Person: Bruña Fernández, Ricardo
Loading...
First Name
Ricardo
Last Name
Bruña Fernández
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Medicina
Department
Radiología, Rehabilitación y Fisioterapia
Area
Radiología y Medicina Física
Identifiers
37 results
Search Results
Now showing 1 - 10 of 37
Item Age and APOE genotype affect the relationship between objectively measured physical activity and power in the alpha band, a marker of brain disease(Alzheimer's Research & Therapy, 2020) De Frutos Lucas, Jaisalmer; Cuesta Prieto, Pablo; Ramírez Toraño, Federico; Nebreda Pérez, Alberto; Cuadrado Soto, Esther; Peral Suárez, África; López Sanz, David; Bruña Fernández, Ricardo; Marcos-de Pedro, Silvia; Delgado Losada, María Luisa; López Sobaler, Ana María; Rodríguez Rojo, Inmaculada Concepción; Barabash Bustelo, Ana; Serrano Rodríguez, Juan Manuel; Laws, Simon M.; Marcos Dolado, Alberto; López Sánchez, Ramón; Brown, Belinda M.; Maestu Unturbe, FernandoBACKGROUND: Electrophysiological studies show that reductions in power within the alpha band are associated with the Alzheimer’s disease (AD) continuum. Physical activity (PA) is a protective factor that has proved to reduce AD risk and pathological brain burden. Previous research has confirmed that exercise increases power in the alpha range. However, little is known regarding whether other non-modifiable risk factors for AD, such as increased age or APOE ε4 carriage, alter the association between PA and power in the alpha band. METHODS: The relationship between PA and alpha band power was examined in a sample of 113 healthy adults using magnetoencephalography. Additionally, we explored whether ε4 carriage and age modulate this association. The correlations between alpha power and gray matter volumes and cognition were also investigated. RESULTS: We detected a parieto-occipital cluster in which PA positively correlated with alpha power. The association between PA and alpha power remained following stratification of the cohort by genotype. Younger and older adults were investigated separately, and only younger adults exhibited a positive relationship between PA and alpha power. Interestingly, when four groups were created based on age (younger-older adult) and APOE (E3/E3-E3/E4), only younger E3/E3 (least predicted risk) and older E3/E4 (greatest predicted risk) had associations between greater alpha power and higher PA. Among older E3/E4, greater alpha power in these regions was associated with improved memory and preserved brain structure. CONCLUSION: PA could protect against the slowing of brain activity that characterizes the AD continuum, where it is of benefit for all individuals, especially E3/E4 older adults.Item Searching for Primary Predictors of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Multivariate Follow-Up Study(Journal of Alzheimer's Disease, 2016) López García, María Eugenia; Turrero Nogués, Agustín; Cuesta Prieto, Pablo; López Sanz, David; Bruña Fernández, Ricardo; Marcos Dolado, Alberto; Gil Gregorio, Pedro; Yus, Miguel; Barabash Bustelo, Ana; Cabranes Díaz, José Antonio; Maestu Unturbe, Fernando; Fernández Lucas, Alberto AmableRecent proposals of diagnostic criteria within the healthy aging-Alzheimer’s disease (AD) continuum stressed the role of biomarker information. More importantly, such information might be critical to predict those mild cognitive impairment (MCI) patients at a higher risk of conversion to AD. Usually, follow-up studies utilize a reduced number of potential markers although the conversion phenomenon may be deemed as multifactorial in essence. In addition, not only biological but also cognitive markers may play an important role. Considering this background, we investigated the role of cognitive reserve, cognitive performance in neuropsychological testing, hippocampal volumes, APOE genotype, and magnetoencephalography power sources to predict the conversion to AD in a sample of 33 MCI patients. MCIs were followed up during a 2-year period and divided into two subgroups according to their outcome: The “stable” MCI group (sMCI, 21 subjects) and the “progressive” MCI group (pMCI, 12 subjects). Baseline multifactorial information was submitted to a hierarchical logistic regression analysis to build a predictive model of conversion to AD. Results indicated that the combination of left hippocampal volume, occipital cortex theta power, and clock drawing copy subtest scores predicted conversion to AD with a 100% of sensitivity and 94.7% of specificity. According to these results it might be suggested that anatomical, cognitive, and neurophysiological markers may be considered as “first order” predictors of progression to AD, while APOE or cognitive reserve proxies might play a more secondary role.Item Resting-state connectivity and network parameter analysis in alcohol-dependent males. A simultaneous EEG-MEG study(Journal of Neuroscience Research, 2020) Sion, Ana Mª Alexandra; Bruña Fernández, Ricardo; Martínez Maldonado, Andrés; Domínguez Centeno, Isabel; Torrado-Carvajal, Ángel; Rubio Valladolid, Gabriel; Pereda, Ernesto; Jurado Barba, RosaThere is supporting evidence of alcohol negative effects on the brain: neuroimaging and psychophysiological studies finding anatomical and functional connectivity (FC) changes associated with the dependence process. Thus, the aim of this work was to evaluate brain FC and network characteristics of alcohol-dependent individuals in resting state. For this study, we included males diagnosed with alcohol dependence (N = 25) and a group of healthy individuals (N = 23). Simultaneous EEG-MEG (electroencephalographic and magnetoencephalographic) activity was recorded in 5 min of eyes-closed resting state. EEG-MEG activity was preprocessed and FC was computed through the leakage-corrected version of phase locking value (ciPLV). Additionally, local (degree, efficiency, clustering) and