Current Tobacco-Smoking and Neural Responses to Infant Cues in Mothers

SYNOPSIS

Objective. Smoking has a detrimental impact on maternal physical health and exposes children to secondhand smoke, but the extent to which it affects maternal brain and behavior is not well-known and may have implications for parent and child development. We examined how current smoking status might relate to maternal neural responses to infant cues using event-related potentials. Design. Current smoking (n = 35) and non-smoking (n = 35) mothers viewed photographs of emotional infant faces while electroencephalography was simultaneously recorded. Results. The latency of the face-specific N170 event-related potential component was delayed in smoking mothers compared to non-smoking mothers, and the P300 was differentiated by smoking status. Conclusions. Postpartum smoking is associated with event-related potential measures that may reflect modulation of infant face perception in motherhood.

ACKNOWLEDGMENTS

The authors wish to thank Marion Mayes, Max Greger-Moser, Jasmine Coleman, and Amanda Ng for their help with data collection as well as Cortney Booth for matching participant groups.

FUNDING

This work was supported by the NIH (NIDA) grants P01 DA022446, R01 DA026437, and R21 DA030665, as well as the Anna Freud Centre (UK), the Connecticut Mental Health Center, the National Alliance for Medical Image Computing (NA-MIC) U54 EB005149, the National Center on Addiction and Substance Abuse, and a NIMH T32 postdoctoral fellowship (MH018268). This publication was also made possible by CTSA, Grant Number UL1 RR024139, from the National Center for Research Resources, a component of the NIH, and NIH roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of any of the funding agencies.

Additional information

Funding

This work was supported by the NIH (NIDA) grants P01 DA022446, R01 DA026437, and R21 DA030665, as well as the Anna Freud Centre (UK), the Connecticut Mental Health Center, the National Alliance for Medical Image Computing (NA-MIC) U54 EB005149, the National Center on Addiction and Substance Abuse, and a NIMH T32 postdoctoral fellowship (MH018268). This publication was also made possible by CTSA, Grant Number UL1 RR024139, from the National Center for Research Resources, a component of the NIH, and NIH roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of any of the funding agencies.