Gene networks show associations with seed region connectivity

Interpretability of bi-level variable selection methods

Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities or high correlations. Here, it may be more appropriate to identify groups and their predictive members, a task that can be accomplished with bi-level selection procedures. To investigate whether such techniques lead to increased interpretability, group exponential LASSO (GEL), sparse group LASSO (SGL), composite minimax concave penalty (cMCP), and least absolute shrinkage, and selection operator (LASSO) as reference methods were used to select predictors in time-to-event, regression, and classification tasks in bootstrap samples from a cohort of 1001 patients. Different groupings based on prior knowledge, correlation structure, and random assignment were compared in terms of selection relevance, group consistency, and collinearity tolerance. The results show that bi-level selection methods are superior to LASSO in all criteria. The cMCP demonstrated superiority in selection relevance, while SGL was convincing in group consistency. An all-round capacity was achieved by GEL: the approach jointly selected correlated and content-related predictors while maintaining high selection relevance. This method seems recommendable when variables are grouped, and interpretation is of primary interest.

Molecular mechanisms underlying human spatial cognitive ability revealed with neurotransmitter and transcriptomic mapping

Mental rotation, one of the cores of spatial cognitive abilities, is closely associated with spatial processing and general intelligence. Although the brain underpinnings of mental rotation have been reported, the cellular and molecular mechanisms remain unexplored. Here, we used magnetic resonance imaging, a whole-brain spatial distribution atlas of 19 neurotransmitter receptors, transcriptomic data from Allen Human Brain Atlas, and mental rotation performances of 356 healthy individuals to identify the genetic/molecular foundation of mental rotation. We found significant associations of mental rotation performance with gray matter volume and fractional amplitude of low-frequency fluctuations in primary visual cortex, fusiform gyrus, primary sensory-motor cortex, and default mode network. Gray matter volume and fractional amplitude of low-frequency fluctuations in these brain areas also exhibited significant sex differences. Importantly, spatial correlation analyses were conducted between the spatial patterns of gray matter volume or fractional amplitude of low-frequency fluctuations with mental rotation and the spatial distribution patterns of neurotransmitter receptors and transcriptomic data, and identified the related genes and neurotransmitter receptors associated with mental rotation. These identified genes are localized on the X chromosome and are mainly involved in trans-synaptic signaling, transmembrane transport, and hormone response. Our findings provide initial evidence for the neural and molecular mechanisms underlying spatial cognitive ability.

Structural covariances of prefrontal subregions selectively associate with dopamine-related gene coexpression and schizophrenia

Evidence highlights that dopamine (DA) system dysregulation and prefrontal cortex (PFC) dysfunction may underlie the pathophysiology of schizophrenia. However, the associations among DA genes, PFC morphometry, and schizophrenia have not yet been fully clarified. Based on the brain gene expression dataset from Allen Human Brain Atlas and structural magnetic resonance imaging data (NDIS = 1727, NREP = 408), we first identified 10 out of 22 PFC subregions whose gray matter volume (GMV) covariance profiles were reliably associated with their DA genes coexpression profiles, then four out of the identified 10 PFC subregions demonstrated abnormally increased GMV covariance with the hippocampus, insula, and medial frontal areas in schizophrenia patients (NCASE = 100; NCONTROL = 102). Moreover, based on a schizophrenia postmortem expression dataset, we found that the DA genes coexpression of schizophrenia was significantly reduced between the middle frontal gyrus and hippocampus, in which 21 DA genes showed significantly unsynchronized expression changes, and the 21 genes' brain expression were enriched in brain activity invoked by working memory, reward, speech production, and episodic memory. Our findings indicate the DA genes selectively regulate the structural covariance of PFC subregions by their coexpression profiles, which may underlie the disrupted GMV covariance and impaired cognitive functions in schizophrenia.

