Decoding motor expertise from fine-tuned oscillatory network organization

First published: 11 March 2022Can motor expertise be robustly predicted by the organization of frequency-specific oscillatory brain networks? To answer this question, we recorded high-density electroencephalography (EEG) in expert Tango dancers and naïves while viewing and judging the correctness of Tango-specific movements and during resting. We calculated task-related and resting-state connectivity at different frequency-bands capturing task performance (delta [δ], 1.5–4 Hz), error monitoring (theta [θ], 4–8 Hz), and sensorimotor experience (mu [μ], 8–13 Hz), and derived topographical features using graph analysis. These features, together with canonical expertise measures (i.e., performance in action discrimination, time spent dancing Tango), were fed into a data-driven computational learning analysis to test whether behavioral and brain signatures robustly classified individuals depending on their expertise level. Unsurprisingly, behavioral measures showed optimal classification (100%) between dancers and naïves. When considering brain models, the task-based classification performed well (~73%), with maximal discrimination afforded by theta-band connectivity, a hallmark signature of error processing. Interestingly, mu connectivity during rest outperformed (100%) the task-based approach, matching the optimal classification of behavioral measures and thus emerging as a potential trait-like marker of sensorimotor network tuning by intense training. Overall, our findings underscore the power of fine-tuned oscillatory network signatures for capturing expertise-related differences and their potential value in the neuroprognosis of learning outcomes.Basque Government; Consejo Nacional de Investigaciones Científicas y Técnicas; (CONICET) Ikerbasque, Basque Foundation for Science; Spanish State Research Agency, Grant/Award Number: SEV-2015-0490; Programa Interdisciplinario de Investigaci on Experimental en Comunicaci on y Cognici on (PIIECC), Facultad de Humanidades, USACH; ANID; FONDECYT Regular, Grant/Award Numbers: 1210195, 1210176; Global Brain Health Institute (GBHI

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease

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 49 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

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer's disease with magnetoencephalography

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer's disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships

Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke

Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal. For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level. After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design)

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease

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 49 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

La imagen y la narrativa como herramientas para el abordaje psicosocial en escenarios de violencia. Departamentos Nariño y Valle del Cauca.

La imagen y la narrativa como herramientas para el abordaje psicosocial en escenarios de violencia. Departamentos Nariño y Valle del Cauca.Éste documento expone de manera contundente, el análisis realizado al caso seleccionado del libro Voces: Historias de Violencia y Esperanza en Colombia con el relato de Gloria, una mujer que logró transformar a partir de un proceso emancipatorio, su papel de víctima a sobreviviente. Como producto de éste análisis del caso, surgen nueve preguntas (estratégicas, circulares y reflexivas) que permiten un acercamiento psicosocial en el proceso de la protagonista del caso. A continuación, se presentan las estrategias para el abordaje psicosocial al caso propuesto Pandurí, abarcando desde los emergentes psicosociales latentes posterior la situación de violencia, hasta las acciones a desarrollar como mecanismo de apoyo en situaciones de crisis. Finalmente, se retoma el informe analítico como resultado del proceso de fotovoz realizado previamente en el desarrollo del diplomado.This document shows bluntly, the analysis made to the select case in the book Voices: stories of violence and hope in Colombia with Gloria´s story, a woman who transformed from an emancipatory process, her role from victim to survivor. As a result of this analysis, come up nine questions (strategic, circular and reflexive) that allows a psychosocial approach on the process of the protagonist case. Next, you will find the strategies of the psychosocial approach to the proposed case Pandurí, ranging from latent psychosocial emergent post-violence’s situation, till action to develop as a support mechanism in crisis situations. Finally, we tack back the analytic report as a result of a photo-voice process that we have made previously on the academic process

MEG functional network disorganization associates with cerebrospinal fluid biomarkers in early Alzheimer’s disease

To determine whether functional connectivity patterns, as an index of synaptic dysfunction, associate with cerebrospinal fluid (CSF) biomarkers (i.e., phospho-tau and amyloid beta -A 42- levels) in patients with Mild Cognitive Impairment due to Alzheimer?s disease. We also assessed orrelations of aberrant functional connections with structural connectivity abnormalities and with cognitive deficit

How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?

