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Background. Serious mental illness (SMI) represents a category of psychiatric disorders characterized by specific difficulties of personal and social functioning, derived from suffering severe and persistent mental health problems. Aims. We wanted to look into differences in cognitive performance among different SMI patients. Methods. Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) screening was applied in one sample of SMI patients (n = 149) and another of healthy comparison participants (n = 35). Within the SMI sample, three different subsamples were formed: one with 97 patients with schizophrenia, a second with 29 patients with mood disorders, and a third with 23 patients with personality disorder. We performed a comparative study within and between groups. Results. Analysis of covariance was performed. Significant differences were found for cognitive functioning including attention and memory. Conclusions. RBANS can be recommended for the detection of neurocognitive deficits in psychiatric disorders, especially in Schizophrenia.
Gabriel De la Torre; Sandra Doval; David López-Sanz; Manuel García-Sedeño; Miguel Ramallo; Macarena Bernal; Sara González-Torre. Neurocognitive Impairment in Severe Mental Illness. Comparative study with Spanish Speaking Patients. Brain Sciences 2021, 11, 389 .
AMA StyleGabriel De la Torre, Sandra Doval, David López-Sanz, Manuel García-Sedeño, Miguel Ramallo, Macarena Bernal, Sara González-Torre. Neurocognitive Impairment in Severe Mental Illness. Comparative study with Spanish Speaking Patients. Brain Sciences. 2021; 11 (3):389.
Chicago/Turabian StyleGabriel De la Torre; Sandra Doval; David López-Sanz; Manuel García-Sedeño; Miguel Ramallo; Macarena Bernal; Sara González-Torre. 2021. "Neurocognitive Impairment in Severe Mental Illness. Comparative study with Spanish Speaking Patients." Brain Sciences 11, no. 3: 389.
Background Alzheimer's disease (AD) is a neurodegenerative disorder, clinically defined by a progressive loss of memory and other cognitive and functional abilities. One of the most studied phases in the prognosis of AD is the Mild cognitive impairment (MCI) since it entails a higher risk of developing this type of dementia. The majority longitudinal studies from MCI to AD utilize both a reduce number of potential prediction markers and a shorten length of follow‐up. Therefore, the present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological (i.e. magnetoencephalography, MEG), and neuroanatomical (i.e. magnetic resonance imaging (MRI) volumetry) factors may predict differences in time to progression from MCI to AD during an extended follow‐up. Method To this end, a sample of 121 MCIs was followed‐up during a 5‐years period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the “progressive” MCI (pMCI; n= 46); and (ii) the “stable” MCI group (sMCI; n= 75). Kaplan‐Meier survival analyses were applied to explore each variable’s relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model that may allow the estimation of differences in time to progression. Result Results indicated that the final model included three variables (in order of relevance): Left parahippocampal volume (corrected by intracranial volume, LP_ ICV), Delayed recall (DR), and Left Inferior Occipital lobe individual alpha peak frequency (LIOL_IAF). Those MCIs with LP_ ICV volume, DR score and LIOL_IAPF value lower than the defined cutoff had 6‐times, 5.5‐times and 3‐times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were “unfavourable” (i.e. values below the cutoff), a 100% of cases progressed to AD at the end of follow‐ up, while a combination of “favourable” categories yielded a 94.7% of stable cases at the end of follow‐up. Conclusion Our results highlighted the relevance of neurophysiological markers as predictors of conversion, and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.
María Eugenia López García; Agustín Turrero; Pablo Cuesta; Inmaculada Concepción Rodríguez Rojo; Ana Barabash; Alberto Marcos Dolado; Fernando Maestú; Alberto Fernandez. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease. Alzheimer's & Dementia 2020, 16, 1 .
AMA StyleMaría Eugenia López García, Agustín Turrero, Pablo Cuesta, Inmaculada Concepción Rodríguez Rojo, Ana Barabash, Alberto Marcos Dolado, Fernando Maestú, Alberto Fernandez. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease. Alzheimer's & Dementia. 2020; 16 (S5):1.
Chicago/Turabian StyleMaría Eugenia López García; Agustín Turrero; Pablo Cuesta; Inmaculada Concepción Rodríguez Rojo; Ana Barabash; Alberto Marcos Dolado; Fernando Maestú; Alberto Fernandez. 2020. "A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease." Alzheimer's & Dementia 16, no. S5: 1.
