Magnetoencephalogram background activity analysis in Alzheimer's disease patients using auto mutual information

Gómez, Carlos; Hornero, Roberto; Fernández, Alberto; Abásolo, Daniel; Escudero, Javier; López, M.

doi:10.1109/iembs.2006.260317

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…In contrast to the prediction in Introduction, we presently found that the decreased alpha-to-beta power in task-relevant regions was associated with the increased regularity of oscillatory signals. Although regularity (periodicity) of neural oscillation has been measured using autocorrelation analysis (Red'ka and Mayorov, 2015), spectral entropy (Gomez-Pilar et al, 2016;Poza, et al, 2012), sample entropy (Gomez and Hornero, 2010), auto-mutual information analysis (Gomez, et al, 2007), Q factor (Lemercier et al, 2017), and lagged coherence (Fransen, et al, 2015), no study has reported a perception-or attention-related --15 increase in regularity in sensory areas (even an attention-related decrease in regularity of the beta rhythm was reported (Fransen, et al, 2015)). We recorded neuromagnetic signals from…”

Section: Discussionmentioning

confidence: 99%

“…Regularity (or periodicity) is one of the most fundamental information in oscillatory signals. Indeed, previous studies reported altered regularity of neural oscillations in patients with mental disorders such as Alzheimer's disease (Gomez et al, 2007;Poza et al, 2012). We hypothesized that, if the alpha/beta ERD represents weakened (less-regulated) pulses of inhibition, this would be associated with decreased regularity of oscillations in the same frequency band.…”

Section: Introductionmentioning

confidence: 91%

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Desynchronizing to be faster? Perceptual- and attentional-modulation of brain rhythms at the sub-millisecond scale

2019

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Desynchronizing to be faster? Perceptual- and attentional-modulation of brain rhythms at the sub-millisecond scale

2019

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“…In order to estimate the signal for each frequency band we simply computed the Fourier transform of the signal and reconstructed it via inverse Fourier transform in the range of frequencies specified above. γ band was disregarded for all subjects due to lack of power in that frequency range (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50).…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Many studies pointed to the same direction: entropy and complexity tend to be decreased in AD patients and their signals become more predictable. For example, Gómez et al [31,32] and Hornero and his collaborators [33] found that AD patients had, in group-average, a diminished auto mutual information (AMI) decreasing rate, with local differences found in many channels. The AMI decreasing rate is an indicator of how predictable future values of a time series are, based on past ones.…”

Section: Introductionmentioning

confidence: 99%

Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands

Echegoyen

López‐Sanz

Martínez

et al. 2020

Entropy

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We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

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Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor

et al. 2010

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Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild-moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD-AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD-AMI values than PD (P = 0.003). BD-AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment.

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Magnetoencephalogram background activity analysis in Alzheimer's disease patients using auto mutual information

Cited by 10 publications

References 20 publications

Desynchronizing to be faster? Perceptual- and attentional-modulation of brain rhythms at the sub-millisecond scale

Desynchronizing to be faster? Perceptual- and attentional-modulation of brain rhythms at the sub-millisecond scale

Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands

Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor

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