scholarly journals Direction of Information Flow in Alzheimer's Disease and MCI Patients

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Fabrizio Vecchio ◽  
Claudio Babiloni
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Information Flow ◽  
Additive Model ◽  
Functional Coupling ◽  
Directed Transfer Function ◽  
Directional Flow ◽  
Eeg Data ◽  
Rest Condition ◽  
Amnesic Mild Cognitive Impairment

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) atδ(2–4 Hz),θ(4–8 Hz),α1 (8–10 Hz),α2 (10–12 Hz),β1 (13–20 Hz),β2 (20–30 Hz), andγ(30–40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects forαandβrhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher atθ,α1,α2, andβ1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

scholarly journals Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
François-B. Vialatte ◽  
Justin Dauwels ◽  
Monique Maurice ◽  
Toshimitsu Musha ◽  
Andrzej Cichocki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frequency Domain ◽  
Matched Control ◽  
Cost Effective ◽  
Eeg Signals ◽  
Time Frequency ◽  
Eyes Closed ◽  
Eeg Data ◽  
Rest Condition

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients.Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest wereθ(3.5–7.5 Hz),α1(7.5–9.5 Hz),α2(9.5–12.5 Hz), andβ(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models.Results. Enhanced EEG power in theθrange is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies.Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Discrimination of Alzheimer's disease and normal aging by EEG data

1997 ◽  
Vol 103 (2) ◽  
pp. 241-248 ◽  
Author(s):  
C. Besthorn ◽  
R. Zerfass ◽  
C. Geiger-Kabisch ◽  
H. Sattel ◽  
S. Daniel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Normal Aging ◽  
Eeg Data

Global Functional Coupling of Resting EEG Rhythms is Abnormal in Mild Cognitive Impairment and Alzheimer’s Disease

2009 ◽  
Vol 23 (4) ◽  
pp. 224-234 ◽  
Author(s):  
Claudio Babiloni ◽  
Giovanni Frisoni ◽  
Fabrizio Vecchio ◽  
Roberta Lizio ◽  
Michela Pievani ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Networks ◽  
Elderly Subjects ◽  
Functional Coupling ◽  
Eeg Rhythms ◽  
Eyes Closed ◽  
Global Cognition

Alzheimer’s disease (AD) is typically associated with an impairment of brain networks and global cognitive function in aging. In this vein, the present study tested the hypothesis that the functional coupling of resting cortical electroencephalographic (EEG) rhythms is progressively abnormal in amnesic mild cognitive impairment (MCI) and AD subjects. Eyes-closed resting EEG data were recorded (10–20 system) in 33 mild AD, 52 amnesic MCI, and 47 normal elderly subjects (Nold). EEG rhythms of interest were delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), and gamma (30–40 Hz). The global functional coupling of the EEG rhythms was indexed by means of spectral coherence for all combinations of electrode pairs (i.e., total coherence). The main results showed that the total coherence of delta rhythms was higher in the AD than the MCI group. It was also higher in the MCI than the Nold group. Furthermore, the delta total coherence was negatively correlated with global cognition (Mini Mental State Examination score) across the Nold, MCI, and AD subjects. Finally, the alpha1 total coherence was lower in the AD group than in the MCI and Nold groups. These results suggest that in the AD process an impairment of brain networks and global cognition is associated with a frequency-specific modulation of the global functional coupling of resting EEG rhythms.

The Value of Quantitative EEG Measures in the Early Diagnosis of Alzheimer’s Disease

2011 ◽  
pp. 132-140
Author(s):  
Hideaki Tanaka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Parahippocampal Gyrus ◽  
Data Sets ◽  
Electromagnetic Tomography ◽  
Eyes Closed ◽  
Eeg Data ◽  
Z Value

There is growing interest in the discovery of clinically useful, robust biomarkers for Alzheimer’s disease (AD) and pre-AD; the ability to accurately diagnose AD or to predict conversion from a preclinical state to AD would aid in both prevention and early intervention. This study aimed to evaluate the usefulness of a statistical assessment of cortical activity using electroencephalograms (EEGs) with normative data and the ability of such an assessment to contribute to the diagnosis of AD. 15 patients with AD and 8 patients with mild cognitive impairment (MCI) were studied. Eyes-closed resting EEGs were digitally recorded at 200 Hz from 20 electrodes placed according to the international 10/20 system on the scalp, and 20 artifact-free EEG epochs lasting 2.56 ms were selected. Each EEG epoch was down-sampled to 100 Hz and matched to the normal data sets. The selected EEGs from each subject were analyzed by standardized Low Resolution Electromagnetic Tomography (sLORETA) and statistically compared with the age-matched normal data sets at all frequencies. This procedure resulted in cortical z values for each EEG frequency with 0.39 Hz frequency resolution for each subject. Some of the AD and MCI patients presented a peak of negative z value around 20 Hz, revealing hypoactivity of the parahippocampal gyrus and the insula in the sLORETA cortical image. In addition, severe cases of AD showed decreased parietal activation. These results were in agreement with evidence from statistical neuroimaging using MRI/SPECT. Submission of normal EEG data sets to sLORETA might be useful for the detection of diagnostic and predictive markers of AD and MCI in individual patients.

scholarly journals EEG Characterization of the Alzheimer’s Disease Continuum by Means of Multiscale Entropies

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 544 ◽  
Author(s):  
Aarón Maturana-Candelas ◽  
Carlos Gómez ◽  
Jesús Poza ◽  
Nadia Pinto ◽  
Roberto Hornero
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Activity ◽  
P Values ◽  
Eeg Data ◽  
High Prevalence ◽  
Average Slope ◽  
The Brain

Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal–Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.

scholarly journals Computer Model of Synapse Loss During an Alzheimer’s Disease-like Pathology in Hippocampal Subregions DG, CA3 and CA1—the Way to Chaos and Information Transfer

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 408 ◽  
Author(s):  
Świetlik ◽  
Białowąs ◽  
Moryś ◽  
Kusiak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lyapunov Exponent ◽  
Information Flow ◽  
Shannon Entropy ◽  
Control Model ◽  
Maximal Lyapunov Exponent ◽  
Synapse Loss ◽  
Entropy Transfer ◽  
Pathological Model

The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

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