The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the “progressive” MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the “stable” MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan–Meier survival analyses were applied to explore each variable’s relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were “unfavorable” (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.
María Eugenia López; Agustín Turrero; Pablo Cuesta; Inmaculada Concepción Rodríguez-Rojo; Ana Barabash; Alberto Marcos; Fernando Maestu; Alberto Fernández. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease. GeroScience 2020, 42, 1715 -1732.
AMA StyleMaría Eugenia López, Agustín Turrero, Pablo Cuesta, Inmaculada Concepción Rodríguez-Rojo, Ana Barabash, Alberto Marcos, Fernando Maestu, Alberto Fernández. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease. GeroScience. 2020; 42 (6):1715-1732.
Chicago/Turabian StyleMaría Eugenia López; Agustín Turrero; Pablo Cuesta; Inmaculada Concepción Rodríguez-Rojo; Ana Barabash; Alberto Marcos; Fernando Maestu; Alberto Fernández. 2020. "A multivariate model of time to conversion from mild cognitive impairment to Alzheimer’s disease." GeroScience 42, no. 6: 1715-1732.
To analyse magnetoencephalogram (MEG) signals with Lempel-Ziv Complexity (LZC) to identify the regions of the brain showing changes related to cognitive decline and Alzheimeŕs Disease (AD). LZC was used to study MEG signals in the source space from 99 participants (36 male, 63 female, average age: 71.82 ± 4.06) in three groups (33 subjects per group): healthy (control) older adults, older adults with subjective cognitive decline (SCD), and adults with mild cognitive impairment (MCI). Analyses were performed in broadband (2-45 Hz) and in classic narrow bands (theta (4-8 Hz), alpha (8-12 Hz), low beta (12-20 Hz), high beta (20-30 Hz), and, gamma (30-45 Hz)). LZC was significantly lower in subjects with MCI than in those with SCD. Moreover, subjects with MCI had significantly lower MEG complexity than controls and SCD subjects in the beta frequency band. Lower complexity was correlated with smaller hippocampal volumes. Brain complexity - measured with LZC - decreases in MCI patients when compared to SCD and healthy controls. This decrease is associated with a decrease in hippocampal volume, a key feature in AD progression. This is the first study to date characterising the changes of brain activity complexity showing the specific spatial pattern of the alterations as well as the morphological correlations throughout preclinical stages of AD.
Elizabeth Shumbayawonda; David López-Sanz; Ricardo Bruña; Noelia Serrano; Alberto Fernández; Fernando Maestu; Daniel Abasolo. Complexity changes in preclinical Alzheimer’s disease: An MEG study of subjective cognitive decline and mild cognitive impairment. Clinical Neurophysiology 2020, 131, 437 -445.
AMA StyleElizabeth Shumbayawonda, David López-Sanz, Ricardo Bruña, Noelia Serrano, Alberto Fernández, Fernando Maestu, Daniel Abasolo. Complexity changes in preclinical Alzheimer’s disease: An MEG study of subjective cognitive decline and mild cognitive impairment. Clinical Neurophysiology. 2020; 131 (2):437-445.
Chicago/Turabian StyleElizabeth Shumbayawonda; David López-Sanz; Ricardo Bruña; Noelia Serrano; Alberto Fernández; Fernando Maestu; Daniel Abasolo. 2020. "Complexity changes in preclinical Alzheimer’s disease: An MEG study of subjective cognitive decline and mild cognitive impairment." Clinical Neurophysiology 131, no. 2: 437-445.
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.
Ignacio Echegoyen; David López-Sanz; Johann H. Martínez; Fernando Maestú; Javier M. Buldú. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands. Entropy 2020, 22, 116 .
AMA StyleIgnacio Echegoyen, David López-Sanz, Johann H. Martínez, Fernando Maestú, Javier M. Buldú. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands. Entropy. 2020; 22 (1):116.
Chicago/Turabian StyleIgnacio Echegoyen; David López-Sanz; Johann H. Martínez; Fernando Maestú; Javier M. Buldú. 2020. "Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands." Entropy 22, no. 1: 116.