global (efficiency, characteristic path length) network parameters were computed. Connectivity analysis showed an increase in phase-lagged synchronization, mainly between frontal and frontotemporal regions, in high beta band, and a decrease in interhemispheric gamma, for alcohol-dependent individuals. Network analysis revealed intergroup differences at the local level for high beta, indicating higher degree, clustering, and efficiency, mostly at frontal nodes, together with a decrease in these measures at more posterior sites for patients’ group. The hyper-synchronization in beta, next to the hypo-synchronization in gamma, could indicate an alteration in communication between hemispheres, but also a possible functional compensation mechanism in neural circuits. This could be also supported by network characteristic data, where local alterations in communication are observed.Item 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) Rodríguez Rojo, Inmaculada Concepción; Cuesta Prieto, Pablo; López García, María Eugenia; De Frutos Lucas, Jaisalmer; Bruña Fernández, Ricardo; Pereda de Pablo, Ernesto; Barabash Bustelo, Ana; Montejo, Pedro; Montenegro Peña, María Mercedes; Marcos Dolado, Alberto; López-Higes, Ramón; Fernández Lucas, Alberto Amable; Maestu Unturbe, FernandoThe 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.Item Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia(Alzheimer's Research and Therapy, 2019) López Sanz, David; Bruña Fernández, Ricardo; Delgado Losada, María Luisa; López Sánchez, Ramón; Marcos Dolado, Alberto; Maestu Unturbe, Fernando; Walter, StefanBackground 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. Methods 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. Results 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. Conclusions 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.Item A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease(Journal of Medical Systems, 2015) Cámara, Carmen; Warwick, Kevin; Bruña Fernández, Ricardo; Aziz, Tipu; del Pozo, Francisco; Maestu Unturbe, FernandoParkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients.Item Cognitive training modulates brain hypersynchrony in a population at risk for Alzheimer’s disease(Journal of Alzheimer's Disease, 2022) Suárez Méndez, Isabel; Bruña Fernández, Ricardo; López Sanz, David; Montejo, Pedro; Montenegro Peña, María Mercedes; Delgado Losada, María Luisa; Marcos Dolado, Alberto; López Sánchez, Ramón; Maestu Unturbe, FernandoBackground: Recent studies demonstrated that brain hypersynchrony is an early sign of dysfunction in Alzheimer's disease (AD) that can represent a proxy for clinical progression. Conversely, non-pharmacological interventions, such as cognitive training (COGTR), are associated with cognitive gains that may be underpinned by a neuroprotective effect on brain synchrony. Objective: To study the potential of COGTR to modulate brain synchrony and to eventually revert the hypersynchrony phenomenon that characterizes preclinical AD. Methods: The effect of COGTR was examined in a sample of healthy controls (HC, n = 41, 22 trained) and individuals with subjective cognitive decline (SCD, n = 49, 24 trained). Magnetoencephalographic activity and neuropsychological scores were acquired before and after a ten-week COGTR intervention aimed at improving cognitive function and daily living performance. Functional connectivity (FC) was analyzed using the phase-locking value. A mixed-effects ANOVA model with factors time (pre-intervention/post-intervention), training (trained/non-trained), and diagnosis (HC/SCD) was used to investigate significant changes in FC. Results: We found an average increase in alpha-band FC over time, but the effect was different in each group (trained and non-trained). In the trained group (HC and SCD), we report a reduction in the increase in FC within temporo-parietal and temporo-occipital connections. In the trained SCD group, this reduction was stronger and showed a tentative correlation with improved performance in different cognitive tests. Conclusion: COGTR interventions could mitigate aberrant increases in FC in preclinical AD, promoting brain synchrony normalization in groups at a higher risk of developing dementia.Item Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms(Entropy, 2019) Shumbayawonda, Elizabeth; Abásolo, Daniel; López Sanz, David; Bruña Fernández, Ricardo; Maestú Unturbe, Fernando; Fernández Lucas, Alberto AmableThe 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.Item Enhancement of posterior brain functional networks in bilingual older adults(Bilingualism: Language and Cognition, 2019) De Frutos Lucas, Jaisalmer; López Sanz, David; Cuesta Prieto, Pablo; Bruña Fernández, Ricardo; Fuente, Sofía de la; Serrano Martínez, Noelia; López García, María Eugenia; Delgado Losada, María Luisa; López Sánchez, Ramón; Marcos Dolado, Alberto; Maestu Unturbe, FernandoBilingualism 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.Item A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer’s Disease(Cerebral Cortex Communications, 2021) Abbas, Kausar; Marcos de Pedro, Silvia; Gómez-Ruiz, Natividad; Pereda, Ernesto; Goñi, Joaquín; López Sánchez, Ramón; Maestu Unturbe, Fernando; Marcos Dolado, Alberto; Barabash Bustelo, Ana; Bruña Fernández, Ricardo; Ramírez Toraño, FedericoThe concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer’s disease, that is, early signs of subnetworks impairment due to Alzheimer’s disease.The sample in this study consisted of 123 first-degree Alzheimer’s disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer’s disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer’s disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer’s disease structural-connectome-trait presents a posterior–posterior and posterior–temporal pattern, supplying new evidences to the cascading network failure model.