GARCIA adolfo Martin
congresos y reuniones científicas
Brain network organization in frontotemporal dementia across countries, recordings and methods
SEDEÑO, L., PIGUET, O., GARCÍA, A. M., FITTIPALDI, S., ABREVAYA, S., GARCÍA-CORDERO, I., BAEZ, S., DE LA FUENTE, L., KUMFOR, F., TORRALVA, T., HODGES, J. & IBÁÑEZ, A.
Conferencia; 10th International Conference on Frontotemporal Dementias; 2016
International Society for Frontotemporal Dementias
Behavioral-variant frontotemporal dementia (bvFTD) is characterized by marked functional connectivity alterations, which may represent a key biomarker contributing to its early diagnosis. However, to achieve this, it is critical to assess the robustness and replicability of functional connectivity alterations across heterogeneous contexts (due to differences in MRI equipment and acquisition parameters, clinical diagnostic teams and participants' socio-cultural profiles). We explored whether graph measures could robustly distinguish bvFTD patients from controls. To this end, we enrolled patients and controls from three international clinical centers (Argentina, Australia and Colombia) with extensive experience in neurodegeneration. To assess the specificity of our results, we replicated our study with three disease control groups: fronto-insular stroke, Alzheimer disease, and primary progressive aphasia. All participants underwent an fMRI resting-state recording with different MRI acquisition parameters. Based on the graph-theory approach, we measured several global and local indexes. Compared to controls, bvFTD presented consistent alterations in all measures across centers. In addition, they discriminate bvFTD patients from the other disease control groups. To our knowledge, this is the first multicenter report to assess bvFTD via graph-theory. This approach was robust enough to discriminate bvFTD patients from healthy controls and also from other neurological patients. The consistency of these findings in a highly heterogeneous context highlights graph-theory method as a potential gold-standard approach for brain network analysis. This represents a critical step in its possible clinical application as a biomarker signature for neurodegenerative diseases.