Objective: Although, the apolipoprotein E (APOE) genotype is widely recognized as one of the most important risk factors for Alzheimer's disease (AD) development, the neural mechanisms by which the ε4 allele promotes the AD occurring remain under debate. The aim of this study was to evaluate neurobiological effects of the APOE-genotype on the pattern of the structural covariance in mild cognitive impairment (MCI) subjects. Methods: We enrolled 95 MCI subjects and 49 healthy controls. According to APOE-genotype, MCI subjects were divided into three groups: APOEε4 non-carriers (MCIε4−/−, n = 55), APOEε4 heterozygous carriers (MCIε4+/−, n = 31), and APOEε4 homozygous carriers (MCIε4+/+, n = 9) while all controls were APOEε4 non-carriers. In order to explore their brain structural pattern, T1-weighted anatomical brain 1.5-T MRI scans were collected. A whole-brain voxel-based morphometry analysis was performed, and all significant regions (p < 0.05 family-wise error, whole brain) were selected as a region of interest for the structural covariance analysis. Moreover, in order to evaluate the progression of the disease, a clinical follow-up was performed for 2 years. Results: The F-test showed in voxel-based morphometry analysis a strong overall difference among the groups in the middle frontal and temporal gyri and in the bilateral hippocampi, thalami, and parahippocampal gyri, with a grading in the atrophy in these latter three structures according to the following order: MCIε4+/+ > MCIε4+/− > MCIε4−/− > controls. Structural covariance analysis revealed a strong structural association between the left thalamus and the left caudate and between the right hippocampus and the left caudate (p < 0.05 family-wise error, whole brain) in the MCIε4 carrier groups (MCIε4+/+ > MCIε4+/−), whereas no significant associations were observed in MCIε4−/− subjects. Of note, the 38% of MCIs enrolled in this study developed AD within 2 years of follow-up. Conclusion: This study improves the knowledge on neurobiological effect of APOE ε4 in early pathophysiological phenomena underlying the MCI-to-AD evolution, as our results demonstrate changes in the structural association between hippocampal formation and thalamo-striatal connections occurring in MCI ε4 carriers. Our results strongly support the role of subcortical structures in MCI ε4 carriers and open a clinical window on the role of these structures as early disease markers.
Fabiana Novellino; María Eugenia López; Maria Grazia Vaccaro; YuS Miguel; María Luisa Delgado; Fernando Maestu. Association Between Hippocampus, Thalamus, and Caudate in Mild Cognitive Impairment APOEε4 Carriers: A Structural Covariance MRI Study. Frontiers in Neurology 2019, 10, 1 .
AMA StyleFabiana Novellino, María Eugenia López, Maria Grazia Vaccaro, YuS Miguel, María Luisa Delgado, Fernando Maestu. Association Between Hippocampus, Thalamus, and Caudate in Mild Cognitive Impairment APOEε4 Carriers: A Structural Covariance MRI Study. Frontiers in Neurology. 2019; 10 ():1.
Chicago/Turabian StyleFabiana Novellino; María Eugenia López; Maria Grazia Vaccaro; YuS Miguel; María Luisa Delgado; Fernando Maestu. 2019. "Association Between Hippocampus, Thalamus, and Caudate in Mild Cognitive Impairment APOEε4 Carriers: A Structural Covariance MRI Study." Frontiers in Neurology 10, no. : 1.
Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.
Sandra Pusil; María Eugenia López; Pablo Cuesta; Ricardo Bruña; Ernesto Pereda; Fernando Maestu. Hypersynchronization in mild cognitive impairment: the ‘X’ model. Brain 2019, 142, 3936 -3950.
AMA StyleSandra Pusil, María Eugenia López, Pablo Cuesta, Ricardo Bruña, Ernesto Pereda, Fernando Maestu. Hypersynchronization in mild cognitive impairment: the ‘X’ model. Brain. 2019; 142 (12):3936-3950.
Chicago/Turabian StyleSandra Pusil; María Eugenia López; Pablo Cuesta; Ricardo Bruña; Ernesto Pereda; Fernando Maestu. 2019. "Hypersynchronization in mild cognitive impairment: the ‘X’ model." Brain 142, no. 12: 3936-3950.
