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Lopez Naranjo C, Razzaq FA, Li M, Wang Y, Bosch‐Bayard JF, Lindquist MA, Gonzalez Mitjans A, Garcia R, Rabinowitz AG, Anderson SG, Chiarenza GA, Calzada‐Reyes A, Virues‐Alba T, Galler JR, Minati L, Bringas Vega ML, Valdes‐Sosa PA. EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition. Hum Brain Mapp 2024; 45:e26698. [PMID: 38726908 PMCID: PMC11082925 DOI: 10.1002/hbm.26698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.
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Affiliation(s)
- Carlos Lopez Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Hangzhou Dianzi UniversityZhejiangHangzhouChina
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | | | - Anisleidy Gonzalez Mitjans
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Montreal Neurological Institute‐HospitalMcGill UniversityMontrealQuebecCanada
| | - Ronaldo Garcia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | - Simon G. Anderson
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health ResearchUniversity of the West IndiesCave HillBarbados
| | - Giuseppe A. Chiarenza
- Centro Internazionale Disturbi di Apprendimento, Attenzione, Iperattività (CIDAAI)MilanItaly
| | | | | | - Janina R. Galler
- Division of Pediatric Gastroenterology and NutritionMassachusetts General Hospital for ChildrenBostonMassachusettsUSA
| | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Center for Mind/Brain Science (CIMeC)University of TrentoTrentoItaly
| | - Maria L. Bringas Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Center for NeuroscienceLa HabanaCuba
| | - Pedro A. Valdes‐Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Center for NeuroscienceLa HabanaCuba
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Roger K, Vannasing P, Tremblay J, Bringas Vega ML, Bryce CP, Rabinowitz A, Valdes-Sosa PA, Galler JR, Gallagher A. Early childhood malnutrition impairs adult resting brain function using near-infrared spectroscopy. Front Hum Neurosci 2024; 17:1287488. [PMID: 38298205 PMCID: PMC10827877 DOI: 10.3389/fnhum.2023.1287488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/15/2023] [Indexed: 02/02/2024] Open
Abstract
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.
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Affiliation(s)
- Kassandra Roger
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Phetsamone Vannasing
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Julie Tremblay
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Maria L. Bringas Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Arielle Rabinowitz
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Pedro Antonio Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Janina R. Galler
- Division of Pediatric Gastroenterology and Nutrition, MassGeneral Hospital for Children, Boston, MA, United States
| | - Anne Gallagher
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
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Razzaq FA, Calzada-Reyes A, Tang Q, Guo Y, Rabinowitz AG, Bosch-Bayard J, Galan-Garcia L, Virues-Alba T, Suarez-Murias C, Miranda I, Riaz U, Bernardo Lagomasino V, Bryce C, Anderson SG, Galler JR, Bringas-Vega ML, Valdes-Sosa PA. Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline. Front Neurosci 2023; 17:1149102. [PMID: 37781256 PMCID: PMC10540225 DOI: 10.3389/fnins.2023.1149102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 07/18/2023] [Indexed: 10/03/2023] Open
Abstract
Objective This study compares the complementary information from semi-quantitative EEG (sqEEG) and spectral quantitative EEG (spectral-qEEG) to detect the life-long effects of early childhood malnutrition on the brain. Methods Resting-state EEGs (N = 202) from the Barbados Nutrition Study (BNS) were used to examine the effects of protein-energy malnutrition (PEM) on childhood and middle adulthood outcomes. sqEEG analysis was performed on Grand Total EEG (GTE) protocol, and a single latent variable, the semi-quantitative Neurophysiological State (sqNPS) was extracted. A univariate linear mixed-effects (LME) model tested the dependence of sqNPS and nutritional group. sqEEG was compared with scores on the Montreal Cognitive Assessment (MoCA). Stable sparse classifiers (SSC) also measured the predictive power of sqEEG, spectral-qEEG, and a combination of both. Multivariate LME was applied to assess each EEG modality separately and combined under longitudinal settings. Results The univariate LME showed highly significant differences between previously malnourished and control groups (p < 0.001); age (p = 0.01) was also significant, with no interaction between group and age detected. Childhood sqNPS (p = 0.02) and adulthood sqNPS (p = 0.003) predicted MoCA scores in adulthood. The SSC demonstrated that spectral-qEEG combined with sqEEG had the highest predictive power (mean AUC 0.92 ± 0.005). Finally, multivariate LME showed that the combined spectral-qEEG+sqEEG models had the highest log-likelihood (-479.7). Conclusion This research has extended our prior work with spectral-qEEG and the long-term impact of early childhood malnutrition on the brain. Our findings showed that sqNPS was significantly linked to accelerated cognitive aging at 45-51 years of age. While sqNPS and spectral-qEEG produced comparable results, our study indicated that combining sqNPS and spectral-qEEG yielded better performance than either method alone, suggesting that a multimodal approach could be advantageous for future investigations. Significance Based on our findings, a semi-quantitative approach utilizing GTE could be a valuable diagnostic tool for detecting the lasting impacts of childhood malnutrition. Notably, sqEEG has not been previously explored or reported as a biomarker for assessing the longitudinal effects of malnutrition. Furthermore, our observations suggest that sqEEG offers unique features and information not captured by spectral quantitative EEG analysis and could lead to its improvement.
