GEFF: Graph embedding for functional fingerprinting.

Autor: Abbas K; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA., Amico E; Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland., Svaldi DO; Indiana University School of Medicine, Indiana University, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University, Indianapolis, IN, USA., Tipnis U; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA., Duong-Tran DA; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA., Liu M; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA., Rajapandian M; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA., Harezlak J; Department of Epidemiology and Biostatistics, Indiana University, IN, USA., Ances BM; Washington University School of Medicine, Washington University, St Louis, MO, USA., Goñi J; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA. Electronic address: jgonicor@purdue.edu.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2020 Nov 01; Vol. 221, pp. 117181. Date of Electronic Publication: 2020 Jul 20.
DOI: 10.1016/j.neuroimage.2020.117181
Abstrakt: It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.
(Copyright © 2020. Published by Elsevier Inc.)
Databáze: MEDLINE
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