Transcriptional signal and cell specificity of genes related to cortical structural differences of post-traumatic stress disorder

Due to the diversity of traumatic events, the diagnosis of Post-traumatic Stress Disorder is heterogeneous. The pathogenesis has been explored in the fields of brain imaging and genomics separately, but the results are inconsistent. Previous research evidenced that there existed structural differences between PTSD and healthy controls in multiple brain regions. This study further looked into the differences of brain structure in PTSD at the whole brain level and analyzed the difference-related genomes. The brain structure imaging data of 36 patients and 32 healthy controls were taken as morphological indexes. Partial least squares regression and transcriptome data were used to extract genomes related to structural differences. Additional data sets were used to study transcription characteristics of genome. Morphological differences were found in cingulate gyrus between patients and control group. Differentially expressed genes related to Morphometric similarity networks difference space were also observed. The obtained genes (i.e., RORA, PRKG1 and FKBP5) were proved to be related to the disorder with no significant correlation with other mental illnesses. In the subsequent cell type analysis, astrocytes, excitatory neurons and inhibitory neurons were evidenced to have the most significant correlation with these genes. This study found morphologically different brain regions related to PTSD. The related genome transcription analysis connects the structural differences and molecular mechanisms.

Toward Best Practices for Imaging Transcriptomics of the Human Brain

Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.

Alzheimer's genetic risk effects on cerebral blood flow across the lifespan are proximal to gene expression

Cerebrovascular dysregulation such as altered cerebral blood flow (CBF) can be observed in Alzheimer's disease (AD) and may precede symptom onset. Genome wide association studies show that AD has a polygenic aetiology, providing a tool for studying AD susceptibility across the lifespan. Here, we ascertain whether the AD genetic risk effects on CBF previously observed (Chandler et al., 2019) are also present in later life. Consistent with our prior observations, AD genetic risk score (AD-GRS) was associated with reduced CBF in the ADNI sample. The regional association between AD-GRS and CBF were also spatially similar. Furthermore, CBF was related to the regional mRNA transcript expression of AD risk genes proximal to AD-GRS risk loci. These observations suggest that AD risk alleles may reduce neurovascular process such as CBF, potentially via mechanisms such as regional expression of proximal AD risk genes as an antecedent AD pathophysiology. Our observations help establish processes that underpin AD genetic risk-related reductions in CBF as a therapeutic target prior to the onset of neurodegeneration.

Gene expression associated with human brain activations in facial expression recognition

Previous studies identified some genetic loci of emotion, but few focused on human emotion-related gene expression. In this study, the facial expression recognition (FER) task-based high-resolution fMRI data of 203 subjects in the Human Connectome Project (HCP) and expression data of the six healthy human postmortem brain tissues in the Allen Human Brain Atlas (AHBA) were used to conduct a transcriptome-neuroimaging spatial association analysis. Finally, 371 genes were identified to be significantly associated with FER-related brain activations. Enrichment analyses revealed that FER-related genes were mainly expressed in the brain, especially neurons, and might be related to cell junction organization, synaptic functions, and nervous system development regulation, indicating that FER was a complex polygenetic biological process involving multiple pathways. Moreover, these genes exhibited higher enrichment for psychiatric diseases with heavy emotion impairments. This study provided new insight into understanding the FER-related biological mechanisms and might be helpful to explore treatment methods for emotion-related psychiatric disorders.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Divergent connectomic organization delineates genetic evolutionary traits in the human brain

The relationship between human brain connectomics and genetic evolutionary traits remains elusive due to the inherent challenges in combining complex associations within cerebral tissue. In this study, insights are provided about the relationship between connectomics, gene expression and divergent evolutionary pathways from non-human primates to humans. Using in vivo human brain resting-state data, we detected two co-existing idiosyncratic functional systems: the segregation network, in charge of module specialization, and the integration network, responsible for information flow. Their topology was approximated to whole-brain genetic expression (Allen Human Brain Atlas) and the co-localization patterns yielded that neuron communication functionalities-linked to Neuron Projection-were overrepresented cell traits. Homologue-orthologue comparisons using dN/dS-ratios bridged the gap between neurogenetic outcomes and biological data, summarizing the known evolutionary divergent pathways within the Homo Sapiens lineage. Evidence suggests that a crosstalk between functional specialization and information flow reflects putative biological qualities of brain architecture, such as neurite cellular functions like axonal or dendrite processes, hypothesized to have been selectively conserved in the species through positive selection. These findings expand our understanding of human brain function and unveil aspects of our cognitive trajectory in relation to our simian ancestors previously left unexplored.

Imaging Transcriptomics of Brain Disorders

Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.

