BACKGROUND:Patients with heart failure (HF) show abnormal autonomic activities, which may stem from altered functional connectivity (FC) between different brain sites. METHODS AND RESULTS:We evaluate insular and cerebellar FC with other brain areas, before, during, and after the Valsalva challenge, with functional magnetic resonance imaging in 35 HF and 35 control subjects. Significant insular FC emerged with striatum, thalamus, and anterior cingulate. While left and right cerebellar cortices showed significant FC with each other constituting the cerebellum network, the insula and cerebellum networks showed significant negative FC with each other at baseline, challenge, and recovery phases. The challenge induced increased FC within the insula and the cerebellum networks in both HF and controls. However, patients with HF showed more increased insular network FC, but less enhanced cerebellar FC. During the recovery phase, the negative FC between the insular network and cerebellum enhanced significantly in controls, but not in HF. Lower left ventricle ejection fraction was correlated with lower insula network FC, and impaired negative FC between cerebellum and the insula network in HF. CONCLUSIONS:Increased insular FC in patients with HF might contribute to exaggerated sympathetic tone. While impaired cerebellar FC and diminished negative interactions between cerebellum and insular systems may indicate impaired parasympathetic functions in HF.

Patients with obstructive sleep apnea (OSA) show autonomic, mood, cognitive, and breathing dysfunctions that are linked to increased morbidity and mortality, which can be improved with early screening and intervention. The gold standard and other available methods for OSA diagnosis are complex, require whole-night data, and have significant wait periods that potentially delay intervention. Our aim was to examine whether using faster and less complicated machine learning models, including support vector machine (SVM) and random forest (RF), with brain diffusion tensor imaging (DTI) data can classify OSA from healthy controls. We collected two DTI series from 59 patients with OSA [age: 50.2 ± 9.9 years; body mass index (BMI): 31.5 ± 5.6 kg/m2 ; apnea-hypopnea index (AHI): 34.1 ± 21.2 events/h 23 female] and 96 controls (age: 51.8 ± 9.7 years; BMI: 26.2 ± 4.1 kg/m2 ; 51 female) using a 3.0-T magnetic resonance imaging scanner. Using DTI data, mean diffusivity maps were calculated from each series, realigned and averaged, normalised to a common space, and used to conduct cross-validation for model training and selection and to predict OSA. The RF model showed 0.73 OSA and controls classification accuracy and 0.85 area under the curve (AUC) value on the receiver-operator curve. Cross-validation showed the RF model with comparable fitting over SVM for OSA and control data (SVM; accuracy, 0.77; AUC, 0.84). The RF ML model performs similar to SVM, indicating the comparable statistical fitness to DTI data. The findings indicate that RF model has similar AUC and accuracy over SVM, and either model can be used as a faster OSA screening tool for subjects having brain DTI data.