Seminario del Grupo de Física Estadística y de los Sistemas Complejos de la Universidad de Granada, impartido por Jesús M. Cortés (Biocruces Health Research Institute, España).
Abstract: The chronological age (CHA) differs from the biological one. While the former is defined as the time running as birth, the latter quantifies the maturity level that an individual (or an organ) has at the operational level. In relation with the brain, the discrepancy between the brain age and CHA might work as a biomarker for quantifying deterioration as a result of disease or improvement after some treatment or therapy, which has unlimited applications. Here, I will talk about recent results from our laboratory addressing whether or not the CHA of an individual can be predicted from the brain connectivity patterns. In particular, by building functional (resting-state) and structural (tractography) brain networks from neuroimages of healthy participants (N=155) ranging from 10 to 80 years, we apply machine learning methods to define the brain-connectome age (BCA) –the age estimator resulting from the structure-function connectome–. We next compared BCA to CHA to find that the connectivity between basal ganglia and thalamus to frontal areas, a.k.a. the fronto-striato-thalamic circuit, makes the major contribution to age estimation. Notably, the same methodology can be generally applied to identify the structural-functional brain network patterns correlating to other biomarkers than CHA.
- Fecha: Miércoles, 31 de Octubre de 2018.
- Lugar: Seminario de Física Computacional (F6). Departamento de Física de la Materia (Planta baja del edificio de Física). Facultad de Ciencias.
- Horario: 12:00.
- Organiza: Grupo de Física Estadística y de los Sistemas Complejos de la Universidad de Granada
- Más información:
- Reference: http://jesuscortes.info/jesusweb/publications/Betal_HBM2018.pdf
- Daniel Manzano
Electromagnetism and Condensed Matter Department
University of Granada
Facultad de Ciencias, Av. Fuentenueva s/n
Granada 18071, Spain
Phone: +34 958241000 Ext: 20569
manzano@onsager.ugr.es