Robust associations between white matter microstructure and general intelligence

Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.

Pain vs. comfort diary: A fully remote app-based experiment

BACKGROUND

Focusing on pain while completing a pain diary might have detrimental effects on pain intensity. Inverted comfort ratings might be used instead.

METHODS

A fully remote app-based registered experiment was conducted to investigate the effects of a pain versus comfort diary on 7-day recall ratings of pain intensity during a 3-week period. The diary included questions about past, current and expected pain or comfort. Randomization took place by the study app, thereby controlling for effects of experimenter bias.

RESULTS

Contrary to the study hypothesis, multilevel regression showed a more pronounced decrease in 7-day recall ratings of pain in the group who rated pain intensity daily (n = 184) than in the group who rated comfort daily (n = 205, B = -0.17, p = 0.034). There were no between-group differences in secondary outcomes (comfort, depressive symptoms, pain interference and happiness). Exploratory analyses revealed more pronounced decreases in pain intensity in participants who experienced less frequent pain in the previous 6 months. Correlations between pain and comfort ratings decreased from -0.39 at baseline to -0.06 after 3 weeks.

CONCLUSIONS

The findings do not support the potential beneficial effects of replacing diary ratings of pain intensity with diary ratings of comfort. The unexpected decreases among those who completed daily pain diaries might have been due to the inclusion of questions about expected pain. Decreasing correlations between pain and comfort ratings suggest that comfort ratings are not merely inverted pain ratings; rather, they appear to assess a domain distinct from pain intensity.

SIGNIFICANCE

The positive effects of pain diaries on pain trajectories appear to constitute a reliable effect and not a methodological artefact. Pain diaries should be investigated systematically to identify ways to optimize their effects on clinical outcomes. Comfort diaries, however, do not appear to be an efficacious substitute for pain diaries; if the current findings replicate, they indicate that primary care practitioners should continue to use pain diaries in clinical care.

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.