Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study.

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and ß) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis.

OBJECTIVE: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. METHODS: We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. RESULTS: For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. CONCLUSIONS: Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SIGNIFICANCE: Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

The Brain Dynamics of Syllable Duration and Semantic Predictability in Spanish.

This study examines the neural dynamics underlying the prosodic (duration) and the semantic dimensions in Spanish sentence perception. Specifically, we investigated whether adult listeners are aware of changes in the duration of a pretonic syllable of words that were either semantically predictable or unpredictable from the preceding sentential context. Participants listened to the sentences with instructions to make prosodic or semantic judgments, while their EEG was recorded. For both accuracy and RTs, the results revealed an interaction between duration and semantics. ERP analysis exposed an interactive effect between task, duration and semantic, showing that both processes share neural resources. There was an enhanced negativity on semantic process (N400) and an extended positivity associated with anomalous duration. Source estimation for the N400 component revealed activations in the frontal gyrus for the semantic contrast and in the parietal postcentral gyrus for duration contrast in the metric task, while activation in the sub-lobar insula was observed for the semantic task. The source of the late positive components was located on posterior cingulate. Hence, the ERP data support the idea that semantic and prosodic levels are processed by similar neural networks, and the two linguistic dimensions influence each other during the decision-making stage in the metric and semantic judgment tasks.

Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer's Disease Using Machine Learning and Resting State Electroencephalography.

BACKGROUND: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer's disease (AD). OBJECTIVE: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer's disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). METHODS: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. RESULTS: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. CONCLUSION: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.

Assessment of changes in the electrical activity of the brain during general anesthesia using portable electroencephalography / Evaluación de los cambios de la actividad eléctrica cerebral durante anestesia general usando electroencefalografía portable

Abstract Introduction The analysis of the electrical activity of the brain using scalp electrodes with electroencephalography (EEG) could reveal the depth of anesthesia of a patient during surgery. However, conventional EEG equipment, due to its price and size, are not a practical option for the operating room and the commercial units used in surgery do not provide access to the electrical activity. The availability of low-cost portable technologies could provide for further research on the brain activity under general anesthesia and facilitate our quest for new markers of depth of anesthesia. Objective To assess the capabilities of a portable EEG technology to capture brain rhythms associated with the state of consciousness and the general anesthesia status of surgical patients anesthetized with propofol. Methods Observational, cross-sectional study that reviewed 10 EEG recordings captured using OpenBCI portable low-cost technology, in female patients undergoing general anesthesia with propofol. The signal from the frontal electrodes was analyzed with spectral analysis and the results were compared against the reports in the literature. Results The signal captured with frontal electrodes, particularly α rhythm, enabled the distinction between resting with eyes closed and with eyes opened in a conscious state, and sustained anesthesia during surgery. Conclusions It is possible to differentiate a resting state from sustained anesthesia, replicating previous findings with conventional technologies. These results pave the way to the use of portable technologies such as the OpenBCI tool, to explore the brain dynamics during anesthesia.

Resumen Introducción El análisis de la actividad eléctrica cerebral mediante electrodos ubicados sobre el cuero cabelludo con electroencefalografía (EEG) podría permitir conocer la profundidad anestésica de un paciente durante cirugía. Sin embargo, los equipos de EEG convencionales, por su precio y tamaño, no son una alternativa práctica en quirófanos y los equipos comerciales usados en cirugía no permiten acceder a la actividad eléctrica. Disponer de tecnologías portables y de bajo costo aumentaría el número de investigaciones sobre la actividad cerebral bajo anestesia general y facilitaría la búsqueda de nuevos marcadores para la profundidad anestésica. Objetivo Evaluar la capacidad de una tecnología EEG portable de adquirir ritmos cerebrales relacionados con el estado consciente y el estado de anestesia general de pacientes en cirugía anestesiados con propofol. Métodos Estudio observacional de corte transversal en el que se analizaron datos de 10 registros EEG obtenidos mediante tecnología portable y de bajo costo OpenBCI, de pacientes de sexo femenino que fueron sometidas a anestesia general con propofol. La señal obtenida de los electrodos frontales se analizó mediante análisis espectral y se contrastaron los resultados con lo descrito en la literatura. Resultados La señal obtenida con electrodos frontales, especialmente el ritmo α, permitió diferenciar el reposo con ojos cerrados y ojos abiertos en estado consciente, del estado de mantenimiento de la anestesia durante cirugía. Conclusiones Se logra la diferenciación de estado de reposo y de mantenimiento de la anestesia replicando hallazgos previos de tecnologías convencionales. Estos resultados abren la posibilidad de utilizar las tecnologías portables como el OpenBCI para investigar la dinámica cerebral durante la anestesia.

Resting-state EEG alpha/theta ratio related to neuropsychological test performance in Parkinson's Disease.

