Tracking Development of the Corpus Callosum in Fetal and Early Postnatal Baboons Using Magnetic Resonance Imaging

Although the maturation of the corpus callosum (CC) can serve as a sensitive marker for normative antenatal and postnatal brain development, little is known about its development across this critical period. While high-resolution magnetic resonance imaging can provide an opportunity to examine normative brain development in humans, concerns remain over the exposure of developing fetuses to non-essential imaging. Nonhuman primates can provide a valuable model for normative brain maturation. Baboons share several important developmental characteristics with humans, including a highly orchestrated pattern of cerebral development. Developmental changes in total CC area and its subdivisions were examined across the antenatal (weeks 17 – 26 of 28 weeks total gestation) and early postnatal (to week 32) period in baboons (Papio hamadryas anubis). Thirteen fetal and sixteen infant baboons were studied using high-resolution MRI. During the period of primary gyrification, the total area of the CC increased by a magnitude of five. By postnatal week 32, the total CC area attained only 51% of the average adult area. CC subdivisions showed non-uniform increases in area, throughout development. The splenium showed the most maturation by postnatal week 32, attaining 55% of the average adult value. The subdivisions of the genu and anterior midbody showed the least maturation by postnatal week 32, attaining 50% and 49% of the average adult area. Thus, the CC of baboons shows continued growth past the postnatal period. These age-related changes in the developing baboon CC are consistent with the developmental course in humans

Brain-wide versus genome-wide vulnerability biomarkers for severe mental illnesses

Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual\u27s brain-wide similarity to the expected SMI patterns derived from meta-analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual\u27s similarity to genome-wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI-MDD (Cohen\u27s d = 0.20, p = 1 × 1

Initial Incidence of White Matter Hyperintensities on MRI in Astronauts

Introduction: Previous literature has described the increase in white matter hyperintensity (WMH) burden associated with hypobaric exposure in the U-2 and altitude chamber operating personnel. Although astronauts have similar hypobaric exposure pressures to the U2 pilot population, astronauts have far fewer exposures and each exposure would be associated with a much lower level of decompression stress due to rigorous countermeasures to prevent decompression sickness. Therefore, we postulated that the WMH burden in the astronaut population would be less than in U2 pilots. Methods: Twenty-one post-flight de-identified astronaut MRIs (5 mm slice thickness FLAIR sequences) were evaluated for WMH count and volume. The only additional data provided was an age range of the astronauts (43-57) and if they had ever performed an EVA (13 yes, 8 no). Results: WMH count in these 21 astronaut MRI was 21.0 +/- 24.8 (mean+/- SD) and volume was 0.382 +/- 0.602 ml, which was significantly higher than previously published results for the U2 pilots. No significant differences between EVA and no EVA groups existed. Age range of astronaut population is not directly comparable to the U2 population. Discussion: With significantly less frequent (sometimes none) and less stressful hypobaric exposures, yet a much higher incidence of increased WMH, this indicates the possibility of additional mechanisms beyond hypobaric exposure. This increase unlikely to be attributable just to the differences in age between astronauts and U2 pilots. Forward work includes continuing review of post-flight MRI and evaluation of pre to post flight MRI changes if available. Data mining for potential WMH risk factors includes collection of age, sex, spaceflight experience, EVA hours, other hypobaric exposures, hyperoxic exposures, radiation, high performance aircraft experience and past medical history. Finally, neurocognitive and vision/eye results will be evaluated for any evidence of impairment linked to increased WMH

Regional vulnerability indices in youth with persistent and distressing psychoticlike experiences

IMPORTANCE: Distressing and persistent psychoticlike experiences (PLEs) in youth are associated with greater odds of developing psychiatric conditions in adulthood. Despite this risk, it is unclear whether early PLEs show similar brain patterns compared with adults with psychiatric and neurologic conditions. OBJECTIVE: To examine the degree to which persistent and distressing PLEs exhibit neural metrics that show similarity to adults with chronic psychiatric and neurologic conditions. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used Adolescent Brain Cognitive Development (ABCD) Study examining the persistence and distress associated with PLEs across the first 3 waves of data with baseline structural magnetic resonance imaging data. Analyzed data were collected between September 1, 2016, and September 27, 2021. Children were recruited from 21 research sites across the US. EXPOSURES: Psychoticlike experiences were assessed using the Prodromal Questionnaire-Brief Child Version, and participants were categorized into groups based on the persistence and distress associated with PLEs. MAIN OUTCOMES AND MEASURES: Cortical and subcortical regional vulnerability indices (RVIs) were created by quantifying the similarity of participants\u27 baseline neuroimaging measures to the expected patterns found in adult neuropsychiatric samples. The PLE groups were compared on the following RVI cortical and subcortical metrics: schizophrenia spectrum disorders, bipolar disorder, major depressive disorder, Parkinson disease, Alzheimer disease, and metabolic diseases. RESULTS: Analyses examined PLE groups created from 8242 children in the ABCD sample (52.5% male; mean [SD] age, 9.93 [0.63] years; and 56.3% White), including persistent distressing PLEs (n = 329), transient distressing PLEs (n = 396), persistent nondistressing PLEs (n = 234), transient nondistressing PLEs (n = 390), and low distressing PLEs (n = 6893) groups. Participants with persistent or transient distressing PLEs broadly showed increased subcortical RVI scores across most RVI metrics, with persistent distressing PLEs additionally showing increased scores for cortical RVI metrics. The greatest effect sizes were found for persistent distressing PLEs with cortical RVI-schizophrenia spectrum disorders (β estimate, 1.055; 95% CI, 0.326-1.786) and RVI-Alzheimer disease (β estimate, 2.473; 95% CI, 0.930-4.018). CONCLUSIONS AND RELEVANCE: In this cohort study of ABCD participants, the findings suggest that especially the persistent distressing PLEs in children were associated with neural metrics resembling those observed in adults with severe psychiatric and neurologic conditions. These findings support the potential use of brain-based risk scores for early identification and precision medicine approaches in the assessment of PLEs