The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently introduced non-linear signal processing algorithm based on symbolic dynamics, was used to evaluate the complexity of MEG signals in source space. PLZC was estimated in a broad band of frequencies (2–45 Hz), as well as in narrow bands (i.e., theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz)) in a sample of 98 healthy elderly subjects (49 males, 49 female) aged 65–80 (average age of 72.71 ± 4.22 for males and 72.67 ± 4.21 for females). PLZC was significantly higher for females than males in the high beta band at posterior brain regions including the precuneus, and the parietal and occipital cortices. Further statistical analyses showed that higher complexity values over highly overlapping regions than the ones mentioned above were associated with larger hippocampal volumes only in females. These results suggest that sex differences in healthy aging can be identified from the analysis of magnetoencephalograms with novel signal processing methods.
Elizabeth Shumbayawonda; Daniel Abásolo; David López-Sanz; Ricardo Bruña; Fernando Maestu; Alberto Fernández. Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms. Entropy 2019, 21, 798 .
AMA StyleElizabeth Shumbayawonda, Daniel Abásolo, David López-Sanz, Ricardo Bruña, Fernando Maestu, Alberto Fernández. Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms. Entropy. 2019; 21 (8):798.
Chicago/Turabian StyleElizabeth Shumbayawonda; Daniel Abásolo; David López-Sanz; Ricardo Bruña; Fernando Maestu; Alberto Fernández. 2019. "Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms." Entropy 21, no. 8: 798.
Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease.
Sandra Pusil; Stavros I. Dimitriadis; María Eugenia López; Ernesto Pereda; Fernando Maestu. Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease. NeuroImage: Clinical 2019, 24, 101972 .
AMA StyleSandra Pusil, Stavros I. Dimitriadis, María Eugenia López, Ernesto Pereda, Fernando Maestu. Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease. NeuroImage: Clinical. 2019; 24 ():101972.
Chicago/Turabian StyleSandra Pusil; Stavros I. Dimitriadis; María Eugenia López; Ernesto Pereda; Fernando Maestu. 2019. "Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease." NeuroImage: Clinical 24, no. : 101972.
The need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI) is evident. MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings on the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys) function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, 𝜃, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine 90 anatomical regions of interest (ROIs). A DFCG was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 MCI patients and 22 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments, which further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. An estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). The NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (n μstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded in gaining a high classification accuracy on the blind dataset (85%), which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could properly manipulate the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.
Stavros I. Dimitriadis; María Eugenia López; Fernando Maestu; Ernesto Pereda. Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. Frontiers in Neuroscience 2019, 13, 542 .
AMA StyleStavros I. Dimitriadis, María Eugenia López, Fernando Maestu, Ernesto Pereda. Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. Frontiers in Neuroscience. 2019; 13 ():542.
Chicago/Turabian StyleStavros I. Dimitriadis; María Eugenia López; Fernando Maestu; Ernesto Pereda. 2019. "Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment." Frontiers in Neuroscience 13, no. : 542.
Alzheimer's disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials. Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm. The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic. According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.
David López-Sanz; Ricardo Bruña; María Luisa Delgado-Losada; Ramón López-Higes; Alberto Marcos-Dolado; Fernando Maestú; Stefan Walter. Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia. Alzheimer's Research & Therapy 2019, 11, 49 .
AMA StyleDavid López-Sanz, Ricardo Bruña, María Luisa Delgado-Losada, Ramón López-Higes, Alberto Marcos-Dolado, Fernando Maestú, Stefan Walter. Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia. Alzheimer's Research & Therapy. 2019; 11 (1):49.
Chicago/Turabian StyleDavid López-Sanz; Ricardo Bruña; María Luisa Delgado-Losada; Ramón López-Higes; Alberto Marcos-Dolado; Fernando Maestú; Stefan Walter. 2019. "Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia." Alzheimer's Research & Therapy 11, no. 1: 49.
Bilingualism has been said to improve cognition and even delay the onset of Alzheimer's disease (AD). This research aimed to investigate whether bilingualism leaves a neurophysiological trace even when people are highly educated. We expected bilinguals to present better preserved brain functional networks, which could be a trace of higher cognitive reserve. With this purpose, we conducted a magnetoencephalographic study with a group of healthy older adults. We estimated functional connectivity using phase-locking value and found five clusters in parieto-occipital regions in which bilinguals exhibited greater functional connectivity than monolinguals. These clusters included brain regions typically implicated in language processing. Furthermore, these functional changes correlated with caudate volumes (a key region in language shifting and control) in the bilingual sample. Interestingly, decreased Functional Connectivity between posterior brain regions had already been identified as an indicator of aging/preclinical AD but, according to our study, bilingualism seems to exert the opposite effect.
