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Zhou Y, Müller HG, Zhu C, Chen Y, Wang JL, O'Muircheartaigh J, Bruchhage M, Deoni S, Bruchhage M, Carnell S, Deoni S, D’Sa V, Huentelman M, Klepac-Ceraj V, LeBourgeois M, Müller HG, O’Muircheartaigh J, Wang JL. Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education. Sci Rep 2023; 13:2984. [PMID: 36804963 PMCID: PMC9941570 DOI: 10.1038/s41598-023-29797-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/10/2023] [Indexed: 02/22/2023] Open
Abstract
The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother's education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.
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Affiliation(s)
- Yidong Zhou
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA.
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Changbo Zhu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Yaqing Chen
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Muriel Bruchhage
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, USA.,Department of Diagnostic Imaging, Rhode Island Hospital, Providence, USA.,Institute of Social Sciences, Stavanger University, Stavanger, 4021, Norway
| | - Sean Deoni
- Maternal, Newborn, and Child Health Discovery and Tools, Bill and Melinda Gates Foundation, Seattle, WA, USA
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Elayouty A, Scott M, Miller C. Time-Varying Functional Principal Components for Non-Stationary EpCO$$_2$$ in Freshwater Systems. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2022. [DOI: 10.1007/s13253-022-00494-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractOutgassing of carbon dioxide (CO$$_2$$
2
) from river surface waters, estimated using partial pressure of dissolved CO$$_2$$
2
, has recently been considered an important component of the global carbon budget. However, little is still known about the high-frequency dynamics of CO$$_2$$
2
emissions in small-order rivers and streams. To analyse such highly dynamic systems, we propose a time-varying functional principal components analysis (FPCA) for non-stationary functional time series (FTS). This time-varying FPCA is performed in the frequency domain to investigate how the variability and auto-covariance structures in a FTS change over time. This methodology, and the associated proposed inference, enables investigation of the changes over time in the variability structure of the diurnal profiles of the partial pressure of CO$$_2$$
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and identification of the drivers of those changes. By means of a simulation study, the performance of the time-varying dynamic FPCs is investigated under different scenarios of complete and incomplete FTS. Although the time-varying dynamic FPCA has been applied here to study the daily processes of consuming and producing CO$$_2$$
2
in a small catchment of the river Dee in Scotland, this methodology can be applied more generally to any dynamic time series.Supplementary materials accompanying this paper appear online.
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Wright ID, Reimherr M, Liechty J. A Machine Learning Approach to Classification for Traders in Financial Markets. Stat (Int Stat Inst) 2022. [DOI: 10.1002/sta4.465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Isaac D. Wright
- Department of Statistics, Pennsylvania, State University State College PA USA
| | - Matthew Reimherr
- Department of Statistics, Pennsylvania, State University State College PA USA
| | - John Liechty
- Department of Marketing, Pennsylvania, State University State College PA USA
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Fan J, Müller H. Conditional distribution regression for functional responses. Scand Stat Theory Appl 2021. [DOI: 10.1111/sjos.12525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jianing Fan
- Department of Statistics University of California Davis California USA
| | - Hans‐Georg Müller
- Department of Statistics University of California Davis California USA
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Petersen A, Müller HG. Discussion: A Spatial Modeling Approach for Linguistic Object Data: Analyzing Dialect Sound Variations Across Great Britain, by Shahin Tavakoli et al. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2019.1635478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Alexander Petersen
- Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, CA
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Dai X, Müller HG, Wang JL, Deoni SCL. Age-dynamic networks and functional correlation for early white matter myelination. Brain Struct Funct 2019; 224:535-551. [PMID: 30392094 PMCID: PMC6420858 DOI: 10.1007/s00429-018-1785-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 10/26/2018] [Indexed: 12/24/2022]
Abstract
The maturation of the myelinated white matter throughout childhood is a critical developmental process that underlies emerging connectivity and brain function. In response to genetic influences and neuronal activities, myelination helps establish the mature neural networks that support cognitive and behavioral skills. The emergence and refinement of brain networks, traditionally investigated using functional imaging data, can also be interrogated using longitudinal structural imaging data. However, few studies of structural network development throughout infancy and early childhood have been presented, likely owing to the sparse and irregular nature of most longitudinal neuroimaging data, which complicates dynamic analysis. Here, we overcome this limitation and investigate through concurrent correlation the co-development of white matter myelination and volume, and structural network development of white matter myelination between brain regions as a function of age, using statistically well-supported methods. We show that the concurrent correlation of white matter myelination and volume is overall positive and reaches a peak at 580 days. Brain regions are found to differ in overall magnitudes and patterns of time-varying association throughout early childhood. We introduce time-dynamic developmental networks based on temporal similarity of association patterns in the levels of myelination across brain regions. These networks reflect groups of brain regions that share similar patterns of evolving intra-regional connectivity, as evidenced by levels of myelination, are biologically interpretable and provide novel visualizations of brain development. Comparing the constructed networks between different maternal education groups, we found that children with higher and lower maternal education differ significantly in the overall magnitude of the time-dynamic correlations.
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Affiliation(s)
- Xiongtao Dai
- Department of Statistics, Iowa State University, Ames, IA, 50011, USA.
| | - Hans-Georg Müller
- Department of Statistics, University of California Davis, Davis, CA, 95616, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California Davis, Davis, CA, 95616, USA
| | - Sean C L Deoni
- Advanced Baby Imaging Lab, Brown University School of Engineering, Providence, RI, 02912, USA
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