IntroductionEven though infant crying is a common phenomenon in humans’ early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns.MethodsMultimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants.ResultsWe found correlations between most of the features extracted from the signals depending on the infant’s arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy.ConclusionOur findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians

Using magnetoencephalography to monitor the progression from mild cognitive impairment to Alzheimer disease: an approach based on resting state functional connectivity

El estudio del cerebro es uno de los campos más interesantes y complejos de la ciencia actual. Tanto la investigación del cerebro sano como el estudio de neuropatologías de alta prevalencia mundial llenan las páginas de todas las revistas científicas del mundo. Dentro de las neuropatologías, el Alzheimer es una enfermedad neurodegenerativa, actualmente considerada como el tipo más común de demencia correspondiente al 60-70% de los casos en la población mundial. Esta enfermedad esta clínicamente definida por una pérdida progresiva de memoria y otras habilidades cognitivas y funcionales. Histológicamente, el Alzheimer se caracteriza por la presencia de placas de amiloide, ovillos neurofibrilares y atrofia cerebral. Muchos de los estudios científicos actuales se centran en etapas tempranas de la enfermedad para crear tratamientos diagnósticos que puedan prevenir el avance de esta enfermedad. Una de las fases más investigadas es el deterioro cognitivo leve. En esta etapa, el individuo presenta una disminución leve pero apreciable y medible de las capacidades cognitivas, entre ellas la memoria, sin afectar por completo el desarrollo de las actividades diarias de la persona. Estos pacientes tienen un alto riesgo de desarrollar Alzheimer, especialmente aquellos que presentan deterioro de la memoria. Estos primeros signos de deterioro pueden ser medidos a través de biomarcadores como la acumulación de beta-amiloide y tau en el líquido cefalorraquídeo, evaluación neuropsicológica y estudio de atrofia cerebral, especialmente de los hipocampos, por medio de resonancia magnética. Dentro de este contexto, la Magnetoencefalografía aparece como una técnica de neuroimagen que permite estudiar los campos magnéticos cerebrales, específicamente la medición de potenciales post-sinápticos, la cual puede ser utilizada para estudiar alteraciones de redes funcionales en neuropatologías como el Alzheimer. De esta manera, la conectividad funcional ofrece una visión integradora, donde las funciones del cerebro se llevan a cabo mediante la comunicación e interacción de diversas regiones cerebrales. Adicionalmente, la conectividad efectiva permite conocer la dirección del flujo de información entre regiones cerebrales. Junto con las herramientas mencionadas anteriormente, la teoría de grafos permite la caracterización de las redes funcionales encontradas a partir de la conectividad. Por lo tanto, el propósito de esta tesis es estudiar los patrones de las alteraciones de la conectividad funcional en pacientes con deterioro cognitivo leve que evolucionan posteriormente a Alzheimer. Así mismo, asociar estos patrones a diversos biomarcadores como la acumulación de las proteínas beta-amiloide y tau así como también, al deterioro de memoria y atrofia de hipocampo. Finalmente, de cara al estudio de la enfermedad y al diagnóstico de la misma proponer un modelo que permita predecir que sujetos con deterioro cognitivo leve evolucionaran a Alzheimer. En el capítulo 3, se presenta un estudio en sujetos ancianos sanos a los cuales se les ha administrado escopolamina, un anti-muscarínico que produce un efecto anti-colinérgico e induce alteraciones cognitivas (como la perdida temporal de memoria) similares a las que se observan en Alzheimer. En este estudio, se analizaron los patrones cerebrales a través de la conectividad funcional. Además, para caracterizar estas redes encontradas se aplicó teoría de grafos. Así, se obtuvieron alteraciones en la conectividad funcional como el decremento de la sincronización en bandas de alta frecuencias y una organización de la red parecida a la que se ve en los pacientes con Alzheimer. El capítulo 4, muestra una población de pacientes con deterioro cognitivo leve a los cuales se les extrajo líquido cefalorraquídeo para estudiar la asociación de la acumulación patológica de beta-amiloide y tau con los cambios en los patrones de conectividad funcional. También, se estudiaron alteraciones en la conectividad estructural y se relacionaron con las alteraciones en la conectividad funcional. Así, se encontró que los pacientes con valores anormales de proteínas beta-amiloide y tau en el líquido cefalorraquídeo mostraban una reducción e incremento de la conectividad funcional afectando a diferentes regiones cerebrales entre ellas el cingulado anterior y posterior, el córtex orbito-frontal y las áreas temporales mediales en diferentes bandas de frecuencias. Además, se encontró una reducción en la conectividad del cingulado posterior mediada por la proteína tau y esto fue asociado al deterioro de la integridad axonal del cingulum hipocampal. Finalmente, los biomarcadores del líquido cefalorraquídeo y los valores obtenidos de la evaluación neuropsicológica predijeron la mayoría de alteraciones de la conectividad