Jaisalmer De Frutos-Lucas; David López-Sanz; Pablo Cuesta; Ricardo Bruña; Sofía De La Fuente; Noelia Serrano; María Eugenia López; María Luisa Delgado-Losada; Ramón López-Higes; Alberto Marcos; Fernando Maestu. Enhancement of posterior brain functional networks in bilingual older adults. Bilingualism: Language and Cognition 2019, 23, 387 -400.
AMA StyleJaisalmer De Frutos-Lucas, David López-Sanz, Pablo Cuesta, Ricardo Bruña, Sofía De La Fuente, Noelia Serrano, María Eugenia López, María Luisa Delgado-Losada, Ramón López-Higes, Alberto Marcos, Fernando Maestu. Enhancement of posterior brain functional networks in bilingual older adults. Bilingualism: Language and Cognition. 2019; 23 (2):387-400.
Chicago/Turabian StyleJaisalmer De Frutos-Lucas; David López-Sanz; Pablo Cuesta; Ricardo Bruña; Sofía De La Fuente; Noelia Serrano; María Eugenia López; María Luisa Delgado-Losada; Ramón López-Higes; Alberto Marcos; Fernando Maestu. 2019. "Enhancement of posterior brain functional networks in bilingual older adults." Bilingualism: Language and Cognition 23, no. 2: 387-400.
It is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings.The activity of different brain rhythms {δ, θ, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nμstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation.We succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.
Stavros I Dimitriadis; Maria-Eugenia Lopez; Fernando Maestu; Ernesto Pereda. Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. 2019, 619437 .
AMA StyleStavros I Dimitriadis, Maria-Eugenia Lopez, Fernando Maestu, Ernesto Pereda. Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. . 2019; ():619437.
Chicago/Turabian StyleStavros I Dimitriadis; Maria-Eugenia Lopez; Fernando Maestu; Ernesto Pereda. 2019. "Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment." , no. : 619437.
Hippocampal atrophy is one of the main hallmarks of Alzheimer's disease (AD). However, there is still controversy about whether this sign is a robust finding during the early stages of the disease, such as in mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Considering this background, we proposed a new marker for assessing hippocampal atrophy: the local surface roughness (LSR). We tested this marker in a sample of 307 subjects (normal control (NC) = 70, SCD = 87, MCI = 137, AD = 13). In addition, 97 patients with MCI were followed‐up over a 3‐year period and classified as stable MCI (sMCI) (n = 61) or progressive MCI (pMCI) (n = 36). We did not find significant differences using traditional markers, such as normalized hippocampal volumes (NHV), between the NC and SCD groups or between the sMCI and pMCI groups. However, with LSR we found significant differences between the sMCI and pMCI groups and a better ability to discriminate between NC and SCD. The classification accuracy of the LSR for NC and SCD was 68.2%, while NHV had a 57.2% accuracy. In addition, the classification accuracy of the LSR for sMCI and pMCI was 74.3%, and NHV had a 68.3% accuracy. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on hippocampal markers and conversion times. The LSR marker showed better prediction of conversion to AD than NHV. These results suggest the relevance of considering the LSR as a new hippocampal marker for the AD continuum.
Carlos Platero; María Eugenia López; María Del Carmen Tobar; Miguel Yus; Fernando Maestu. Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness. Human Brain Mapping 2018, 40, 1666 -1676.
AMA StyleCarlos Platero, María Eugenia López, María Del Carmen Tobar, Miguel Yus, Fernando Maestu. Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness. Human Brain Mapping. 2018; 40 (5):1666-1676.
Chicago/Turabian StyleCarlos Platero; María Eugenia López; María Del Carmen Tobar; Miguel Yus; Fernando Maestu. 2018. "Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness." Human Brain Mapping 40, no. 5: 1666-1676.
Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multi-neuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, which enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, which allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multi-dimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor-inhibitory cognitive task. The results show that out method exhibits similar performance compared with other classification methods, indicating the effectiveness of our approach. In addition, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication, or early detection of diseases.
Gianluca Susi; Luis Antón Toro; Leonides Canuet; María Eugenia López; Fernando Maestu; Claudio R. Mirasso; Ernesto Pereda. A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP. Frontiers in Neuroscience 2018, 12, 780 .
