EEG oscillations during word processing predict MCI conversion to Alzheimer's disease

Abstract

Only a subset of mild cognitive impairment (MCI) patients progress to develop a form of dementia. A prominent feature of Alzheimer's disease (AD) is a progressive decline in language. We investigated if subtle anomalies in EEG activity of MCI patients during a word comprehension task could provide insight into the likelihood of conversion to AD. We studied 25 amnestic MCI patients, a subset of whom developed AD within 3-years, and 11 elderly controls. In the task, auditory category descriptions (e.g., ‘a type of wood’) were followed by a single visual target word either semantically congruent (i.e., oak) or incongruent with the preceding category. We found that the MCI convertors group (i.e. patients that would go on to convert to AD in 3-years) had a diminished early posterior-parietal theta (3–5 Hz) activity induced by first presentation of the target word (i.e., access to lexico-syntactic properties of the word), compared to MCI non-convertors and controls. Moreover, MCI convertors exhibited oscillatory signatures for processing the semantically congruent words that were different from non-convertors and controls. MCI convertors thus showed basic anomalies for lexical and meaning processing. In addition, both MCI groups showed anomalous oscillatory signatures for the verbal learning/memory of repeated words: later alpha suppression (9–11 Hz), which followed first presentation of the target word, was attenuated for the second and third repetition in controls, but not in either MCI group. Our findings suggest that a subtle breakdown in the brain network subserving language comprehension can be foretelling of conversion to AD.

Behavioral and neural network abnormalities in human APP transgenic mice resemble those of App knock-in mice and are modulated by familial Alzheimer’s disease mutations but not by inhibition of BACE1

“…We focused our behavioral analysis on learning and memory, because these cognitive functions are severely impaired by AD, and our electrophysiological analysis on electroencephalographic (EEG) recordings, because such recordings can be readily obtained also in humans. Indeed, various types of neural network dysfunction have been detected by EEG in AD patients [57][58][59][60][61][62][63] and related mouse models [53,54,60,[64][65][66][67][68]. We are particularly interested in non-convulsive epileptiform activity, because we and others recently showed that this activity is more prevalent in AD patients than is widely recognized [57,58,60,62,63], its detection predicts faster cognitive decline in AD [63], it could promote disease progression through multiple mechanisms [69], and the relationship between epileptiform activity and APP/Aβ is a matter of debate [26,70].…”

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

“…, to capture movement-intentions (Wolpaw et al, 1991;Lotte et al, 2007;Tangermann et al, 2008). For biomarkers applications, the focus is on predicting medical diagnosis and other clinical endpoints Sami et al, 2018;Mazaheri et al, 2018).…”

Assessment of Perceived Attractiveness, Usability, and Societal Impact of a Multimodal Robotic Assistant for Aging Patients With Memory Impairments

“…Physical and mental tiredness, increased stress level and a need for multitasking negatively influences the capacity of the working memory and therefore the execution of everyday tasks ( 20 ). It is worth remembering that for a person with memory impairments most of the activities, even if typically automatic, constantly remain in the loop of active and conscious processing ( 21 – 23 ). Otherwise the less significant steps of the executive action are forgotten or performed inadequately.…”