Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Poil, Simon-Shlomo; Haan, Willem de; Flier, Wiesje M. van der; Mansvelder, Huibert D.; Scheltens, Philip; Linkenkaer‐Hansen, Klaus

doi:10.3389/fnagi.2013.00058

Cited by 170 publications

(136 citation statements)

References 101 publications

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“…In addition to imaging biomarkers, one study found that the six electroencephalography biomarkers on electroencephalography could predicte MCI to AD conversion in the Alzheimer’s Center in Netherlands (43). Two studies used clinical tests to predict MCI to AD conversion.…”

Section: Resultsmentioning

confidence: 99%

Advancing Alzheimer’s research: A review of big data promises

Zhang

Simon

2017

International Journal of Medical Informatics

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Objective To review the current state of science using big data to advance AD research and practice. In particular, we analyzed the types of research foci addressed and corresponding methods employed and study findings reported using big data in AD. Method Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria. Results Thirty-eight articles were included in this review. They can be categorized into six research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3) and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n= 16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis. Conclusion Big data are increasingly used to address AD related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-tapped opportunity for advancing AD research.

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Section: Resultsmentioning

confidence: 99%

Advancing Alzheimer’s research: A review of big data promises

Zhang

Simon

2017

International Journal of Medical Informatics

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“…Subtle but consistent deviations in the electromagnetic neuronal dynamics have been shown to precede explicit cognitive manifestations in AD [291] which could enable a future role of EEG/MEG biomarkers not only as a clinical diagnosis and treatment option, but also as a new mode for AD stage discovery. Dramatic progresses in dense-array active-EEG and MEG sensor technology, as well as in advanced signal processing techniques [292] have generated a recent surge of interest to use these promising capabilities in the context of improved clinical AD diagnosis. The added value of the EEG/MEG markers as an inexpensive, fast, and time-resolved tool is set to be explored rigorously both as a standalone approach and as a complementary measure together with other biomarker modalities.…”

Section: Neuroelectrical and Neuromagnetic Markersmentioning

confidence: 99%

“…A next generation of more sophisticated resting-state signal analysis approaches [292] is set to improve upon and to replace band-power markers in the next decade by capturing better the complex characteristics and dynamics of progressive neurodegeneration and aging. Promising methods involve brain connectivity [296], global synchronization, synchronization likelihood [291], detrended fluctuation analysis, approximate entropy, mutual information, source localization, and a host of further non-linear signal features.…”

Section: Resting-state Neuroelectrical/neuromagnetic Markersmentioning

confidence: 99%

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Hampel

Lista

Teipel

et al. 2014

Biochemical Pharmacology

105

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Recent advances in understanding the molecular mechanisms underlying various paths toward the pathogenesis of Alzheimer's disease (AD) has begun to provide new insight for interventions to modify disease progression. The evolving knowledge gained from multidisciplinary basic research has begun to identify new concepts for treatments and distinct classes of therapeutic targets; as well as putative disease-modifying compounds that are now being tested in clinical trials. There is a mounting consensus that such disease modifying compounds and/or interventions are more likely to be effectively administered as early as possible in the cascade of pathogenic processes preceding and underlying the clinical expression of AD. The budding sentiment is that "treatments" need to be applied before various molecular mechanisms converge into an irreversible pathway leading to morphological, metabolic and functional alterations that characterize the pathophysiology of AD. In light of this, biological indicators of pathophysiological mechanisms are desired to chart and detect AD throughout the asymptomatic early molecular stages into the prodromal and early dementia phase. A major conceptual development in the clinical AD research field was the recent proposal of new diagnostic criteria, which specifically incorporate the use of biomarkers as defining criteria for preclinical stages of AD. This paradigm shift in AD definition, conceptualization, operationalization, detection and diagnosis represents novel fundamental opportunities for the modification of interventional trial designs. This perspective summarizes not only present knowledge regarding biological markers but also unresolved questions on the status of surrogate indicators for detection of the disease in asymptomatic people and diagnosis of AD

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“…Changes in power were analyzed by using the Neurophysiological Biomarker Toolbox 15 and compared with 5-minute EEG recordings of 15 agematched (9-11 years old) healthy subjects with normal development. These data were collected as part of the Dutch Dyslexia Programme (DDP).…”

Section: Figurementioning

confidence: 99%

Paradoxical Benzodiazepine Response: A Rationale for Bumetanide in Neurodevelopmental Disorders?

Bruining¹,

Passtoors²,

Goriounova

et al. 2015

Pediatrics

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The diuretic agent bumetanide has recently been put forward as a novel, promising treatment of behavioral symptoms in autism spectrum disorder (ASD) and related conditions. Bumetanide can decrease neuronal chloride concentrations and may thereby reinstate g-aminobutyric acid (GABA)-ergic inhibition in patients with neurodevelopmental disorders. However, strategies to select appropriate candidates for bumetanide treatment are lacking. We hypothesized that a paradoxical response to GABA-enforcing agents such as benzodiazepines may predict the efficacy of bumetanide treatment in neurodevelopmental disorders. We describe a case of a 10-year-old girl with ASD, epilepsy, cortical dysplasia, and a 15q11.2 duplication who had exhibited marked behavioral arousal after previous treatment with clobazam, a benzodiazepine. We hypothesized that this response indicated the presence of depolarizing excitatory GABA and started bumetanide treatment with monitoring of behavior, cognition, and EEG. The treatment resulted in a marked clinical improvement in sensory behaviors, rigidity, and memory performance, which was substantiated by questionnaires and cognitive assessments. At baseline, the girl's EEG showed a depression in absolute a power, an electrographic sign previously related to ASD, which was normalized with bumetanide treatment. The effects of bumetanide on cognition and EEG seemed to mirror the "nonparadoxical" responses to benzodiazepines in healthy subjects. In addition, temporal lobe epilepsy and cortical dysplasia have both been linked to disturbed chloride homeostasis and seem to support our assumption that the observed paradoxical response was due to GABA-mediated excitation. This case highlights that a paradoxical behavioral response to GABA-enforcing drugs may constitute a framework for targeted treatment with bumetanide. PATIENT PRESENTATIONThe patient, a 10-year-old girl, was the fourth child of nonconsanguineous healthy parents. The family history was unremarkable, and the pregnancy and birth were uneventful. The child displayed an apparently normal development up to 4 years of age, when she began experiencing epileptic seizures. She was diagnosed with focal epilepsy with right temporoparietal localization, with focal seizures occurring in clusters.An MRI revealed a focal cortical dysplasia of the right temporal lobe. Coinciding with the onset of epilepsy, learning difficulty and behavioral rigidity emerged but were not evaluated at that time by a child psychiatrist. At the age of 8 years, she was referred to our clinic for diagnostic evaluation of increasing learning disability, behavioral inflexibility, and emotional disturbance. We observed a delay in language development, rigidity, and repetitive behaviors with peculiar

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Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Cited by 170 publications

References 101 publications

Advancing Alzheimer’s research: A review of big data promises

Advancing Alzheimer’s research: A review of big data promises

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Paradoxical Benzodiazepine Response: A Rationale for Bumetanide in Neurodevelopmental Disorders?

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