Genetic effects on the cerebellar role in working memory: Same brain, different genes?

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n= 430; aged 16-30. years), that the cerebellum is strongly, and reliably (n=30 rescans), activated during an n-back working memory task, particularly lobules I-IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.

Extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture

We extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3D images to analyze single gene effects on brain fiber architecture. We collected 4 Tesla diffusion tensor images (DTI) and genotype data from 258 healthy adult twins and their non-twin siblings. After high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (SNP), Val66Met (dbSNP number rs6265), of the BDNF gene (brain-derived neurotrophic factor) with fractional anisotropy (FA) derived from each subject's DTI scan, by fitting structural equation models (SEM) from quantitative genetics. We also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (FWHM) of the Gaussian smoothing applied to the FA images. Raw FA maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (FDR) procedure. The BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in FA. Our study generated the first maps to visualize the effect of the BDNF gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.

Gene network effects on brain microstructure and intellectual performance identified in 472 twins

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 2.1 SD years; 193 male/279 female).Wecombined clustering with genome-wide scanning to find brain systems withcommongenetic determination.Wethen filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.

BDNF gene effects on brain circuitry replicated in 455 twins

Brain-derived neurotrophic factor (BDNF) plays a key role in learning and memory, but its effects on the fiber architecture of the living brain are unknown. We genotyped 455 healthy adult twins and their non-twin siblings (188 males/267 females; age: 23.7 ± 2.1. years, mean ± SD) and scanned them with high angular resolution diffusion tensor imaging (DTI), to assess how the BDNF Val66Met polymorphism affects white matter microstructure. By applying genetic association analysis to every 3D point in the brain images, we found that the Val-BDNF genetic variant was associated with lower white matter integrity in the splenium of the corpus callosum, left optic radiation, inferior fronto-occipital fasciculus, and superior corona radiata. Normal BDNF variation influenced the association between subjects' performance intellectual ability (as measured by Object Assembly subtest) and fiber integrity (as measured by fractional anisotropy; FA) in the callosal splenium, and pons. BDNF gene may affect the intellectual performance by modulating the white matter development. This combination of genetic association analysis and large-scale diffusion imaging directly relates a specific gene to the fiber microstructure of the living brain and to human intelligence.

Genome-wide interaction analysis reveals replicated epistatic effects on brain structure

The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 1011 pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.

Exhaustive search of the SNP-sNP interactome identifies epistatic effects on brain volume in two cohorts

The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.

Diffusion imaging protocol effects on genetic associations

Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA skeletons derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

Discovery and replication of dopamine-related gene effects on caudate volume in young and elderly populations (N1198) using genome-wide search

The caudate is a subcortical brain structure implicated in many common neurological and psychiatric disorders. To identify specific genes associated with variations in caudate volume, structural magnetic resonance imaging and genome-wide genotypes were acquired from two large cohorts, the Alzheimer's Disease NeuroImaging Initiative (ADNI; N=734) and the Brisbane Adolescent/Young Adult Longitudinal Twin Study (BLTS; N=464). In a preliminary analysis of heritability, around 90% of the variation in caudate volume was due to genetic factors. We then conducted genome-wide association to find common variants that contribute to this relatively high heritability. Replicated genetic association was found for the right caudate volume at single-nucleotide polymorphism rs163030 in the ADNI discovery sample (P=2.36 × 10 -6) and in the BLTS replication sample (P=0.012). This genetic variation accounted for 2.79 and 1.61% of the trait variance, respectively. The peak of association was found in and around two genes, WDR41 and PDE8B, involved in dopamine signaling and development. In addition, a previously identified mutation in PDE8B causes a rare autosomal-dominant type of striatal degeneration. Searching across both samples offers a rigorous way to screen for genes consistently influencing brain structure at different stages of life. Variants identified here may be relevant to common disorders affecting the caudate.

Orthographic effects on picture naming in Chinese: A 4T erfMRI study

The presence of a phonological relationship between a context word (cap) and the name of a target picture (cat) facilitates picture naming in the picture–word interference task in alphabetic languages such as English and Dutch. Neuroimaging studies of picture naming in English suggest distinct regions of brain activity are involved in phonological priming relative to a baseline control.

The Value of Financial Planning Advice: Process and Outcome Effects on Consumer Well-being [Time 1 and 2 Survey Summary Results]

While anecdotal evidence indicates financial advice affects consumers’ financial well-being, this research project is motivated by the absence of empirically-grounded research relating to the extent to which, and, importantly, how, financial planning advice contributes to broader client well-being. Accordingly, the aim of this project is to establish how the quality of financial planning advice can be optimised to add value, not only to clients’ financial situation, but also to broader aspects of their well-being. This broader construct of well-being captures a range of process and outcome factors that map to concepts of security, control, choice, mastery, and life satisfaction (Irving, 2012; Gallery, Gallery, Irving & Newton, 2011; Irving, Gallery, and Gallery, 2009). Financial planning is commonly purported to confer not only tangible benefits, but also intangible benefits, such as increased security and peace of mind that are considered as important, if not more important, than material outcomes. Such claims are intuitively appealing; however, little empirical evidence exists for the notion that engaging with a financial planner or adviser promotes peace of mind, feelings of security, and expands choices and possibilities. Nor is there evidence signalling what mechanisms might underpin such client benefits. In addressing this issue, we examine the financial planning advice (including financial product advice) provided to retail clients, and consider the short- and longer-term impacts on clients’ financial satisfaction and broader well-being. To this end, we examine both process (e.g., how financial planning advice is given) and outcome (e.g., financial situation) effects.

