Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks

Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies have predominately focused on brain functional connectivity (i.e. Fisher z-score), which requires manual processing steps for feature extraction from fMRI images. Deep learning approaches (i.e., Convolutional Neural Networks) have achieved remarkable success on learning directly from image data, yet have not been applied on fetal fMRI for understanding fetal neurodevelopment. Here, we bridge this gap by applying a novel application of deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data. Specifically, we test a supervised CNN framework as a data-driven approach to isolate variation in fMRI signals that relate to younger v.s. older fetal age groups. Based on the learned CNN, we further perform sensitivity analysis to identify brain regions in which changes in BOLD signal are strongly associated with fetal brain age. The findings demonstrate that deep CNNs are a promising approach for identifying spontaneous functional patterns in fetal brain activity that discriminate age groups. Further, we discovered that regions that most strongly differentiate groups are largely bilateral, share similar distribution in older and younger age groups, and are areas of heightened metabolic activity in early human development.Comment: 9 page

BDNF Genotype Modulates Resting Functional Connectivity in Children

A specific polymorphism of the brain-derived neurotrophic factor (BDNF) gene is associated with alterations in brain anatomy and memory; its relevance to the functional connectivity of brain networks, however, is unclear. Given that altered hippocampal function and structure has been found in adults who carry the methionine (met) allele of the BDNF gene and the molecular studies elucidating the role of BDNF in neurogenesis and synapse formation, we examined the association between BDNF gene variants and neural resting connectivity in children and adolescents. We observed a reduction in hippocampal and parahippocampal to cortical connectivity in met-allele carriers within both default-mode and executive networks. In contrast, we observed increased connectivity to amygdala, insula and striatal regions in met-carriers, within the paralimbic network. Because of the known association between the BDNF gene and neuropsychiatric disorder, this latter finding of greater connectivity in circuits important for emotion processing may indicate a new neural mechanism through which these gene-related psychiatric differences are manifest. Here we show that the BDNF gene, known to regulate synaptic plasticity and connectivity in the brain, affects functional connectivity at the neural systems level. In addition, we demonstrate that the spatial topography of multiple high-level resting state networks in healthy children and adolescents is similar to that observed in adults

Frontal alpha asymmetry in response to stressor moderates the relation between parenting hassles and child externalizing problems

Inequitable urban environments are associated with toxic stress and altered neural social stress processing that threatens the development of self-regulation. Some children in these environments struggle with early onset externalizing problems that are associated with a variety of negative long-term outcomes. While previous research has linked parenting daily hassles to child externalizing problems, the role of frontal alpha asymmetry (FAA) as a potential modifier of this relationship has scarcely been explored. The present study examined mother-child dyads, most of whom were living in low socioeconomic status households in an urban environment and self-identified as members of racial minority groups. Analyses focused on frustration task electroencephalography (EEG) data from 67 children (mean age = 59.0 months, SD = 2.6). Mothers reported the frequency of their daily parenting hassles and their child’s externalizing problems. Frustration task FAA moderated the relationship between parenting daily hassles and child externalizing problems, but resting FAA did not. More specifically, children with left frontal asymmetry had more externalizing problems as their mothers perceived more hassles in their parenting role, but parenting hassles and externalizing problems were not associated among children with right frontal asymmetry. These findings lend support to the motivational direction hypothesis and capability model of FAA. More generally, this study reveals how individual differences in lateralization of cortical activity in response to a stressor may confer differential susceptibility to child behavioral problems with approach motivation (i.e., left frontal asymmetry) predicting externalizing problems under conditions of parental stress

Automated brain masking of fetal functional MRI with open data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing

An impairment in sniffing contributes to the olfactory impairment in Parkinson's disease

Although the presence of an olfactory impairment in Parkinson's disease (PD) has been recognized for 25 years, its cause remains unclear. Here we suggest a contributing factor to this impairment, namely, that PD impairs active sniffing of odorants. We tested 10 men and 10 women with clinically typical PD, and 20 age- and gender-matched healthy controls, in four olfactory tasks: (i) the University of Pennsylvania smell identification test; (ii and iii) detection threshold tests for the odorants vanillin and propionic acid; and (iv) a two-alternative forced-choice detection paradigm during which sniff parameters (airflow peak rate, mean rate, volume, and duration) were recorded with a pneomatotachograph-coupled spirometer. An additional experiment tested the effect of intentionally increasing sniff vigor on olfactory performance in 20 additional patients. PD patients were significantly impaired in olfactory identification (P < 0.0001) and detection (P < 0.007). As predicted, PD patients were also significantly impaired at sniffing, demonstrating significantly reduced sniff airflow rate (P < 0.01) and volume (P < 0.002). Furthermore, a patient's ability to sniff predicted his or her performance on olfactory tasks, i.e., the more poorly patients sniffed, the worse their performance on olfaction tests (P < 0.009). Finally, increasing sniff vigor improved olfactory performance in those patients whose baseline performance had been poorest (P < 0.05). These findings implicate a sniffing impairment as a component of the olfactory impairment in PD and further depict sniffing as an important component of human olfaction

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

Child sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas

What Is a Representative Brain? Neuroscience Meets Population Science

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain–behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective—population neuroscience—that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas

Behavioral Coping Phenotypes and Associated Psychosocial Outcomes of Pregnant and Postpartum Women During the COVID-19 Pandemic

The impact of COVID-19-related stress on perinatal women is of heightened public health concern given the established intergenerational impact of maternal stress-exposure on infants and fetuses. There is urgent need to characterize the coping styles associated with adverse psychosocial outcomes in perinatal women during the COVID-19 pandemic to help mitigate the potential for lasting sequelae on both mothers and infants. This study uses a data-driven approach to identify the patterns of behavioral coping strategies that associate with maternal psychosocial distress during the COVID-19 pandemic in a large multicenter sample of pregnant women (N = 2876) and postpartum women (N = 1536). Data was collected from 9 states across the United States from March to October 2020. Women reported behaviors they were engaging in to manage pandemic-related stress, symptoms of depression, anxiety and global psychological distress, as well as changes in energy levels, sleep quality and stress levels. Using latent profile analysis, we identified four behavioral phenotypes of coping strategies. Critically, phenotypes with high levels of passive coping strategies (increased screen time, social media, and intake of comfort foods) were associated with elevated symptoms of depression, anxiety, and global psychological distress, as well as worsening stress and energy levels, relative to other coping phenotypes. In contrast, phenotypes with high levels of active coping strategies (social support, and self-care) were associated with greater resiliency relative to other phenotypes. The identification of these widespread coping phenotypes reveals novel behavioral patterns associated with risk and resiliency to pandemic-related stress in perinatal women. These findings may contribute to early identification of women at risk for poor long-term outcomes and indicate malleable targets for interventions aimed at mitigating lasting sequelae on women and children during the COVID-19 pandemic

Toward understanding the impact of trauma on the early developing human brain

Boletín informativo que reseña un paisaje seleccionado entre los inscritos en el Registro de Paisajes de Interés Cultural de Andalucía y ofrece información en materia de Paisaje Cultural