Literature DB >> 27351350

Brain functional network connectivity development in very preterm infants: The first six months.

Lili He1, Nehal A Parikh2.   

Abstract

Nearly 10% of premature infants are born very preterm at 32weeks gestational age or less in the United States. Up to 35% of these very preterm survivors are at risk for cognitive and behavioral deficits. Yet accurate diagnosis of such deficits cannot be made until early childhood. Resting-state fMRI provides noninvasive assessment of the brain's functional networks and is a promising tool for early prognostication. In our present study, we enrolled a cohort of very preterm infants soon after birth and performed resting state fMRI at 32, 39 and additionally at 52weeks postmenstrual age. Using group probabilistic independent component analysis, we identified the following resting-state networks: visual, auditory, motor, somatosensory, cerebellum, brainstem, subcortical gray matter, default mode, executive control, and frontoparietal network. We observed increasing functional connectivity strength from 32 to 52weeks postmenstrual age for the auditory, somatosensory, visual, subcortical gray matter, executive control, and frontoparietal networks. Future studies with neurodevelopmental follow-up are needed to potentially identify prognostic biomarkers of long-term cognitive and behavioral deficits.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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Year:  2016        PMID: 27351350     DOI: 10.1016/j.earlhumdev.2016.06.002

Source DB:  PubMed          Journal:  Early Hum Dev        ISSN: 0378-3782            Impact factor:   2.079


  17 in total

1.  Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth.

Authors:  Maria Jalbrzikowski; Fuchen Liu; William Foran; Kathryn Roeder; Bernie Devlin; Beatriz Luna
Journal:  Schizophr Bull       Date:  2020-02-26       Impact factor: 9.306

Review 2.  Advanced neuroimaging and its role in predicting neurodevelopmental outcomes in very preterm infants.

Authors:  Nehal A Parikh
Journal:  Semin Perinatol       Date:  2016-11-15       Impact factor: 3.300

3.  Functional Connectome of the Fetal Brain.

Authors:  Elise Turk; Marion I van den Heuvel; Manon J Benders; Roel de Heus; Arie Franx; Janessa H Manning; Jasmine L Hect; Edgar Hernandez-Andrade; Sonia S Hassan; Roberto Romero; René S Kahn; Moriah E Thomason; Martijn P van den Heuvel
Journal:  J Neurosci       Date:  2019-11-04       Impact factor: 6.167

4.  Differential age-dependent development of inter-area brain connectivity in term and preterm neonates.

Authors:  Takeshi Arimitsu; Naomi Shinohara; Yasuyo Minagawa; Eiichi Hoshino; Masahiro Hata; Takao Takahashi
Journal:  Pediatr Res       Date:  2022-01-29       Impact factor: 3.953

5.  Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments?

Authors:  Elveda Gozdas; Nehal A Parikh; Stephanie L Merhar; Jean A Tkach; Lili He; Scott K Holland
Journal:  Brain Struct Funct       Date:  2018-07-10       Impact factor: 3.270

Review 6.  Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.

Authors:  Han Zhang; Dinggang Shen; Weili Lin
Journal:  Neuroimage       Date:  2018-07-07       Impact factor: 6.556

7.  Analysis of Spontaneous Preterm Labor and Birth and Its Major Causes Using Artificial Neural Network.

Authors:  Yun Sook Kim
Journal:  J Korean Med Sci       Date:  2019-04-29       Impact factor: 2.153

8.  Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality.

Authors:  Chandler R L Mongerson; Russell W Jennings; David Borsook; Lino Becerra; Dusica Bajic
Journal:  Front Pediatr       Date:  2017-08-14       Impact factor: 3.418

9.  Altered Functional Connectivity Following an Inflammatory White Matter Injury in the Newborn Rat: A High Spatial and Temporal Resolution Intrinsic Optical Imaging Study.

Authors:  Edgar Guevara; Wyston C Pierre; Camille Tessier; Luis Akakpo; Irène Londono; Frédéric Lesage; Gregory A Lodygensky
Journal:  Front Neurosci       Date:  2017-07-04       Impact factor: 4.677

10.  A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes.

Authors:  Hailong Li; Nehal A Parikh; Lili He
Journal:  Front Neurosci       Date:  2018-07-24       Impact factor: 4.677

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