funcional. Por último, en el capítulo 5, se realizó un estudio longitudinal de pacientes con deterioro cognitivo leve. Estos pacientes fueron evaluados durante tres años y luego divididos en dos grupos de acuerdo al diagnóstico clínico final. Así se crearon dos grupos: pacientes con deterioro cognitivo leve "estables" y pacientes con deterioro cognitivo "progresivo", estos últimos son aquellos pacientes que durante esos tres años convirtieron a Alzheimer. Se registraron dos medidas de Magnetoencefalografía al comienzo del estudio y luego 3 años después para cada paciente. De estos dos grupos se estudió la conectividad funcional y efectiva. Así como la asociación de la valoración neuropsicológica y los volúmenes de hipocampo con la conectividad funcional. Además, con estas variables se propuso un modelo que permite realizar la clasificación de los sujetos en "estable" o "progresivo". Por otro lado, se realizó un análisis de redes con el PLV, estudiando el coeficiente de participación, este índice permite conocer los nodos que están altamente involucrados con otras sub-redes en el cerebro. También, se calculó el in- y el out-degree a partir de la conectividad efectiva con la transferencia de entropía de fase. Finalmente, para complementar los análisis de red, se decidió estudiar el coeficiente de participación múltiple de una red multiplexada de cuatro capas correspondientes a las bandas de frecuencias analizadas. Este análisis puede dar información adicional de las regiones que participan activamente en las cuatro capas de esta red multiplexada. Los pacientes que evolucionaron a Alzheimer mostraron una disrupción de la conectividad funcional en la banda theta y beta en comparación con los que permanecieron estables. La alteración de esta conectividad en theta correlacionó con dos test neuropsicológicos usualmente relacionados con la memoria: el recuerdo inmediato y demorado, también, con los volúmenes de hipocampo en theta y beta. Los cambios en la conectividad funcional en las bandas theta y beta junto con los test neuropsicológicos de dígitos inversos y el test del trazo ambos relacionados con funciones ejecutivas y de la atención, funciones deterioradas en el transcurso de la enfermedad de Alzheimer predijeron la conversión a Alzheimer. Igualmente, la conectividad en theta y beta de la primera medida de Magnetoencefalografía junto con el recuerdo inmediato y el test de dígitos directos predijeron la conversión a Alzheimer, aunque no con una precisión tan alta como el modelo anterior. Las redes con direccionalidad obtenidas de la conectividad efectiva mostraron que los conversores presentaban una red más desorganizada y desconectada en comparación a los estables. Los hallazgos encontrados aquí corroboran la teoría de la disrupción de las redes funcional y efectiva en los pacientes con Alzheimer. En este capítulo, la Magnetoencefalografía representa una herramienta novedosa y prometedora de la disrupción sináptica en el diagnóstico y predicción de Alzheimer. Así mismo, es importante resaltar la importancia de combinar diferentes perspectivas para apoyar el diagnóstico y predicción utilizando variables neuropsicológicas y anatómicas como los volúmenes de hipocampo cuyas alteraciones representan los déficits cognitivos y atrofia cerebral presentes en Alzheimer. Esta tesis pretende aportar un grano de arena en el estudio del cerebro y la enfermedad de Alzheimer y reforzar la Magnetoencefalografía junto con la conectividad funcional como herramientas novedosas con inmenso potencial en el estudio de las redes funcionales cerebrales y sus alteraciones, así como en la evolución del deterioro cognitivo leve a la enfermedad de Alzheimer. ----------ABSTRACT---------- Alzheimer's disease (AD) is a neurodegenerative disease, currently considered the most common type of dementia corresponding to 60-70% of the cases in the world population. This disease is clinically defined by a progressive loss of memory and other cognitive and functional abilities. Histologically, AD is characterized by the presence of amyloid plaques, neurofibrillary tangles and brain atrophy. Recent studies have focused on early stages of the disease to generate diagnostic treatments that can prevent the progression of this disease. One of the most studied phases is Mild Cognitive Impairment (MCI). In this stage, the individual presents a slight but appreciable and measurable decrease in cognitive abilities, including memory, without completely affecting the development of the person's daily activities. These patients have a high risk of developing AD, especially those who have memory impairment. These first signs of decline can be measured through biomarkers such as the accumulation of beta-amyloid and p-tau in the cerebrospinal fluid (CSF), neuropsychological evaluation and the study of cerebral atrophy, especially of the hippocampus, by means of magnetic resonance imaging. In this context, Magnetoencephalography (MEG), a promising neuroimaging technique, measures the post-synaptic potentials generated by billions of neurons, which can be used to study functional networks alterations in neuropathologies such as AD. Thus, functional connectivity (FC) offers an integrative vision, where brain functions are carried out through the communication and interaction of various brain regions. Additionally, effective connectivity (EC) defines