AMA StyleGianluca Susi, Luis Antón Toro, Leonides Canuet, María Eugenia López, Fernando Maestu, Claudio R. Mirasso, Ernesto Pereda. A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP. Frontiers in Neuroscience. 2018; 12 ():780.
Chicago/Turabian StyleGianluca Susi; Luis Antón Toro; Leonides Canuet; María Eugenia López; Fernando Maestu; Claudio R. Mirasso; Ernesto Pereda. 2018. "A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP." Frontiers in Neuroscience 12, no. : 780.
Cognitive flexibility is critical for humans living in complex societies with ever growing multitasking demands. Yet the low frequency neural dynamics of distinct task-specific and domain-general mechanisms sub-serving mental flexibility are still ill defined. Here we estimated phase electroencephalogram synchronization by using inter-trial phase coherence (ITPC) at the source space while twenty six young participants were intermittently cued to switch or repeat their perceptual categorization rule of Gabor gratings varying in color and thickness (switch task). Therefore, the aim of this study was to examine whether proactive control is associated with connectivity only in the frontoparietal theta network, or also involves distinct neural connectivity within the delta band, as distinct neural signatures while preparing to switch or repeat a task set, respectively. To this end, we focused the analysis on late-latencies (from 500 to 800 msec post-cue onset), since they are known to be associated with top-down cognitive control processes. We confirmed that proactive control during a task switch was associated with frontoparietal theta connectivity. But importantly, we also found a distinct role of delta band oscillatory synchronization in proactive control, engaging more posterior frontotemporal regions as opposed to frontoparietal theta connectivity. Additionally, we built a regression model by using the ITPC results in delta and theta bands as predictors, and the behavioral accuracy in the switch task as the criterion, obtaining significant results for both frequency bands. All these findings support the existence of distinct proactive cognitive control processes related to functionally distinct though highly complementary theta and delta frontoparietal and temporoparietal oscillatory networks at late-latency temporal scales.
María Eugenia López; Sandra Pusil; Ernesto Pereda; Fernando Maestu; Francisco Barceló. Dynamic low frequency EEG phase synchronization patterns during proactive control of task switching. NeuroImage 2018, 186, 70 -82.
AMA StyleMaría Eugenia López, Sandra Pusil, Ernesto Pereda, Fernando Maestu, Francisco Barceló. Dynamic low frequency EEG phase synchronization patterns during proactive control of task switching. NeuroImage. 2018; 186 ():70-82.
Chicago/Turabian StyleMaría Eugenia López; Sandra Pusil; Ernesto Pereda; Fernando Maestu; Francisco Barceló. 2018. "Dynamic low frequency EEG phase synchronization patterns during proactive control of task switching." NeuroImage 186, no. : 70-82.
The pathophysiological processes undermining brain functioning decades before the onset of the clinical symptoms associated with dementia are still not well understood. Several heritability studies have reported that the Brain Derived Neurotrophic Factor (BDNF) Val66Met genetic polymorphism could contribute to the acceleration of cognitive decline in aging. This mutation may affect brain functional connectivity (FC), especially in those who are carriers of the BDNF Met allele. The aim of this work was to explore the influence of the BDNF Val66Met polymorphism in whole brain eyes-closed, resting-state magnetoencephalography (MEG) FC in a sample of 36 cognitively intact (CI) older females. All of them were ε3ε3 homozygotes for the apolipoprotein E (APOE) gene and were divided into two subgroups according to the presence of the Met allele: Val/Met group (n = 16) and Val/Val group (n = 20). They did not differ in age, years of education, Mini-Mental State Examination scores, or normalized hippocampal volumes. Our results showed reduced antero-posterior gamma band FC within the Val/Met genetic risk group, which may be caused by a GABAergic network impairment. Despite the lack of cognitive decline, these results might suggest a selective brain network vulnerability due to the carriage of the BDNF Met allele, which is linked to a potential progression to dementia. This neurophysiological signature, as tracked with MEG FC, indicates that age-related brain functioning changes could be mediated by the influence of particular genetic risk factors.
