Approved research

Quantification of morphological changes of brain structures in patients affected by COVID-19

Lay summary

There is evidence reported in the scientific literature about brain damage caused by COVID-19. However, the methods and biomarkers reported for the diagnosis and quantification of morphological changes are diverse, especially those associated with symptoms of long-COVID, this being an area of science under constant research. The trend in the search and validation of new biomarkers will continue in the future because many patients suffer from the consequences of the infection, and its long-term repercussions are still unknown. Within the study of MRI images of patients affected with SARS-CoV2, the frequent use of volumetric measurements can be observed, to find morphological anomalies in brain structures. In the case of metrics such as volume or cortical thickness, present limitations of not offering much information if it is not compared with a measurement before the disease. Consequently, it is necessary to find other metrics that are invariant to scaling, rotation, or translation and that can more accurately quantify the atrophic changes presented by patients affected with COVID-19.

Aims:

To Evaluate discrete compactness and normalized tortuosity, as imaging biomarkers to quantify atrophic changes in brain structures of patients diagnosed with COVID-19.

To validate the proposed biomarkers to provide support tools to experts in the clinical monitoring and evolution of sequelae in affected patients.

This project will be conducted for approximately 36 months, but we hope to produce initial results in the next 12 months.

In this research, it is expected to find statistically significant differences (p<0.05) in the brain structures analyzed for the new biomarkers proposed in this study. In this way, these biomarkers in conjunction with machine learning techniques can be used to more effectively quantify the brain morphological deterioration caused by COVID-19. These results will provide tools to experts to more accurately quantify the clinical diagnosis of patients affected by COVID-19.