Abstract
The goal of this study is to identify the quantitative electroencephalographic (qEEG) signature of early childhood malnutrition [protein-energy malnutrition (PEM)]. To this end, archival digital EEG recordings of 108 participants in the Barbados Nutrition Study (BNS) were recovered and cleaned of artifacts (46 children who suffered an episode of PEM limited to the first year of life) and 62 healthy controls). The participants of the still ongoing BNS were initially enrolled in 1973, and EEGs for both groups were recorded in 1977–1978 (at 5–11 years). Scalp and source EEG Z-spectra (to correct for age effects) were obtained by comparison with the normative Cuban Human Brain Mapping database. Differences between both groups in the z spectra (for all electrode locations and frequency bins) were assessed by t-tests with thresholds corrected for multiple comparisons by permutation tests. Four clusters of differences were found: (a) increased theta activity (3.91–5.86 Hz) in electrodes T4, O2, Pz and in the sources of the supplementary motor area (SMA); b) decreased alpha1 (8.59–8.98 Hz) in Fronto-central electrodes and sources of widespread bilateral prefrontal are; (c) increased alpha2 (11.33–12.50 Hz) in Temporo-parietal electrodes as well as in sources in Central-parietal areas of the right hemisphere; and (d) increased beta (13.67–18.36 Hz), in T4, T5 and P4 electrodes and decreased in the sources of bilateral occipital-temporal areas. Multivariate Item Response Theory of EEGs scored visually by experts revealed a neurophysiological latent variable which indicated excessive paroxysmal and focal abnormality activity in the PEM group. A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under ROC curves (AUC). Thus, qEEG differentiate between the two nutrition groups (PEM vs Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83). Since PEM is a global public health problem with lifelong neurodevelopmental consequences, our finding of consistent differences between PEM and controls, both in qualitative and quantitative EEG analysis, suggest that this technology may be a source of scalable and affordable biomarkers for assessing the long-term brain impact of early PEM.
Highlights
Reviewed by: Zhimin Song, Emory University, United States Santiago Canals, Instituto de Neurociencias de Alicante (IN), Spain
A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under receiving operating curves (ROC) curves (AUC). Quantitative electroencephalography (qEEG) differentiate between the two nutrition groups (PEM vs. Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83)
All data and programs for used in this study will be available via the open source portal CBRAIN3 (Sherif et al, 2014). This is the first study to identify quantitative EEG in individuals who suffered from Protein-energy malnutrition (PEM) during the first year of life
Summary
Reviewed by: Zhimin Song, Emory University, United States Santiago Canals, Instituto de Neurociencias de Alicante (IN), Spain. There are several earlier studies using visual inspection of the EEG (Stoch and Smythe, 1967; Baraitser and Evans, 1969) which found marked differences (e.g., slower theta, less alpha) in malnourished children compared to controls These EEG patterns were later confirmed by computerized studies of the EEG spectrum (Bartel et al, 1979; Robinson et al, 1995), using different methodologies (quiet sleep and photic stimulation versus awake condition, no stimulation). Significant problems in cognitive and behavioral function, soft neurological signs and health outcomes have been documented in childhood, adolescence and middle adulthood following a history of PEM (Peter et al, 2016; Waber et al, 2016) Using these EEG data, we have been afforded a unique opportunity to evaluate the sensitivity of both qEEG and qEEGt analysis. We carried out analyses of the visual inspection of the EEG to identify the predictive value of our quantitative analyses as compared with traditional visual EEG clinical evaluation
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