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Affiliation(s)
- Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | | | | | | | - Ileana Miranda
- National Center for Animal and Plant Health, CENSA, San José de las Lajas, Mayabeque, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Cyralene Bryce
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
| | - Simon G. Anderson
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
| | - Janina R. Galler
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
- Division of Pediatric Gastroenterology and Nutrition, MassGeneral Hospital for Children, Boston, MA, United States
| | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, La Habana, Cuba
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Roger K, Vannasing P, Tremblay J, Bringas Vega ML, Bryce CP, Rabinowitz AG, Valdés-Sosa PA, Galler JR, Gallagher A. Impact of Early Childhood Malnutrition on Adult Brain Function: An Evoked-Related Potentials Study. Front Hum Neurosci 2022; 16:884251. [PMID: 35845242 PMCID: PMC9283562 DOI: 10.3389/fnhum.2022.884251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
More than 200 million children under the age of 5 years are affected by malnutrition worldwide according to the World Health Organization. The Barbados Nutrition Study (BNS) is a 55-year longitudinal study on a Barbadian cohort with histories of moderate to severe protein-energy malnutrition (PEM) limited to the first year of life and a healthy comparison group. Using quantitative electroencephalography (EEG), differences in brain function during childhood (lower alpha1 activity and higher theta, alpha2 and beta activity) have previously been highlighted between participants who suffered from early PEM and controls. In order to determine whether similar differences persisted into adulthood, our current study used recordings obtained during a Go-No-Go task in a subsample of the original BNS cohort [population size (N) = 53] at ages 45-51 years. We found that previously malnourished adults [sample size (n) = 24] had a higher rate of omission errors on the task relative to controls (n = 29). Evoked-Related Potentials (ERP) were significantly different in participants with histories of early PEM, who presented with lower N2 amplitudes. These findings are typically associated with impaired conflict monitoring and/or attention deficits and may therefore be linked to the attentional and executive function deficits that have been previously reported in this cohort in childhood and again in middle adulthood.
