Pedro A. Valdés‐Sosa

University of Electronic Science and Technology of China

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Maria L. Bringas‐Vega

University of Washington

Jorge Bosch‐Bayard

Universidad Autónoma de Madrid

Deirel Paz-Linares

Cuban Neuroscience Center

Ariosky Areces-González

University of Electronic Science and Technology of China

Eduardo Martínez‐Montes

Cuban Neuroscience Center

Mitchell Valdés-Sosa

Cuban Neuroscience Center

R. Biscay

Mathematics Research Center

Alan C. Evans

McGill University

Eduardo Henrik Aubert

Universidade de São Paulo

Fuleah A. Razzaq

University of Electronic Science and Technology of China

Cooperative Institutions

Cuban Neuroscience Center

196

University of Electronic Science and Technology of China

169

McGill University

Harvard University

University of Cambridge

Montreal Neurological Institute and Hospital

Chinese Academy of Sciences

University College London

Inserm

Centre National de la Recherche Scientifique

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Temperament, character and QEEG in children with ADHD-C and ADHD-C+ODD

International Journal of Psychophysiology (2014)

Giuseppe A. Chiarenza Jorge Bosch‐Bayard Stefania Villa Marco P. Chiarenza Lı́dice Galán

Ninth

10.1016/j.ijpsycho.2014.08.765

Cite

Citations (1)

Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles

Entropy (2022)

Ania Mesa-Rodríguez Augusto González E. Estévez-Rams Pedro A. Valdés‐Sosa

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity–entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.

Expression (computer science)

Data set

10.3390/e24121744

Cite

Citations (3)

New tools for the development of computational neuroanatomy

Revista Cubana de Investigaciones Biomédicas (2006)

Susana Chao González Pedro A. Valdés‐Sosa Eduardo Henrik Aubert Lı́dice Galán

Neuroanatomy

Source

Cite

Citations (0)

Early childhood malnutrition impairs adult resting brain function using near-infrared spectroscopy

Frontiers in Human Neuroscience (2024)

Kassandra Roger Phetsamone Vannasing Julie Tremblay Maria L. Bringas‐Vega Cyralene P. Bryce

Introduction Early childhood malnutrition affects 200+ million children under 5 years of age worldwide and is associated with persistent cognitive, behavioral and psychiatric impairments in adulthood. However, very few studies have investigated the long-term effects of childhood protein-energy malnutrition (PEM) on brain function using a functional hemodynamic brain imaging technique. Objective and methods This study aims to investigate functional brain network alterations using near infrared spectroscopy (NIRS) in adults, aged 45–51 years, from the Barbados Nutrition Study (BNS) who suffered from a single episode of malnutrition restricted to their first year of life (n = 26) and controls (n = 29). A total of 55 individuals from the BNS cohort underwent NIRS recording at rest. Results and discussion Using functional connectivity and permutation analysis, we found patterns of increased Pearson’s correlation with a specific vulnerability of the frontal cortex in the PEM group (ps < 0.05). Using a graph theoretical approach, mixed ANCOVAs showed increased segregation (ps = 0.0303 and 0.0441) and decreased integration ( p = 0.0498) in previously malnourished participants compared to healthy controls. These results can be interpreted as a compensatory mechanism to preserve cognitive functions, that could also be related to premature or pathological brain aging. To our knowledge, this study is the first NIRS neuroimaging study revealing brain function alterations in middle adulthood following early childhood malnutrition limited to the first year of life.

Functional near-infrared spectroscopy

10.3389/fnhum.2023.1287488

Cite

Citations (1)

Chaotic dynamics and synchronization under tripartite couplings: Analyses and experiments using single-transistor oscillators as metaphors of neural dynamics

Chaos Solitons & Fractals (2024)

Ludovico Minati Laura Sparacino Luca Faes Hiroyuki Ito Chunbiao Li

Dynamics

10.1016/j.chaos.2024.115567

Cite

Citations (0)

162. Sparse connectivity in the motor circuit of basal ganglia: A multivariate autoregressive analysis

Clinical Neurophysiology (2008)

Rafael Rodríguez‐Rojas M. Vega Hernández José M. Sánchez‐Bornot Maylén Carballo Barreda J. Bosh

10.1016/j.clinph.2008.04.178

Cite

Citations (0)

An Age-Adjusted EEG Source Classifier Accurately Detects School-Aged Barbadian Children That Had Protein Energy Malnutrition in the First Year of Life

Frontiers in Neuroscience (2019)

