Introduction to the Special Issue, Pathways Between Genes, Brain, and Behavior

In the past 10 years or so, with the sequencing of the human genome and rapid advances in the development of high throughput techniques, the field of behavior genetics has increasingly moved toward the detection of actual genes and environmental factors. However, the field is still in the relatively early stages of understanding some of the basic facts about the complex genetic underpinnings of brain structure and function and their relationship to behavior. The 15 articles in this special issue were selected to represent the diversity of methodologies applied to the complexity of pathways linking genes, brain, and behavior. While providing strong evidence for the role of genes in individual differences in brain structure and function, these papers also demonstrate that environmental experiences alter neurobiological pathways, and that genetic factors may further moderate the impact of environmental experience. Most importantly, the breadth of studies proves that in order to be able to trace the pathways between genes, brain, and behavior, we need experts in genetics, neuroscience, psychology, and psychiatry

Modifying the minimum criteria for diagnosing amnestic MCI to improve prediction of brain atrophy and progression to Alzheimer’s disease

Mild cognitive impairment (MCI) is a heterogeneous condition with variable outcomes. Improving diagnosis to increase the likelihood that MCI reliably reflects prodromal Alzheimer's Disease (AD) would be of great benefit for clinical practice and intervention trials. In 230 cognitively normal (CN) and 394 MCI individuals from the Alzheimer's Disease Neuroimaging Initiative, we studied whether an MCI diagnostic requirement of impairment on at least two episodic memory tests improves 3-year prediction of medial temporal lobe atrophy and progression to AD. Based on external age-adjusted norms for delayed free recall on the Rey Auditory Verbal Learning Test (AVLT), MCI participants were further classified as having normal (AVLT+, above -1 SD, n = 121) or impaired (AVLT -, -1 SD or below, n = 273) AVLT performance. CN, AVLT+, and AVLT- groups differed significantly on baseline brain (hippocampus, entorhinal cortex) and cerebrospinal fluid (amyloid, tau, p-tau) biomarkers, with the AVLT- group being most abnormal. The AVLT- group had significantly more medial temporal atrophy and a substantially higher AD progression rate than the AVLT+ group (51% vs. 16%, p <0.001). The AVLT+ group had similar medial temporal trajectories compared to CN individuals. Results were similar even when restricted to individuals with above average (based on the CN group mean) baseline medial temporal volume/thickness. Requiring impairment on at least two memory tests for MCI diagnosis can markedly improve prediction of medial temporal atrophy and conversion to AD, even in the absence of baseline medial temporal atrophy. This modification constitutes a practical and cost-effective approach for clinical and research settings.Peer reviewe

Genetic and environmental influences on sleep quality in middle‐aged men: a twin study

Poor sleep quality is a risk factor for a number of cognitive and physiological age-related disorders. Identifying factors underlying sleep quality are important in understanding the etiology of these age-related health disorders. We investigated the extent to which genes and the environment contribute to subjective sleep quality in middle-aged male twins using the classical twin design. We used the Pittsburgh Sleep Quality Index to measure sleep quality in 1218 middle-aged twin men from the Vietnam Era Twin Study of Aging (mean age = 55.4 years; range 51-60; 339 monozygotic twin pairs, 257 dizygotic twin pairs, 26 unpaired twins). The mean PSQI global score was 5.6 [SD = 3.6; range 0-20]. Based on univariate twin models, 34% of variability in the global PSQI score was due to additive genetic effects (heritability) and 66% was attributed to individual-specific environmental factors. Common environment did not contribute to the variability. Similarly, the heritability of poor sleep-a dichotomous measure based on the cut-off of global PSQI>5-was 31%, with no contribution of the common environment. Heritability of six of the seven PSQI component scores (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, and daytime dysfunction) ranged from 0.15 to 0.31, whereas no genetic influences contributed to the use of sleeping medication. Additive genetic influences contribute to approximately one-third of the variability of global subjective sleep quality. Our results in middle-aged men constitute a first step towards examination of the genetic relationship between sleep and other facets of aging.Accepted manuscrip

Underdiagnosis of mild cognitive impairment: A consequence of ignoring practice effects

INTRODUCTION: Longitudinal testing is necessary to accurately measure cognitive change. However, repeated testing is susceptible to practice effects, which may obscure true cognitive decline and delay detection of mild cognitive impairment (MCI). METHODS: We retested 995 late-middle-aged men in a ∼6-year follow-up of the Vietnam Era Twin Study of Aging. In addition, 170 age-matched replacements were tested for the first time at study wave 2. Group differences were used to calculate practice effects after controlling for attrition effects. MCI diagnoses were generated from practice-adjusted scores. RESULTS: There were significant practice effects on most cognitive domains. Conversion to MCI doubled after correcting for practice effects, from 4.5% to 9%. Importantly, practice effects were present although there were declines in uncorrected scores. DISCUSSION: Accounting for practice effects is critical to early detection of MCI. Declines, when lower than expected, can still indicate practice effects. Replacement participants are needed for accurately assessing disease progression.Published versio

A test for common genetic and environmental vulnerability to depression and diabetes