Mapping gene transcription and neurocognition across human neocortex

Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here, we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial-dorsolateral gradient of gene assemblies that separate affective and perceptual domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation and intrinsic network affiliation. In addition, this molecular-psychological signature strengthens over neurodevelopment and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.

Brain Gene Expression Pattern Correlated with the Differential Brain Activation by Pain and Touch in Humans

Genes involved in pain and touch sensations have been studied extensively, but very few studies have tried to link them with neural activities in the brain. Here, we aimed to identify genes preferentially correlated to painful activation patterns by linking the spatial patterns of gene expression of Allen Human Brain Atlas with the pain-elicited neural responses in the human brain, with a parallel, control analysis for identification of genes preferentially correlated to tactile activation patterns. We identified 1828 genes whose expression patterns preferentially correlated to painful activation patterns and 411 genes whose expression patterns preferentially correlated to tactile activation pattern at the cortical level. In contrast to the enrichment for astrocyte and inhibitory synaptic transmission of genes preferentially correlated to tactile activation, the genes preferentially correlated to painful activation were mainly enriched for neuron and opioid- and addiction-related pathways and showed significant overlap with pain-related genes identified in previous studies. These findings not only provide important evidence for the differential genetic architectures of specific brain activation patterns elicited by painful and tactile stimuli but also validate a new approach to studying pain- and touch-related genes more directly from the perspective of neural responses in the human brain.

Integration of human whole-brain transcriptome and neuroimaging data: Practical considerations of current available methods

The Allen Human Brain Atlas (AHBA) is the first example of human brain transcriptomic mappings and detailed anatomical annotations which, for the first time, has allowed the integration of human brain transcriptomics with neuroimaging. This has been made possible because the AHBA offered an original dataset that contains mRNA expression measures for >20,000 genes covering the whole brain and, critically, in a standard stereotaxic space. In recent years many different methods have been used to integrate this data set with brain imaging data, although this endeavour has lacked harmony in terms of the workflow of data processing and subsequent analyses. In this work we discuss five main issues that experience has highlighted as in need of thorough consideration when integrating the AHBA with neuroimaging. These concerns are corroborated by comparing the performance of three different publicly available methods in correlating the same measures of serotonin receptors density with the correspondent AHBA mRNA maps. In this representative case, we were able to show how these methods can lead to discrepant results, suggesting that processing options are not neutral. We believe that the field should take into serious consideration these issues as they could undermine reproducibility and, in the end, the intrinsic value of the AHBA. We also advise on possible strategies to overcome these discrepancies. Finally, we encourage authors towards practices that will improve reproducibility such as transparency in reporting, algorithm and data sharing, collaboration.

Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

Major depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

The correlation between brain structural changes in major depressive disorder (MDD) and gene expression is unclear. Here, the authors explore the correlation between cell type-specific gene expression changes and cortical structural difference in individuals with major depressive disorder.

Imaging Transcriptomics in Neurodegenerative Diseases

Imaging transcriptomics investigates the relationship between neuroanatomical/neuroimaging features and gene expression. The spatial and temporal distribution of the expressed genes and their pattern of spreading over time can contribute to elucidating cellular and molecular processes involved in neurodegeneration. In this study, we review recent findings regarding the correlation between neuroimaging and expression data in neurodegenerative diseases with a focus on Alzheimer's disease and Parkinson's disease. An association between gene expression data and different neuroimaging neurodegeneration features, such as R2 relaxation time and volumetric cortical atrophy, was established. Several positive and negative expression correlations were identified, and they confirmed the focal nature of neurodegeneration. Positively correlated genes were associated with cell motility, immune system activity, neuroinflammation, and microglia. Data from connectome studies support the hypothesis of selective network vulnerability and a temporal spreading pattern in neurodegenerative pathologies. Genes related to cellular mobility and transport are overexpressed in the neuroimaging-defined delineated areas of degeneration. In addition, expression enrichment of genes involved in immunological processes in vulnerable regions-such as the Toll-like receptor, a receptor involved in innate immunity-plays a major role in neuroinflammation in neurodegenerative diseases. However, substantial limitations must be overcome in future studies: the lack of high-quality resolution expression data, the lack of standardized study protocols, and insufficient sensitive early stage neuroimaging markers of degeneration. Identifying neuroimaging and expression prodromal biomarkers and investigating their causal relation in the preclinical disease stage may enable early targeted therapy before the onset of irreversible brain changes.

