Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: a multicenter study

• Autores: Moguilner, Sebastian Gabriel; García, Adolfo Martín; Sanz Perl Hernandez, Yonatan; Tagliazucchi, Enzo Rodolfo; Piguet, Olivier; Kumfor, Fiona; Reyes, Pablo; Matallana, Diana; Sedeño, Lucas; Ibañez, Agustin Mariano
• Año de publicación: 2021
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: AD; BVFTD; COPULA-BASED DEPENDENCE MEASURE; DYNAMIC FUNCTIONAL CONNECTIVITY; FMRI RESTING-STATE CONNECTIVITY
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.
Fil: Moguilner, Sebastian Gabriel. University of California; Estados Unidos. Trinity College; Irlanda. Fundación Escuela de Medicina Nuclear; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of California; Estados Unidos. Trinity College; Irlanda. Universidad de San Andrés; Argentina. Universidad Catolica de Cuyo. Facultad de Educacion.; Argentina
Fil: Sanz Perl Hernandez, Yonatan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Piguet, Olivier. The University Of Sydney; Australia
Fil: Kumfor, Fiona. The University Of Sydney; Australia
Fil: Reyes, Pablo. Pontificia Universidad Javeriana; Colombia. Hospital Universitario Fundación Santa Fe; Colombia. Hospital Universitario San Ignacio; Colombia
Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia. Hospital Universitario Fundación Santa Fe; Colombia. Hospital Universitario San Ignacio; Colombia
Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of California; Estados Unidos. Trinity College; Irlanda. Universidad de San Andrés; Argentina. Universidad Autónoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile