Holz and coauthors clarify the influence of maternal smoking during pregnancy on the neural circuitry of response inhibition and its association with related behavioral phenotypes such as attention-deficit/hyperactivity disorder and novelty seeking in the mother’s offspring.
Gaysina et al examine the relationship between maternal smoking during pregnancy and offspring
conduct problems among children reared by genetically related and genetically unrelated mothers.
Three studies using distinct but complementary research designs were used. Possible covariates were
controlled for in the analyses. See the Editorial by Slotkin.
This case-control study investigates the contributions of glutamate and γ-aminobutyric acid to mismatch negativity and digit sequencing task performance, an assessment of verbal working memory, in schizophrenia.
This study uses data from the Nurses’ Health Study to estimate the association between social integration and suicide.
Chiappelli et al investigate whether the level of KYNA changes following psychological stress and whether this change is associated with stress-related behavior. Javitt provides commentary in a related editorial.
Nicotine-dependent smokers exhibit craving and brain activation in the prefrontal and limbic regions when presented with cigarette-related cues. Bupropion hydrochloride treatment reduces cue-induced craving in cigarette smokers; however, the mechanism by which bupropion exerts this effect has not yet been described.
To assess changes in regional brain activation in response to cigarette-related cues from before to after treatment with bupropion (vs placebo).
Randomized, double-blind, before-after controlled trial.
Academic brain imaging center.
Thirty nicotine-dependent smokers (paid volunteers).
Participants were randomly assigned to receive 8 weeks of treatment with either bupropion or a matching placebo pill (double-blind).
Subjective cigarette craving ratings and regional brain activations (blood oxygen level-dependent response) in response to viewing cue videos.
Bupropion-treated participants reported less craving and exhibited reduced activation in the left ventral striatum, right medial orbitofrontal cortex, and bilateral anterior cingulate cortex from before to after treatment when actively resisting craving compared with placebo-treated participants. When resisting craving, reduction in self-reported craving correlated with reduced regional brain activation in the bilateral medial orbitofrontal and left anterior cingulate cortices in all participants.
Treatment with bupropion is associated with improved ability to resist cue-induced craving and a reduction in cue-induced activation of limbic and prefrontal brain regions, while a reduction in craving, regardless of treatment type, is associated with reduced activation in prefrontal brain regions.
Although psychiatric disorders are, to date, diagnosed on the basis of behavioral symptoms and course of illness, the interest in neurobiological markers of psychiatric disorders has grown substantially in recent years. However, current classification approaches are mainly based on data from a single biomarker, making it difficult to predict disorders characterized by complex patterns of symptoms.
To integrate neuroimaging data associated with multiple symptom-related neural processes and demonstrate their utility in the context of depression by deriving a predictive model of brain activation.
Two groups of participants underwent functional magnetic resonance imaging during 3 tasks probing neural processes relevant to depression.
Participants were recruited from the local population by use of advertisements; participants with depression were inpatients from the Department of Psychiatry, Psychosomatics, and Psychotherapy at the University of Wuerzburg, Wuerzburg, Germany.
We matched a sample of 30 medicated, unselected patients with depression by age, sex, smoking status, and handedness with 30 healthy volunteers.
Accuracy of single-subject classification based on whole-brain patterns of neural responses from all 3 tasks.
Integrating data associated with emotional and affective processing substantially increases classification accuracy compared with single classifiers. The predictive model identifies a combination of neural responses to neutral faces, large rewards, and safety cues as nonredundant predictors of depression. Regions of the brain associated with overall classification comprise a complex pattern of areas involved in emotional processing and the analysis of stimulus features.
Our method of integrating neuroimaging data associated with multiple, symptom-related neural processes can provide a highly accurate algorithm for classification. The integrated biomarker model shows that data associated with both emotional and reward processing are essential for a highly accurate classification of depression. In the future, large-scale studies will need to be conducted to determine the practical applicability of our algorithm as a biomarker-based diagnostic aid.