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Previous studies of conduct disorder (CD) have reported structural and functional alterations in the limbic system. However, the white matter tracts that connect limbic regions have not been comprehensively studied. The uncinate fasciculus (UF), a tract connecting limbic to prefrontal regions, has been implicated in CD. However, CD-related alterations in other limbic tracts, such as the cingulum and the fornix, have not been investigated. Furthermore, few studies have examined the influence of sex and none have been adequately powered to test whether the relationship between CD and structural connectivity differs by sex. We examined whether adolescent males and females with CD exhibit differences in structural connectivity compared with typically developing controls.
We acquired diffusion-weighted magnetic resonance imaging data from 101 adolescents with CD (52 females) and 99 controls (50 females). Data were processed for deterministic spherical deconvolution tractography. Virtual dissections of the UF, the three subdivisions of the cingulum [retrosplenial cingulum (RSC), parahippocampal and subgenual cingulum], and the fornix were performed and measures of fractional anisotropy (FA) and hindrance-modulated orientational anisotropy (HMOA) were analysed.
The CD group had lower FA and HMOA in the right RSC tract relative to controls. Importantly, these effects were moderated by sex – males with CD significantly lower FA compared to male controls, whereas CD and control females did not differ.
Our results highlight the importance of considering sex when studying the neurobiological basis of CD. Sex differences in RSC connectivity may contribute to sex differences in the clinical presentation of CD.
To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.
To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.
Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.
Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.