Literature DB >> 29501876

T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance.

John D Lewis1, Alan C Evans2, Jussi Tohka3.   

Abstract

Knowing the maturational schedule of typical brain development is critical to our ability to identify deviations from it; such deviations have been related to cognitive performance and even developmental disorders. Chronological age can be predicted from brain images with considerable accuracy, but with limited spatial specificity, particularly in the case of the cerebral cortex. Methods using multi-modal data have shown the greatest accuracy, but have made limited use of cortical measures. Methods using complex measures derived from voxels throughout the brain have also shown great accuracy, but are difficult to interpret in terms of cortical development. Measures based on cortical surfaces have yielded less accurate predictions, suggesting that perhaps cortical maturation is less strongly related to chronological age than is maturation of deep white matter or subcortical structures. We question this suggestion. We show that a simple metric based on the white/gray contrast at the inner border of the cortex is a good predictor of chronological age. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Further, our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are not merely random errors, but are strongly related to IQ, suggesting that this metric is sensitive to aspects of brain development that reflect cognitive performance.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Age prediction; Brain-age; Cognitive performance; Cortical thickness; IQ; White/gray-contrast

Mesh:

Year:  2018        PMID: 29501876     DOI: 10.1016/j.neuroimage.2018.02.050

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  18 in total

1.  Structural Associations of Cortical Contrast and Thickness in First Episode Psychosis.

Authors:  Carolina Makowski; John D Lewis; Claude Lepage; Ashok K Malla; Ridha Joober; Martin Lepage; Alan C Evans
Journal:  Cereb Cortex       Date:  2019-12-17       Impact factor: 5.357

2.  Anatomical context improves deep learning on the brain age estimation task.

Authors:  Camilo Bermudez; Andrew J Plassard; Shikha Chaganti; Yuankai Huo; Katherine S Aboud; Laurie E Cutting; Susan M Resnick; Bennett A Landman
Journal:  Magn Reson Imaging       Date:  2019-06-24       Impact factor: 2.546

3.  Multivariate Patterns of Brain-Behavior-Environment Associations in the Adolescent Brain and Cognitive Development Study.

Authors:  Amirhossein Modabbernia; Delfina Janiri; Gaelle E Doucet; Abraham Reichenberg; Sophia Frangou
Journal:  Biol Psychiatry       Date:  2020-08-24       Impact factor: 13.382

4.  Maturational trajectories of pericortical contrast in typical brain development.

Authors:  Stefan Drakulich; Anne-Charlotte Thiffault; Emily Olafson; Olivier Parent; Aurelie Labbe; Matthew D Albaugh; Budhachandra Khundrakpam; Simon Ducharme; Alan Evans; Mallar M Chakravarty; Sherif Karama
Journal:  Neuroimage       Date:  2021-03-22       Impact factor: 6.556

5.  Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

Authors:  Donald J Hagler; SeanN Hatton; M Daniela Cornejo; Carolina Makowski; Damien A Fair; Anthony Steven Dick; Matthew T Sutherland; B J Casey; Deanna M Barch; Michael P Harms; Richard Watts; James M Bjork; Hugh P Garavan; Laura Hilmer; Christopher J Pung; Chelsea S Sicat; Joshua Kuperman; Hauke Bartsch; Feng Xue; Mary M Heitzeg; Angela R Laird; Thanh T Trinh; Raul Gonzalez; Susan F Tapert; Michael C Riedel; Lindsay M Squeglia; Luke W Hyde; Monica D Rosenberg; Eric A Earl; Katia D Howlett; Fiona C Baker; Mary Soules; Jazmin Diaz; Octavio Ruiz de Leon; Wesley K Thompson; Michael C Neale; Megan Herting; Elizabeth R Sowell; Ruben P Alvarez; Samuel W Hawes; Mariana Sanchez; Jerzy Bodurka; Florence J Breslin; Amanda Sheffield Morris; Martin P Paulus; W Kyle Simmons; Jonathan R Polimeni; Andre van der Kouwe; Andrew S Nencka; Kevin M Gray; Carlo Pierpaoli; John A Matochik; Antonio Noronha; Will M Aklin; Kevin Conway; Meyer Glantz; Elizabeth Hoffman; Roger Little; Marsha Lopez; Vani Pariyadath; Susan Rb Weiss; Dana L Wolff-Hughes; Rebecca DelCarmen-Wiggins; Sarah W Feldstein Ewing; Oscar Miranda-Dominguez; Bonnie J Nagel; Anders J Perrone; Darrick T Sturgeon; Aimee Goldstone; Adolf Pfefferbaum; Kilian M Pohl; Devin Prouty; Kristina Uban; Susan Y Bookheimer; Mirella Dapretto; Adriana Galvan; Kara Bagot; Jay Giedd; M Alejandra Infante; Joanna Jacobus; Kevin Patrick; Paul D Shilling; Rahul Desikan; Yi Li; Leo Sugrue; Marie T Banich; Naomi Friedman; John K Hewitt; Christian Hopfer; Joseph Sakai; Jody Tanabe; Linda B Cottler; Sara Jo Nixon; Linda Chang; Christine Cloak; Thomas Ernst; Gloria Reeves; David N Kennedy; Steve Heeringa; Scott Peltier; John Schulenberg; Chandra Sripada; Robert A Zucker; William G Iacono; Monica Luciana; Finnegan J Calabro; Duncan B Clark; David A Lewis; Beatriz Luna; Claudiu Schirda; Tufikameni Brima; John J Foxe; Edward G Freedman; Daniel W Mruzek; Michael J Mason; Rebekah Huber; Erin McGlade; Andrew Prescot; Perry F Renshaw; Deborah A Yurgelun-Todd; Nicholas A Allgaier; Julie A Dumas; Masha Ivanova; Alexandra Potter; Paul Florsheim; Christine Larson; Krista Lisdahl; Michael E Charness; Bernard Fuemmeler; John M Hettema; Hermine H Maes; Joel Steinberg; Andrey P Anokhin; Paul Glaser; Andrew C Heath; Pamela A Madden; Arielle Baskin-Sommers; R Todd Constable; Steven J Grant; Gayathri J Dowling; Sandra A Brown; Terry L Jernigan; Anders M Dale
Journal:  Neuroimage       Date:  2019-08-12       Impact factor: 7.400

6.  Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan.

Authors:  Sheng He; Diana Pereira; Juan David Perez; Randy L Gollub; Shawn N Murphy; Sanjay Prabhu; Rudolph Pienaar; Richard L Robertson; P Ellen Grant; Yangming Ou
Journal:  Med Image Anal       Date:  2021-04-30       Impact factor: 13.828

7.  Mapping the neuroanatomical impact of very preterm birth across childhood.

Authors:  Marlee M Vandewouw; Julia M Young; Sarah I Mossad; Julie Sato; Hilary A E Whyte; Manohar M Shroff; Margot J Taylor
Journal:  Hum Brain Mapp       Date:  2019-11-05       Impact factor: 5.038

8.  Developmental changes of cortical white-gray contrast as predictors of autism diagnosis and severity.

Authors:  Gleb Bezgin; John D Lewis; Alan C Evans
Journal:  Transl Psychiatry       Date:  2018-11-16       Impact factor: 6.222

9.  Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants.

Authors:  Joshua Corps; Islem Rekik
Journal:  Sci Rep       Date:  2019-07-04       Impact factor: 4.379

10.  Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction.

Authors:  Angela Lombardi; Nicola Amoroso; Domenico Diacono; Alfonso Monaco; Sabina Tangaro; Roberto Bellotti
Journal:  Brain Sci       Date:  2020-06-11
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