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  • Showing results for Weighted[Title] AND Symbolic[Title] AND Dependence[Title] AND Metric[Title] AND wSDM[Title] AND fMRI[Title] AND connectivity[Title] AND multicentric[Title] AND validation[Title] AND frontotemporal[Title] AND dementia[Title]. Your search for Weighted Symbolic Dependence Metric (wSDM) for fMRI restingstate connectivity: A multicentric validation for frontotemporal dementia retrieved no results.
Sci Rep. 2018 Jul 25;8(1):11181. doi: 10.1038/s41598-018-29538-9.

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

Author information

1
Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro University, Buenos Aires, Argentina.
2
Fundación Escuela de Medicina Nuclear (FUESMEN) and Comisión Nacional de Energía Atómica (CNEA), Buenos Aires, Argentina.
3
Instituto Balseiro and Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo (UNCuyo), Mendoza, Argentina.
4
National Scientific and Technical Research Council (CONICET), Av. Rivadavia 1917, C1033AAJ, Buenos Aires, Argentina.
5
Faculty of Education, National University of Cuyo (UNCuyo), Sobremonte 74, C5500, Mendoza, Argentina.
6
Instituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Ciudad de Buenos Aires, Argentina.
7
Departamento de Educación Física y Salud, Instituto Superior de Educación Física, Universidad de la República, Montevideo, Uruguay.
8
Neurologia Cognitiva. Hospital Cesar Milstein., Buenos Aires, Argentina.
9
Universidad Icesi, Departamento de Estudios Psicologicos, Cali, Colombia.
10
Intellectus Memory and Cognition Center, Aging Institute, Mental Health and Psychiatry Department, Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogotá, Colombia.
11
Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia.
12
Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Diagonal Las Torres, 2640, Santiago de Chile, Chile.
13
Universidad Autónoma del Caribe, Calle 90, No 46-112, C2754, Barranquilla, Colombia.
14
Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro University, Buenos Aires, Argentina. lsedeno@ineco.org.ar.
15
National Scientific and Technical Research Council (CONICET), Av. Rivadavia 1917, C1033AAJ, Buenos Aires, Argentina. lsedeno@ineco.org.ar.

Abstract

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-linear brain dynamics. To circumvent this limitation, we developed a "weighted Symbolic Dependence Metric" (wSDM) measure. 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. We compared this measure with a linear connectivity metric (Pearson's R) in its capacity to identify patients with behavioral variant frontotemporal dementia (bvFTD) and controls based on resting-state data. We recruited participants from two international centers with different MRI recordings to assess the consistency of our measure across heterogeneous conditions. First, a seed-analysis comparison of the salience network (a specific target of bvFTD) and the default-mode network (as a complementary control) between patients and controls showed that wSDM yields better identification of resting-state networks. Moreover, machine learning analysis revealed that wSDM yielded higher classification accuracy. These results were consistent across centers, highlighting their robustness despite heterogeneous conditions. Our findings underscore the potential of wSDM to assess fMRI-derived FC data, and to identify sensitive biomarkers in bvFTD.

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