Avoiding Bias in Longitudinal Image Processing

<div>Talk given during the "How Not to Analyze Your Data: A Skeptical Introduction to Modeling Methods" workshop at the 2013 Organization for Human Brain Mapping (OHBM) conference in Seattle, 16-20 June.</div><div><br></div

Data_Sheet_1_Construct validity of questionnaires for the original and revised reinforcement sensitivity theory.docx

This study highlights psychometric properties and evidence of construct validity on parcel-level for questionnaires on the original and revised reinforcement sensitivity theory. Our data (N = 1,076) suggest good to very good psychometric properties and moderate to excellent internal consistencies. Confirmatory factor analysis (CFA) models suggest a very good model fit for the first-order, four factor models of the Carver-White BIS/BAS scales, Reinforcement Sensitivity Theory – Personality Questionnaire (RST-PQ), the two-factor model of revised Reinforcement Sensitivity Theory-Questionnaire (rRST-Q) and for the bifactor model of the Conflict Monitoring Questionnaire (CMQ-44). The CMQ-44 extends the psychometric measurement of previous trait-(r)BIS and trait-BAS scales. Factor scores of CMQ-44 cognitive demand correlate positively with factor scores of Carver-White BIS and all Carver-White BAS subfactors except RST-PQ-Impulsivity suggesting that CMQ-44 cognitive demand addresses Carver-White trait-BIS specifically and more generally the trait-BAS core. CMQ-44 anticipation of negative consequences and response adaptation correlate negatively with trait-BAS, whereas the second-order factor performance monitoring extends the rRST trait-space and correlates positively with trait-BAS.</p

Twin and sibling resemblance (ICCs) for the Affective Neuroscience Personality Scale.

<p>Twin and sibling resemblance (ICCs) for the Affective Neuroscience Personality Scale.</p

Demographic characteristics of the total sample and the subsamples of twins and siblings.

<p>Demographic characteristics of the total sample and the subsamples of twins and siblings.</p

Multivariate independent pathway model of the relations between the six ANPS scales.

<p>The best fitting model, with an independent pathway specification for additive genetic influences (A), and a cholesky decomposition for non-additive genetic influences (D) and non-shared environmental influences (E). For a better illustration, the model only shows A and E influences. D influences are not shown in the Figure, but were modeled the same way as E influences using a cholesky decomposition. For simplicity, the model is shown only for one member of a pair.</p

Standardized estimates for additive genetic, non-additive genetic, and non-shared environmental influences on the six ANPS scales as well as genetic and environmental correlations based on the best fitting model (please see Fig 1 for additional information).

<p>Standardized estimates for additive genetic, non-additive genetic, and non-shared environmental influences on the six ANPS scales as well as genetic and environmental correlations based on the best fitting model (please see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151405#pone.0151405.g001" target="_blank">Fig 1</a> for additional information).</p

Nicotinergic Modulation of Attention-Related Neural Activity Differentiates Polymorphisms of DRD2 and CHRNA4 Receptor Genes

<div><p>Cognitive and neuronal effects of nicotine show high interindividual variability. Recent findings indicate that genetic variations that affect the cholinergic and dopaminergic neurotransmitter system impact performance in cognitive tasks and effects of nicotine. The current pharmacogenetic functional magnetic resonance imaging (fMRI) study aimed to investigate epistasis effects of CHRNA4/DRD2 variations on behavioural and neural correlates of visuospatial attention after nicotine challenge using a data driven partial least squares discriminant analysis (PLS-DA) approach. Fifty young healthy non-smokers were genotyped for CHRNA4 (rs1044396) and DRD2 (rs6277). They received either 7 mg transdermal nicotine or a matched placebo in a double blind within subject design prior to performing a cued target detection task with valid and invalid trials. On behavioural level, the strongest benefits of nicotine in invalid trials were observed in participants carrying both, the DRD2 T- and CHRNA4 C+ variant. Neurally, we were able to demonstrate that different DRD2/CHRNA4 groups can be decoded from the pattern of brain activity in invalid trials under nicotine. Neural substrates of interindividual variability were found in a network of attention-related brain regions comprising the pulvinar, the striatum, the middle and superior frontal gyri, the insula, the left precuneus, and the right middle temporal gyrus. Our findings suggest that polymorphisms in the CHRNA4 and DRD2 genes are a relevant source of individual variability in pharmacological studies with nicotine.</p></div

Model-Fitting results and parameter estimations based on the extended classical twin design.

<p>Model-Fitting results and parameter estimations based on the extended classical twin design.</p

Phenotypic correlations among ANPS scales (correlations for twin1above the diagonal and correlations for twin2 below the diagonal).

<p>Phenotypic correlations among ANPS scales (correlations for twin1above the diagonal and correlations for twin2 below the diagonal).</p

Visual cueing paradigm (A) and behavioural results (B).

<p><b>A.</b> Scheme of the three task conditions; valid (120), invalid (30), catch (20), and zero (50, no cue and no target, not depicted) trials were presented in randomized order with a SOA of 2000 ms. <b>B.</b> Difference of the validity effect (slowing of RTs due to invalidly cued trials as compared to valid trials) between nicotine and placebo. The CHRNA4 C+ and DRD2 T- genotype group shows a significant benefit from nicotine. The significant three-way interaction of <i>genotype group</i> x <i>treatment</i> x <i>condition</i> as displayed in the current figure was identified by post-hoc ANOVAs to be driven by the <i>genotype group</i> x <i>treatment</i> interaction during invalid trials.</p