Voxel based whole-brain Diffusion Tensor Imaging analysis.

<p>The right external capsule was the only white matter area that emerged as significant when comparing FA maps from individuals with CD relative to age- and IQ-matched healthy controls (two-sample t test, P<0.05, corrected for the entire volume of the white-matter in the whole brain). The CD group showed increased FA values in this region relative to the healthy control group. MNI: Montreal Neurological Institute (x, y, z) coordinates. The color bar ranging from red (bottom) to yellow (top) represents T statistics.</p

Examples of reconstructions of the right Uncinate Fascicle (left panel) or the left Inferior Frontal-Occipital Fascicle (right panel) pathways in two individual subjects.

<p>Examples of reconstructions of the right Uncinate Fascicle (left panel) or the left Inferior Frontal-Occipital Fascicle (right panel) pathways in two individual subjects.</p

Mean fractional anisotropy (FA) values and number of streamlines reconstructed in the uncinate fascicle and inferior frontal occipital fascicle of individuals with CD and healthy controls.

<p>Mean fractional anisotropy (FA) values and number of streamlines reconstructed in the uncinate fascicle and inferior frontal occipital fascicle of individuals with CD and healthy controls.</p

Characteristics of the participants included in the diffusion tensor imaging analyses.

<p>Key: SD, Standard Deviation; IQ, intelligence quotient; YPI, Youth Psychopathic traits Inventory; CD, Conduct Disorder; STAI, Spielberger State-Trait Anxiety Inventory; ADHD, Attention-Deficit/Hyperactivity Disorder.</p

The distribution of model fit over the population is bimodal and best described by four Gaussian components.

A. Kernel density contours are shown, while the inset plots peaks of the landscape in 3D. B. 4-component mixture-of-Gaussians fitted to this joint distribution of integrated likelihoods, with participants clustered according to the Gaussian they are most likely to belong to. As can also be seen in the 3D rendering. Apart from the prominent peaks (cluster3, green, Nc3 = 176 participants, and 4, blue, Nc4 = 171) there are somewhat less prominent concentrations (1, black, Nc1 = 94 and 2, red, Nc2 = 113).</p

Plots of the fractional anisotropy (FA) values within the bilateral Uncinate Fascicle and the Inferior-Frontal-Occipital Fascicle in individuals with Conduct Disorder (CD) and healthy controls as obtained from tractography analyses.

<p>Error bars represent the standard error of the mean.</p

Raw performance in the all conditions.

The middle two quartiles are in solid color, with the ‘avoid loss’ conditions in pink/purple and ‘win’ in gold/yellow. Just non-overlapping notches represent p = 0.05 for the uncorrected difference between two medians. One star denotes p pcor A. Performance weighted towards early trials, the weighing decreasing linearly to 0 for the middle of the task. No-Go to win (NG2W) showed median success rate slightly below chance, in line with the literature. B. Late trials. Performance reaches maximum for at least a quarter of participants in both appetitive conditions, but a quarter (long follow-up) or more (baseline) participants still perform below chance in NG2W. Long follow-up shows better performance than baseline in all except the easy Go-to-Win (G2W) conditions.</p

Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood - Fig 5

Model comparison based on Mean Prediction probability per trial (Ppt) in the long follow-up sample, showing that the difference in Ppt between the two best models is similar if one uses model-fitting vs. out-of-sample based methods A. ΔPpt estimated through a model fit measure, namely mean integrated likelihood per trial, N2 = 556. Both models have mean Ppt about 0.64. B. ΔPpt estimated by out-of-sample prediction of the 48th and 96th trials for each participant on a test subsample of N = 255. This out-of-sample comparison is more variable, but the resampling-based 95% confidence interval (CI) of the median difference is -0.0012 to 0.0026, consistent with A. If it were desirable to further reduce this CI, the estimate could be averaged over rotated out-of-sample trials, at the very considerable computational cost of re-estimating the entire model fit for each left-out sample.</p

Stability in descriptive estimates of Pavlovian bias, assessed by the interaction between the fractions of total correct answers in the four conditions, ((G2W-NG2W)+(NG2AL-G2AL))/2.

A. Stability assessed by baseline vs. long follow-up estimates. A positive correlation is detectable, rho~0.15, p = 5.4e-4. B. Difference in performance between the appetitive (two left) and aversive (two right) conditions. The horizontal lines show the median appetitive bias at baseline (G2W-NG2W; first violin plot; salmon) vs. long follow-up (second plot, in green). This significant difference (p = 0.0027) drives an overall reduction in the estimate of Pavlovian bias (p = 0.0019). The aversive context, NG2AL-G2AL, shows no significant change on its own (blue and mauve; p = 0.35). White boxes are interquartile ranges.</p

Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood

Pavlovian influences are important in guiding decision-making across health and psychopathology. There is an increasing interest in using concise computational tasks to parametrise such influences in large populations, and especially to track their evolution during development and changes in mental health. However, the developmental course of Pavlovian influences is uncertain, a problem compounded by the unclear psychometric properties of the relevant measurements. We assessed Pavlovian influences in a longitudinal sample using a well characterised and widely used Go-NoGo task. We hypothesized that the strength of Pavlovian influences and other ‘psychomarkers’ guiding decision-making would behave like traits. As reliance on Pavlovian influence is not as profitable as precise instrumental decision-making in this Go-NoGo task, we expected this influence to decrease with higher IQ and age. Additionally, we hypothesized it would correlate with expressions of psychopathology. We found that Pavlovian effects had weak temporal stability, while model-fit was more stable. In terms of external validity, Pavlovian effects decreased with increasing IQ and experience within the task, in line with normative expectations. However, Pavlovian effects were poorly correlated with age or psychopathology. Thus, although this computational construct did correlate with important aspects of development, it does not meet conventional requirements for tracking individual development. We suggest measures that might improve psychometric properties of task-derived Pavlovian measures for future studies.</div