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Affiliation(s)
- Kassandra Roger
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Phetsamone Vannasing
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Julie Tremblay
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
| | - Maria L. Bringas Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | - Pedro A. Valdés-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Janina R. Galler
- Division of Pediatric Gastroenterology and Nutrition, MassGeneral Hospital for Children, Boston, MA, United States
| | - Anne Gallagher
- LION Lab, Sainte-Justine University Hospital Research Center, University of Montreal, Montreal, QC, Canada
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Bringas Vega ML, Pedroso Ibáñez I, Razzaq FA, Zhang M, Morales Chacón L, Ren P, Galan Garcia L, Gan P, Virues Alba T, Lopez Naranjo C, Jahanshahi M, Bosch-Bayard J, Valdes-Sosa PA. The Effect of Neuroepo on Cognition in Parkinson's Disease Patients Is Mediated by Electroencephalogram Source Activity. Front Neurosci 2022; 16:841428. [PMID: 35844232 PMCID: PMC9280298 DOI: 10.3389/fnins.2022.841428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/30/2022] [Indexed: 11/14/2022] Open
Abstract
We report on the quantitative electroencephalogram (qEEG) and cognitive effects of Neuroepo in Parkinson's disease (PD) from a double-blind safety trial (https://clinicaltrials.gov/, number NCT04110678). Neuroepo is a new erythropoietin (EPO) formulation with a low sialic acid content with satisfactory results in animal models and tolerance in healthy participants and PD patients. In this study, 26 PD patients were assigned randomly to Neuroepo (n = 15) or placebo (n = 11) groups to test the tolerance of the drug. Outcome variables were neuropsychological tests and resting-state source qEEG at baseline and 6 months after administering the drug. Probabilistic Canonical Correlation Analysis was used to extract latent variables for the cognitive and for qEEG variables that shared a common source of variance. We obtained canonical variates for Cognition and qEEG with a correlation of 0.97. Linear Mixed Model analysis showed significant positive dependence of the canonical variate cognition on the dose and the confounder educational level (p = 0.003 and p = 0.02, respectively). Additionally, in the mediation equation, we found a positive dependence of Cognition with qEEG for (p = < 0.0001) and with dose (p = 0.006). Despite the small sample, both tests were powered over 89%. A combined mediation model showed that 66% of the total effect of the cognitive improvement was mediated by qEEG (p = 0.0001), with the remaining direct effect between dose and Cognition (p = 0.002), due to other causes. These results suggest that Neuroepo has a positive influence on Cognition in PD patients and that a large portion of this effect is mediated by brain mechanisms reflected in qEEG.
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Affiliation(s)
- Maria L. Bringas Vega
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- International Center of Neurological Restoration (CIREN), La Habana, Cuba
| | | | - Fuleah A. Razzaq
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Zhang
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Peng Ren
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Peng Gan
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Carlos Lopez Naranjo
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Marjan Jahanshahi
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jorge Bosch-Bayard
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Pedro A. Valdes-Sosa
- Ministry of Education (MOE) Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
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6
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Li M, Wang Y, Lopez-Naranjo C, Hu S, Reyes RCG, Paz-Linares D, Areces-Gonzalez A, Hamid AIA, Evans AC, Savostyanov AN, Calzada-Reyes A, Villringer A, Tobon-Quintero CA, Garcia-Agustin D, Yao D, Dong L, Aubert-Vazquez E, Reza F, Razzaq FA, Omar H, Abdullah JM, Galler JR, Ochoa-Gomez JF, Prichep LS, Galan-Garcia L, Morales-Chacon L, Valdes-Sosa MJ, Tröndle M, Zulkifly MFM, Abdul Rahman MRB, Milakhina NS, Langer N, Rudych P, Koenig T, Virues-Alba TA, Lei X, Bringas-Vega ML, Bosch-Bayard JF, Valdes-Sosa PA. Harmonized-Multinational qEEG norms (HarMNqEEG). Neuroimage 2022; 256:119190. [PMID: 35398285 DOI: 10.1016/j.neuroimage.2022.119190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/05/2022] [Indexed: 12/14/2022] Open
Abstract
This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.
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Affiliation(s)
- Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiang Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | | | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - Aini Ismafairus Abd Hamid
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alexander N Savostyanov
- Humanitarian Institute, Novosibirsk State University, Novosibirsk 630090, Russia; Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | | | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany; Center for Stroke Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos A Tobon-Quintero
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Research Department, Institución Prestadora de Servicios de Salud IPS Universitaria, Colombia
| | - Daysi Garcia-Agustin
- Cuban Center for Neurocience, La Habana, Cuba; The Cuban center aging longevity and health, Havana Cuba
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | | | - Faruque Reza
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hazim Omar
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Universiti Sains Malaysia Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Massachusetts General Hospital for Children, Boston, MA, United States Massachusetts General Hospital for Children, Boston, MA, United States
| | - John F Ochoa-Gomez
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Leslie S Prichep
- Research & Development, BrainScope Company, Inc. Bethesda, MD, United States; Department of Psychiatry (Ret.), Brain Research Laboratories, NYU School of Medicine, New York, NY, United States
| | | | - Lilia Morales-Chacon
- Department of Clinical Neurophysiology, International Center for Neurological Restoration, Playa, Havana 11300, Cuba
| | | | - Marius Tröndle
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Mohd Faizal Mohd Zulkifly
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Muhammad Riddha Bin Abdul Rahman
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
| | - Natalya S Milakhina
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | - Nicolas Langer
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Pavel Rudych
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Department of Information Technologies Novosibirsk State University, Novosibirsk 630090, Russia; Federal Research Center for Information and Computational Technologies, Biomedical Data Processing Lab, Novosibirsk 630090, Russia
| | - Thomas Koenig
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | | | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
| | - Jorge F Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada.
