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Cited by 117Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

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Predicting individual brain maturity using dynamic functional connectivity

Open access
Qin, Jian, Chen, Shan-Guang, Hu, Dewen, Zeng, Ling-Li, Fan, Yi-Ming, Chen, Xiao-Ping, Shen, Hui·2015·Frontiers in Human Neuroscience
DOI 10.3389/fnhum.2015.00418PMC PMC4503925
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Abstract

Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7-30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.

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Crossref · 10.3389/fnhum.2015.00418

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Figures

FIGURE 1 Flowchart of maturity prediction using the dynamics of resting-state functional connectivity. AAL, automated anatomical labeling; ALFF-FC, amplitude of low-frequency fluctuation of functional connectivity; FC, functional connectivity; PLS, partial least-squares.
FIGURE 1 Flowchart of maturity prediction using the dynamics of resting-state functional connectivity. AAL, automated anatomical labeling; ALFF-FC, amplitude of low-frequency fluctuation of functional connectivity; FC, functional connectivity; PLS, partial least-squares.CC BY · PubMed Central
FIGURE 2 Relationship between brain score and maturation age.(A) Individual brain scores, reflecting the maturity of the brain, were equal to the dot products of connectivity salience and ALFF-FC coefficient vectors. Scatter plots of the age and brain score were constructed to view the correlation between these two parameters, and age was treated as a continuous variable. (B) In the reliability test, 1000 bootstrap resamples were performed to estimate the distribution and 95% confidence intervals of correlation coefficients between brain score and age.
FIGURE 2 Relationship between brain score and maturation age.(A) Individual brain scores, reflecting the maturity of the brain, were equal to the dot products of connectivity salience and ALFF-FC coefficient vectors. Scatter plots of the age and brain score were constructed to view the correlation between these two parameters, and age was treated as a continuous variable. (B) In the reliability test, 1000 bootstrap resamples were performed to estimate the distribution and 95% confidence intervals of correlation coefficients between brain score and age.CC BY · PubMed Central
FIGURE 3 Maturation-dependent connections and regions in the dynamics of functional connectivity from the analysis on the total of subjects. Only connections with bootstrap ratios over 3.0 were selected and considered predictive connections. (A) Functional connections that decreased with maturation during the PLS analysis (shown in green) and maturation-dependent ROIs are displayed on the surface of the brain. Connection lines are scaled according to the bootstrap ratios. The color and scale of ROIs represent the functional networks and region weights (1/2 the sum of connection bootstrap ratios to and from that region). In contrast, (B) functional connections that increased with maturation are displayed in orange.
FIGURE 3 Maturation-dependent connections and regions in the dynamics of functional connectivity from the analysis on the total of subjects. Only connections with bootstrap ratios over 3.0 were selected and considered predictive connections. (A) Functional connections that decreased with maturation during the PLS analysis (shown in green) and maturation-dependent ROIs are displayed on the surface of the brain. Connection lines are scaled according to the bootstrap ratios. The color and scale of ROIs represent the functional networks and region weights (1/2 the sum of connection bootstrap ratios to and from that region). In contrast, (B) functional connections that increased with maturation are displayed in orange.CC BY · PubMed Central
FIGURE 4 Maturation-related networks in the dynamics of functional connectivity. SMN, sensorimotor network; OCC, occipital network; FPN, fronto-parietal network; DMN, default mode network; CON, cingulo-opercular network; CER, cerebellum. The first group includes 85 subjects with TR = 2500 ms and the second group contains 98 subjects with TR = 645 ms. (A) Negative network weight (1/2 the sum of the bootstrap ratios of connections that decreased with age affiliated with corresponding networks) reflects the “network-distribution” of connections that decrease with age in dynamics. (B) The positive network weight, which was calculated by a similar method, represents the distribution of connections that increase with age. (C) The sum of all of the connection bootstrap ratios within each network and between networks are shown to the left and right of the black line, respectively.
