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Prédiction de la détérioration cognitive chez les sujets âgés normaux et avec troubles cognitifs légers à l'aide d'un nouveau marqueur éléctrophysiologique de la mémoire de travail

English title Prediction of cognitive deterioration in healthy elderly controls and MCI based on a new electrophysiological marker of working memory
Applicant Giannakopoulos Panteleimon
Number 103770
Funding scheme Project funding (Div. I-III)
Research institution Département de Psychiatrie Hôpitaux Universitaires de Genève HUG
Institution of higher education University of Geneva - GE
Main discipline Neurology, Psychiatry
Start/End 01.04.2004 - 31.03.2007
Approved amount 216'785.00
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Keywords (5)

Alzheimer; EEG; marqueur biologique; potentiels évoqués; troubles cognitifs

Lay Summary (English)

Lead
Lay summary
The main objective of our studies is to identify an easily applicable EEG marker that permits to identify cases at high risk for dementia among the highly heterogeneous group of patients with mild cognitive impairment (MCI). Recent studies described several changes of endogenous event related potentials (ERP) and brain rhythm synchronization during memory activation in patients with Alzheimer's disease (AD). To examine whether memory-related EEG parameters may predict cognitive decline in MCI, we assessed P200 and N200 latencies as well as beta event-related synchronization (ERS) in 16 elderly controls (EC), 29 MCI cases and 10 patients with AD during the successful performance of a pure attentional detection task as compared to a highly working memory demanding 2-back task. At one year follow-up, sixteen MCI patients showed progressive cognitive decline (PMCI) and thirteen remained stable (SMCI). Both P200 and N200 latencies in the 2-back task were longer in PMCI and AD cases compared to EC and SMCI cases. During the interval 1000 ms to 1700 ms after stimulus, beta ERS at parietal electrodes was of lower amplitude in PMCI and AD compared to EC and SMCI cases. Univariate models showed that P200, N200 and log% beta values were significantly related to the SMCI/PMCI distinction with areas under the receiver operating characteristic curve of 0.93, 0.78 and 0.72, respectively. The combination of all three EEG hallmarks was the stronger predictor of MCI deterioration with 90% of correctly classified MCI cases. Our data reveal that PMCI and clinically overt AD share the same pattern of working memory-related EEG activation characterized by increased P200-N200 latencies and decreased beta ERS. They also show that P200 latency during the 2-back task may be a simple and promising EEG marker of rapid cognitive decline in MCI.Since working memory also needs the activation of selective attention, we aimed to explore whether EEG parameters associated with this latter function may contribute to distinguish between PMCI and SMCI. Theta frequency band is known to react to selective attention paradigms. Global theta oscillatory activity (GTOA) is composed of a posterior component phase-locked to the stimulus and a frontal induced component, the ITOA, modulated by the level of focused attention to the stimulus. Analysis of ITOA may be a sensitive method to identify early alterations of the directed-attention network in MCI patients. Time-frequency analysis at baseline was used to assess GTOA and ITOA (4-6 Hz) during n-back working memory tasks in 29 MCI patients and 13 elderly controls (EC). At 1-year follow-up, 13 MCI patients were still stable (SMCI) and 16 had progressed (PMCI). Baseline task performance was similar in SMCI and PMCI cases. Unlike GTOA, frontal ITOA at baseline was significantly reduced in PMCI as compared to EC and SMCI. Such reduction was equally present in all n-back tasks that are similar in terms of focused attention requirements. Despite maintenance of performance, the ITOA decrease suggests early deficits in the directed-attention network in PMCI, whereas this network is functionally preserved in SMCI. Clinically, the decrease of the ITOA was also significantly related to the cognitive outcome (area under the ROC curve: 78%, sensitivity: 0.81, specificity: 0.62, correctly classified cases: 72.4%). However, the inclusion of this parameter in multiple logistic regression models did not improve the sensitivity and specificity values and percentage of correctly classified cases obtained using the working memory load-dependent EEG parameters (P200 and N200 latencies as well as beta ERS). Altogether, our results show that it is possible to identify simple EEG markers that predict MCI conversion to AD. Future perspectives include the analysis of the predictive value of these EEG markers in an independent validation sample.
Direct link to Lay Summary Last update: 21.02.2013

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Associated projects

Number Title Start Funding scheme
116193 Neurophysiological prediction of cognitive decline in mild cognitive impairment: analysis of attention components 01.04.2007 Project funding (Div. I-III)
59110 Identification of early dysfunction of corticocortical circuits in mild cognitive impairment and Alzheimer's disease 01.04.2000 Project funding (Div. I-III)

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