Individual-Level Identification of Gene Expression Associated with Volume Differences among Neocortical Areas

The human cerebral cortex is the source of many complex behaviors and is a vulnerable target of various neuropsychiatric disorders, but transcriptional profiles linked to cerebral cortical volume (CCV) differences across brain areas remain unknown. Here, we screened CCV-related genes using an across-sample spatial correlation analysis in 6 postmortem brains and then individually validated these correlations in 1091 subjects with different ages and ethnicities. We identified 62 genes whose transcriptional profiles were repeatedly associated with CCV in more than 90% of individuals. CCV-related genes were specifically expressed in neurons and in developmental periods from middle childhood to young adulthood, were enriched in ion channels and developmental processes, and showed significant overlap with genes linked to brain functional activity and mental disorders. The identified genes represent the conserved transcriptional architecture of the human cerebral cortex, suggesting a link between conserved gene transcription and neocortical structural properties.

Neural Transcription Correlates of Multimodal Cortical Phenotypes during Development

During development, cellular events such as cell proliferation, migration, and synaptogenesis determine the structural organization of the brain. These processes are driven in part by spatiotemporally regulated gene expression. We investigated how the genetic signatures of specific neural cell types shape cortical organization of the human brain throughout infancy and childhood. Using a transcriptional atlas and in vivo magnetic resonance imaging (MRI) data, we demonstrated time-dependent associations between the expression levels of neuronal and glial genes and cortical macro- and microstructure. Neonatal cortical phenotypes were associated with prenatal glial but not neuronal gene expression. These associations reflect cell migration and proliferation during fetal development. Childhood cortical phenotypes were associated with neuronal and astrocyte gene expression related to synaptic signaling processes, reflecting the refinement of cortical connections. These findings indicate that sequential developmental stages contribute to distinct MRI measures at different time points. This helps to bridge the gap between the genetic mechanisms driving cellular changes and widely used neuroimaging techniques.

Functional network dynamics in a neurodevelopmental disorder of known genetic origin

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9-associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data-driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting-state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

A practical guide to linking brain-wide gene expression and neuroimaging data

The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

Age-Related Changes in Topological Degradation of White Matter Networks and Gene Expression in Chronic Schizophrenia

Current hypotheses stipulate core symptoms of schizophrenia (SZ) result from the brain's incapacity to integrate neural processes. Converging diffusion magnetic resonance imaging and graph theory studies provide evidence of macrostructural alterations in SZ. However, age-related topological changes within and between white matter (WM) networks and its relationship to gene expression with disease progression remain incompletely understood. This cross-sectional study uses network modeling to investigate changes in WM network organization with disease progression in chronic SZ as well its relationship with gene expression in healthy brains. First, we replicate prior findings demonstrating altered global WM network topology in SZ. Novel results show significantly altered age-related network degradation patterns in patients compared with controls. Specifically, controls show stereotyped, linear global network decline with age. In contrast, patients show nonlinear network decline with age. Further analysis reveals lack of significant topological decline in younger adult patients, which is subsequently followed by stereotyped linear decline in older adult patients. Node-specific analyses show significant topological differences in frontal and limbic regions of younger adult patients compared with age-matched controls, which become less pronounced with age in older adult patients compared with age-matched controls. Lastly, we show several gene expression profiles, including DISC1, are associated with age-related changes in WM disconnectivity. Together, these findings provide novel WM topological and genetic evidence supporting neurodevelopmental models of SZ, suggesting that network remodeling continues throughout the third decade of life before stabilizing.

Network Neuroscience and Personality

Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.

