P2–069: Index of alpha/theta ratio of the electroencephalogram: A new marker for Alzheimer's disease

Schmidt, Magali; Kanda, Paulo Afonso Medeiros; Oliveira, Helder; Baratho, Regina; Jorge, Mário Silva; Nitrini, Ricardo; Anghinah, Renato

doi:10.1016/j.jalz.2013.05.712

Cited by 6 publications

(9 citation statements)

References 17 publications

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“…Having a set of QEEG features that could detect patients in the early stages of Parkinson's disease would be useful in providing treatment and care to the individuals. Schmidt et al (2013) carried out such a study for Alzheimer's Disease (AD) and investigated alpha/theta spectral ratio as a measure to distinguish healthy individuals from patients with AD. Han et al (2013) recorded EEG's in Parkinson's disease patients and healthy controls and found an increase of relative powers in the delta, theta bands, while observing a decrease of relative powers in the alpha, beta bands.…”

Section: Introductionmentioning

confidence: 99%

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)

Chaturvedi

Hatz

Gschwandtner

et al. 2017

Front. Aging Neurosci.

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Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection.Background: Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease. We wanted to investigate the differences in high-resolution QEEG measures between groups of PD patients and healthy individuals, and come up with a small subset of features that could accurately distinguish between the two groups.Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8 ± 7.0 year; female/male 17/33) and 41 healthy controls (age 71.1 ± 7.7 year; female/male 20/22). Data was processed to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Median, peak frequencies were also obtained and alpha1/theta ratios were calculated. Machine learning methods were applied to the data and compared. Additionally, penalized Logistic regression using LASSO was applied to the data in R and a subset of best-performing features was obtained.Results: Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region.Conclusion: The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity.

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Section: Introductionmentioning

confidence: 99%

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)

Chaturvedi

Hatz

Gschwandtner

et al. 2017

Front. Aging Neurosci.

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“…Because absolute power depends on factors unrelated to brain-activity per se (e.g., geometry of the head and brain), it is uninformative to compare absolute power values between groups of participants. To eliminate these confounds, researchers often normalize (e.g., [3], [13], [19], [31]) by dividing each power value by the total power (the approach we take here for this conventional measure) or compute ratios of power (e.g., [56]) in one frequency to another (e.g., theta:alpha). Thus, prior measures of slow rhythmic activity during rest have always been relative to power at other frequencies.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging

et al. 2015

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Rhythmic brain activity at low frequencies (<12 Hz) during rest are thought to increase in neurodegenerative disease, but findings in healthy neurocognitive aging are mixed. Here we address two reasons conventional spectral analyses may have led to inconsistent results. First, spectral-power measures are compared to a baseline condition; when resting activity is the signal of interest, it is unclear what the baseline should be. Second, conventional methods do not clearly differentiate power due to rhythmic versus non-rhythmic activity. The Better OSCillation detection method (BOSC; [10], [65]) avoids these problems by using the signal’s own spectral characteristics as a reference to detect elevations in power lasting a few cycles. We recorded electroencephalographic (EEG) signal during rest, alternating eyes open and closed, in healthy younger (18–25 years) and older (60–74 years) participants. Topographic plots suggested the conventional and BOSC analyses measured different sources of activity, particularly at frequencies, like delta (1–4 Hz), at which rhythms are sporadic (but topographies were more similar in the 8–12 Hz alpha band). There was little theta-band activity meeting the BOSC method’s criteria, suggesting prior findings of theta power in healthy aging may reflect non-rhythmic signal. In contrast, delta oscillations were present at higher levels than theta in both age groups. In sum, applying strict and standardized criteria for rhythmicity, slow rhythms appear present in the resting brain at delta and alpha, but not theta frequencies, and appear unchanged in healthy aging.

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“…Certain qEEG parameters were shown to be associated with dementia in patients with PD [4][5][6][7][8][9] or with Alzheimer's disease. 9,10 For instance, Caviness et al 8 identified an association between slowing of EEG rhythms and the cognitive state of PD patients, in particular in the theta and alpha frequency bands. Babiloni et al 6 further discussed localized associations of brain signals with the cognitive states in both Parkinson's disease and Alzheimer's disease patients.…”

Section: Introductionmentioning

confidence: 99%

Power spectra for screening parkinsonian patients for mild cognitive impairment

Bousleiman

Zimmermann

Ahmed

et al. 2014

Ann Clin Transl Neurol

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ObjectiveMild cognitive impairment in Parkinson’s disease (PD-MCI) is diagnosed based on the results of a standardized set of cognitive tests. We investigate whether quantitative EEG (qEEG) measures could identify differences between cognitively normal PD (PD-CogNL) and PD-MCI patients.MethodsHigh-resolution EEG was recorded in 53 patients with Parkinson’s disease (PD). Relative power in five frequency bands was calculated globally and for ten regions. Peak and median frequencies were determined. qEEG results were compared between groups. Effect sizes of all variables were calculated. The best separating variable was used to demonstrate subject-wise classification.ResultsLower mean values were observed in global alpha1 power and alpha1 power in five brain regions (left hemisphere: frontal, central, temporal, occipital; right hemisphere: temporal, P < 0.05), differentiating between PD-CogNL and PD-MCI groups. Effect sizes were high, ranging from 0.79 to 0.87. Median frequency was 8.56 ± 0.74 Hz and was not different between the groups. The variable with the best subject-wise classification was the power in the alpha1 band in the right temporal region. The area under the corresponding receiver operating characteristic (ROC) curve was 0.72. The optimal classification threshold yielded a sensitivity of 65.9% and a specificity of 66.7%. The positive and negative predictive values were 87.1% and 36.4%, respectively.InterpretationReduction in alpha1 band power in nondemented PD patients, particularly in the right temporal region, is highly indicative of MCI in PD patients. The results might be used to assist in time-efficient diagnosis of PD-MCI and avoid the drawbacks of test–retest effect in repeated neuropsychological testing.

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P2–069: Index of alpha/theta ratio of the electroencephalogram: A new marker for Alzheimer's disease

Cited by 6 publications

References 17 publications

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)

Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging

Power spectra for screening parkinsonian patients for mild cognitive impairment

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