| | - Pedro Antonio Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
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7
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Bosch-Bayard J, Razzaq FA, Lopez-Naranjo C, Wang Y, Li M, Galan-Garcia L, Calzada-Reyes A, Virues-Alba T, Rabinowitz AG, Suarez-Murias C, Guo Y, Sanchez-Castillo M, Rogers K, Gallagher A, Prichep L, Anderson SG, Michel CM, Evans AC, Bringas-Vega ML, Galler JR, Valdes-Sosa PA. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity. Neuroimage 2022; 254:119144. [PMID: 35342003 DOI: 10.1016/j.neuroimage.2022.119144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 02/07/2023] Open
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.
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Affiliation(s)
- Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | | | - Arielle G Rabinowitz
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Kassandra Rogers
- LION Lab, Sainte-Justine University Hospital Research Centre, University of Montreal, Montreal, QC, Canada
| | - Anne Gallagher
- LION Lab, Sainte-Justine University Hospital Research Centre, University of Montreal, Montreal, QC, Canada
| | | | - Simon G Anderson
- Caribbean Institute for Health Research, University of the West Indies, Barbados
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Mucosal Immunology and Biology Research Center, Mass General Hospital for Children, Boston, MA, USA
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada; Cuban Neuroscience Center, La Habana, Cuba.
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8
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Morton SU, Leyshon BJ, Tamilia E, Vyas R, Sisitsky M, Ladha I, Lasekan JB, Kuchan MJ, Grant PE, Ou Y. A Role for Data Science in Precision Nutrition and Early Brain Development. Front Psychiatry 2022; 13:892259. [PMID: 35815018 PMCID: PMC9259898 DOI: 10.3389/fpsyt.2022.892259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Multimodal brain magnetic resonance imaging (MRI) can provide biomarkers of early influences on neurodevelopment such as nutrition, environmental and genetic factors. As the exposure to early influences can be separated from neurodevelopmental outcomes by many months or years, MRI markers can serve as an important intermediate outcome in multivariate analyses of neurodevelopmental determinants. Key to the success of such work are recent advances in data science as well as the growth of relevant data resources. Multimodal MRI assessment of neurodevelopment can be supplemented with other biomarkers of neurodevelopment such as electroencephalograms, magnetoencephalogram, and non-imaging biomarkers. This review focuses on how maternal nutrition impacts infant brain development, with three purposes: (1) to summarize the current knowledge about how nutrition in stages of pregnancy and breastfeeding impact infant brain development; (2) to discuss multimodal MRI and other measures of early neurodevelopment; and (3) to discuss potential opportunities for data science and artificial intelligence to advance precision nutrition. We hope this review can facilitate the collaborative march toward precision nutrition during pregnancy and the first year of life.
