Brain oscillatory responses to an auditory-verbal working memory task in mild cognitive impairment and Alzheimer's disease

Karrasch, Mira; Laine, Matti; Rinne, Juha O.; Rapinoja, Pekka; Sinervä, Eija; Krause, Christina M.

doi:10.1016/j.ijpsycho.2005.04.006

Cited by 74 publications

(56 citation statements)

References 61 publications

(72 reference statements)

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“…In [195] event-related desynchronization (ERD) and synchronization (ERS) was studied in MCI and AD patients during a working-memory task; the 1-20Hz frequency band was analyzed. The AD patients were reported to make more errors than the controls and the MCI group.…”

Section: Working Memory Tasks In MCI and Admentioning

confidence: 99%

Diagnosis of Alzheimers Disease from EEG Signals: Where Are We Standing?

Dauwels¹,

Vialatte²,

Cichocki³

2010

CAR

368

188

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This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper first describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study fluctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis.Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing techniques are outlined, for example, based on blind source separation and other non-linear filtering paradigms.In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.

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Section: Working Memory Tasks In MCI and Admentioning

confidence: 99%

Diagnosis of Alzheimers Disease from EEG Signals: Where Are We Standing?

Dauwels¹,

Vialatte²,

Cichocki³

2010

CAR

368

188

View full text Add to dashboard Cite

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“…A recent study by Babiloni et al (2009) indicated an information flux in the direction of parietal to frontal. Also, EEG functional coupling for alpha and beta rhythms was stronger in normal elderly than in MCI and/or AD patients (Karrasch et al 2006). Hogan et al (2003) examined memory-related EEG power and coherence over temporal and central recording sites in patients with early AD and a normal control group.…”

Section: Introductionmentioning

confidence: 99%

Sensory evoked and event related oscillations in Alzheimer’s disease: a short review

Yener¹,

Başar²

2010

Cogn Neurodyn

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Diagnosis and treatment of Alzheimer's disease (AD) depend on clinical evaluation and there is a strong need for an objective tool as a biomarker. Our group has investigated brain oscillatory responses in a small group of AD subjects. We found that the de novo (untreated) AD group differs from both the cholinergically-treated AD group and aged-matched healthy controls in theta and delta responses over left frontal-central areas after cognitive stimulation. On the contrary, the difference observed in AD groups upon a sensory visual stimulation includes response increase over primary or secondary visual sensorial areas compared to controls. These findings imply at least two different neural networks, depending on type of stimulation (i.e. cognitive or sensory). The default mode defined as activity in resting state in AD seems to be affected electrophysiologically. Coherences are also very valuable in observing the group differences, especially when a cognitive stimulus is applied. In healthy controls, higher coherence values are elicited after a cognitive stimulus than after a sensory task. Our findings support the notion of disconnectivity of cortico-cortical connections in AD. The differences in comparison of oscillatory responses upon sensory and cognitive stimulations and their role as a biomarker in AD await further investigation in series with a greater number of subjects.

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“…Specifically, a decrease in amplitude and increase in latency of the P 300 component of the ERP, a positive peak that occurs 300 ms after the stimulus, has been linked to cognitive decline and AD [5][6][7][8][9]. Various signal processing approaches on the raw EEG or the P 300 has been conducted since then, which verified the presence of a statistical correlation, albeit a weak one that has mixed success in patient specific diagnosis [10][11][12][13]. Previous studies have shown that discrete wavelet coefficients of the ERPs, and not that of just the P 300 components, are more beneficial in patient specific AD diagnosis, particularly when the ERPs in response to different types of stimuli are combined [14][15][16][17].…”

mentioning

confidence: 96%