Quantitative EEG Tomography of Early Childhood Malnutrition

EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition

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.

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

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.

Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline

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.

Impact of Early Childhood Malnutrition on Adult Brain Function: An Evoked-Related Potentials Study

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.

The Effect of Neuroepo on Cognition in Parkinson's Disease Patients Is Mediated by Electroencephalogram Source Activity

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.

Harmonized-Multinational qEEG norms (HarMNqEEG)

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.

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

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.

A Role for Data Science in Precision Nutrition and Early Brain Development

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.

Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective

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.

Malnutrition Essentials for Neurologists and Neurosurgeons: A Review of the Literature

Malnutrition 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.

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

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.

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

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.