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Affiliation(s)
- Sarah U Morton
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | | | - Eleonora Tamilia
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Rutvi Vyas
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - Michaela Sisitsky
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - Imran Ladha
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States
| | | | | | - P Ellen Grant
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States.,Department of Radiology, Boston Children's Hospital, Boston, MA, United States
| | - Yangming Ou
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, United States.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States.,Department of Radiology, Boston Children's Hospital, Boston, MA, United States
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9
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Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective. Neuroimage 2021; 231:117828. [PMID: 33549754 DOI: 10.1016/j.neuroimage.2021.117828] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/28/2021] [Accepted: 01/31/2021] [Indexed: 02/08/2023] Open
Abstract
Approximately one in five children worldwide suffers from childhood malnutrition and its complications, including increased susceptibility to inflammation and infectious diseases. Due to improved early interventions, most of these children now survive early malnutrition, even in low-resource settings (LRS). However, many continue to exhibit neurodevelopmental deficits, including low IQ, poor school performance, and behavioral problems over their lifetimes. Most studies have relied on neuropsychological tests, school performance, and mental health and behavioral measures. Few studies, in contrast, have assessed brain structure and function, and to date, these have mainly relied on low-cost techniques, including electroencephalography (EEG) and evoked potentials (ERP). The use of more advanced methods of neuroimaging, including magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), has been limited by cost factors and lack of availability of these technologies in developing countries, where malnutrition is nearly ubiquitous. This report summarizes the current state of knowledge and evidence gaps regarding childhood malnutrition and the study of its impact on neurodevelopment. It may help to inform the development of new strategies to improve the identification, classification, and treatment of neurodevelopmental disabilities in underserved populations at the highest risk for childhood malnutrition.
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10
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Tahta A, Turgut YB, Sahin C. Malnutrition Essentials for Neurologists and Neurosurgeons: A Review of the Literature. JOURNAL OF PEDIATRIC NEUROLOGY 2021. [DOI: 10.1055/s-0040-1721852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractMalnutrition still causes deaths in the world today and protein energy malnutrition (PEM) is characterized by increased oxidative stress, immune deficiency, and development of various infections. Even today, however, it is an underrecognized and undertreated entity in neurology and neurosurgery. In this article, we therefore seek to review the available literature regarding various factors affecting surgical outcome of children with malnutrition undergoing some neurosurgical interventions including shunt surgery and traumatic brain injury in intensive care unit, in addition to its effects upon oxidative stress status and immunity. Furthermore, we attempt to provide essential knowledge of malnutrition affecting surgical outcome of patients with PEM. Based on available evidence in the published literature, it is concluded that it is a serious public health problem characterized by increased oxidative stress, immune deficiency, and development of various infections.
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Affiliation(s)
- Alican Tahta
- Department of Neurosurgery, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Yasar B. Turgut
- Department of Internal Medicine, Faculty of Medicine, Mugla Sitki Kocman University, Mugla, Turkey
| | - Cem Sahin
- Department of Internal Medicine, Faculty of Medicine, Mugla Sitki Kocman University, Mugla, Turkey
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11
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Bosch-Bayard J, Galan L, Aubert Vazquez E, Virues Alba T, Valdes-Sosa PA. Resting State Healthy EEG: The First Wave of the Cuban Normative Database. Front Neurosci 2020; 14:555119. [PMID: 33335467 PMCID: PMC7736237 DOI: 10.3389/fnins.2020.555119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 11/09/2020] [Indexed: 12/02/2022] Open
Affiliation(s)
- Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China.,McGill Centre for Integrative Neurosciences MCIN, Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Cuban Neuroscience Center, La Habana, Cuba
| | | | | | | | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China.,Cuban Neuroscience Center, La Habana, Cuba
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12
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Bringas Vega ML, Guo Y, Tang Q, Razzaq FA, Calzada Reyes A, Ren P, Paz Linares D, Galan Garcia L, Rabinowitz AG, Galler JR, Bosch-Bayard J, Valdes Sosa PA. An Age-Adjusted EEG Source Classifier Accurately Detects School-Aged Barbadian Children That Had Protein Energy Malnutrition in the First Year of Life. Front Neurosci 2019; 13:1222. [PMID: 31866804 PMCID: PMC6905178 DOI: 10.3389/fnins.2019.01222] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 10/29/2019] [Indexed: 01/22/2023] Open
Abstract
We have identified an electroencephalographic (EEG) based statistical classifier that correctly distinguishes children with histories of Protein Energy Malnutrition (PEM) in the first year of life from healthy controls with 0.82% accuracy (area under the ROC curve). Our previous study achieved similar accuracy but was based on scalp quantitative EEG features that precluded anatomical interpretation. We have now employed BC-VARETA, a novel high-resolution EEG source imaging method with minimal leakage and maximal sparseness, which allowed us to identify a classifier in the source space. The EEGs were recorded in 1978 in a sample of 108 children who were 5-11 years old and were participants in the 45+ year longitudinal Barbados Nutrition Study. The PEM cohort experienced moderate-severe PEM limited to the first year of life and were age, handedness and gender-matched with healthy classmates who served as controls. In the current study, we utilized 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 rhythms that increase with normal maturation. Our findings indicate that the PEM group showed a significant decrease in alpha activity, suggestive of a delay in brain development. Childhood malnutrition is still a serious worldwide public health problem and its consequences are particularly severe when present during early life. Deficits during this critical period are permanent and predict impaired cognitive and behavioral functioning later in life. Our EEG source classifier may provide a functionally interpretable diagnostic technology to study the effects of early childhood malnutrition on the brain, and may have far-reaching applicability in low resource settings.
