Weighted symbolic dependence dynamics (wSDD) for fMRI resting-state connectivity: A multicentric validation for frontotemporal dementia

SEDEÑO, LUCAS; MATALLANA, DIANA; CERVETTO, SABRINA; HESSE, EUGENIA; MOGUILNER, SEBASTIÁN; IBÁÑEZ, AGUSTÍN; REYES, PABLO; MELLONI, MARGHERITA; MIKULAN, EZEQUIEL; MANES, FACUNDO; HERRERA, EDUAR; GARCÍA-CORDERO, INDIRA; GARCÍA, ADOLFO M.

Buenos Aires

Workshop; AAIC Satellite Symposium; 2018

Alzheimer's Association International Conference

We developed a novel fMRI connectivity measure, the wSDD. Using symbolic transforms, we factor in local and global temporal features of the BOLD signal to weigh a robust copula-based dependence measure by symbolic similarity capturing both linear and non-linear associations. To test our wSDD, we compared it with a widely used linear connectivity metric (namely, Pearson?s R) in its capacity to identify patients with behavioral variant frontotemporal dementia (bvFTD) and controls based on resting-state data. Importantly, by recruiting participants from two international centers with different MRI recordings, we also assessed which metric proved more consistent across heterogeneous acquisition conditions. First, a seed-analysis comparison of the salience network (a specific target of bvFTD) between patients and controls showed that wSDD yields better identification of resting-state networks. Moreover, machine learning analysis revealed that wSDD yielded higher classification accuracy. Crucially, these results were consistent in both centers, highlighting their robustness despite heterogeneous acquisition parameters and sociocultural contexts. Our findings underscore the potential of wSDD to assess fMRI-derived FC data, and, more particularly, to identify sensitive biomarkers in bvFTD and other neurodegenerative diseases.