Weighted Symbolic Dependence Metric (wSDM) for fMRI resting-state connectivity: A multicentric validation for frontotemporal dementia

MOGUILNER SEBASTIAN; GARCIA ADOLFO; MIKULAN EZEQUIEL; HESSE EUGENIA; GARCÍA CORDERO INDIRA; MELLONI MARGHERITA; SABRINA CERVETTO; SERRANO CECILIA; HERRERA, EDUAR; PABLOS REYES; DIANA MATALLANA; MANES FACUNDO; IBÁÑEZ, AGUSTÍN; SEDEÑO LUCAS

Scientific Reports

Nature

Año: 2018

The search for biomarkers of neurodegenerative diseases via fMRI functional connectivity(FC) research has yielded inconsistent results. Yet, most FC studies are blind to non-linearbrain dynamics. To circumvent this limitation, we developed a ?weighted SymbolicDependence Metric? (wSDM) measure. Using symbolic transforms, we factor in local and global temporal features of the BOLD signal to weigh a robust copula-based dependencemeasure by symbolic similarity, capturing both linear and non-linear associations. Wecompared this measure with a linear connectivity metric (Pearson?s R) in its capacity toidentify patients with behavioral variant frontotemporal dementia (bvFTD) and controlsbased on resting-state data. We recruited participants from two international centers withdifferent MRIs recordings to assess the consistency of our measure across heterogeneousconditions. First, a seed-analysis comparison of the salience network (a specific target ofbvFTD) and the default-mode network (as a complementary control) between patients andcontrols showed that wSDM yields better identification of resting-state networks. Moreover,machine learning analysis revealed that wSDM yielded higher classification accuracy. Theseresults were consistent across centers, highlighting their robustness despite heterogeneousconditions. Our findings underscore the potential of wSDM to assess fMRI-derived FC data,and to identify sensitive biomarkers in bvFTD.