13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Higher polygenic risk score for schizophrenia (szPGRS) has been associated with lower cognitive function and might be a predictor of decline in brain structure in apparently healthy populations. Age-related declines in structural brain connectivity—measured using white matter diffusion MRI —are evident from cross-sectional data. Yet, it remains unclear how graph theoretical metrics of the structural connectome change over time, and whether szPGRS is associated with differences in ageing-related changes in human brain connectivity. Here, we studied a large, relatively healthy, same-year-of-birth, older age cohort over a period of 3 years (age ∼ 73 years, N = 731; age ∼76 years, N = 488). From their brain scans we derived tract-averaged fractional anisotropy (FA) and mean diffusivity (MD), and network topology properties. We investigated the cross-sectional and longitudinal associations between these structural brain variables and szPGRS. Higher szPGRS showed significant associations with longitudinal increases in MD in the splenium (β = 0.132, p FDR  = 0.040), arcuate (β = 0.291, p FDR  = 0.040), anterior thalamic radiations (β = 0.215, p FDR  = 0.040) and cingulum (β = 0.165, p FDR  = 0.040). Significant declines over time were observed in graph theory metrics for FA-weighted networks, such as mean edge weight (β = −0.039, p FDR  = 0.048) and strength (β = −0.027, p FDR  = 0.048). No significant associations were found between szPGRS and graph theory metrics. These results are consistent with the hypothesis that szPGRS confers risk for ageing-related degradation of some aspects of structural connectivity.

          Highlights

          • Large longitudinal MRI study of brain structure in narrow age range older age cohort.

          • Longitudinal changes occur in white matter tracts from age 73 to 76.

          • Graph theory metrics also showed longitudinal reductions over that period.

          • Microstructural changes in white matter correlate with changes in brain’s network.

          • Higher genetic risk for schizophrenia is associated with changes in brain structure.

          Related collections

          Most cited references62

          • Record: found
          • Abstract: found
          • Article: not found

          Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry.

          Magnetic resonance imaging volumetry studies report inverted U-patterns with increasing white-matter (WM) volume into middle age suggesting protracted WM maturation compared with the cortical gray matter. Diffusion tensor imaging (DTI) is sensitive to degree and direction of water permeability in biological tissues, providing in vivo indices of WM microstructure. The aim of this cross-sectional study was to delineate age trajectories of WM volume and DTI indices in 430 healthy subjects ranging 8-85 years of age. We used automated regional brain volume segmentation and tract-based statistics of fractional anisotropy, mean, and radial diffusivity as markers of WM integrity. Nonparametric regressions were used to fit the age trajectories and to estimate the timing of maximum development and deterioration in aging. Although the volumetric data supported protracted growth into the sixth decade, DTI indices plateaued early in the fourth decade across all tested regions and then declined slowly into late adulthood followed by an accelerating decrease in senescence. Tractwise and voxel-based analyses yielded regional differences in development and aging but did not provide ample evidence in support of a simple last-in-first-out hypothesis of life-span changes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond

            Background Cognitive ageing is a major burden for society and a major influence in lowering people's independence and quality of life. It is the most feared aspect of ageing. There are large individual differences in age-related cognitive changes. Seeking the determinants of cognitive ageing is a research priority. A limitation of many studies is the lack of a sufficiently long period between cognitive assessments to examine determinants. Here, the aim is to examine influences on cognitive ageing between childhood and old age. Methods/Design The study is designed as a follow-up cohort study. The participants comprise surviving members of the Scottish Mental Survey of 1947 (SMS1947; N = 70,805) who reside in the Edinburgh area (Lothian) of Scotland. The SMS1947 applied a valid test of general intelligence to all children born in 1936 and attending Scottish schools in June 1947. A total of 1091 participants make up the Lothian Birth Cohort 1936. They undertook: a medical interview and examination; physical fitness testing; extensive cognitive testing (reasoning, memory, speed of information processing, and executive function); personality, quality of life and other psycho-social questionnaires; and a food frequency questionnaire. They have taken the same mental ability test (the Moray House Test No. 12) at age 11 and age 70. They provided blood samples for DNA extraction and testing and other biomarker analyses. Here we describe the background and aims of the study, the recruitment procedures and details of numbers tested, and the details of all examinations. Discussion The principal strength of this cohort is the rarely captured phenotype of lifetime cognitive change. There is additional rich information to examine the determinants of individual differences in this lifetime cognitive change. This protocol report is important in alerting other researchers to the data available in the cohort.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              Genome-wide association studies establish that human intelligence is highly heritable and polygenic

              General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan 1,2 . Data from twin and family studies are consistent with a high heritability of intelligence 3 , but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 SNPs and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted approximately 1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample (P = 0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence.
                Bookmark

                Author and article information

                Contributors
                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                1 December 2018
                December 2018
                : 183
                : 884-896
                Affiliations
                [a ]Division of Psychiatry, University of Edinburgh, Edinburgh, UK
                [b ]Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
                [c ]Department of Psychology, University of Edinburgh, Edinburgh, UK
                [d ]Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK
                [e ]MRC Centre for Reproductive Health, University of Edinburgh, UK
                [f ]Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
                [g ]Department of Psychology, University of Texas, Austin, TX, USA
                Author notes
                []Corresponding author. University of Edinburgh, Royal Edinburgh Hospital, Morning side Park, Edinburgh, EH10 5HF, UK. c.alloza@ 123456sms.ed.ac.uk
                Article
                S1053-8119(18)30773-0
                10.1016/j.neuroimage.2018.08.075
                6215331
                30179718
                a20bca40-64e7-4df0-a980-34777b91dcee
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 May 2018
                : 28 August 2018
                : 31 August 2018
                Categories
                Article

                Neurosciences
                schizophrenia,ageing,structural connectivity,longitudinal,genetics
                Neurosciences
                schizophrenia, ageing, structural connectivity, longitudinal, genetics

                Comments

                Comment on this article