OBJECTIVE: To determine possible associations of hemispheric-regional alpha/theta ratio (α/θ) with neuropsychological test performance in Parkinson's Disease (PD) non-demented patients. METHODS: 36 PD were matched to 36 Healthy Controls (HC). The α/θ in eight hemispheric regions was computed from the relative power spectral density of the resting-state quantitative electroencephalogram (qEEG). Correlations between α/θ and performance in several neuropsychological tests were conducted, significant findings were included in a moderation analysis. RESULTS: The α/θ in all regions was lower in PD than in HC, with larger effect sizes in the posterior regions. Right parietal, and right and left occipital α/θ had significant positive correlations with performance in Judgement of Line Orientation Test (JLOT) in PD. Adjusted moderation analysis indicated that right, but not left, occipital α/θ influenced the JLOT performance related to PD. CONCLUSIONS: Reduction of the occipital α/θ, in particular on the right side, was associated with visuospatial performance impairment in PD. SIGNIFICANCE: Visuospatial impairment in PD, which is highly correlated with the subsequent development of dementia, is reflected in α/θ in the right posterior regions. The right occipital α/θ may represent a useful qEEG marker for evaluating the presence of early signs of cognitive decline in PD and the subsequent risk of dementia.

Active information storage in Parkinson's disease: a resting state fMRI study over the sensorimotor cortex.

Parkinson's disease (PD), the second most frequent neurodegenerative disease, affects significantly life quality by a combination of motor and cognitive disturbances. Although it is traditionally associated with basal ganglia dysfunction, cortical alterations are also involved in disease symptoms. Our objective is to evaluate the alterations in brain dynamics in de novo and recently treated PD subjects using a nonlinear method known as Active Information Storage. In the current research, Active Information Storage (AIS) was used to study the complex dynamics in motor cortex spontaneous activity captured using resting state functional Magnetic Resonance Imaging (rs-fMRI) at early-stage in non-medicated and recently medicated PD subjects. Supplementary to AIS, the fractional Amplitude of Low Frequency Fluctuation (fALFF), which is a better-established technique of analysis of rs-fMRI signals, was also evaluated. Compared to healthy subjects, the AIS values were significantly reduced in PD patients over the analyzed motor cortex regions; differences were also found at less extent using the fALFF measure. Correlations between AIS and fALFF values showed that the measures seem to capture similar neuronal phenomena in rs-fMRI data. The highest sensitivity when detecting group differences revealed by AIS, and not captured by traditional linear approaches, suggests that this measure is a promising tool for the analysis of rs-fMRI neural data in PD.

Conectividad funcional en adultos mayores a partir de resonanciamagnética funcional como un posible indicador para la enfermedad deAlzheimer / Functional connectivity in senior adults from fMRI as a possible indicator for Alzheimer’s Disease

La enfermedad de Alzheimer es una enfermedad neurodegenerativa y la causa de demenciamás frecuente en el mundo. Esta enfermedad se caracteriza por muerte neuronal que lleva a una pérdida deconectividad cerebral general. Técnicas como la conectividad funcional a partir de imágenes de resonanciamagnética pueden brindar información acerca de la interacción entre regiones cerebrales y, por tanto, puedeser un indicador del Alzheimer.Objetivo: evaluar la conectividad funcional a partir de imágenes de resonancia magnética funcional en estadode reposo en adultos mayores como posible biomarcador para la enfermedad de Alzheimer.Materiales y métodos: en una población de 35 sujetos de edad avanzada (10 pacientes con Alzheimer de75 ± 2,87 años, 10 pacientes con deterioro cognitivo leve de 74,9 ± 2,88 años, y 15 personas sanas de 75,35 ±2,91 años), se compararon las redes de conectividad funcional obtenidas a través de la correlación temporal dela señal BOLD y elementos de la teoría de grafos. Se calcularon las medidas de las redes (costo y grado medio),y se correlacionaron estas medidas con las escalas neuropsicológicas ADNI-mem y ADAS-Cog.Resultados: en los pacientes con Alzheimer hay una disminución de la conectividad en comparación con loscontroles sanos y los pacientes con deterioro cognitivo leve. Se encontró una correlación significativa entre elcosto de las redes en los sujetos sanos y las escalas neuropsicológicas.Conclusión: se confirma la desconexión existente en la enfermedad de Alzheimer y se muestra que la alteraciónde la actividad cerebral en el deterioro cognitivo y Alzheimer se puede medir mediante el algoritmo basado engrafos desarrollado en este trabajo...

Alzheimer’s disease is a neurodegenerative disease and the most common cause of dementiain the world. This disease is characterized by neuronal cell death leading to an overall loss of brain connectivity.Functional connectivity from magnetic resonance images can provide information about the interactionbetween brain regions and therefore may be an indicator of Alzheimer. Objective: assessing functional connectivity from functional magnetic resonance images at rest in elderly asa potential biomarker for Alzheimer’s disease.Materials and methods: in a population of 35 elderly subjects (10 patients with Alzheimer 75 ± 2,87 years,10 patients with mild cognitive impairment 74,9 ± 2,88 years and 15 healthy individuals 75,35 ± 2,91years),functional connectivity networks obtained through the temporal correlation of BOLD signal and elementsof the graph theory were compared. Network indexes (cost and average degree) were calculated, and furthercorrelated with the neuropsychological scales, ADNI-mem, and ADAS-Cog...