The good news is that frame censoring does a fantastic job of reducing motion overestimation scores. That means if you motion censor your data after standard processing then you are much less likely to get spurious results due to motion.

Over 40% of brain-behavior effects were likely overestimated due to motion. There was no correlation between a trait’s a priori correlation with motion and its motion overestimation score. Calculating the motion impact score was necessary to discover the problematic brain-behavior effects!

The principle behind the motion impact score is simple! We know head motion varies from second-to-second while behavioral traits are stable over time. We just compare high- and low-motion portions of the fMRI scans. The effect of behavior is the same and cancels out, leaving the motion impact.

We realized that seemingly-unrelated behavioral traits like matrix reasoning ability are, surprisingly, correlated with in-scanner head motion. Could brain-behavior associations of more motion-correlated traits be more impacted by residual head motion? We developed a tool to find out!

Unfortunately, even with fancy processing pipelines and motion censoring, the effect size of residual head motion on functional connectivity can still be larger than the effect of many behavioral traits.

Ever wondered if your interesting brain-behavior correlation was over- or under-estimated due to head motion, but were afraid to ask? We’ve created a motion impact score for detecting spurious brain-behavior associations, now available in Nature Communications!
doi.org/10.1038/s414...

Once again, if you give @gordonneuro.bsky.social et al a network, they're going to want to characterize its subnetworks... A thought provoking read on the AMN (née CON) and the brain architecture of the decision-action-feedback loop. I always feel smarter after reading these kinds of papers!

Stimulants helped children with ADHD or insufficient sleep do better on cognitive tasks. They seem to mask the effects of fatigue and elevate the perceived salience of goal-oriented actions, helping you do tasks you find boring without directly affecting your capacity to pay attention.

Stimulants also increased connectivity of salience network, which encodes anticipated reward, influencing motivation and task-switching. We hypothesize stimulants elevate the perceived salience of mundane tasks (e.g. schoolwork), facilitating attention without directly acting on attention networks.

The arousal-related differences in connectivity associated with stimulants were so strong that they completely negated brain differences related to not getting enough sleep!

Arousal is associated with decreased connectivity in somatomotor networks – exactly the pattern we see in children taking stimulants. The stimulant pattern is highly similar to EEG- and physiologically-derived maps of arousal and concordant with norepinephrine density.

We were so surprised, we replicated our findings in a precision imaging drug trial of healthy adults taking an experimentally controlled dose of Ritalin (methylphenidate). The stimulant trial brain maps were highly correlated with those from ABCD p < 0.001.

My med school textbook says stimulants like Ritalin treat hyperactivity by “stimulating” the brain’s attention and cognitive control systems. We studied children taking stimulants in the ABCD Study, and the largest differences were actually in arousal and reward networks! Check out our preprint!