracted. In part two, SLs are computed, not among all electrodes, but between loci of group 1 and loci of group 2 in all sub-bands and the band-limited EEG. This part leads to more accurate detection of deficit connections, and not just deficit areas, but more discriminative SLs in sub-bands with finer resolutions. In part three, a classification technique, radial basis function neural network, is used to distinguish ADHD from normal subjects. The methodology was applied to EEG data obtained from 47 ADHD and 7 control individuals with eyes closed. The Radial Basis Function (RBF) neural network classifier yielded a high accuracy of 95.6% for diagnosis of the ADHD in the feature space discovered in this research with a variance of 0.7%.

Changes in local and distant EEG activities before, during and after the observation and execution of sequential finger movements

AIM OF THE STUDY

To consider cortical oscillations at local and distant/large scale levels during the time course of motor events under both an observation and an execution condition.

METHODS

Local and distant changes in EEG cortical oscillations were respectively assessed by the Event-Related Desynchronization/Synchronization technique and the Synchronization Likelihood technique. Data collected prior to, during, and after observation and execution of complex sequential finger movements were used to investigate these changes. EEGs were recorded from 19 active sites across the cortex of 10 subjects. Sensorimotor activity was examined in alpha frequency bands.

RESULTS

Local power changes and global interregional synchronizations were two distinct phenomena, which occurred simultaneously and displayed different spatiotemporal patterns.

DISCUSSION AND CONCLUSIONS

These findings demonstrate the complementary character of both analysis techniques. Results are discussed in light of the recent findings from the cognitive and behavioural neuroscience literature.

Long-term effects of temporal lobe epilepsy on local neural networks: a graph theoretical analysis of corticography recordings

PURPOSE

Pharmaco-resistant temporal lobe epilepsy (TLE) is often treated with surgical intervention at some point. As epilepsy surgery is considered a last resort by most physicians, a long history of epileptic seizures prior to surgery is not uncommon. Little is known about the effects of ongoing TLE on neural functioning. A better understanding of these effects might influence the moment of surgical intervention. Functional connectivity (interaction between spatially distributed brain areas) and network structure (integration and segregation of information processing) are thought to be essential for optimal brain functioning. We report on the impact of TLE duration on temporal lobe functional connectivity and network characteristics.

METHODS

Functional connectivity of the temporal lobe at the time of surgery was assessed by means of interictal electrocorticography (ECoG) recordings of 27 TLE patients by using the phase lag index (PLI). Graphs (abstract network representations) were reconstructed from the PLI matrix and characterized by the clustering coefficient C (local clustering), the path length L (overall network interconnectedness), and the "small world index" S (network configuration).

RESULTS

Functional connectivity (average PLI), clustering coefficients, and the small world index were negatively correlated with TLE duration in the broad frequency band (0.5-48 Hz).

DISCUSSION

Temporal lobe functional connectivity is lower in patients with longer TLE history, and longer TLE duration is correlated with more random network configuration. Our findings suggest that the neural networks of TLE patients become more pathological over time, possibly due to temporal lobe changes associated with long-standing lesional epilepsy.

Advanced time-series analysis of MEG data as a method to explore olfactory function in healthy controls and Parkinson's disease patients

OBJECTIVES

To determine whether time-series analysis of magnetoencephalography (MEG) data is a suitable method to study brain activity related to olfactory information processing, and to detect differences in odor-induced brain activity between patients with Parkinson's disease (PD) and controls.

METHODS

Whole head 151-channel MEG recordings were obtained in 21 controls and 20 patients with PD during a 10-min olfactory stimulus paradigm, consisting of 10 alternating rest-stimulus cycles (30 s each), using phenylethyl alcohol administered by means of a Burghart olfactometer. Relative spectral power and synchronization likelihood (SL; an unbiased measure of functional connectivity) were calculated for delta, theta, alpha1, alpha2, beta, and gamma frequency bands.

RESULTS

In controls, olfactory stimulation produced an increase in theta power and a decrease in beta power. In patients with PD, there was a decrease in alpha1 power. No significant interaction between group and condition was found for spectral power. SL analysis revealed a significantly different response to olfactory stimulation in patients with PD compared to controls. In controls, the odor stimulus induced a decrease in local beta band SL. The response in patients with PD involved a decrease in intrahemispheric alpha2 band SL.

