Cerebral hypoperfusion in post-COVID-19 cognitively impaired subjects revealed by arterial spin labeling MRI.

Cognitive impairment is one of the most prevalent symptoms of post Severe Acute Respiratory Syndrome COronaVirus 2 (SARS-CoV-2) state, which is known as Long COVID. Advanced neuroimaging techniques may contribute to a better understanding of the pathophysiological brain changes and the underlying mechanisms in post-COVID-19 subjects. We aimed at investigating regional cerebral perfusion alterations in post-COVID-19 subjects who reported a subjective cognitive impairment after a mild SARS-CoV-2 infection, using a non-invasive Arterial Spin Labeling (ASL) MRI technique and analysis. Using MRI-ASL image processing, we investigated the brain perfusion alterations in 24 patients (53.0 ± 14.5 years, 15F/9M) with persistent cognitive complaints in the post COVID-19 period. Voxelwise and region-of-interest analyses were performed to identify statistically significant differences in cerebral blood flow (CBF) maps between post-COVID-19 patients, and age and sex matched healthy controls (54.8 ± 9.1 years, 13F/9M). The results showed a significant hypoperfusion in a widespread cerebral network in the post-COVID-19 group, predominantly affecting the frontal cortex, as well as the parietal and temporal cortex, as identified by a non-parametric permutation testing (p < 0.05, FWE-corrected with TFCE). The hypoperfusion areas identified in the right hemisphere regions were more extensive. These findings support the hypothesis of a large network dysfunction in post-COVID subjects with cognitive complaints. The non-invasive nature of the ASL-MRI method may play an important role in the monitoring and prognosis of post-COVID-19 subjects.

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability.

Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process.