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However, current efforts would set an upper bound on which genetic correlations would be detected so genetic correlations identified in larger samples must be smaller than those currently observed. Primary causal environmental influences in the absence of genetic effects, for example infection, are driving the observed brain structural differences. However, though environmental influences have been shown to have effects on brain structure and function, , a primary causal role for these influences in schizophrenia is still difficult to establish. Rare variation contributing to schizophrenia risk that is unmeasured in these common variant association studies could be driving the observed brain structural differences.
This is unlikely, given that common variation, when considered in aggregate, is the greatest contributor to risk for schizophrenia in the population and even individuals harboring a rare mutation also have a polygenic common variant burden. Developmental fetal and infant imaging perhaps would detect genetic correlations unobserved in adults. Given the intimate relationship between brain structure and function, this seems unlikely. Finally, brain changes that predispose to schizophrenia risk happen at the cellular or subcellular level and do not manifest at the gross level at which brain images are taken with MRI.
Genetic association studies continue to identify both common and rare variants that impact brain structures at the gross anatomical level. While such macroscale imaging is valuable for identifying some affected brain regions and subregions, a typical MRI voxel comprises a brain volume of roughly 1 cubic millimeter: a space that may contain tens of thousands of neurons and millions of synapses.
Understanding the cellular and molecular impact of genetic variation demands imaging on microscopic and ultrascopic scales Fig. For example, if an allele at a particular variant is strongly associated with reduced cortical thickness as measured by MRI in an adult, there are many cellular and circuit changes that could lead to this macroscale alteration.
Is the total number of cortical cells reduced, or are they more densely packed? Do neurons in this region have fewer dendritic arborizations?
Are relative contributions of specific cell types altered? Is synaptic density or structure disrupted?
Is the spatial architecture and pattern of connectivity within the structure impacted? None of these questions can be adequately addressed with MRI. We expect that data illuminating these features will be critical to further map causal pathways from genetic risk loci to neuropsychiatric dysfunction, and also may explain why genetic correlations are not observed at gross brain structural levels with some neuropsychiatric disorders. Measuring phenotypes manifested at the cellular and molecular levels, closer to the effects of a genetic risk factor on the causal chain, will likely be more mechanistically informative and have higher effect sizes — though until association studies are conducted, we will not know the effect sizes.
Most techniques involve a series of immersions in chemical solvents that dehydrate the sample, dissolve away lipids, and induce chemical modifications to create a uniform refractive index, thus minimizing destructive interference. Brain banks are therefore a key resource in order to accomplish this goal. There are of course some barriers to completing such a study.
Third, collecting large sample sizes will likely involve the concerted efforts of multiple different brain banks working together, which can introduce technical variation. Fourth, tissue clearing allows labeling most molecules assuming an antibody or probe exists and is able to diffuse within the tissue with specific expression in a cell type.
It is often not clear which specific cell types are driving an imaging genetic association, so it may be difficult to design the experiment labeling proteins of interest.
When trying to explain the specific cell types underlying an observed gross brain structural hit, the proximity on the genome or functional evidence linking to a specific gene may guide cell types to probe. This could lead to future hypotheses about cell fates, spatial architecture, and density.
Fifth, tissue clearing and light sheet microscopy are not currently possible within an entire intact human brain. Given that cortical thickness in humans is on average 2. For example, using a nuclear label for sparse inhibitory neurons in the cortex will be much easier and more accurate to segment than all nuclei.
Raw data for a sample size of donors for the cortical wall with four different types of cells labeled would be TB alone, not including copies of data made in downstream processing. Clearly, large computational resources would be needed to tackle this problem. When important phenotypes occur in small subcellular structures, such as synapses, electron microscopy techniques that can resolve ultrastructures may be more appropriate, and are discussed below.
Such ultrastructural phenotypes will be difficult to capture with gross anatomical imaging or optical microscopy. Scanning electron microscopy SEM focuses and sweeps a beam of electrons across a fixed and dehydrated biological sample.
As the electron beam interacts with molecules within the sample, emitted electrons are detected to create a micrograph image with lateral pixel resolution down to 3. Volumetric ultrascale imaging techniques could be employed to understand the underlying subcellular influences of genetic variation associated with gross brain structure, and may identify novel genetic associations to structural phenotypes that are undetectable using tools that are limited to measuring gross brain structure.
