Nailin Yao

Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index

Abstract Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

The Role of Intrinsic Brain Functional Connectivity in Vulnerability and Resilience to Bipolar Disorder

OBJECTIVE Bipolar disorder is a heritable disorder characterized by mood dysregulation associated with brain functional dysconnectivity. Previous research has focused on the detection of risk- and disease-associated dysconnectivity in individuals with bipolar disorder and their first-degree relatives. The present study seeks to identify adaptive brain connectivity features associated with resilience, defined here as avoidance of illness or delayed illness onset in unaffected siblings of patients with bipolar disorder. METHOD Graph theoretical methods were used to examine global and regional brain network topology in head-motion-corrected resting-state functional MRI data acquired from 78 patients with bipolar disorder, 64 unaffected siblings, and 41 healthy volunteers. RESULTS Global network properties were preserved in patients and their siblings while both groups showed reductions in the cohesiveness of the sensorimotor network. In the patient group, these sensorimotor network abnormalities were coupled with reduced integration of core default mode network regions in the ventromedial cortex and hippocampus. Conversely, integration of the default mode network was increased in the sibling group compared with both the patient group and the healthy volunteer group. CONCLUSIONS The authors found that trait-related vulnerability to bipolar disorder was associated with reduced resting-state cohesiveness of the sensorimotor network in patients with bipolar disorder. However, integration of the default mode network emerged as a key feature differentiating disease expression and resilience between the patients and their siblings. This is indicative of the presence of neural mechanisms that may promote resilience, or at least delay illness onset.

Epigenetic Age Acceleration Assessed with Human White-Matter Images

The accurate estimation of age using methylation data has proved a useful and heritable biomarker, with acceleration in epigenetic age predicting a number of age-related phenotypes. Measures of white matter integrity in the brain are also heritable and highly sensitive to both normal and pathological aging processes across adulthood. We consider the phenotypic and genetic interrelationships between epigenetic age acceleration and white matter integrity in humans. Our goal was to investigate processes that underlie interindividual variability in age-related changes in the brain. Using blood taken from a Mexican-American extended pedigree sample (n = 628; age = 23.28–93.11 years), epigenetic age was estimated using the method developed by Horvath (2013). For n = 376 individuals, diffusion tensor imaging scans were also available. The interrelationship between epigenetic age acceleration and global white matter integrity was investigated with variance decomposition methods. To test for neuroanatomical specificity, 16 specific tracts were additionally considered. We observed negative phenotypic correlations between epigenetic age acceleration and global white matter tract integrity (ρpheno = −0.119, p = 0.028), with evidence of shared genetic (ρgene = −0.463, p = 0.013) but not environmental influences. Negative phenotypic and genetic correlations with age acceleration were also seen for a number of specific white matter tracts, along with additional negative phenotypic correlations between granulocyte abundance and white matter integrity. These findings (i.e., increased acceleration in epigenetic age in peripheral blood correlates with reduced white matter integrity in the brain and shares common genetic influences) provide a window into the neurobiology of aging processes within the brain and a potential biomarker of normal and pathological brain aging. SIGNIFICANCE STATEMENT Epigenetic measures can be used to predict age with a high degree of accuracy and so capture acceleration in biological age, relative to chronological age. The white matter tracts within the brain are also highly sensitive to aging processes. We show that increased biological aging (measured using epigenetic data from blood samples) is correlated with reduced integrity of white matter tracts within the human brain (measured using diffusion tensor imaging) with data from a large sample of Mexican-American families. Given the family design of the sample, we are also able to demonstrate that epigenetic aging and white matter tract integrity also share common genetic influences. Therefore, epigenetic age may be a potential, and accessible, biomarker of brain aging.

Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity

Despite over 400 peer‐reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide‐spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time‐series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between‐group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex‐level analyses, between‐group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757–3770, 2017.