the direction of the flow of information between brain regions, considering a simultaneous interaction of several neural elements to explicitly quantify the effect one element has on another. Finally, graph theory allows the characterization of the functional networks derived from a connectivity analysis. Therefore, the aim of this thesis is to study the functional connectivity alterations in patients with MCI that subsequently evolve to AD. Likewise, we also seek to associate these patterns to diverse biomarkers such as the accumulation of beta-amyloid and tau proteins as well as memory impairment and hippocampal atrophy. Finally, in order to study the disease, we propose a model that allows the prediction of which subjects with mild cognitive impairment will develop AD. Chapter 3 presents healthy elderly subjects under scopolamine administration. Scopolamine is an anti-muscarinic that produces an anti-cholinergic effect and induces cognitive alterations (such as temporary memory loss) similar to those observed in AD. In this study, MEG brain patterns were analyzed through FC. In addition, graph theory was applied to characterize these networks. Thus, FC alterations were obtained, such as the decrease in synchronization in high frequency bands and a network organization similar to that found in AD patients. Chapter 4 shows MCI patients from whom CSF was extracted to study the association between pathological accumulation of beta-amyloid and p-tau with changes in MEG FC patterns. Also, alterations in structural connectivity were studied and related to alterations in FC. Thus, it was found that patients with abnormal accumulation of beta-amyloid and p-tau in the CSF, showed a significant reduction and increase in FC affecting different brain regions including the anterior and posterior cingulate, the orbito-frontal cortex and the medial temporal areas in different frequency bands. In addition, a reduction in the connectivity of the posterior cingulate mediated by the p-tau protein was found; this was associated with the degeneration of the axonal integrity of the hippocampal cingulum. Finally, the biomarkers of the CSF and the scores obtained from the neuropsychological assessment predicted most of the alterations in FC. Lastly, in Chapter 5, a longitudinal study of MCI patients was conducted. These patients were follow-up for three years and then split into two groups according to the final clinical outcome, as "stable" MCI (sMCI) and patients with "progressive" MCI (pMCI), the latter being those patients who during the three years converted to AD. Two MEG measurements were recorded at the beginning of the study and then 3 years later for each patient. FC and EC were studied between both groups as well as the association of neuropsychological assessment and hippocampal volumes with FC. In addition, with these variables, a model was proposed to classify the subjects in "sMCI" or "pMCI”. On the other hand, a network analysis was performed with the FC, studying the participation coefficient; this index highlights the nodes that are highly involved with other sub-networks in the brain. Also, the in- and out-degree was computed from the EC with the phase transfer entropy. Finally, to complement the network analysis, we studied the multiple participation coefficient, which is computed for a multiplex network consisting of four layers corresponding to the frequency bands analyzed. This analysis provides additional information about the regions that actively participate in the four layers of this multiplex network. Patients who developed AD showed a disruption of FC in the theta and beta bands compared to those who remained stable. The alteration of this connectivity in the theta correlated with two neuropsychological tests usually related to memory: the immediate and delayed recall, also, with hippocampus volumes in theta and beta. Changes in FC in theta and beta band along with the neuropsychological tests of inverse digits and the trail making test, both related to attentional and executive functions, predicted the conversion to AD with high accuracy. Likewise, the FC in theta and beta band of the first MEG measurement together with the immediate recall and the direct digit test predicted the conversion to AD, although not with a precision as high as the previous model. The directed networks obtained from EC showed that pMCI presented a more disorganized and disconnected network in comparison to sMCI. The findings found here corroborate the theory of FC and EC disrupted networks in patients with AD. In this chapter, MEG revealed itself as a promising tool to measure synaptic disruption in AD patients. Likewise, it is important to highlight the importance of combining different perspectives to support diagnosis and prediction using neuropsychological and anatomical variables such as hippocampal volumes, whose alterations represent the cognitive deficits and cerebral atrophy observed in AD. This thesis aims to provide a contribution to the study of AD and support MEG along with FC as tools with immense potential in the study of functional brain networks and their alterations in MCI and its progression to AD

Effects of spaceflight on the EEG alpha power and functional connectivity

Abstract Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN’s alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space