Showing papers by "Lauren E. Salminen published in 2018"

The genetic architecture of the human cerebral cortex

Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 35,660 individuals with replication in 15,578 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 206 nominally significant loci (P ≤ 5 x 10 -8 ); 150 survived multiple testing correction (P ≤ 8.3 x 10 -10 ; 140 surface area; 10 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson9s disease, insomnia, depression and ADHD. NOTE: K.L.G. and N.J. contributed to this work as co-first authors for this preprint. J.N.P., L.C.-C., J.B., D.P.H., P.A.L., F.P. contributed to this work as co-second authors for this preprint. J.L.S., P.M.T., S.E.M. contributed to this work as co-last authors for this preprint.

Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer's Disease in the Aging Brain.

Abstract: Brain aging is a multifaceted process that remains poorly understood. Despite significant advances in technology, progress towards identifying reliable risk factors for suboptimal brain health requires realistically complex analytic methods to explain relationships between genetics, biology, and environment. Here we show the utility of a novel unsupervised machine learning technique - Correlation Explanation (CorEx) - to discover how individual measures from structural brain imaging, genetics, plasma, and CSF markers can jointly provide information on risk for Alzheimer’s disease (AD). We examined 829 participants (Mage: 75.3 ± 6.9 years; 350 women and 479 men) from the Alzheimer’s Disease Neuroimaging Initiative database to identify multivariate predictors of cognitive decline and brain atrophy over a one-year period. Our sample included 231 cognitively normal individuals, 397 with mild cognitive impairment (MCI), and 201 with AD as their baseline diagnosis. Analyses revealed latent factors based on data-driven combinations of plasma markers and brain metrics, that were aligned with established biological pathways in AD. These factors were able to improve disease prediction along the trajectory from normal cognition and MCI to AD, with an area under the receiver operating curve of up to 99%, and prediction accuracy of up to 89.9% on independent “held out” testing data. Further, the most important latent factors that predicted AD consisted of a novel set of variables that are essential for cardiovascular, immune, and bioenergetic functions. Collectively, these results demonstrate the strength of unsupervised network measures in the detection and prediction of AD.

Genetic determinants of cortical structure (thickness, surface area and volumes) among disease free adults in the CHARGE Consortium

Abstract: Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions Our discovery sample comprised 22,822 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses We identified genetic heterogeneity between cortical measures and brain regions, and 161 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions These data are a rich resource for studies of the biological mechanisms behind cortical development and aging

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Abstract: Background and Purpose Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here we used a novel machine learning method - evolving partitions to improve classification (EPIC) - to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. Methods We used EPIC with repeated cross-validation to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n=40) versus controls (n=57), and classification of ELS within the PTSD (ELS+ n=16; ELS-n=24) and control groups (ELS+ n=16; ELS- n=41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. Results On average, EPIC classified PTSD with 69% accuracy (SD=5%), and ELS with 64% accuracy in the PTSD group (SD=10%), and 62% accuracy in controls (SD=6%). EPIC selected unique sets of individual features that classified each group with 75-85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. Conclusions EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.

White matter fiber bundle lengths are shorter in cART naive HIV: an analysis of quantitative diffusion tractography in South Africa.

Abstract: This study examines white matter microstructure using quantitative tractography diffusion magnetic resonance imaging (qtdMRI) in HIV+ individuals from South Africa who were naive or early in the initiation of antiretroviral therapy. Fiber bundle length (FBL) metrics, generated from qtdMRI, for whole brain and six white matter tracts of interest (TOI) were assessed for 135 HIV+ and 21 HIV− individuals. The association between FBL metrics, measures of disease burden, and neuropsychological performance were also investigated. Results indicate significantly reduced sum of whole brain fiber bundle lengths (FBL, p < 0.001), but not average whole brain FBL in the HIV+ group compared to the HIV− controls. The HIV+ group exhibited significantly shorter sum of FBL in all six TOIs examined: the anterior thalamic radiation, cingulum bundle, inferior and superior longitudinal fasciculi, inferior frontal occipital fasciculus, and the uncinate fasciculus. Additionally, average FBLs were significantly shorter select TOIs including the inferior longitudinal fasciculus, cingulum bundle, and the anterior thalamic radiation. Shorter whole brain FBL sum metrics were associated with poorer neuropsychological performance, but were not associated with markers of disease burden. Taken together these findings suggest HIV affects white matter architecture primarily through reductions in white matter fiber numbers and, to a lesser degree, the shortening of fibers along a bundle path.

Altered cortical brain structure and increased risk for disease seen decades after perinatal exposure to maternal smoking: A study of 9,000 adults in the UK Biobank

Abstract: Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early life smoke exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44-80) who were exposed (n=2,510) or unexposed (n=6,079) to maternal smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volume. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE-) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n=109, unexposed, n=315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.