Resting EEG sources correlate with attentional span in mild cognitive impairment and Alzheimer's disease

Abstract

Previous evidence has shown that resting delta and alpha electroencephalographic (EEG) rhythms are abnormal in patients with Alzheimer's disease (AD) and its potential preclinical stage (mild cognitive impairment, MCI). Here, we tested the hypothesis that these EEG rhythms are correlated with memory and attention in the continuum across MCI and AD. Resting eyes-closed EEG data were recorded in 34 MCI and 53 AD subjects. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography (LORETA). These sources were correlated with neuropsychological measures such as Rey list immediate recall (word short-term memory), Rey list delayed recall (word medium-term memory), Digit span forward (immediate memory for digits probing focused attention), and Corsi span forward (visuo-spatial immediate memory probing focused attention). A statistically significant negative correlation (Bonferroni corrected, P < 0.05) was observed between Corsi span forward score and amplitude of occipital or temporal delta sources across MCI and AD subjects. Furthermore, a positive correlation was shown between Digit span forward score and occipital alpha 1 sources (Bonferroni corrected, P < 0.05). These results suggest that cortical sources of resting delta and alpha rhythms correlate with neuropsychological measures of immediate memory based on focused attention in the continuum of MCI and AD subjects.

Predicting dementia in Parkinson disease by combining neurophysiologic and cognitive markers

“…20 It is tempting to speculate that these disruptions give rise to clinical impairments in global attention and visuospatial processes and might explain the progressive decline in cognitive (attentional and visuospatial) functions across the spectrum from MCI to dementia, both in AD and PDD. 21,22 The fact that the discriminatory capacity of oscillatory slowing was highest in temporal and occipital regions supports this line of thought, as these regions have previously been implicated in visuospatial cognitive Figure 1 Spatial distribution of the MEG power predictors that contributed significantly to risk discrimination (A) Theta power, (B) beta power. HR 5 hazard ratio; MEG 5 magnetoencephalography.…”

“…We estimated broadband time series of neuronal activation for voxels with peak activation for 6 frequency bands (delta [0.5-4 Hz], theta [4][5][6][7][8], alpha1 [8][9][10], alpha2 [10][11][12][13], beta [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and gamma [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], using an average time window of 232 (range: 118-433) seconds as input for the beamformer computations. This resulted in a total of 6 sets (one for each frequency band) of 78 time series (one for each AAL region).…”

Alzheimer's disease: relationship between cognitive aspects and power and coherence EEG measures

“…Positive correlations with the coherence of the alpha and beta activities appeared in the tests involving memory, specifically the word list recall, and praxis recall, for both the general casuistry and in the Alzheimer group. Connections between alpha rhythm and memory have already been pointed out in the literature 28 , especially for immediate memory 7,16 . These relationships would be supported by physiological aspects in which the alpha…”

“…Cross-sectional studies with the coherence measures of the qEEG failed to show correlation with the MMSE, although only global coherence measures or those restricted to the alpha and beta bands were used in these studies 7,14,15 . On the other hand, there are a few studies assessing correlations between qEEG measures and specific cognitive aspects 15,16 , but in these studies the coherence measurements were also limited with respect to the bands and locations studied. A study of relationships between the variables of the resting qEEG and specific cognitive aspects could contribute to knowledge concerning their physiopathology, and also contribute to the choice of variables to be used in qEEG studies during specific tasks with a view to diagnostic utility.…”

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

“…Interestingly, various studies have constructed source models from the EEG of MCI and AD patients [108,153,154,155,156,157,158,159,160]. We will discuss some of those studies later on in this paper.…”