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167 results on '"Valdes, A."'

2. Brain health in diverse settings: How age, demographics and cognition shape brain function

Author

Hernan Hernandez, Sandra Baez, Vicente Medel, Sebastian Moguilner, Jhosmary Cuadros, Hernando Santamaria-Garcia, Enzo Tagliazucchi, Pedro A. Valdes-Sosa, Francisco Lopera, John Fredy OchoaGómez, Alfredis González-Hernández, Jasmin Bonilla-Santos, Rodrigo A. Gonzalez-Montealegre, Tuba Aktürk, Ebru Yıldırım, Renato Anghinah, Agustina Legaz, Sol Fittipaldi, Görsev G. Yener, Javier Escudero, Claudio Babiloni, Susanna Lopez, Robert Whelan, Alberto A Fernández Lucas, Adolfo M. García, David Huepe, Gaetano Di Caterina, Marcio Soto-Añari, Agustina Birba, Agustin Sainz-Ballesteros, Carlos Coronel, Eduar Herrera, Daniel Abasolo, Kerry Kilborn, Nicolás Rubido, Ruaridh Clark, Ruben Herzog, Deniz Yerlikaya, Bahar Güntekin, Mario A. Parra, Pavel Prado, and Agustin Ibanez

Subjects
Age, Cognition, Education, Individual differences, Sex, Brain dynamics, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Published
2024
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3. Brain health in diverse settings: How age, demographics and cognition shape brain function

Author

Hernandez, Hernan, Baez, Sandra, Medel, Vicente, Moguilner, Sebastian, Cuadros, Jhosmary, Santamaria-Garcia, Hernando, Tagliazucchi, Enzo, Valdes-Sosa, Pedro A., Lopera, Francisco, OchoaGómez, John Fredy, González-Hernández, Alfredis, Bonilla-Santos, Jasmin, Gonzalez-Montealegre, Rodrigo A., Aktürk, Tuba, Yıldırım, Ebru, Anghinah, Renato, Legaz, Agustina, Fittipaldi, Sol, Yener, Görsev G., Escudero, Javier, Babiloni, Claudio, Lopez, Susanna, Whelan, Robert, Lucas, Alberto A Fernández, García, Adolfo M., Huepe, David, Caterina, Gaetano Di, Soto-Añari, Marcio, Birba, Agustina, Sainz-Ballesteros, Agustin, Coronel, Carlos, Herrera, Eduar, Abasolo, Daniel, Kilborn, Kerry, Rubido, Nicolás, Clark, Ruaridh, Herzog, Ruben, Yerlikaya, Deniz, Güntekin, Bahar, Parra, Mario A., Prado, Pavel, and Ibanez, Agustin

Published
2024
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5. Harmonized-Multinational qEEG norms (HarMNqEEG)

Author

Li, Min, Wang, Ying, Lopez-Naranjo, Carlos, Hu, Shiang, Reyes, Ronaldo César García, Paz-Linares, Deirel, Areces-Gonzalez, Ariosky, Hamid, Aini Ismafairus Abd, Evans, Alan C., Savostyanov, Alexander N., Calzada-Reyes, Ana, Villringer, Arno, Tobon-Quintero, Carlos A., Garcia-Agustin, Daysi, Yao, Dezhong, Dong, Li, Aubert-Vazquez, Eduardo, Reza, Faruque, Razzaq, Fuleah Abdul, Omar, Hazim, Abdullah, Jafri Malin, Galler, Janina R., Ochoa-Gomez, John F., Prichep, Leslie S., Galan-Garcia, Lidice, Morales-Chacon, Lilia, Valdes-Sosa, Mitchell J., Tröndle, Marius, Zulkifly, Mohd Faizal Mohd, Abdul Rahman, Muhammad Riddha Bin, Milakhina, Natalya S., Langer, Nicolas, Rudych, Pavel, Koenig, Thomas, Virues-Alba, Trinidad A., Lei, Xu, Bringas-Vega, Maria L., Bosch-Bayard, Jorge F., and Valdes-Sosa, Pedro Antonio

Published
2022
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6. Activation network improves spatiotemporal modelling of human brain communication processes

Author

Xucheng Liu, Ze Wang, Shun Liu, Lianggeng Gong, Pedro A. Valdes Sosa, Benjamin Becker, Tzyy-Ping Jung, Xi-jian Dai, and Feng Wan

Subjects
Functional MRI, Dynamic functional network connectivity, Brain network, Topological analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p

Published
2024
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9. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity

Author

Bosch-Bayard, Jorge, Razzaq, Fuleah Abdul, Lopez-Naranjo, Carlos, Wang, Ying, Li, Min, Galan-Garcia, Lidice, Calzada-Reyes, Ana, Virues-Alba, Trinidad, Rabinowitz, Arielle G., Suarez-Murias, Carlos, Guo, Yanbo, Sanchez-Castillo, Manuel, Roger, Kassandra, Gallagher, Anne, Prichep, Leslie, Anderson, Simon G., Michel, Christoph M., Evans, Alan C., Bringas-Vega, Maria L., Galler, Janina R., and Valdes-Sosa, Pedro A.