FIGURE 4 Maturation-related networks in the dynamics of functional connectivity. SMN, sensorimotor network; OCC, occipital network; FPN, fronto-parietal network; DMN, default mode network; CON, cingulo-opercular network; CER, cerebellum. The first group includes 85 subjects with TR = 2500 ms and the second group contains 98 subjects with TR = 645 ms. (A) Negative network weight (1/2 the sum of the bootstrap ratios of connections that decreased with age affiliated with corresponding networks) reflects the “network-distribution” of connections that decrease with age in dynamics. (B) The positive network weight, which was calculated by a similar method, represents the distribution of connections that increase with age. (C) The sum of all of the connection bootstrap ratios within each network and between networks are shown to the left and right of the black line, respectively.CC BY · PubMed Central
FIGURE 5 Influence of the sliding windows size on the results of partial least-squares analysis. SMN, sensorimotor network; OCC, occipital network; FPN, fronto-parietal network; (A) Correlation coefficients of the brain score and age with each time window size are shown in blue, and the green circle above indicate the confidence intervals. (B) Average MAE (±SE) curves for age prediction are shown in blue, and their confidence intervals with each time window size are shown in green. (C) The height of the bar in the figure indicates the upper border of the permuted P-value for correlation coefficients of the brain score and age with each time window size. (D) The figure reflects the stability of the region bootstrap ratios (1/2 the sum of the connection bootstrap ratios to and from the regions) with changing time window size, suggesting the stability of the spatial patterns of maturation-dependent connections. Due to the large number of regions, only the “predictive regions” were selected and shown. Positive values indicate regions with connections mainly showing increased dynamics with age, and negative values denote regions with connections showing decreased dynamics.
FIGURE 5 Influence of the sliding windows size on the results of partial least-squares analysis. SMN, sensorimotor network; OCC, occipital network; FPN, fronto-parietal network; (A) Correlation coefficients of the brain score and age with each time window size are shown in blue, and the green circle above indicate the confidence intervals. (B) Average MAE (±SE) curves for age prediction are shown in blue, and their confidence intervals with each time window size are shown in green. (C) The height of the bar in the figure indicates the upper border of the permuted P-value for correlation coefficients of the brain score and age with each time window size. (D) The figure reflects the stability of the region bootstrap ratios (1/2 the sum of the connection bootstrap ratios to and from the regions) with changing time window size, suggesting the stability of the spatial patterns of maturation-dependent connections. Due to the large number of regions, only the “predictive regions” were selected and shown. Positive values indicate regions with connections mainly showing increased dynamics with age, and negative values denote regions with connections showing decreased dynamics.CC BY · PubMed Central

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Metadata

Authors
Qin, J., Chen, S.-G., Hu, D., Zeng, L.-L., Fan, Y.-M., Chen, X.-P., & Shen, H.
Affiliations
National University of Defense Technology (CN)
Corresponding author
Shen, Hui
ORCID
Venue
Frontiers in Human Neuroscience
Publisher
Frontiers Media SA
Published
2015-07-16
Volume
9
Pages
418-418
ISSN
1662-5161 (ISSN-L)
DOI
10.3389/fnhum.2015.00418
PMC
PMC4503925
OpenAlex
W1587698431
Cited by
117
References
73
Citations by year
2026: 2 · 2025: 6 · 2024: 10 · 2023: 11 · 2022: 17 · 2021: 16 · 2020: 12 · 2019: 12 · 2018: 12 · 2017: 11 · 2016: 8
FWCI
6.21
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Key Laboratory of Human Factors Engineering
  • National Natural Science Foundation of China (2013CB329401, 61375034, 61375111)
  • National Key Research and Development Program of China (61375034, 61375111, 2013CB329401)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords
dynamic functional connectivity, brain maturation, developmental trajectories, individual differences, default mode network, cerebellum, visual network, cingulo-opercular network, resting-state functional connectivity

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