Prevalence of multiple sclerosis in Valladolid, northern Spain

Producción CientíficaThe aim of this study was to ascertain the prevalence of multiple sclerosis (MS) in a northern Spanish region and to compare it with that from the most recent epidemiological studies in the country. MS prevalence was studied for a period of 2 years using multiple sources of information in the province of Valladolid, with a sample comprising a total population of 92,632. Patients were classified according to the Poser criteria. The crude prevalence of definite and probable MS was 58.3 per 100,000 (95% confidence interval: 43.7–75.7). The same methods have been used in ascertaining similar prevalence rates in Vélez-Málaga, Osona, and Gijón and a slightly lower rate in Teruel. Our survey confirms Spain as a high-risk area for MS, with prevalence rates over 50 per 100,00

Exploring the alterations in the distribution of neural network weights in dementia due to alzheimer’s disease

Producción CientíficaAlzheimer’s disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional (FEDER) - (project PGC2018-098214- A-I00)Comisión Europea y Fondo Europeo de Desarrollo Regional (FEDER) - (Cooperation Programme Interreg V-A Spain-Portugal POCTEP 2014–20200

Analysis of Spontaneous EEG Activity in Alzheimer’s Disease Using Cross-Sample Entropy and Graph Theory

Producción CientíficaThe aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer’s disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p < 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD.Ministerio de Economía y Competitividad (TEC2014-53196-R)Junta de Castilla y León (proyecto VA037U16 y BIO/VA08/15

Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum.

peer reviewedThe characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility

Characterizing the fluctuations of dynamic resting-state electrophysiological functional connectivity: Reduced neuronal coupling variability in mild cognitive impairment and dementia due to Alzheimer's disease

OBJECTIVE: The characterization of brain functional connectivity is a helpful tool in the study of the neuronal substrates and mechanisms that are altered in Azheimer's Disease (AD) and mild cognitive impairment (MCI). Recently, there has been a shift towards the characterization of dynamic functional connectivity (dFC), discarding the assumption of connectivity stationarity during the resting-state. The majority of these studies have been performed with functional magnetic resonance imaging (fMRI) recordings, with only a small subset being based on magnetoencephalography/electroencephalography (MEG/EEG). However, only these modalities enable the characterization of potentially fast brain dynamics, which is mandatory for an accurate understanding of the transmission and processing of neuronal information. The aim of this study was to characterize the dFC of resting-state EEG activity in AD and MCI. APPROACH: Three measures: the phase lag index (PLI), leakage-corrected magnitude squared coherence (MSCOH) and leakage-corrected amplitude envelope correlation (AEC) were computed for 45 patients with dementia due to AD, 51 subjects with MCI due to AD and 36 cognitively healthy controls. All measures were estimated in epochs of 60 s using a sliding window approach. An epoch length of 15 s was used to provide reliable results. We tested whether the observed PLI, MSCOH and AEC fluctuations reflected actual variations in functional connectivity, as well as whether between-group differences could be found. MAIN RESULTS: We found dFC using PLI, MSCOH and AEC, with AEC having the highest number of statistically significant connections, followed by MSCOH and PLI. Furthermore, a significant reduction in AEC dFC for patients with AD compared to controls was found in the alpha (8-13 Hz) and beta-1 (13-30 Hz) bands. SIGNIFICANCE: Our results suggest that patients with AD (and MCI subjects to a lesser degree) show less variation in neuronal connectivity during resting-state, supporting the notion that dFC can be found at the EEG time scale and is abnormal in the MCI-AD continuum. Measures of dFC have the potential of being used as biomarkers of AD. Moreover, they could also suggest that AD resting-state networks may operate at a state of low firing activity induced by the observed reduction in coupling strength. Furthermore, the statistically significant correlation between dFC and relative power in the beta-1 band could be related to pathologically high levels of neural activity inducing a loss of dFC. These findings show that the stability of neuronal coupling is affected in AD and MCI

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as “canonical” frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the “Connectivity-based Meta-Bands” (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the “canonical” frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity

39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Producción CientíficaDementia due to Alzheimer’s disease (AD) is a common disorder with a great impact on the patients’ quality of life. The aim of this pilot study was to characterize spontaneous electroencephalography (EEG) activity in dementia due to AD using bispectral analysis. Five minutes of EEG activity were recorded from 17 patients with moderate dementia due to AD and 19 age-matched controls. Bispectrum results revealed that AD patients are characterized by an increase of phase coupling at low frequencies in comparison with controls. Additionally, some bispectral features calculated from the bispectrum showed significant differences between both groups (p < 0.05, Mann-Whitney U test with Bonferroni’s correction). Finally, a stepwise logistic regression analysis with a leave-one-out cross-validation procedure was used for classification purposes. An accuracy of 86.11% (sensitivity = 88.24%; specificity =84.21%) was achieved. This study suggests the usefulness of bispectral analysis to provide further insights into the underlying brain dynamics associated with ADThis research was supported in part by ‘Ministerio de Economía y Competitividad (MINECO)’ and FEDER under project TEC2014-53196-R, and ‘Consejería de Educación de la Junta de Castilla y León’ under project VA037U16. F. Vaquerizo-Villar was in receipt of a ‘Promoción de Empleo Joven e Implantación de la Garantía Juvenil en I+D+i’ grant from MINECO and University of Valladolid. C. Gómez, F. Vaquerizo-Villar, J. Poza, Saúl J. Ruiz, 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]). 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

World Congress on Medical Physics & Biomedical Engineering (IUPESM 2018)

Producción CientíficaThe aim of this study was to characterize the dynamic functional connectivity of resting-state electroencephalographic (EEG) activity in Alzheimer’s disease (AD). The magnitude squared coherence (MSCOH) of 50 patients with dementia due to AD and 28 cognitively healthy controls was computed. MSCOH was estimated in epochs of 60 s subdivided in overlapping windows of different lengths (1, 2, 3, 5 and 10 s; 50% overlap). The effect of epoch length was tested on MSCOH and it was found that MSCOH stabilized at a window length of 3 s. We tested whether the MSCOH fluctuations observed reflected actual changes in functional connectivity by means of surrogate data testing, with the standard deviation of MSCOH chosen as the test statistic. The results showed that the variability of the measure could be due to dynamic functional connectivity. Furthermore, a significant reduction in the dynamic MSCOH connectivity of AD patients compared to controls was found in the delta (0–4 Hz) and beta-1 (13–30 Hz) bands. This indicated that AD patients show lesser variation in neural connectivity during resting state. Finally, a correlation between relative power and standard deviation was found, suggesting that an increase/peak in power spectrum could be a pre-requisite for dynamic functional connectivity in a specific frequency band.This study has been partially funded by projects TEC2014-53196-R of ‘Ministerio de Economía y Competitividad’ and FEDER, the project ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ (Inter-regional cooperation program VA Spain-Portugal POCTEP 2014–2020) of the European Commission and FEDER, and project VA037U16 of the ‘Junta de Castilla y León and FEDER. P. Núñez and S. J. Ruiz are in receipt of predoctoral grants co-financed by the ‘Junta de Castilla y León’ and ESF

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

XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2017)

Producción CientíficaEl objetivo de este estudio fue caracterizar las propiedades dinámicas de la conectividad funcional de la actividad electroencefalográfica (EEG) espontánea en la enfermedad de Alzheimer (EA). Se calculó el módulo al cuadrado de la coherencia (MSCOH) de 62 enfermos con demencia debida a EA y 36 controles cognitivamente sanos. Se estimó la MSCOH en tramas de 30 segundos con ventanas no solapadas de diferentes longitudes (1, 2, 3, 5 y 10 segundos) y se estudió:(i) la variabilidad de la conectividad funcional entre cada par de electrodos en la banda alfa (8 - 13 Hz) en función del tamaño de la ventana; (ii) si las fluctuaciones observadas reflejaban cambios reales de conectividad funcional mediante un análisis de subrogación; y (iii) la estabilidad de la medida en función del tamaño de la ventana. Los resultados muestran una reducción significativa de la conectividad dinámica de los enfermos de EA respecto a los controles en las ventanas de 2 y 3 segundos. Asimismo, se concluye que parte de la variabilidad en la medida se podría deber a conectividad funcional dinámica.Este estudio ha sido parcialmente financiado por los proyectos TEC2014-53196-R del Ministerio de Economía y Competitividad y FEDER, el proyecto ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ (Programa de cooperación inter-regional V-A España-Portugal POCTEP 2014-2020) de la Comisión Europea y FEDER, y el proyecto VA037U16 de la Consejería de Educación y FEDER. P. Núñez y S. J. Ruiz disfrutan de una beca predoctoral cofinanciada por la Junta de Castilla y León y FEDER