Neurophysiological correlates in Mild Cognitive Impairment detected using group Independent Component Analysis

Abstract

Alzheimer's disease is the most prevalent cause of dementia. Mild Cognitive Impairment (MCI) is defined as a grey area between intact cognitive functioning and clinical dementia. Electroencephalography (EEG) has been used to identify biomarkers in dementia. Currently, there is a great interest in translating the study from raw signals to signal generators, trying to keep the relationship with neurophysiology. In the current study, EEG recordings during an encoding task were acquired in MCI subjects and healthy controls. Data was decomposed using group Independent Component Analysis (gICA) and the most neuronal components were analyzed using Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC). MCI subjects exhibited an increase of PIC in the theta band, while controls showed increase in PsIC in the alpha band. Correlation between PIC and PsIC and clinical scales were also found. Those findings indicate that the methodology proposed based in gICA can help to extract information from EEG recordings with neurophysiological meaning.

Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

“…The use of independent component analysis (ICA) in EEG has evolved from a technique oriented to help in artifact detection and correction [76,77], to identify neural process and biomarkers [27,33,78,79]. Using recordings obtained at rest in 144 subjects, Congedo et al [80] reported seven sources distributed across the cortex, where three of them were also related to the Precuneus, that would serve to assessment and diagnosis of abnormal brain functioning.…”

“…The analysis was based in gICA, to extract the neuronal sources related to resting state, and inverse solution methods to locate the neuronal sources in the brain cortex. The concatenation of subject data for gICA analysis has been applied to study Mild Cognitive Impairment [33], Alzheimer disease [34] and Attention-Deficit Hyperactivity Disorder [35].…”

Reproducible Neuronal Components found using Group Independent Component Analysis in Resting State Electroencephalographic Data

“…The same author, in a subsequent work, discussed how the gICA approach gives replicable physiological components that could help in diagnosis and assessment of abnormal brain functioning (Congedo, John, Ridder, & Prichep, 2010). The concatena-tion of subject data for gICA analysis has been applied to the study of Mild Cognitive Impairment (Ochoa et al, 2015), Alzheimer’s disease (Zervakis, Michalopoulos, Ior-danidou, & Sakkalis, 2011) and Attention-Deficit Hyperactivity Disorder (Ponomarev, Mueller, Candrian, Grin-Yatsenko, & Kropotov, 2014).…”

Reproducible Neuronal Components found using Group Independent Component Analysis in Resting State Electroencephalographic Data

“…The same author, in a subsequent work, discussed how the gICA approach gives replicable physiological components that could help in diagnosis and assessment of abnormal brain functioning . The concatenation of subject data for gICA analysis has been applied to the study of Mild Cognitive Impairment (Ochoa et al, 2015), Alzheimer's disease (Zervakis, Michalopoulos, Iordanidou, & Sakkalis, 2011) and Attention-Deficit Hyperactivity Disorder (Ponomarev, Mueller, Candrian, Grin-Yatsenko, & Kropotov, 2014).…”