Inmaculada C. Rodríguez-Rojo; Pablo Cuesta; María Eugenia López; Jaisalmer De Frutos-Lucas; Ricardo Bruña; Ernesto Pereda; Ana Barabash; Pedro Montejo; Mercedes Montenegro-Peña; Alberto Marcos; Ramón López-Higes; Alberto Fernández; Fernando Maestu. BDNF Val66Met Polymorphism and Gamma Band Disruption in Resting State Brain Functional Connectivity: A Magnetoencephalography Study in Cognitively Intact Older Females. Frontiers in Neuroscience 2018, 12, 684 .
AMA StyleInmaculada C. Rodríguez-Rojo, Pablo Cuesta, María Eugenia López, Jaisalmer De Frutos-Lucas, Ricardo Bruña, Ernesto Pereda, Ana Barabash, Pedro Montejo, Mercedes Montenegro-Peña, Alberto Marcos, Ramón López-Higes, Alberto Fernández, Fernando Maestu. BDNF Val66Met Polymorphism and Gamma Band Disruption in Resting State Brain Functional Connectivity: A Magnetoencephalography Study in Cognitively Intact Older Females. Frontiers in Neuroscience. 2018; 12 ():684.
Chicago/Turabian StyleInmaculada C. Rodríguez-Rojo; Pablo Cuesta; María Eugenia López; Jaisalmer De Frutos-Lucas; Ricardo Bruña; Ernesto Pereda; Ana Barabash; Pedro Montejo; Mercedes Montenegro-Peña; Alberto Marcos; Ramón López-Higes; Alberto Fernández; Fernando Maestu. 2018. "BDNF Val66Met Polymorphism and Gamma Band Disruption in Resting State Brain Functional Connectivity: A Magnetoencephalography Study in Cognitively Intact Older Females." Frontiers in Neuroscience 12, no. : 684.
Understanding how the heart influences brain dynamics will suppose a deep change for the neuroscience, psychology and medicine. A mainstay questions is the heart modulation of resting state brain networks and its relation with both the cardiac dynamics and the cognitive status. We evaluated the heart evoked basal networks for controls and two groups of mild cognitive impairment patients, stable and progressive to Alzheimer’s disease without cardiovascular alteration symptoms. Our results in controls show that a healthy cognitive performance correlates with the heart modulation of brain dynamics in areas of the default mode network, and that the heart influence on brain networks varies along the cardiac cycle and the spectral band. However, the cognitive deficit produced by dementia correlates with the lack of heart modulation on brain activity. The heart influence on brain networks is disrupted in patients by producing hypersynchronization, accompanied by decreased cardiac complexity. We designed a surrogate and predictive procedure based on machine learning to compare the heart evoked results with the neural activity no locked to heartbeats. Based on our longitudinal data, we conclude that the prediction to progression to Alzheimer’s disease is higher when considering the heart - brain interaction than when taking into account only the brain dynamics. We can conclude that brain networks in control subjects were more responsive to the heart cycle, allowing a wealthier, more complex pattern of oscillations. Our results highlight the role of heart in cognitive neuroscience by showing that basal brain networks are modulated by the cardiac dynamics.
Nazareth Castellanos; Gustavo G. Diez; Ernesto Pereda; Maria Eugenia Lopez; Ricardo Bruna; Myriam G. Bartolomé; Fernando Maestu; Miriam G Bartolome. Heart evoked brain oscillatory networks and its interruption in early stages of Alzheimer’s disease. 2018, 407361 .
AMA StyleNazareth Castellanos, Gustavo G. Diez, Ernesto Pereda, Maria Eugenia Lopez, Ricardo Bruna, Myriam G. Bartolomé, Fernando Maestu, Miriam G Bartolome. Heart evoked brain oscillatory networks and its interruption in early stages of Alzheimer’s disease. . 2018; ():407361.
Chicago/Turabian StyleNazareth Castellanos; Gustavo G. Diez; Ernesto Pereda; Maria Eugenia Lopez; Ricardo Bruna; Myriam G. Bartolomé; Fernando Maestu; Miriam G Bartolome. 2018. "Heart evoked brain oscillatory networks and its interruption in early stages of Alzheimer’s disease." , no. : 407361.