CONCLUSION

This is the first study to show that time-series analysis of MEG data, including spectral power and SL, can be used to detect odor-induced changes in brain activity. In addition, differences in odor-induced brain activity were found between patients with PD and controls using analysis of SL, but not of spectral power.

Functional brain connectivity and neurocognitive functioning in patients with long-standing type 1 diabetes with and without microvascular complications: a magnetoencephalography study

OBJECTIVE

Hyperglycemia-associated microvascular disease may underlie changes in cerebral functioning and cognitive performance in patients with type 1 diabetes. Functional connectivity, an indicator of functional interactions and information exchange between brain regions, provides a measure of cerebral functioning. This study addresses functional connectivity and cognition in type 1 diabetic patients with and without proliferative retinopathy, relative to healthy control subjects, using magnetoencephalography.

RESEARCH DESIGN AND METHODS

Fluctuations in magnetic field at scalp for Delta, theta, lower and upper alpha, beta, and lower and upper gamma frequency bands were measured using magnetoencephalography. Synchronization likelihood, a measure of functional connectivity, was computed. Using neuropsychological tests, cognitive functioning was assessed and its associations with functional connectivity were determined.

RESULTS

Compared with control subjects, type 1 diabetic patients performed poorer on general cognitive ability, information processing speed, and motor speed, irrespective of their microvascular complication status. Functional connectivity, however, was lowest for type 1 diabetic patients with retinopathy, compared with type 1 diabetic patients without microvascular complications and control subjects, whereas type 1 diabetic patients without microvascular complications showed an increase relative to control subjects. Positive associations were found between functional connectivity and executive functioning, memory, information processing speed, motor speed, and attention.

CONCLUSIONS

Compared with healthy control subjects, functional connectivity and cognition differed in type 1 diabetic patients irrespective of microvascular complication status, indicating that chronic hyperglycemia, among other factors, may negatively affect brain functioning even before microvascular damage becomes manifest. The association found between synchronization likelihood and cognition suggests functional connectivity plays a significant role in cognitive functioning.

Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

BACKGROUND

Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5-4 Hz, 4-8 Hz, 8-10 Hz, 10-13 Hz, 13-30 Hz and 30-45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks.

RESULTS

In AD, the clustering coefficient decreased in the lower alpha and beta bands (p < 0.001), and the characteristic path length decreased in the lower alpha and gamma bands (p < 0.05) compared to controls. In FTLD no significant differences with controls were found in these measures. The degree correlation decreased in both alpha bands in AD compared to controls (p < 0.05), but increased in the FTLD lower alpha band compared with controls (p < 0.01).

CONCLUSION

With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively) more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.

Altered sleep brain functional connectivity in acutely depressed patients

Recent evidence suggests that problems in information processing within neural networks may underlie depressive disease. In this study, we investigated whether sleep functional brain networks are abnormally organized during a major depressive episode (MDE). We characterized spatial patterns of functional connectivity by computing the "synchronization likelihood" (SL) of 19 sleep EEG channels in 11 acutely depressed patients [42 (20-51) years] and 14 healthy controls [32.9 (27-42) years]. To test whether disrupting an optimal pattern ["small-world network" (SWN)] of functional brain connectivity underlies MDE, graph theoretical measures were then applied to the resulting synchronization matrices, and a clustering coefficient (C, measure of local connectedness) and a shortest path length (L, measure of overall network integration) were determined. In the depressed group, the mean SL was lower in the delta, theta and sigma frequency bands. Acutely depressed patients showed a significantly lower path length in the theta and delta frequency bands, whereas the cluster coefficient showed no significant changes. The present study provides further support that sleep functional brain networks exhibit "small-world" properties. Sleep neuronal functional networks in depressed patients are characterized by a functional reorganization with a lower mean level of global synchronization and loss of SWN characteristics. These results argue for considering an MDE as a problem of neuronal network organization and a problem of information processing.