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

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

On the uniqueness of the Horrocks-Mumford-bundle

SIGLECopy held by FIZ Karlsruhe; available from UB/TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Producción CientíficaMild cognitive impairment (MCI) is a pathology characterized by an abnormal cognitive state. MCI patients are considered to be at high risk for developing dementia. The aim of this study is to characterize the changes that MCI causes in the patterns of brain information flow. For this purpose, spontaneous EEG activity from 41 MCI patients and 37 healthy controls was analyzed by means of an effective connectivity measure: the phase slope index (PSI). Our results showed statistically significant decreases in PSI values mainly at delta and alpha frequency bands for MCI patients, compared to the control group. These abnormal patterns may be due to the structural changes in the brain suffered by patients: decreased hippocampal volume, atrophy of the medial temporal lobe, or loss of gray matter volume. This study suggests the usefulness of PSI to provide further insights into the underlying brain dynamics associated with MCI.Competitividad’ and ‘European Regional Development Fund’ under project TEC2014-53196-R, by ‘European Commission’ and ‘European Regional Development Fund’ under project ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ (‘Cooperation Programme Interreg V-A Spain- Portugal POCTEP 2014-2020’), and by ‘Consejería de Educación de la Junta de Castilla y León’ under project VA037U16. P. Núñez and S. J. Ruiz are in receipt of predoctoral grants co-financed by the ‘Junta de Castilla y León’ and ESF. N. Pinto’s work is partially financed through the FCT postdoctoral grant SFRH/BPD/97414/2013 and projects POCI-01-0145- FEDER-007274 and UID/MAT/00144/2013. C. Gómez, Saúl J. Ruiz-Gómez, J. Poza, A. Maturana-Candelas, P. Núñez, and R. Hornero are with the Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Paseo Belén 15, 47011 Valladolid, Spain (e-mail: [email protected]). N. Pinto is with the Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), the Institute for Research and Innovation in Health Sciences, and the Center of Mathematics of University of Porto, Portugal. M. A. Tola is with the Department of Neurology, Hospital Universitario Río Hortega, Valladolid, Spain. M. Cano is with the Department of Clinical Neurophysiology, Hospital Universitario Río Hortega, Valladolid, Spain

Genome-wide scan for five brain oscillatory phenotypes identifies a new QTL associated with theta EEG band

Brain waves, measured by electroencephalography (EEG), are a powerful tool in the investigation of neurophysiological traits and a noninvasive and cost-effective alternative in the diagnostic of some neurological diseases. In order to identify novel Quantitative Trait Loci (QTLs) for brain wave relative power (RP), we collected resting state EEG data in five frequency bands (δ, θ, α, β1, and β2) and genome- wide data in a cohort of 105 patients with late onset Alzheimer’s disease (LOAD), 41 individuals with mild cognitive impairment and 45 controls from Iberia, correcting for disease status. One novel association was found with an interesting candidate for a role in brain wave biology, CLEC16A (C-type lectin domain family 16), with a variant at this locus passing the adjusted genome-wide significance threshold after Bonferroni correction. This finding reinforces the importance of immune regulation in brain function. Additionally, at a significance cutoff value of 5 × 10−6, 18 independent association signals were detected. These signals comprise brain expression Quantitative Loci (eQTLs) in caudate basal ganglia, spinal cord, anterior cingulate cortex and hypothalamus, as well as chromatin interactions in adult and fetal cortex, neural progenitor cells and hippocampus. Moreover, in the set of genes showing signals of association with brain wave RP in our dataset, there is an overrepresentation of loci previously associated with neurological traits and pathologies, evidencing the pleiotropy of the genetic variation modulating brain function.European Commission | Ref. 1317_AD-EEGWAFundação para a Ciência e a Tecnologia | Ref. POCI-01-0145-FEDER-007274Fundação para a Ciência e a Tecnologia | Ref. CEECIND/00684/2017Fundação para a Ciência e a Tecnologia | Ref. IF/01262/2014Fundação para a Ciência e a Tecnologia | Ref. SFRH/BPD/97414/2013Fundação para a Ciência e a Tecnologia | Ref. CEECIND/02609/2017Ministerio de Economía, Industria y Competitividad (España) | Ref. RYC-2015-18241Ministerio de Ciencia, Innovación y Universidades (España) | Ref. PGC2018-098214-A-I00Instituto de Salud Carlos II