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Affiliation(s)
- Maria L. Bringas Vega
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Peng Ren
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Deirel Paz Linares
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | | | | | - Janina R. Galler
- Division of Pediatric Gastroenterology and Nutrition, Massachusetts General Hospital for Children, Boston, MA, United States
| | - Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Pedro A. Valdes Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
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13
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Léveillé P, Knoth IS, Denis MH, Morin G, Barlaam F, Nyalendo C, Daneault C, Marcotte JE, Rosiers CD, Ferland G, Lippé S, Mailhot G. Association between fat-soluble nutrient status and auditory and visual related potentials in newly diagnosed non-screened infants with cystic fibrosis: A case-control study. Prostaglandins Leukot Essent Fatty Acids 2019; 150:21-30. [PMID: 31568924 DOI: 10.1016/j.plefa.2019.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 08/28/2019] [Accepted: 09/04/2019] [Indexed: 11/26/2022]
Abstract
Nutritional deficiencies often precede the diagnosis of cystic fibrosis (CF) in infants, and occur at a stage where the rapidly developing brain is more vulnerable to insult. We aim to compare fat-soluble nutrient status of newly diagnosed non-screened infants with CF to that of healthy infants, and explore the association with neurodevelopment evaluated by electroencephalography (EEG). Our results show that CF infants had lower levels of all fat-soluble vitamins and docosahexaenoic acid (DHA) compared to controls. The auditory evoked potential responses were higher in CF compared to controls whereas the visual components did not differ between groups. DHA levels were correlated with auditory evoked potential responses. Although resting state frequency power was similar between groups, we observed a negative correlation between DHA levels and low frequencies. This study emphasizes the need for long-term neurodevelopmental follow-up of CF infants and pursuing intervention strategies in the future.
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Affiliation(s)
- Pauline Léveillé
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada; Department of Psychology, Université de Montréal, Montréal, Quebec, H3T 1C5, Canada
| | - Inga-Sophia Knoth
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada; Department of Psychology, Université de Montréal, Montréal, Quebec, H3T 1C5, Canada
| | - Marie-Hélène Denis
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada
| | - Geneviève Morin
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada
| | - Fanny Barlaam
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada; Department of Psychology, Université de Montréal, Montréal, Quebec, H3T 1C5, Canada
| | - Carine Nyalendo
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada; Department of Clinical Biochemistry, Université de Montréal, Montréal, Quebec, H3T 1C5, Canada
| | - Caroline Daneault
- Montreal Heart Institute Research Centre, Montréal, Quebec H1T 1C8, Canada
| | | | - Christine Des Rosiers
- Montreal Heart Institute Research Centre, Montréal, Quebec H1T 1C8, Canada; Department of Nutrition, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada
| | - Guylaine Ferland
- Montreal Heart Institute Research Centre, Montréal, Quebec H1T 1C8, Canada; Department of Nutrition, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada
| | - Sarah Lippé
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada; Department of Psychology, Université de Montréal, Montréal, Quebec, H3T 1C5, Canada
| | - Geneviève Mailhot
- Research Centre of Sainte-Justine University Health Center, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada; Department of Nutrition, Université de Montréal, Montreal, Quebec, H3T 1C5, Canada.
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