We investigated how the organization of functional brain networks was related to cognitive reserve (CR) during a memory task in healthy aging. We obtained the magnetoencephalographic functional networks of 20 elders with a high or low CR level to analyse the differences at network features. We reported a negative correlation between synchronization of the whole network and CR, and observed differences both at the node and at the network level in: the average shortest path and the network outreach. Individuals with high CR required functional networks with lower links to successfully carry out the memory task. These results may indicate that those individuals with low CR level exhibited a dual pattern of compensation and network impairment, since their functioning was more energetically costly to perform the task as the high CR group. Additionally, we evaluated how the dynamical properties of the different brain regions were correlated to the network parameters obtaining that entropy was positively correlated with the strength and clustering coefficient, while complexity behaved conversely. Consequently, highly connected nodes of the functional networks showed a more stochastic and less complex signal. We consider that network approach may be a relevant tool to better understand brain functioning in aging.
Johann H. Martínez; María Eugenia López; Pedro Ariza; Mario Chavez; José A. Pineda-Pardo; David López-Sanz; Pedro Gil; Fernando Maestu; Javier M. Buldú. Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics. Scientific Reports 2018, 8, 10525 .
AMA StyleJohann H. Martínez, María Eugenia López, Pedro Ariza, Mario Chavez, José A. Pineda-Pardo, David López-Sanz, Pedro Gil, Fernando Maestu, Javier M. Buldú. Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics. Scientific Reports. 2018; 8 (1):10525.
Chicago/Turabian StyleJohann H. Martínez; María Eugenia López; Pedro Ariza; Mario Chavez; José A. Pineda-Pardo; David López-Sanz; Pedro Gil; Fernando Maestu; Javier M. Buldú. 2018. "Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics." Scientific Reports 8, no. 1: 10525.
Since a cure for Alzheimer’s disease (AD) is yet to be discovered, attention has shifted towards prevention. Physical activity (PA) emerged as a notorious lifestyle factor that could influence brain structure and function. The individual alpha peak frequency (IAPF) is a measure that summarizes the spectral content of brain signals and has been proven to be sensitive to both AD pathology and PA interventions. Therefore, our goal was to unravel whether chronic PA modulates IAPF and if APOE ɛ4 carriage moderates this relationship. We analyzed 4-minutes of resting-state magnetoencephalographic recordings from 100 healthy elders that provided self-reported measures of PA, and the IAPF was calculated. We found that IAPF was negatively influenced by age and APOE and positively influenced by PA. The effect of PA on IAPF only remained significant for the ɛ4 non-carriers group. PA is positively associated to higher IAPF in healthy older adults and could potentially act as a protective factor against cognitive decline. Nevertheless, such effect is non-significant among elders who are more vulnerable to developing AD due to their genetic carriage. This investigation offers the first neurophysiological evidences on the combined effects of APOE genotype and PA in healthy elders.
Jaisalmer De Frutos-Lucas; David López-Sanz; Pilar Zuluaga; Inmaculada Concepcion Rodríguez-Rojo; Raúl Luna; María Eugenia López; María Luisa Delgado-Losada; Alberto Marcos; Ana Barabash; Ramón López-Higes; Fernando Maestú; Alberto Fernández. Physical activity effects on the individual alpha peak frequency of older adults with and without genetic risk factors for Alzheimer’s Disease: A MEG study. Clinical Neurophysiology 2018, 129, 1981 -1989.
AMA StyleJaisalmer De Frutos-Lucas, David López-Sanz, Pilar Zuluaga, Inmaculada Concepcion Rodríguez-Rojo, Raúl Luna, María Eugenia López, María Luisa Delgado-Losada, Alberto Marcos, Ana Barabash, Ramón López-Higes, Fernando Maestú, Alberto Fernández. Physical activity effects on the individual alpha peak frequency of older adults with and without genetic risk factors for Alzheimer’s Disease: A MEG study. Clinical Neurophysiology. 2018; 129 (9):1981-1989.
Chicago/Turabian StyleJaisalmer De Frutos-Lucas; David López-Sanz; Pilar Zuluaga; Inmaculada Concepcion Rodríguez-Rojo; Raúl Luna; María Eugenia López; María Luisa Delgado-Losada; Alberto Marcos; Ana Barabash; Ramón López-Higes; Fernando Maestú; Alberto Fernández. 2018. "Physical activity effects on the individual alpha peak frequency of older adults with and without genetic risk factors for Alzheimer’s Disease: A MEG study." Clinical Neurophysiology 129, no. 9: 1981-1989.