Quantitative assessment of cerebral connectivity deficiency and cognitive impairment in children with prenatal alcohol exposure

Lin Gao (Corresponding Author), Celso Grebogi, Ying-Cheng Lai, Julia Stephen, Tongsheng Zhang, Yuanli Li, Haipeng Ren, Dichen Li, Jue Wang, Bjoern Schelter, Linda Sommerlade

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4 Citations (Scopus)
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It is common sense that alcohol consumption during pregnancy would cause cognitive impairment in children. However, recent works suggested the risk of drinking during pregnancy may have been exaggerated. It is a critical issue to determine whether and up to which amount the consumption of alcohol will affect the cognitive development of children. We evaluate time-varying functional connectivity using magnetoencephalogram data from somatosensory evoked response experiments for nineteen teenage subjects with prenatal alcohol exposure and twenty-one healthy control teenage subjects using a new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling. Children exposed to alcohol prenatally are at risk of developing a Fetal Alcohol Spectrum Disorder (FASD) characterized by cerebral connectivity deficiency and impaired cognitive abilities. Through a comparison study of teenage subjects exposed to alcohol prenatally with healthy control subjects, we establish that the inter-hemispheric connectivity is deficient for the former, which may lead to disruption in cortical inter-hemispheric connectivity and deficits in higher order cognitive functions as measured by e.g., IQ test. We provide quantitative evidence that the disruption is correlated with cognitive deficits. These findings could lead to a novel, highly sensitive biomarker for FASD, and support a recommendation of no safe amount of alcohol consumption during pregnancy.
Original languageEnglish
Article number041101
Issue number4
Early online date2 Apr 2019
Publication statusPublished - Apr 2019

Bibliographical note

We would like to thank the patients, their parents, and technicians for their participation in this study. This research was supported by the National Natural Science Foundation (Grant No. 61601361), the Natural Science Foundation of Shaanxi Province in China (Grant No. 2017JM6013), the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing (Contract No. SKL2017CP07), the Xi’an University of Technology, and the National Institutes of Health (NIH) grants (J. Stephen and T. Zhang—Grant Nos. P20AA017068, NCRR P20RR021938, NIGMS P20GM103472, and 1P50AA022534).


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