Corrigendum: Joint Attention and Brain Functional Connectivity in Infants and Toddlers

Eggebrecht, Adam T.; Elison, Jed T.; Feczko, Eric; Todorov, Alexandre A.; Wolff, Jason J.; Kandala, Sridhar; Adams, Chloe M.; Snyder, Abraham Z.; Lewis, John D.; Estes, Annette; Zwaigenbaum, Lonnie; Botteron, Kelly N.; McKinstry, Robert C.; Constantino, John N.; Evans, Alan C.; Hazlett, Heather C.; Dager, Stephen R.; Paterson, Sarah; Schultz, Robert T.; Styner, Martin; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L.; Petersen, Steven E.; Piven, Joseph; Pruett, John R.

doi:10.1093/cercor/bhaa092

Cited by 3 publications

(3 citation statements)

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“…At the network level, the sensorimotor system splits into two sub-networks, i.e., the mouth-and hand-sensorimotor at 6 months, which were also observed in infants and toddlers (22). The visual network is split into central (primary) visual and peripheral (high-order) visual cortices at 9 months and well maintains this pattern until 24 months.…”

Section: Discussionmentioning

confidence: 80%

Generation of Infant-dedicated Fine-grained Functional Parcellation Maps of Cerebral Cortex

Wang

Zhang

et al. 2021

Preprint

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Infancy is a dynamic and immensely important period in human brain development. Studies of infant functional development using resting-state fMRI rely on precisely defined cortical parcellation maps. However, available adult-based functional parcellation maps are not applicable for infants due to their substantial differences in functional organizations. Fine-grained infant-dedicated cortical parcellation maps are highly desired but remain scarce, due to difficulties ranging from acquiring to processing of infant brain MRIs. In this study, leveraging 1,064 high-resolution longitudinal rs-fMRIs from 197 infants from birth to 24 months and advanced infant-dedicated processing tools, we create the first set of infant-specific, fine-grained cortical functional parcellation maps. Besides the conventional folding-based cortical registration, we specifically establish the functional correspondences across individuals using functional gradient densities and generate both age-specific and age-common fine-grained parcellation maps. The first set of comprehensive brain functional developmental maps are accordingly derived, and reveals a complex, hitherto unseen multi-peak fluctuation development pattern in temporal variations of gradient density, network sizes, and local efficiency, with more dynamic changes during the first 9 months than other ages. Our proposed method is applicable in generating fine-grained parcellations for the whole lifespan, and our parcellation maps will be available online to advance the neuroimaging field.

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Section: Discussionmentioning

confidence: 80%

Generation of Infant-dedicated Fine-grained Functional Parcellation Maps of Cerebral Cortex

Wang

Zhang

et al. 2021

Preprint

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“…The Slow-5 reduction was explained as being associated with disruption on the brain network [24,25]. Since in the brain of ASD the disruption on the brain network has been well demonstrated [3,[5][6][7][8][9][10][11][12]21,22], our hypothesis is that there might exist difference between ASD and TD in the power spectrum of spontaneous cerebral hemodynamic oscillation (e.g., around the Slow-5 band), and the difference could serve as characteristic features for predicting ASD. To test this hypothesis, in this study we reanalyzed fNIRS data we collected in a prior study [21] with the aim of exploring new characteristics inherent to ASD.…”

Section: Introductionmentioning

confidence: 91%

“…For examples, studies with magnetic resonance imaging (MRI) [3] and diffuse tensor imaging (DTI) [4] have demonstrated the difference between ASD patients and normal controls in the cranial capacity and fold degree of the cortex [3]. Brain functional studies with positron emission tomography (PET) [5], functional magnetic resonance imaging (fMRI) [3,6,7], single photon emission computed tomography (SPECT) [8], electroencephalography(EEG) [9,10] and magnetoencephalography (MEG) [11,12] have demonstrated that ASD shows abnormalities in functional connection of the brain. For instance, there is convergent evidence demonstrating that ASD exists weaker remote connection, e.g.…”

Section: Introductionmentioning

confidence: 99%

Power spectrum of spontaneous cerebral homodynamic oscillation shows a distinct pattern in autism spectrum disorder

Cheng

et al. 2019

Biomed. Opt. Express

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Spontaneous hemodynamic fluctuations recorded by functional near-infrared spectroscopy (fNIRS) from bilateral temporal lobes were analyzed on 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. By frequency domain analysis, a new characteristic was uncovered that the power spectrum of low frequency cerebral hemodynamic oscillation showed a distinct pattern in ASD. More specifically, at the frequency of 0.0200 Hz, the power of oxygenated hemoglobin was larger for TD than ASD, whereas in the band of 0.0267-0.0333 Hz, the power of deoxygenated hemoglobin was larger for ASD than TD. Using these new features and those identified previously together as feature variables for the support vector machine (SVM) classifier, accurate classification between ASD and TD was achieved with a sensitivity of 90.2%, specificity of 95.1% and accuracy of 92.7%.

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The Effects of Prenatal Exposure to Neighborhood Crime on Neonatal Functional Connectivity

Brady

Rogers

Prochaska

et al. 2022

Biological Psychiatry

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Corrigendum: Joint Attention and Brain Functional Connectivity in Infants and Toddlers

Cited by 3 publications

References 0 publications

Generation of Infant-dedicated Fine-grained Functional Parcellation Maps of Cerebral Cortex

Generation of Infant-dedicated Fine-grained Functional Parcellation Maps of Cerebral Cortex

Power spectrum of spontaneous cerebral homodynamic oscillation shows a distinct pattern in autism spectrum disorder

The Effects of Prenatal Exposure to Neighborhood Crime on Neonatal Functional Connectivity

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