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St-Onge F, Javanray M, Pichet Binette A, Strikwerda-Brown C, Remz J, Spreng RN, Shafiei G, Misic B, Vachon-Presseau É, Villeneuve S. Functional connectome fingerprinting across the lifespan. Netw Neurosci 2023; 7:1206-1227. [PMID: 37781144 PMCID: PMC10473304 DOI: 10.1162/netn_a_00320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 04/24/2023] [Indexed: 10/03/2023] Open
Abstract
Systematic changes have been observed in the functional architecture of the human brain with advancing age. However, functional connectivity (FC) is also a powerful feature to detect unique "connectome fingerprints," allowing identification of individuals among their peers. Although fingerprinting has been robustly observed in samples of young adults, the reliability of this approach has not been demonstrated across the lifespan. We applied the fingerprinting framework to the Cambridge Centre for Ageing and Neuroscience cohort (n = 483 aged 18 to 89 years). We found that individuals are "fingerprintable" (i.e., identifiable) across independent functional MRI scans throughout the lifespan. We observed a U-shape distribution in the strength of "self-identifiability" (within-individual correlation across modalities), and "others-identifiability" (between-individual correlation across modalities), with a decrease from early adulthood into middle age, before improving in older age. FC edges contributing to self-identifiability were not restricted to specific brain networks and were different between individuals across the lifespan sample. Self-identifiability was additionally associated with regional brain volume. These findings indicate that individual participant-level identification is preserved across the lifespan despite the fact that its components are changing nonlinearly.
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Affiliation(s)
- Frédéric St-Onge
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, Canada
- Research Center of the Douglas Mental Health University Institute, Montreal, Canada
| | - Mohammadali Javanray
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, Canada
- Research Center of the Douglas Mental Health University Institute, Montreal, Canada
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
| | | | - Jordana Remz
- Research Center of the Douglas Mental Health University Institute, Montreal, Canada
| | - R. Nathan Spreng
- Research Center of the Douglas Mental Health University Institute, Montreal, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Étienne Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Canada
| | - Sylvia Villeneuve
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, Canada
- Research Center of the Douglas Mental Health University Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
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Yoo K, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. Neuroimage 2019; 197:212-223. [PMID: 31039408 DOI: 10.1016/j.neuroimage.2019.04.060] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 10/26/2022] Open
Abstract
Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.
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Affiliation(s)
| | | | - Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, USA; Interdepartmental Neuroscience Program, Yale University, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA
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