From ‘free’ to fee: Acceptability of airline ancillary fees and the effects on customer behavior

Businesses in various consumer service industries have begun to unbundle their service offerings by introducing numerous fees for products and services that were previously provided as “free.” Anecdotal evidence in the media indicates that these fees cause widespread public displeasure, frustration, and outrage. This paper develops a framework of fee acceptability, negative emotions, and dysfunctional customer behavior, which is tested using data from the airline industry. Findings identify the strongest effects on betrayal in the case of baggage fees, followed by charges for comfort. Also, betrayal has a direct effect on complaining, whereas anger mediates the relationship between betrayal and negative word of mouth.

Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses

Monitoring pedestrian and cyclists movement is an important area of research in transport, crowd safety, urban design and human behaviour assessment areas. Media Access Control (MAC) address data has been recently used as potential information for extracting features from people’s movement. MAC addresses are unique identifiers of WiFi and Bluetooth wireless technologies in smart electronics devices such as mobile phones, laptops and tablets. The unique number of each WiFi and Bluetooth MAC address can be captured and stored by MAC address scanners. MAC addresses data in fact allows for unannounced, non-participatory, and tracking of people. The use of MAC data for tracking people has been focused recently for applying in mass events, shopping centres, airports, train stations etc. In terms of travel time estimation, setting up a scanner with a big value of antenna’s gain is usually recommended for highways and main roads to track vehicle’s movements, whereas big gains can have some drawbacks in case of pedestrian and cyclists. Pedestrian and cyclists mainly move in built distinctions and city pathways where there is significant noises from other fixed WiFi and Bluetooth. Big antenna’s gains will cover wide areas that results in scanning more samples from pedestrians and cyclists’ MAC device. However, anomalies (such fixed devices) may be captured that increase the complexity and processing time of data analysis. On the other hand, small gain antennas will have lesser anomalies in the data but at the cost of lower overall sample size of pedestrian and cyclist’s data. This paper studies the effect of antenna characteristics on MAC address data in terms of travel-time estimation for pedestrians and cyclists. The results of the empirical case study compare the effects of small and big antenna gains in order to suggest optimal set up for increasing the accuracy of pedestrians and cyclists’ travel-time estimation.

Friends or foes: Group influence effects on moderate drinking behaviors

Drunkenness and the addictive consumption of alcohol remains a key social and public health concern. Advancing beyond traditional individualized prevention approaches, this research explores the role of social influences in determining individual and group influence in moderate-drinking decision-making and participatory actions. A social influence model of intentional moderate drinking actions is conceptualized and validated. Results show group norm as the single social influence predictor of intentions and desire to drink moderately, as opposed to well-known social influence factors (e.g., subjective norm, social identity and drinking contextual effects). Significantly, the peer-group is identified as a key influencer supporting moderate drinking practices, and i-intentions to drink moderately predict group-related we-intentions, which suggests that moderate drinking is a shared goal. These findings advance alcohol prevention research drawing attention to the power of group dynamics to support positive changes in youth drinking behaviors.

PTHR1 polymorphisms influence BMD variation through effects on the growing skeleton

We investigated whether polymorphisms in PTHR1 are associated with bone mineral density (BMD), to determine whether the association of this gene with BMD was due to effects on attainment of peak bone mass or effects on subsequent bone loss. The PTHR1 gene, including its 14 exons, their exon-intron boundaries, and 1,500 bp of its promoter region, was screened for polymorphisms by denaturing high-performance liquid chromatography (dHPLC) and sequencing in 36 osteoporotic cases. Eleven single-nucleotide polymorphisms (SNPs), one tetranucleotide repeat, and one tetranucleotide deletion were identified. A cohort of 634 families, including 1,236 men (39%) and 1,926 women (61%) ascertained with probands with low BMD (Z< -2.0) and the Children in Focus subset of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (785 unrelated individuals, mean age 118 months), were genotyped for the five most informative SNPs (minor allele frequency >5%) and the tetranucleotide repeat. In our osteoporosis families, association was noted between lumbar spine BMD and alleles of a known functional tetranucleotide repeat (U4) in the PTHR1 promoter region (P = 0.042) and between two and three marker haplotypes of PTHR1 polymorphisms with lumbar spine, femoral neck, and total hip BMD (P = 0.021-0.047). This association was restricted to the youngest tertile of the population (age 16-39 years, P = 0.013-0.048). A similar association was found for the ALSPAC cohort: two marker haplotypes of SNPs A48609T and C52813T were associated with height (P = 0.006) and total body less head BMD (P = 0.02), corrected for age and gender, confirming the family findings. These findings suggest a role for PTHR1 variation in determining peak BMD.