Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Current perspectives on perinatal mental health and neurobehavioral development: focus on regulation, coregulation and self-regulation

PURPOSE OF REVIEW

Perinatal mental health research provides an important perspective on neurobehavioral development. Here, we aim to review the association of maternal perinatal health with offspring neurodevelopment, providing an update on (self-)regulation problems, hypothesized mechanistic pathways, progress and challenges, and implications for mental health.

RECENT FINDINGS

(1) Meta-analyses confirm that maternal perinatal mental distress is associated with (self-)regulation problems which constitute cognitive, behavioral, and affective social-emotional problems, while exposure to positive parental mental health has a positive impact. However, effect sizes are small. (2) Hypothesized mechanistic pathways underlying this association are complex. Interactive and compensatory mechanisms across developmental time are neglected topics. (3) Progress has been made in multiexposure studies. However, challenges remain and these are shared by clinical, translational and public health sciences. (4) From a mental healthcare perspective, a multidisciplinary and system level approach employing developmentally-sensitive measures and timely treatment of (self-)regulation and coregulation problems in a dyadic caregiver-child and family level approach seems needed. The existing evidence-base is sparse.

SUMMARY

During the perinatal period, addressing vulnerable contexts and building resilient systems may promote neurobehavioral development. A pluralistic approach to research, taking a multidisciplinary approach to theoretical models and empirical investigation needs to be fostered.

Fetal influence on the human brain through the lifespan

Human fetal development has been associated with brain health at later stages. It is unknown whether growth in utero, as indexed by birth weight (BW), relates consistently to lifespan brain characteristics and changes, and to what extent these influences are of a genetic or environmental nature. Here we show remarkably stable and lifelong positive associations between BW and cortical surface area and volume across and within developmental, aging and lifespan longitudinal samples (N = 5794, 4-82 y of age, w/386 monozygotic twins, followed for up to 8.3 y w/12,088 brain MRIs). In contrast, no consistent effect of BW on brain changes was observed. Partly environmental effects were indicated by analysis of twin BW discordance. In conclusion, the influence of prenatal growth on cortical topography is stable and reliable through the lifespan. This early-life factor appears to influence the brain by association of brain reserve, rather than brain maintenance. Thus, fetal influences appear omnipresent in the spacetime of the human brain throughout the human lifespan. Optimizing fetal growth may increase brain reserve for life, also in aging.

Population Neuroscience: Principles and Advances

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.

Clinical decisions in fetal-neonatal neurology II: Gene-environment expression over the first 1000 days presenting as "four great neurological syndromes"

Interdisciplinary fetal-neonatal neurology (FNN) training considers a woman's reproductive and pregnancy health histories when assessing the "four great neonatal neurological syndromes". This maternal-child dyad exemplifies the symptomatic neonatal minority, compared with the silent majority of healthy children who experience preclinical diseases with variable expressions over the first 1000 days. Healthy maternal reports with reassuring fetal surveillance testing preceded signs of fetal distress during parturition. An encephalopathic neonate with seizures later exhibited childhood autistic spectrum behaviors and intractable epilepsy correlated with identified genetic biomarkers. A systems biology approach to etiopathogenesis guides the diagnostic process to interpret phenotypic form and function. Evolving gene-environment interactions expressed by changing phenotypes reflect a dynamic neural exposome influenced by reproductive and pregnancy health. This strategy considers critical/sensitive periods of neuroplasticity beyond two years of life to encompass childhood and adolescence. Career-long FNN experiences reenforce earlier training to strengthen the cognitive process and minimize cognitive biases when assessing children or adults. Prioritizing social determinants of healthcare for persons with neurologic disorders will help mitigate the global burden of brain diseases for all women and children.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

Intrauterine growth and the tangential expansion of the human cerebral cortex in times of food scarcity and abundance

Tangential growth of the human cerebral cortex is driven by cell proliferation during the first and second trimester of pregnancy. Fetal growth peaks in mid-gestation. Here, we explore how genes associated with fetal growth relate to cortical growth. We find that both maternal and fetal genetic variants associated with higher birthweight predict larger cortical surface area. The relative dominance of the maternal vs. fetal variants in these associations show striking variations across birth years (1943 to 1966). The birth-year patterns vary as a function of the epigenetic status near genes differentially methylated in individuals exposed (or not) to famine during the Dutch Winter of 1944/1945. Thus, it appears that the two sets of molecular processes contribute to early cortical development to a different degree in times of food scarcity or its abundance.

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.

Tracking Development of Connectivity in the Human Brain: Axons and Dendrites

The neuron doctrine laid the foundation for our current thinking about the structural and functional organization of the human brain. With the basic units of the nervous system-neurons-being physically separate, their connectivity relies on the conduction of action potentials in axons and their transmission across the synaptic cleft to the dendrites of other neurons. This study reviews available ex vivo data about the cellular composition of the human cerebral cortex, focusing on axons and dendrites, to conceptualize biological sources of signals detected in vivo with magnetic resonance imaging. To bridge the gap between ex vivo and in vivo observations, I then explain the basic principles of virtual histology, an approach that integrates spatially cell- or process-specific transcriptomic data with magnetic resonance signals to facilitate their neurobiological interpretation. Finally, I provide an overview of the initial insights gained in this manner in studies of brain development and maturation, in both health and disease.

A Bio-Social Model during the First 1000 Days Optimizes Healthcare for Children with Developmental Disabilities

Most children with developmental disabilities (DD) live in resource-limited countries (LMIC) or high-income country medical deserts (HICMD). A social contract between healthcare providers and families advocates for accurate diagnoses and effective interventions to treat diseases and toxic stressors. This bio-social model emphasizes reproductive health of women with trimester-specific maternal and pediatric healthcare interactions. Lifelong neuronal connectivity is more likely established across 80% of brain circuitries during the first 1000 days. Maladaptive gene-environment (G x E) interactions begin before conception later presenting as maternal-placental-fetal (MPF) triad, neonatal, or childhood neurologic disorders. Synergy between obstetrical and pediatric healthcare providers can reduce neurologic morbidities. Partnerships between healthcare providers and families should begin during the first 1000 days to address diseases more effectively to moderate maternal and childhood adverse effects. This bio-social model lowers the incidence and lessens the severity of sequalae such as DD. Access to genetic-metabolomic, neurophysiologic and neuroimaging evaluations enhances clinical decision-making for more effective interventions before full expression of neurologic dysfunction. Diagnostic accuracy facilitates developmental interventions for effective preschool planning. A description of a mother-child pair in a HIC emphasizes the time-sensitive importance for early interventions that influenced brain health throughout childhood. Partnership by her parents with healthcare providers and educators provided effective healthcare and lessened adverse effects. Effective educational interventions were later offered through her high school graduation. Healthcare disparities in LMIC and HICMD require that this bio-social model of care begin before the first 1000 days to effectively treat the most vulnerable women and children. Prioritizing family planning followed by prenatal, neonatal and child healthcare improves wellness and brain health. Familiarity with educational neuroscience for teachers applies neurologic diagnoses for effective individual educational plans. Integrating diversity and inclusion into medical and educational services cross socioeconomic, ethnic, racial, and cultural barriers with life-course benefits. Families require knowledge to recognize risks for their children and motivation to sustain relationships with providers and educators for optimal outcomes. The WHO sustainable development goals promote brain health before conception through the first 1000 days. Improved education, employment, and social engagement for all persons will have intergenerational and transgenerational benefits for communities and nations.