Mapping Primary Gyrogenesis During Fetal Development in Primate Brains: High-Resolution in Utero Structural MRI of Fetal Brain Development in Pregnant Baboons

The global and regional changes in the fetal cerebral cortex in primates were mapped during primary gyrification (PG; weeks 17–25 of 26 weeks total gestation). Studying pregnant baboons using high-resolution MRI in utero, measurements included cerebral volume, cortical surface area, gyrification index and length and depth of 10 primary cortical sulci. Seven normally developing fetuses were imaged in two animals longitudinally and sequentially. We compared these results to those on PG that from the ferret studies and analyzed them in the context of our recent studies of phylogenetics of cerebral gyrification. We observed that in both primates and non-primates, the cerebrum undergoes a very rapid transformation into the gyrencephalic state, subsequently accompanied by an accelerated growth in brain volume and cortical surface area. However, PG trends in baboons exhibited some critical differences from those observed in ferrets. For example, in baboons, the growth along the long (length) axis of cortical sulci was unrelated to the growth along the short (depth) axis and far outpaced it. Additionally, the correlation between the rate of growth along the short sulcal axis and heritability of sulcal depth was negative and approached significance (r = −0.60; p < 0.10), while the same trend for long axis was positive and not significant (p = 0.3; p = 0.40). These findings, in an animal that shares a highly orchestrated pattern of PG with humans, suggest that ontogenic processes that influence changes in sulcal length and depth are diverse and possibly driven by different factors in primates than in non-primates

Multiple Imputation Method for High-Dimensional Neuroimaging Data

Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is therefore crucial to conduct appropriate statistical methods to address this issue. While multiple imputation is a popular technique for handling missing data, its application to neuroimaging data is hindered by high dimensionality and complex dependence structures of multivariate neuroimaging variables. To tackle this challenge, we propose a novel approach, named High Dimensional Multiple Imputation (HIMA), based on Bayesian models. HIMA develops a new computational strategy for sampling large covariance matrices based on a robustly estimated posterior mode, which drastically enhances computational efficiency and numerical stability. To assess the effectiveness of HIMA, we conducted extensive simulation studies and real-data analysis using neuroimaging data from a Schizophrenia study. HIMA showcases a computational efficiency improvement of over 2000 times when compared to traditional approaches, while also producing imputed datasets with improved precision and stability.Comment: 13 pages, 5 figure

Fast and powerful heritability inference for family-based neuroimaging studies.

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study

Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression

The joint analysis of multimodal neuroimaging data is critical in the field of brain research because it reveals complex interactive relationships between neurobiological structures and functions. In this study, we focus on investigating the effects of structural imaging (SI) features, including white matter micro-structure integrity (WMMI) and cortical thickness, on the whole brain functional connectome (FC) network. To achieve this goal, we propose a network-based vector-on-matrix regression model to characterize the FC-SI association patterns. We have developed a novel multi-level dense bipartite and clique subgraph extraction method to identify which subsets of spatially specific SI features intensively influence organized FC sub-networks. The proposed method can simultaneously identify highly correlated structural-connectomic association patterns and suppress false positive findings while handling millions of potential interactions. We apply our method to a multimodal neuroimaging dataset of 4,242 participants from the UK Biobank to evaluate the effects of whole-brain WMMI and cortical thickness on the resting-state FC. The results reveal that the WMMI on corticospinal tracts and inferior cerebellar peduncle significantly affect functional connections of sensorimotor, salience, and executive sub-networks with an average correlation of 0.81 (p<0.001).Comment: 20 pages, 5 figures, 2 table

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis : a pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/)