Different Behavioral Experiences Produce Distinctive Parallel Changes in, and Correlate With, Frontal Cortex and Hippocampal Global Post-translational Histone Levels

While it is clear that behavioral experience modulates epigenetic profiles, it is less evident how the nature of that experience influences outcomes and whether epigenetic/genetic "biomarkers" could be extracted to classify different types of behavioral experience. To begin to address this question, male and female mice were subjected to either a Fixed Interval (FI) schedule of food reward, or a single episode of forced swim followed by restraint stress, or no explicit behavioral experience after which global expression levels of two activating (H3K9ac and H3K4me3) and two repressive (H3K9me2 and H3k27me3) post-translational histone modifications (PTHMs), were measured in hippocampus (HIPP) and frontal cortex (FC). The specific nature of the behavioral experience differentiated profiles of PTHMs in a sex- and brain region-dependent manner, with all 4 PTHMs changing in parallel in response to different behavioral experiences. These different behavioral experiences also modified the pattern of correlations of PTHMs both within and across FC and HIPP. Unexpectedly, highly robust correlations were found between global PTHM levels and behavioral performances, suggesting that global PTHMs may provide a higher-order pattern recognition function. Further efforts are needed to determine the generality of such findings and what characteristics of behavioral experience are critical for modulating PTHM responses.

Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral cortex

Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the atomic and subatomic levels, the sources of MR signals are well understood. However, we lack a comprehensive understanding of the macromolecular correlates of MR signal contrast. To address this gap, we used genome-wide measurements to correlate gene expression with MR signal intensity across the cerebral cortex in the Allen Human Brain Atlas (AHBA). We focused on the ratio of T1-weighted and T2-weighted intensities (T1-w/T2-w ratio image), which is considered to be a useful proxy for myelin content. As expected, we found enrichment of positive correlations between myelin-associated genes and the ratio image, supporting its use as a myelin marker. Genome-wide, there was an association with protein mass, with genes coding for heavier proteins expressed in regions with high T1-w/T2-w values. Oligodendrocyte gene markers were strongly correlated with the T1-w/T2-w ratio, but this was not driven by myelin-associated genes. Mitochondrial genes exhibit the strongest relationship, showing higher expression in regions with low T1-w/T2-w ratio. This may be due to the pH gradient in mitochondria as genes up-regulated by pH in the brain were also highly correlated with the ratio. While we corroborate associations with myelin and synaptic plasticity, differences in the T1-w/T2-w ratio across the cortex are more strongly linked to molecule size, oligodendrocyte markers, mitochondria, and pH. We evaluate correlations between AHBA transcriptomic measurements and a group averaged T1-w/T2-w ratio image, showing agreement with in-sample results. Expanding our analysis to the whole brain results in strong positive T1-w/T2-w correlations for immune system, inflammatory disease, and microglia marker genes. Genes with negative correlations were enriched for neuron markers and synaptic plasticity genes. Lastly, our findings are similar when performed on T1-w or inverted T2-w intensities alone. These results provide a molecular characterization of MR contrast that will aid interpretation of future MR studies of the brain.

Developmental Lead and/or Prenatal Stress Exposures Followed by Different Types of Behavioral Experience Result in the Divergence of Brain Epigenetic Profiles in a Sex, Brain Region, and Time-Dependent Manner: Implications for Neurotoxicology

Over a lifetime, early developmental exposures to neurocognitive risk factors, such as lead (Pb) exposures and prenatal stress (PS), will be followed by multiple varied behavioral experiences. Pb, PS and behavioral experience can each influence brain epigenetic profiles. Our recent studies show a greater level of complexity, however, as all three factors interact within each sex to generate differential adult variation in global post-translational histone modifications (PTHMs), which may result in fundamentally different consequences for life-long learning and behavioral function. We have reported that PTHM profiles differ by sex, brain region and time point of measurement following developmental exposures to Pb±PS, resulting in different profiles for each unique combination of these parameters. Imposing differing behavioral experience following developmental Pb±PS results in additional divergence of PTHM profiles, again in a sex, brain region and time-dependent manner, further increasing complexity. Such findings underscore the need to link highly localized and variable epigenetic changes along single genes to the highly-integrated brain functional connectome that is ultimately responsible for governing behavioral function. Here we advance the idea that increased understanding may be achieved through iterative reductionist and holistic approaches. Implications for experimental design of animal studies of developmental exposures to neurotoxicants include the necessity of a 'no behavioral experience' group, given that epigenetic changes in response to behavioral testing can confound effects of the neurotoxicant itself. They also suggest the potential utility of the inclusion of salient behavioral experiences as a potential effect modifier in epidemiological studies.