Published
2022
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16. WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis

Author

Li Dong, Jianfu Li, Qiunan Zou, Yufan Zhang, Lingling Zhao, Xin Wen, Jinnan Gong, Fali Li, Tiejun Liu, Alan C. Evans, Pedro A. Valdes-Sosa, and Dezhong Yao

Subjects
WeBrain, Brainformatics, EEG big data, Distributed cloud computing, Computational ecosystem, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The current evolution of ‘cloud neuroscience’ leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

Published
2021
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35. Neuroimaging and global health

Author

Bringas-Vega, Maria L., primary, Michel, Christoph M., additional, Saxena, Shekar, additional, White, Tonya, additional, and Valdes-Sosa, Pedro A., additional

Published
2022
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40. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity

Author

Jorge Bosch-Bayard, Fuleah Abdul Razzaq, Carlos Lopez-Naranjo, Ying Wang, Min Li, Lidice Galan-Garcia, Ana Calzada-Reyes, Trinidad Virues-Alba, Arielle G. Rabinowitz, Carlos Suarez-Murias, Yanbo Guo, Manuel Sanchez-Castillo, Kassandra Roger, Anne Gallagher, Leslie Prichep, Simon G. Anderson, Christoph M. Michel, Alan C. Evans, Maria L. Bringas-Vega, Janina R. Galler, and Pedro A. Valdes-Sosa

Subjects
Cognitive Neuroscience, Malnutrition, Nutritional Status, Electroencephalography, Protein-Energy Malnutrition, Neurology, Humans, qEEG, Cognitive Dysfunction, EEG, Longitudinal Studies, Longitudinal study, Source analysis, Tomography
Abstract

Protein Energy Malnutrition (PEM) has lifelong consequences on brain development and cognitive function. We studied the lifelong developmental trajectories of resting-state EEG source activity in 66 individuals with histories of Protein Energy Malnutrition (PEM) limited to the first year of life and in 83 matched classmate controls (CON) who are all participants of the 49 years longitudinal Barbados Nutrition Study (BNS). qEEGt source z-spectra measured deviation from normative values of EEG rhythmic activity sources at 5-11 years of age and 40 years later at 45-51 years of age. The PEM group showed qEEGt abnormalities in childhood, including a developmental delay in alpha rhythm maturation and an insufficient decrease in beta activity. These profiles may be correlated with accelerated cognitive decline.

Published
2022
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41. WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis

Author

Fali Li, Qiunan Zou, Yufan Zhang, Dezhong Yao, Jinnan Gong, Tiejun Liu, Xin Wen, Alan C. Evans, Dong Li, Lingling Zhao, Jianfu Li, and Pedro A. Valdes-Sosa

Subjects
Big Data, Computer science, Cognitive Neuroscience, Computer programming, Big data, Maintainability, Cloud computing, Neurosciences. Biological psychiatry. Neuropsychiatry, Brainformatics, Field (computer science), Software, Distributed cloud computing, Humans, Web application, Flexibility (engineering), business.industry, Computational ecosystem, Computational Biology, Electroencephalography, Cloud Computing, Data science, Systems Integration, Neurology, EEG big data, business, WeBrain, RC321-571
Abstract

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

Published
2021

42. Neuroimaging and global health

Author

Maria L. Bringas-Vega, Christoph M. Michel, Shekar Saxena, Tonya White, Pedro A. Valdes-Sosa, and Child and Adolescent Psychiatry / Psychology

Subjects
Neurology, Cognitive Neuroscience, Brain, Humans, Neuroimaging, Global Health
Published
2022
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44. Interoperable atlases of the human brain

Author

Amunts, K., Hawrylycz, M. J., Van Essen, D. C., Van Horn, J. D., Harel, N., Poline, - B.J., De Martino, F., Bjaalie, J. G., Dehaene-Lambertz, G., Dehaene, S., Valdes-Sosa, P., Thirion, B., Zilles, K., Hill, S. L., Abrams, M. B., Tass, P. A., Vanduffel, W., Evans, A. C., and Eickhoff, S. B.

