EEG time signature in Alzheimer´s disease: Functional brain networks falling apart

Smailović, Una; Koenig, Thomas; Laukka, Erika J.; Kalpouzos, Grégoria; Andersson, T.; Winblad, Bengt; Jelić, Vesna

doi:10.1016/j.nicl.2019.102046

Cited by 49 publications

(55 citation statements)

References 60 publications

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“…The topography of microstate class D was altered in AD. Previous studies give conflicting reports on alterations to microstate topographies in AD; in agreement with our results, Smailovic et al 29 found alterations to the topography of class D in AD but also found alterations to class A, while Schumacher et al 28 found alterations to all classes in AD and Nishida et al 26 found alterations to none. Differences in the number of channels is unlikely to explain these inconsistencies in results, as microstates are reliable with 8 or more channels 43 .…”

Section: Discussionsupporting

confidence: 86%

“…Alterations to patterns of transitions between classes have also been reported in neurological disease 26 , 27 , suggesting that studying transitioning behaviour of microstates may give further mechanistic insights into cognitive impairment in AD as well as increasing sensitivity of electrophysiological biomarkers. Indeed, recent studies have identified alterations to spatial patterns and temporal dynamics of EEG microstates in AD and MCI 17 , 26 , 28 , 29 . However, the ‘syntax analysis’ 26 , 27 used to analyse transitioning behaviour in these studies assumes the next microstate depends only on the present microstate (Markovian), and probabilities of transitions do not change over time (stationary).…”

Section: Introductionmentioning

confidence: 99%

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EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

Tait

Tamagnini

Stothart

et al. 2020

Sci Rep

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The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

Tait

Tamagnini

Stothart

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Overall, quantitative EEG analysis provides the most direct and dynamic clinical representation of neuronal and synaptic function in AD patients; however, while it is sensitive to changes in neuronal circuit responses resulting from synaptic dysfunction, it cannot discriminate between the exact mechanisms of action underlying synaptic dys/function. Alterations in quantitative measures derived from EEG data in patients with AD have been widely described and have been shown to be sensitive to disease progression [134,138,139] and to correlate with CSF biomarkers of AD [140]. Furthermore, EEG is non-invasive, robust, efficacious, and widely available in hospitals.…”

Section: Biomarkers Of Synapse Damage or Lossmentioning

confidence: 99%

The clinical promise of biomarkers of synapse damage or loss in Alzheimer’s disease

et al. 2020

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Background: Synapse damage and loss are fundamental to the pathophysiology of Alzheimer's disease (AD) and lead to reduced cognitive function. The goal of this review is to address the challenges of forging new clinical development approaches for AD therapeutics that can demonstrate reduction of synapse damage or loss. The key points of this review include the following: Synapse loss is a downstream effect of amyloidosis, tauopathy, inflammation, and other mechanisms occurring in AD. Synapse loss correlates most strongly with cognitive decline in AD because synaptic function underlies cognitive performance. Compounds that halt or reduce synapse damage or loss have a strong rationale as treatments of AD. Biomarkers that measure synapse degeneration or loss in patients will facilitate clinical development of such drugs. The ability of methods to sensitively measure synapse density in the brain of a living patient through synaptic vesicle glycoprotein 2A (SV2A) positron emission tomography (PET) imaging, concentrations of synaptic proteins (e.g., neurogranin or synaptotagmin) in the cerebrospinal fluid (CSF), or functional imaging techniques such as quantitative electroencephalography (qEEG) provides a compelling case to use these types of measurements as biomarkers that quantify synapse damage or loss in clinical trials in AD. Conclusion: A number of emerging biomarkers are able to measure synapse injury and loss in the brain and may correlate with cognitive function in AD. These biomarkers hold promise both for use in diagnostics and in the measurement of therapeutic successes.

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“…To measure phase synchronization, the characteristics of synchronous signals can be quantitatively estimated at different detection points by calculating the PLI. Firstly, the EEG signals were divided into 5 frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma . Each band-divided signal at time t and point a is represented by the phase fa (t) and the amplitude Aa (t) via the Hilbert transform.…”

Section: Phase Lag Indexmentioning

confidence: 99%

“…Among these methods, those based on EEG are highly effective in clinical application, because they are cost-effective, widely available, and non-invasive (14,15). The pathological progression of AD leads to cortical disconnection; consequently, in EEG signals, it alters functional connectivity measured by the degree of synchronization between different brain regions and complex behavior produced by the interactions among wide spread brain regions (9,11,12,(16)(17)(18)(19)(20)(21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

et al. 2020

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Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among widespread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-andregional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods

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EEG time signature in Alzheimer´s disease: Functional brain networks falling apart

Cited by 49 publications

References 60 publications

EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

The clinical promise of biomarkers of synapse damage or loss in Alzheimer’s disease

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

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