Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning.

BACKGROUND: Functional connectivity between the left dorsolateral prefrontal cortex (DLPFC) and subgenual cingulate (sgACC) may serve as a biomarker for transcranial magnetic stimulation (rTMS) treatment response. The first aim was to establish whether this finding is veridical or artifactually induced by the pre-processing method. Furthermore, alternative biomarkers were identified and the clinical utility for personalized medicine was examined. METHODS: Resting-state fMRI data were collected in medication-refractory depressed patients (n = 70, 16 males) before undergoing neuronavigated left DLPFC rTMS. Seed-based analyses were performed with and without global signal regression pre-processing to identify biomarkers of short-term and long-term treatment response. Receiver Operating Characteristic curve and supervised machine learning analyses were applied to assess the clinical utility of these biomarkers for the classification of categorical rTMS response. RESULTS: Regardless of the pre-processing method, DLPFC-sgACC connectivity was not associated with treatment outcome. Instead, poorer connectivity between the sgACC and three clusters (peak locations: frontal pole, superior parietal lobule, occipital cortex) and DLPFC-central opercular cortex were observed in long-term nonresponders. The identified connections could serve as acceptable to excellent markers. Combining the features using supervised machine learning reached accuracy rates of 95.35% (CI=82.94-100.00) and 88.89% (CI=63.96-100.00) in the cross-validation and test dataset, respectively. LIMITATIONS: The sample size was moderate, and features for machine learning were based on group differences. CONCLUSIONS: Long-term nonresponders showed greater disrupted connectivity in regions involving the central executive network. Our findings may aid the development of personalized medicine for medication-refractory depression.

Coronavirus infections in horses in Saudi Arabia and Oman.

Equine coronaviruses (ECoV) are the only coronavirus known to infect horses. So far, data on ECoV infection in horses remain limited to the USA, France and Japan and its geographic distribution is not well understood. We carried out RT-PCR on 306 nasal and 315 rectal swabs and tested 243 sera for antibodies to detect coronavirus infections in apparently healthy horses in Saudi Arabia and Oman. We document evidence of infection with ECoV and HKU23 coronavirus by RT-PCR. There was no conclusive evidence of Middle East respiratory syndrome coronavirus infection in horses. Serological data suggest that lineage A betacoronavirus infections are commonly infecting horses in Saudi Arabia and Oman but antibody cross-reactivities between these viruses do not permit us to use serological data alone to identify which coronaviruses are causing these infections.