As an example, let us revisit the study of Sekar et al. Similarly, SBFSEM could probe cellular hypotheses to explain genetic effects on the volumes of particular brain areas. The volume of a brain structure could be affected by changes in cell size, the density of neuronal processes, rearrangement of spatial architecture, or a mixture of these effects. All of these possibilities could be investigated with ultrascale electron microscopy on samples from the brain region of interest. There are again limitations to performing such a study, some of which we detail here. The impressive saturated reconstructions by Kasthuri et al.
With such detailed ultrastructural images, data storage and downstream processing present technical bottlenecks. Further, we hope that appraisal of both the promise and limitations associated with these approaches can accelerate their development and eventual application. Microscale and ultrascale imaging genetics will likely identify genetic influences on cellular density, number, arrangement, and synaptic connections that may be able to both explain the cellular basis of gross imaging genetics associations as well as identifying novel associations to brain structure.
Many loci in the genome have a replicable association with risk for neuropsychiatric disorders. To understand how variation at these loci leads to alterations in cognition and behavior, we need to understand the cell types, developmental time periods, brain regions, and biological processes impacted by those variants. To do this, we can map webs of QTLs between genetic variation and multiple endophenotypes leading to disorder symptoms. We provide examples of successful integration of multiple lines of genetic association data to explain the basis of genetic risk for other complex traits, like obesity and diabetes.
Indeed, MRI measurements have demonstrated significant genetic correlations between certain brain structures and ADHD, major depressive disorder, bipolar disorder, and Alzheimer's disease, though the causality of these effects remains to be confirmed. Subsequent modulation of endophenotypes along a causal chain with experiments in model systems can validate the downstream effects of those genetic variants.
Layering multiple levels of genetic association with imaging data and experimental validation will generate important mechanistic connections that can illuminate previously dimly lit causal pathways creating risk for neuropsychiatric illness.
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Abstract Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. From genetic association to mechanism. In Step 2, genetic association with endophenotypes chromatin accessibility, gene expression, and brain structure are used to infer causal pathways leading to risk for a disorder. In Step 3, experimental manipulations in human or animal model systems are used to validate mechanistic hypotheses. Schizophrenia, one of major psychiatric disorders, is thought to be caused by the complicated interaction between genetic and environmental factors.
However, genetic factors with large effective sizes have not been identified. Accumulating evidence suggest that various somatic mutations in the neuronal genome have important roles in the normal physiology of brain. It is possible that altered frequency and pattern of somatic mutations are closely related to the pathogenesis and pathophysiology of psychiatric disorders. In particular, retrotransposon LINE-1 is known to be activated in neural progenitor cells, and somatic new retrotransposition is occurring at this stage. We found that the copy number of LINE-1 was increased in the postmortem brains of patients with schizophrenia, and new insertions have occurred in genes important for neuronal functions.
However, molecular mechanism of increased LINE-1 copy number and causal relationship with schizophrenia remained unclear.
In this project, we try to clarify what kind of brain neurons, neural circuits, and brain regions are affected by retrotransposition using the multiscale approaches. We then revalidate by genomic analysis of target brain cells, circuit and regions using postmortem brain samples. We will develop next-generation trans-omics technologies that allow characterization of psychiatric disorders as multilayered molecular networks.
Current trans-omics technologies are capable of reconstruction of networks that span across "fast" omics layers such as metabolome and phosphoproteome that change in minutes scale. Therefore, in this study plan, we will develop next-generation trans-omics technologies that connect the omics layers of metabolism and gene expression beyond the difference of time scale. With the next-generation technologies, molecular basis of various diseases and biological phenomena including psychiatric disorders, which cannot be handled with the current trans-omics technologies, can be characterized in terms of network reconstruction.
In cooperation with other groups, we apply the next-generation technologies to network reconstruction of various nervous system cells such as patient-derived iPS cells and cultured neural progenitor cells. Then, we provide hypotheses on molecular basis i. Furthermore, we identify the core part of the multi-layered network, and find responsible molecule candidates by analyzing the mathematical model in silico. We employ theoretical and modeling approaches to understand multiscale phenomena involved in mental diseases.
