All neurodegenerative diseases (ND); Alzheimer’s disease (AD); other dementias and Parkinson’s disease (PD) included, have several common features: their incidence increases with an age and the majority of cases are sporadic. Since the generation of „baby boomers“ is aging, a breakout of ND is expected. Therefore, early detection of pathologic processes in the brain is of high importance, especially at presymptomatic stages. Furthermore, its practical availability as a screening method is essential. Hypothesis of our investigation is that the techniques for quantitative ultrasound-based assessment of microstructure and dynamic properties of cerebral tissue would assist in differentiating normal age-related brain changes from ND at an early stage. Thus, a novel non-invasive and inexpensive assessment method of changes in the microstructure and dynamics of brain tissue and anatomical structures is proposed. This method combines primary radiofrequency dynamic ultrasound-based assessment with focal tissue motion analysis technology, and would be analyzed by fusing magnetic resonance imaging and conventional transcranial sonography. This novel method is expected to help finding new biomarkers of ND at an early stage and distinguish normal vs. pathological brain aging in a patient-friendly way. Biomarkers for AD and PD represent important tools supporting the clinical diagnosis and the choice of potential therapeutic options. When applied early, therapeutic interventions are much more effective to sustain healthy aging. Moreover, they would be of great help for the selection of cohorts of homogeneous patients for clinical trials with new disease-modifying derivatives.
Projects funded by the Research Council of Lithuania (RCL), Projects carried out by researchers’ teams
It is expected to create and investigate the parametrical radiofrequency ultrasound based assessment method for early and reliable detection of pathological brain changes in ND by fusing the results of other imaging modalities (MRI, TCS).
Period of project implementation: 2017-09-01 - 2020-09-30
Project coordinator: Lithuanian University of Health Sciences
Project partners: Kaunas University of Technology