Neonatal infant EEG bursts are altered by prenatal maternal depression and serotonin selective reuptake inhibitor use

Prenatal depression outcomes in the next generation: A critical review of recent DOHaD studies and recommendations for future research

Prenatal depression, a common pregnancy-related risk with a prevalence of 10-20 %, may affect in utero development and socioemotional and neurodevelopmental outcomes in the next generation. Although there is a growing body of work that suggests prenatal depression has an independent and long-lasting effect on offspring outcomes, important questions remain, and findings often do not converge. The present review examines work carried out in the last decade, with an emphasis on studies focusing on mechanisms and leveraging innovative technologies and study designs to fill in gaps in research. Overall, the past decade of research continues to suggest that prenatal depression increases risk for offspring socioemotional problems and may alter early brain development by affecting maternal-fetal physiology during pregnancy. However, important limitations remain; lack of diversity in study samples, inconsistent consideration of potential confounders (e.g., genetics, postnatal depression, parenting), and restriction of examination to narrow time windows and single exposures. On the other hand, exciting work has begun uncovering potential mechanisms underlying transmission, including alterations in mitochondria functioning, epigenetics, and the prenatal microbiome. We review the evidence to date, identify limitations, and suggest strategies for the next decade of research to detect mechanisms as well as sources of plasticity and resilience to ensure this work translates into meaningful, actionable science that improves the lives of families.

Depression signal correlation identification from different EEG channels based on CNN feature extraction

Depression is a mental illness and can even lead to suicide if not be diagnosed and treated. Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to extract the features from all the multimodal channel information . In order to simplify the diagnose process and detect clinical depression, the EEG channels with strong depression information should be identified firstly. Therefore, a depression signal correlation identification method based on convolutional neural network (CNN) is proposed. In the method, the labeled multi-channel EEG is used as data. The EEG signals of each channel are divided into neural network training data set and these data is trained by AlexNet network. Then the correlation classification of each channel for depression is identified based on the trained sample. Accuracy and loss functions are used to evaluate classification index.Conversely, the correlation is lower. An experiments is conducted and the results show that the correlation is not consistent. A few of channels are strongly correlated with depression, such as 13, 17, 28, 40, 46, 66 and 69. These EEG channels are selected to diagnose depression.

Early brain activity: Translations between bedside and laboratory

Neural activity is both a driver of brain development and a readout of developmental processes. Changes in neuronal activity are therefore both the cause and consequence of neurodevelopmental compromises. Here, we review the assessment of neuronal activities in both preclinical models and clinical situations. We focus on issues that require urgent translational research, the challenges and bottlenecks preventing translation of biomedical research into new clinical diagnostics or treatments, and possibilities to overcome these barriers. The key questions are (i) what can be measured in clinical settings versus animal experiments, (ii) how do measurements relate to particular stages of development, and (iii) how can we balance practical and ethical realities with methodological compromises in measurements and treatments.

Prenatal antidepressant exposure and sex differences in neonatal corpus callosum microstructure

Prenatal exposure to selective serotonin reuptake inhibitor (SSRI) antidepressants may influence white matter (WM) development, as previous studies report widespread microstructural alterations and reduced interhemispheric connectivity in SSRI-exposed infants. In rodents, perinatal SSRIs had sex-specific disruptions in corpus callosum (CC) axon architecture and connectivity; yet it is unknown whether SSRI-related brain outcomes in humans are sex specific. In this study, the neonate CC was selected as a region-of-interest to investigate whether prenatal SSRI exposure has sex-specific effects on early WM microstructure. On postnatal day 7, diffusion tensor imaging was used to assess WM microstructure in SSRI-exposed (n = 24; 12 male) and nonexposed (n = 48; 28 male) term-born neonates. Fractional anisotropy was extracted from CC voxels and a multivariate discriminant analysis was used to identify latent patterns differing between neonates grouped by SSRI-exposure and sex. Analysis revealed localized variations in CC fractional anisotropy that significantly discriminated neonate groups and correctly predicted group membership with an 82% accuracy. Such effects were identified across three dimensions, representing sex differences in SSRI-exposed neonates (genu, splenium), SSRI-related effects independent of sex (genu-to-rostral body), and sex differences in nonexposed neonates (isthmus-splenium, posterior midbody). Our findings suggest that CC microstructure may have a sex-specific, localized, developmental sensitivity to prenatal SSRI exposure.

Delta brushes are not just a hallmark of EEG in human preterm infants

The delta brush, a well-known characteristic waveform of the human preterm electroencephalogram, represents spontaneous electrical activity. Recent experimental animal model evidence suggests that delta brushes are not only spontaneous intrinsic activity but are also evoked by external sensory stimulation or spontaneous movement. They are also likely to reflect the activity of subplate neurons, which play an important role in early brain development and network organization. Here, evidence about delta brushes in human preterm electroencephalogram is provided along with future perspectives.