In particular, synaptic abnormalities are commonly observed in multiple model animals of mental disease. We utilize computational modeling to bridge synaptic abnormalities, circuit dynamics, and the behavioral output resulting from them. Recent studies have identified that spines undergo constant remodeling even in the absence of spiking and calcium activity of neurons, and that a few model animals of mental disease exhibit abnormal intrinsic spine dynamics.
However, it is unknown how the intrinsic spine dynamics interact with Hebbian synaptic plasticity, which is thought to be the mechanism of learning and memory. It is often assumed that Hebbian synaptic plasticity forms a cell assembly, a mutually interacting group of neurons that encodes memory. However, in recurrently connected networks with pure Hebbian plasticity, cell assemblies typically diverge or fade under ongoing changes of synaptic strength. Previously proposed mechanisms for stabilizing cell assemblies do not robustly reproduce the experimentally reported unimodal and long-tailed distribution of synaptic strength.
Here, we study the role of both normal and abnormal intrinsic spine dynamics in learning and memory. Specifically, we explore how Hebbian plasticity with experimentally observed intrinsic spine dynamics affects the stability of cell assemblies, the distribution of spine volume, and learning performance. Bipolar disorder and schizophrenia are two major mental disorders that cause severe social burden, and there is urgent need to clarify their etiology.
These diseases share common features, such as role of genetic factors and environmental factors during developmental period. Indeed, individual genes and environmental factors that confer a risk are shared by these disorders. Although a number of hypotheses have been proposed in these disorders, they have focused on a single layer of pathophysiological architecture, and understanding of each layer such as molecular, cellular, neural circuit and brain levels, is not connected with that of other layers, which hampers constructive and integrative understanding of these mental disorders as a multiscale phenomenon.
In this study, we aim at constructive understanding of pathophysiology of mental disorders by integrating these layers.
Neuropsychiatric Disorders : An Integrative Approach. : Journal of Neural Transmission Supplementa
For this purpose, the role of genes identified by genetic analysis of families of bipolar disorder and schizophrenia as well as molecules identified by omics analysis of postmortem brain samples, in intracellular and inter-cellular signaling abnormalities, will be analyzed and subject to the construction of mathematical models. Using animal models of the candidate genes, responsible neural circuit will be identified by behavioral and anatomical analyses.
Within that neural circuit, responsible cell types will be identified using omics analysis and the mechanism for the emergence of behavioral changes will be pursued by manipulation of specific neural circuit and by employing mathematical modeling of the responsible neural circuit. Using induced pluripotent stem iPS cells derived from patients with mental disorders, neural cells and cerebral organoids will be generated and cellular pathology underlying mental disorders will be studied using omics analyses. Through these series of studies, mathematical model of mental disorders that incorporate multiple layer facets including molecular, cellular, circuit and behavioral levels, will be constructed and thereby we will aim at constructive understanding of the multiscale phenomena of bipolar disorder and schizophrenia.
Synaptic transmission is the basic element of information processing and storage in the brain. Furthermore, abnormality of synaptic function is implicated not only in memory and learning disorders such as developmental disorder, posttraumatic stress disorder PTSD , dementia, Alzheimer's disease, but also on the pathogenesis of schizophrenia and depression. However, it has not been possible to identify the synaptic abnormality, responsible for a certain pathological situation. In this study, we will establish a system to manipulate synaptic plasticity based on our knowledge and techniques on the molecular mechanism of hippocampal synaptic plasticity.
We will further employ techniques on iPS cells, molecular genetics, animal behavior experiments. By controlling the neural circuit and behavioral at the multiple scale ranging from the molecule to behavior, we will attempt to understand psychiatric disease at multiple laysers. By Fred Ovsiew Editor. Our state and public mental health institutions are the locus of care for numerous patients with neuropsychiatric disorders.
Links between psychiatric symptoms and medical diseases have long been noted; however, a nonintegrated approach has interfered with patients obtaining appropriate treatment. Neuropsychiatry and Mental Health Services examines the importance of an integrated approach to neuropsychiatric conditions and looks at ways to overcome the difficulties in assessing medical disorders in psychiatric populations.
Related Neuropsychiatric Disorders: An Integrative Approach
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