Published
2014
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45. Portable, field-based neuroimaging using high-density diffuse optical tomography

Author

Ana Maria Arbelaez, Margarita Torres, Edward J. Richter, Joseph P. Culver, Arefeh Sherafati, Adam T. Eggebrecht, Christopher D. Smyser, Andrew K. Fishell, Claudia P. Valdes, and Tracy M. Burns-Yocum

Subjects
Male, Sensory processing, Computer science, Image quality, Cognitive Neuroscience, medicine.medical_treatment, Context (language use), Neuroimaging, Portable neuroimaging, Child Nutrition Disorders, 050105 experimental psychology, lcsh:RC321-571, 03 medical and health sciences, Superior temporal gyrus, Wearable Electronic Devices, 0302 clinical medicine, medicine, Image Processing, Computer-Assisted, Humans, Tomography, Optical, 0501 psychology and cognitive sciences, Child, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Optical neuroimaging, Resting state fMRI, Functional connectivity, 05 social sciences, Malnutrition, Brain, Signal Processing, Computer-Assisted, Diffuse optical imaging, Neurology, High-density diffuse optical tomography, Task-evoked responses, Functional near-infrared spectroscopy, Female, Neuroscience, 030217 neurology & neurosurgery
Abstract

Behavioral and cognitive tests in individuals who were malnourished as children have revealed malnutrition-related deficits that persist throughout the lifespan. These findings have motivated recent neuroimaging investigations that use highly portable functional near-infrared spectroscopy (fNIRS) instruments to meet the demands of brain imaging experiments in low-resource environments and enable longitudinal investigations of brain function in the context of long-term malnutrition. However, recent studies in healthy subjects have demonstrated that high-density diffuse optical tomography (HD-DOT) can significantly improve image quality over that obtained with sparse fNIRS imaging arrays. In studies of both task activations and resting state functional connectivity, HD-DOT is beginning to approach the data quality of fMRI for superficial cortical regions. In this work, we developed a customized HD-DOT system for use in malnutrition studies in Cali, Colombia. Our results evaluate the performance of the HD-DOT instrument for assessing brain function in a cohort of malnourished children. In addition to demonstrating portability and wearability, we show the HD-DOT instrument's sensitivity to distributed brain responses using a sensory processing task and measurements of homotopic functional connectivity. Task-evoked responses to the passive word listening task produce activations localized to bilateral superior temporal gyrus, replicating previously published work using this paradigm. Evaluating this localization performance across sparse and dense reconstruction schemes indicates that greater localization consistency is associated with a dense array of overlapping optical measurements. These results provide a foundation for additional avenues of investigation, including identifying and characterizing a child's individual malnutrition burden and eventually contributing to intervention development.

Published
2020

46. Brain status modeling with non-negative projective dictionary learning

Author

Jean-Baptiste Poline, Yuhong Guo, Alan C. Evans, Christian Desrosiers, Noor Al-Sharif, Budhachandra Khundrakpam, Gregory Kiar, Pedro A. Valdes-Sosa, and Mingli Zhang

Subjects
Adult, Male, Cognitive biomarker, Brain development, Adolescent, Computer science, Cognitive Neuroscience, Brain age prediction, Feature selection, Neuroimaging, 050105 experimental psychology, Brain maturity modeling, lcsh:RC321-571, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Sex Factors, Discriminative model, Humans, 0501 psychology and cognitive sciences, Projective test, Association (psychology), Child, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Cerebral Cortex, Projective dictionary learning, business.industry, 05 social sciences, Age Factors, Brain, Pattern recognition, Models, Theoretical, Magnetic Resonance Imaging, Manifold, Diffusion Tensor Imaging, Neurology, Child, Preschool, Connectome, Female, Artificial intelligence, Nerve Net, business, 030217 neurology & neurosurgery, Diffusion MRI
Abstract

Accurate prediction of individuals’ brain age is critical to establish a baseline for normal brain development. This study proposes to model brain development with a novel non-negative projective dictionary learning (NPDL) approach, which learns a discriminative representation of multi-modal neuroimaging data for predicting brain age. Our approach encodes the variability of subjects in different age groups using separate dictionaries, projecting features into a low-dimensional manifold such that information is preserved only for the corresponding age group. The proposed framework improves upon previous discriminative dictionary learning methods by incorporating orthogonality and non-negativity constraints, which remove representation redundancy and perform implicit feature selection. We study brain development on multi-modal brain imaging data from the PING dataset (N = 841, age = 3 − 21 years). The proposed analysis uses our NDPL framework to predict the age of subjects based on cortical measures from T1-weighted MRI and connectome from diffusion weighted imaging (DWI). We also investigate the association between age prediction and cognition, and study the influence of gender on prediction accuracy. Experimental results demonstrate the usefulness of NDPL for modeling brain development.

Published
2020

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