Epigenetic Age Acceleration Assessed In White-Matter Integrity: Towards A Biomarker of Successfully Brain Aging

Background Biological age acceleration as measured by aggregate epigenetic markers has been associated with a number of phenotypes including Parkinson’s, obesity and physical fitness in the elderly. Additionally, epigenetic indices of age acceleration are touted as molecular biomarkers of brain aging, based on findings linking epigenetic age of prefrontal cortex tissue with Alzheimer’s-related phenotypes. Yet, in order to develop a clinically useful brain age acceleration biomarker, peripheral tissue, rather than neural tissue, is preferable. Likewise, in vivo neuroimaging measures are desirable. To that end, we examined the relationship between blood-derived epigenetic measures of age acceleration and white matter integrity, as indexed with diffusion weighted imaging (DWI), in a large randomly ascertained family cohort. Our goal was to investigate processes that underlie inter-individual variability in age-related changes in the white matter tracts. DWI measures of white mater integrity are among the most sensitive imaging measures of aging with demonstrated heritabilities. Methods Using blood DNA methylation data taken from a Mexican-American extended pedigree sample (n=634; mean age=49.16y, range 28.11y-97.52y), epigenetic age was estimated using the method developed by S. Horvath (2013). Epigenetic age acceleration was calculated as epigenetic age regressed upon age, sex, age x sex, age2, age2 x sex, and blood cell count estimates. 379 of these individuals had available DWI scans collected on a Siemens 3T Trio MRI located at the Research Imaging Institute, UTHSCSA. An average fractional anisotropy (FA) map was derived for each subject and skeletonized using the TBSS algorithm, providing FA measures for each of 16 white matter tracts and a global index. Variance decomposition methods were then used to investigate the interrelationship between epigenetic age acceleration and white matter integrity and identify genetic influences on these two phenotypes. Results Consistent with previous reports, both epigenetic age acceleration and white matter integrity measures are heritable in this sample. We observe significant (FDR Discussion Here we demonstrate that age acceleration, as measured via methylation profiles from peripheral blood, is significantly correlated with white matter integrity in a number of tracts within the brain. We also demonstrate evidence of shared genetic influences acting on both age acceleration and white matter integrity. These findings provide an interesting window into the neurobiology of aging processes within the brain and a potential new biomarker of normal and pathological brain aging.

Conceptualizing Major Depression: From Genes to Neuroanatomy to Epidemiology

Because its etiology remains largely unexplained, major depression, like other psychiatric diseases, is understood entirely on the basis of symptomatology. Major depression is the most common mental illness and is responsible for substantial mortality, morbidity, and disability. Arguably we know less about the root causes of major depression than about other major mental illnesses (eg, schizophrenia, bipolar disorder, autism). In the current chapter, we examine the literature on the prevalence, diagnostic heterogeneity, risk factors, neuroanatomy, neurophysiology, heritability, endophenotypes, and genetic architecture of major depressive disorder. In addition, we briefly discuss current treatments. Whereas epidemiological results stress the heterogeneity and complex nature of the illness, neuroimaging-based models typically ignore the diversity of clinical factors, potentially limiting their usefulness. Although certainly influenced by environmental factors, there is ample evidence for a genetic component to major depression. However, to date no specific genomic variant or gene has been implicated for depression.Abstract Because its etiology remains largely unexplained, major depression, like other psychiatric diseases, is understood entirely on the basis of symptomatology. Major depression is the most common mental illness and is responsible for substantial mortality, morbidity, and disability. Arguably we know less about the root causes of major depression than about other major mental illnesses (eg, schizophrenia, bipolar disorder, autism). In the current chapter, we examine the literature on the prevalence, diagnostic heterogeneity, risk factors, neuroanatomy, neurophysiology, heritability, endophenotypes, and genetic architecture of major depressive disorder. In addition, we briefly discuss current treatments. Whereas epidemiological results stress the heterogeneity and complex nature of the illness, neuroimaging-based models typically ignore the diversity of clinical factors, potentially limiting their usefulness. Although certainly influenced by environmental factors, there is ample evidence for a genetic component to major depression. However, to date no specific genomic variant or gene has been implicated for depression.