Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

Abstract

Mild cognitive impairment (MCI) is a stage between healthy aging and dementia. It is known that in this condition the connectivity patterns are altered in the resting state and during cognitive tasks, where an extra effort seems to be necessary to overcome cognitive decline. We aimed to determine the functional connectivity AGE (2014) pattern required to deal with an internally directed cognitive state (IDICS) in healthy aging and MCI. This task differs from the most commonly employed ones in neurophysiology, since inhibition from external stimuli is needed, allowing the study of this control mechanism. To this end, magnetoencephalographic (MEG) signals were acquired from 32 healthy individuals and 38 MCI patients, both in resting state and while performing a subtraction task of two levels of difficulty. Functional connectivity was assessed with phase locking value (PLV) in five frequency bands. Compared to controls, MCIs showed higher PLV values in delta, theta, and gamma bands and an opposite pattern in alpha, beta, and gamma bands in resting state. These changes were associated with poorer neuropsychological performance. During the task, this group exhibited a hypersynchronization in delta, theta, beta, and gamma bands, which was also related to a lower cognitive performance, suggesting an abnormal functioning in this group. Contrary to controls, MCIs presented a lack of synchronization in the alpha band which may denote an inhibition deficit. Additionally, the magnitude of connectivity changes rose with the task difficulty in controls but not in MCIs, in line with the compensation-related utilization of neural circuits hypothesis (CRUNCH) model.

Cognitive workload across the spectrum of cognitive impairments: A systematic review of physiological measures

“…Only patients with MCI demonstrated significantly lower EEG theta power under high demand condition compared to low demand condition (Cummins et al, 2008). A similar pattern was observed in a MEG study (Lopez et al, 2014). When the task difficulty increased (e.g subtraction task), a decline in synchronization changes was noted only in patients with MCI (Lopez et al, 2014).…”

“…Patients with MCI usually invested more overall effort than healthy older adults in performing an identical cognitive task. These results were found in EEG studies (Cummins et al, 2008) and MEG studies (Ahmadlou et al, 2014;Aurtenetxe et al, 2013;Bajo et al, 2010;Lopez et al, 2014;Maestu et al, 2008). Similarly, patients with AD in the early stages needed greater effort than patients with MCI and healthy older adults to execute the task (Hidasi, Czigler, Salacz, Csibri, & Molnár, 2007;van der Hiele et al, 2007;van Deursen, Vuurman, van Kranen-Mastenbroek, Verhey, & Riedel, 2011;van Deursen, Vuurrman, Verhey, van kranen-Mastenbroek, & Riedel, 2008).…”

“…Similarly, Bajo et al (2010) demonstrated an increase in posterior interhemispheric connectivity in the gamma band while patients with MCI performed a working memory task. To some extent, patients with MCI also showed an increase in functional connectivity in most of bands while performing a mental calculation task (Lopez et al, 2014). These changes were also associated with lower cognitive performance, which suggests that the increase in functional connectivity in most of bands was considered a compensatory mechanism for their cognitive decline.…”

“…Studies showed that some EEG/MEG measures were not only sensitive to patients with MCI but also to the task difficulty (Cummins et al, 2008;Lopez et al, 2014).…”

Simultaneous MEG/EEG recordings for the study of source domain brain connectivity in neurodegenerative diseases

“…As oscillatory activity in the brain signals seems to be closely related to cognitive processes, the idea of phase synchronization seems to be a good model to estimate brain connectivity. In particular, PLV has been broadly used as an index to estimate functional connectivity (Gao et al, 2014;López, Garcés, et al, 2014;Arnulfo et al, 2015;Zoefel and VanRullen, 2016;Hassan et al, 2017), mainly because of the simplicity of the required calculations. In addition, this index have proven to be highly reliable in test-retests studies (Colclough et al, 2016;Garcés, Martín-Buro and Maestú, 2016).…”