Molecular genetic research has provided some evidence for the association between depression and metabolic disorders. We sought to determine if molecular findings are reflected in twin analyses testing if common genetic and environmental risk factors contribute to the co-occurrence of diabetes and depression. Data to derive depression and diabetes were collected from 1,237 male-male twins who participated in the 2005 Vietnam Era Twin Study of Aging (VETSA). The 1,237 twins were comprised of 347 MZ pairs, 3 MZ singletons, 267 DZ pairs and 6 unpaired twins. Depression was defined as a score below 46 on the Short Form-36 mental component summary score. Diabetes was defined by self report, use of anti-diabetic medications and insulin. Twin models were fit to estimate the correlation of genetic and environmental contributions to depression and diabetes. Consistent with other studies these data support the association between depression and diabetes (OR = 1.7; 95%CI: 1.1–2.7). Genetic vulnerability accounted for 50% (95%CI: 32%–65%) of the variance in risk for depression and 69% (95%CI: 52%–81%) of the variance in risk for diabetes. The genetic correlation between depression and diabetes was r = 0.19 (95%CI: 0–0.46) and the non-shared environmental correlation was r = 0.09 (95% CI: 0–0.45). Overall there is little evidence that common genetic and environmental factors account for the co-occurrence of depression and diabetes in middle aged men. Further research in female twins and larger cohorts is warranted

Las variaciones de superficie cortical en la corteza dorsolateral prefrontal predicen mejor el futuro desempeño cognitivo que la inteligencia fluida y la memoria operativa

Are cognitive and biological variables useful for predicting future behavioral outcomes? Method: In two independent groups, we measured a set of cognitive (fluid and crystallized intelligence, working memory, and attention control) and biological (cortical thickness and cortical surface area) variables on two occasions separated by six months, to predict behavioral outcomes of interest (performance on an adaptive version of the n-back task) measured twelve and eighteen months later. We followed three stages: discovery, validation, and generalization. In the discovery stage, cognitive/biological variables and the behavioral outcome of interest were assessed in a group of individuals (in-sample). In the validation stage, the cognitive and biological variables were related with a parallel version of the behavioral outcome assessed several months later. In the generalization stage, the validation findings were tested in an independent group of individuals (out-of-sample). Results: The key fi nding revealed that cortical surface area variations within the right dorsolateral prefrontal cortex predict the behavioral outcome of interest in both groups, whereas the cognitive variables failed to show reliable predictive validity. Conclusions: Individual differences in biological variables might predict future behavioral outcomes better than cognitive variables concurrently correlated with these behavioral outcomesAntecedentes: ¿Predicen las variables cognitivas y biológicas el futuro desempeño cognitivo? Método: en dos grupos independientes de participantes se miden variables cognitivas (inteligencia fluida y cristalizada, memoria operativa y control atencional) y biológicas (grosor y superficie cortical) en dos ocasiones separadas por seis meses, para predecir el desempeño en la tarea n-back valorado doce y dieciocho meses después. Se completan tres etapas: descubrimiento, validación y generalización. En la de descubrimiento se valoran en un grupo de individuos las variables cognitivas/biológicas y el desempeño a predecir. En la de validación, se relacionan las mismas variables con una versión paralela de la n-back completada meses después. En la de generalización, los resultados de la validación se replican en un grupo independiente de individuos. Resultados: las variaciones de superficie cortical en la corteza dorsolateral prefrontal derecha predicen el desempeño cognitivo en los dos grupos independientes de individuos, mientras que las variables cognitivas no contribuyen a la predicción del desempeño futuro. Conclusiones: las diferencias individuales en determinadas variables biológicas predicen el desempeño cognitivo mejor que las variables cognitivas que correlacionan concurrentemente con ese desempeñoThis project was supported by PSI2017-82218-P (Ministerio de Economía, Industria y Competitividad, Spain

Genetic network properties of the human cortex based on regional thickness and surface area measures

We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function

Influence of young adult cognitive ability and additional education on later-life cognition

How and when education improves cognitive capacity is an issue of profound societal importance. Education and later-life education-related factors, such as occupational complexity and engagement in cognitive-intellectual activities, are frequently considered indices of cognitive reserve, but whether their effects are truly causal remains unclear. In this study, after accounting for general cognitive ability (GCA) at an average age of 20 y, additional education, occupational complexity, or engagement in cognitive-intellectual activities accounted for little variance in late midlife cognitive functioning in men age 56-66 (n = 1009). Age 20 GCA accounted for 40% of variance in the same measure in late midlife and approximately 10% of variance in each of seven cognitive domains. The other factors each accounted for <1% of the variance in cognitive outcomes. The impact of these other factors likely reflects reverse causation-namely, downstream effects of early adult GCA. Supporting that idea, age 20 GCA, but not education, was associated with late midlife cortical surface area (n = 367). In our view, the most parsimonious explanation of our results, a meta-analysis of the impact of education, and epidemiologic studies of the Flynn effect is that intellectual capacity gains due to education plateau in late adolescence/early adulthood. Longitudinal studies with multiple cognitive assessments before completion of education would be needed to confirm this speculation. If cognitive gains reach an asymptote by early adulthood, then strengthening cognitive reserve and reducing later-life cognitive decline and dementia risk may really begin with improving educational quality and access in childhood and adolescence.Peer reviewe

Genetic Influences on Cortical Regionalization in the Human Brain

SummaryAnimal data demonstrate that the development of distinct cortical areas is influenced by genes that exhibit highly regionalized expression patterns. In this paper, we show genetic patterning of cortical surface area derived from MRI data from 406 adult human twins. We mapped genetic correlations of areal expansion between selected seed regions and all other cortical locations, with the selection of seed points based on results from animal studies. “Marching seeds” and a data-driven, hypothesis-free, fuzzy-clustering approach provided convergent validation. The results reveal strong anterior-to-posterior graded, bilaterally symmetric patterns of regionalization, largely consistent with patterns previously reported in nonhuman mammalian models. Broad similarities in genetic patterning between rodents and humans might suggest a conservation of cortical patterning mechanisms, whereas dissimilarities might reflect the functionalities most essential to each species