Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2015 Apr:12:123-33.
doi: 10.1016/j.dcn.2015.01.003. Epub 2015 Feb 3.

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

John R Pruett Jr  1 , Sridhar Kandala  2 , Sarah Hoertel  3 , Abraham Z Snyder  4 , Jed T Elison  5 , Tomoyuki Nishino  6 , Eric Feczko  7 , Nico U F Dosenbach  8 , Binyam Nardos  9 , Jonathan D Power  10 , Babatunde Adeyemo  11 , Kelly N Botteron  12 , Robert C McKinstry  13 , Alan C Evans  14 , Heather C Hazlett  15 , Stephen R Dager  16 , Sarah Paterson  17 , Robert T Schultz  18 , D Louis Collins  19 , Vladimir S Fonov  20 , Martin Styner  21 , Guido Gerig  22 , Samir Das  23 , Penelope Kostopoulos  24 , John N Constantino  25 , Annette M Estes  26 ; IBIS Network; Steven E Petersen  27 , Bradley L Schlaggar  28 , Joseph Piven  29
Collaborators, Affiliations
Multicenter Study

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

John R Pruett Jr et al. Dev Cogn Neurosci. 2015 Apr.

Abstract

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

Keywords: Development; Functional brain networks; Functional connectivity magnetic resonance imaging (fcMRI); Infant; Multivariate pattern analysis (MVPA); Support vector machine (SVM).

PubMed Disclaimer

Figures

Supplementary Fig. 1
Supplementary Fig. 1
Quality assurance measures before and after fcMRI preprocessing. Age is displayed by column. Row 1 shows the relationship between mean frame displacement (FD) and mean standard deviation (SD) prior to fcMRI preprocessing. Importantly, there is no visible clustering by site. Row 2 shows mean standard deviation post frame censoring and nuisance regression. Subjects are now clustered together without noticeable signal variance or frame displacement variance.
Fig. 1
Fig. 1
75% consensus features shared across risk group-specific SVMs. Lines represent features: green for functional connections that, when stronger, contribute to a classification of 12 months – and orange for functional connections that, when stronger, contribute to a classification of 6 months. Only 75% consensus (across cross-validation folds) features which are common to both the low- and high-risk-trained age-classifying SVMs are shown. Spheres represent involved nodes/seed regions. Node colors are the same as are assigned to adult networks in Power et al. (2011).
Fig. 2
Fig. 2
The classification vector from the n = 128 run. Format is the same as in Fig. 1. Here, only 100% consensus (across all cross-validation folds) features are shown. Node colors are the same as are assigned to adult networks in Power et al. (2011); ASD ALE nodes are from Philip et al. (2012).
See this image and copyright information in PMC

References

    1. American Psychiatric Association . fourth ed., text rev. American Psychiatric Association; Washington, DC: 2000. Diagnostic and Statistical Manual of Mental Disorders.
    1. Ben-Hur A., Ong C.S., Sonnenburg S., Schölkopf B., Rätsch G. Support vector machines and kernels for computational biology. PLoS Comput. Biol. 2008;4(10):e1000173. - PMC - PubMed
    1. Buckner R.L., Head D., Parker J., Fotenos A.F., Marcus D., Morris J.C., Snyder A.Z. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage. 2004;23(2):724–738. - PubMed
    1. Cohen A.L., Fair D.A., Dosenbach N.U., Miezin F.M., Dierker D., Van Essen D.C., Schlaggar B.L., Petersen S.E. Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage. 2008;41:45–57. - PMC - PubMed
    1. Csibra G., Davis G., Spratling M.W., Johnson M.H. Gamma oscillations and object processing in the infant brain. Science. 2000;290(5496):1582–1585. - PubMed

Publication types