Maria L. Bringas‐Vega Yanbo Guo Qin Tang Fuleah A. Razzaq Ana Calzada Reyes

We provide an electroencephalographic (EEG) based statistical classifier that correctly identifies children with histories of Protein Energy Malnutrition (PEM) in the first year of life and distinguished them from controls with 0.82% accuracy (area under the ROC curve). Our previous result in the same participants achieved equivalent accuracy but was based on scalp quantitative EEG features which precluded anatomical interpretation. In contrast, we now employ BC-VARETA, a novel high-resolution EEG source imaging method with minimal leakage and maximal sparseness. This allowed us to identify, to our knowledge for the first time, a classifier in the source space. The EEGs was recorded in 1978 in 108 children who were 5 to 11 years and were participants in the 45+ year longitudinal Barbados Nutrition Study. The PEM cohort experienced moderate-severe protein-energy malnutrition limited to the first year of life; healthy controls were classmates who were matched by age, gender and handedness. In the current study, we employed a machine learning approach based on the elastic net to create a stable sparse classifier. Interestingly, the classifier was driven predominantly by nutrition group differences in alpha activity in the lingual gyrus. This structure is part of the pathway associated with generating alpha rhythm generation which increases with normal maturation. The PEM group showed a significant decrease in alpha activity, suggesting a developmental delay in brain maturation. Childhood malnutrition is still a serious worldwide public health problem. Its consequences are particularly severe when present during early development- deficits during this critical period are permanent and predict impaired cognitive and behavioral functioning over the lifespan. Our EEG source classifier may provide a functionally interpretable diagnostic technology to study the effects on the brain of malnutrition, with wide applicability in low resources settings.

Quantitative electroencephalography

10.3389/fnins.2019.01222

Cite

Citations (21)

Multimodal pipeline for HCP-compatible processing and registration of legacy datasets (MRI, MEG, and EEG)

Ariosky Areces-González Deirel Paz-Linares Usama Riaz Min Li Ying Wang

<p>Extracting cortical features, which are the most relevant at characterizing structure and function for normal or abnormal brain conditions, would greatly benefit from multimodal neuroimage processing following the surface-based style. This style recognizes the natural definition space for such features due to the layered (surface-based) Cortex structural and functional organization. It may therefore be more sensitive and specific than the former volume-based style. The Human Connectome Project (HCP) multimodal pipelines render high-quality surface-based processing for some of the most consistently acquired neuroimaging modalities, with the quality too reliant on their precise acquisition requirements. Relevant international brain initiatives are espoused to develop an HCP-compatible neuroinformatic facility for the quality-ensured processing of international neuroimaging datasets, which may not follow the specific HCP acquisition requirements, also coined as legacy datasets. We appointed some initiatives to introduce multimodal pipelines in two HCP-compatible processing branches. a) Structural: forward- modeling with geometry (sources and head) and Lead Fields defined for legacy MEG, or EEG, in the HCP individual cortical space (Cifti) obtained from legacy MRI. Our pipeline (Ciftify-MEEG) leverages a more diverse neuroinformatic repository than the HCP structural or MEG pipelines. Ciftify-MEEG produces substantial processing illustrated here with EEG examples, incorporating alternative processing paths and corrections based on a quality control loop and upon qualitative and quantitative indicators. b) Functional: identifying spectral topographies and connectomes for the cortical oscillatory activity observed in the MEG or EEG frequency bands. We leverage a novel repository of Bayesian sparse inverse methods that target identifying the topographies and connectomes with actual statistical guarantees, the brain connectivity Variable Resolution Electromagnetic Tomographic Analysis (BC-VARETA). Our pipeline design for BC-VARETA, which we denominate Ciftify-bcVARETA, is integrated into the Ciftify- MEEG outputs and with Bayesian sparse priors structured upon Cifti space information. Ciftify-bcVARETA identification is less biased to forward models and more biased to the observations than the HCP pipeline illustrated here with topographies obtained for MEG and EEG legacy databases.</p>

Human Connectome Project

Leverage (statistics)

Modalities

10.36227/techrxiv.22276549

Cite

Citations (0)

Decomposing EEG Data into Space-Time-Frequency Components Using Parallel Factor Analysis and Its Relation with Cerebral Blood Flow

Lecture notes in computer science (2008)

Fumikazu Miwakeichi Pedro A. Valdés‐Sosa Eduardo Aubert-Vázquez Jorge Bosch‐Bayard Jobu Watanabe

SIGNAL (programming language)

10.1007/978-3-540-69158-7_83

Cite