Evolution of Cortical Functional Networks in Healthy Infants

Hu, Derek K.; Goetz, Parker; To, Phuc Duy; Garner, Cristal; Magers, Amber L.; Skora, Clare; Tran, Nhi; Yuen, Tammy; Hussain, Shaun A.; Shrey, Daniel W.; Lopour, Beth A.

doi:10.3389/fnetp.2022.893826

Cited by 6 publications

(13 citation statements)

References 59 publications

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“…Specifically, while some theories suggest that immature neural networks initially exhibit widespread, chaotic, and random FC, gradually evolving into 'smallworld' functional systems [33,55,[57][58][59][60][61], recent EEG studies employing graph theory analysis have questioned this perspective. Particularly, they have argued that even very young infants demonstrate organized networks, particularly in their frontal and parietal regions [33,62]. The debate also continues regarding whether the developing brain shifts from short-distance to long-distance neural activity [8,56,63,64] or vice versa [39,40,65].…”

Section: Basic Infantile Head and Brain Characteristicsmentioning

confidence: 99%

“…The recommended lower cut-off point by several adult [174, 175] and infantile studies [7, 33, 62, 98] when performing graph theory analysis is between 0.1-0.5. It should be mentioned that thresholding could be skipped when calculating for example weighted graphs and measures, such as the weighted degree [98, 171].…”

Section: Processingmentioning

confidence: 99%

“…Similarly in the coherence and CFC studies, researchers applied different recording, preprocessing and processing procedures in this type of analysis. Firstly, most of them performed low-density electrode recording and processing (<20 electrodes) [33, 62, 89, 98, 181], except Rotem-Kohavi et al [180] who recorded and processed 64 electrodes, Omidvarnia et al [179] who recorded 28–64 channels and processed 25 of them, and Xie et al [7] who recorded 128 electrodes and processed 126. The recording was performed on awake [7, 180] and/or sleeping infants [33, 62, 89, 98, 179], while Hendrikx et al [181] used one-channel EEG (C3–C4) on sedated infants.…”

Section: Processingmentioning

confidence: 99%

“…Firstly, most of them performed low-density electrode recording and processing (<20 electrodes) [33, 62, 89, 98, 181], except Rotem-Kohavi et al [180] who recorded and processed 64 electrodes, Omidvarnia et al [179] who recorded 28–64 channels and processed 25 of them, and Xie et al [7] who recorded 128 electrodes and processed 126. The recording was performed on awake [7, 180] and/or sleeping infants [33, 62, 89, 98, 179], while Hendrikx et al [181] used one-channel EEG (C3–C4) on sedated infants. Next, they performed automated or semi-automated artifact correction procedure on epoched [7, 33, 62, 98] or continuous data [179–181], except Bosch-Bayard et al [89], who visually inspected and chose the least noisy epochs.…”

Section: Processingmentioning

confidence: 99%

“…Moreover, graph theory analysis has increasingly accompanied linear and non-linear FC EEG studies. This integration is used to explore and visualize the evolving architecture of neural networks in both healthy term [7,33,62,89,98,[178][179][180] and preterm infants [179,181].…”

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confidence: 99%

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EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

Tsolisou

2023

Brain Science Advances

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Over the last decades, infantile brain networks have received increased scientific attention due to the elevated need to understand better the maturational processes of the human brain and the early forms of neural abnormalities. Electroencephalography (EEG) is becoming a popular tool for the investigation of functional connectivity (FC) of the immature brain, as it is easily applied in awake, non-sedated infants. However, there are still no universally accepted standards regarding the preprocessing and processing analyses which address the peculiarities of infantile EEG data, resulting in comparability difficulties between different studies. Nevertheless, during the last few years, there is a growing effort in overcoming these issues, with the creation of age-appropriate pipelines. Although FC in infants has been mostly measured via linear metrics and particularly coherence analysis, non-linear methods, such as cross-frequency-coupling (CFC), may be more valuable for the investigation of network communication and early network development. Additionally, graph theory analysis often accompanies linear and non-linear FC computation offering a more comprehensive understanding of the infantile network architecture. The current review attempts to gather the basic information on the preprocessing and processing techniques that are usually employed by infantile FC studies, while providing guidelines for future studies.

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Section: Basic Infantile Head and Brain Characteristicsmentioning

confidence: 99%

Section: Processingmentioning

confidence: 99%

Section: Processingmentioning

confidence: 99%

Section: Processingmentioning

confidence: 99%

mentioning

confidence: 99%

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EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

Tsolisou

2023

Brain Science Advances

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Interrater reliability of interictal EEG waveforms in Lennox–Gastaut Syndrome

Hu,

Rana,

Adams

et al. 2023

Epilepsia Open

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ObjectiveIdentification of EEG waveforms is critical for diagnosing Lennox‐Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS.MethodsA novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15‐second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control).ResultsLabeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected “nothing,” suggesting that reviewers had different thresholds for applying named labels.SignificanceClassification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision making.

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EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition

Lopez Naranjo,

Razzaq,

et al. 2024

Human Brain Mapping

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Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate “mediators”. Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency‐resolved functional connectivity measures as a mediator using the full EEG cross‐spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive‐definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a “compressed CST.” The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix‐valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well‐studied 50+‐year Barbados Nutrition Study dataset by comparing EEGs collected in school‐age children (5–11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.

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Evolution of Cortical Functional Networks in Healthy Infants

Cited by 6 publications

References 59 publications

EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

Interrater reliability of interictal EEG waveforms in Lennox–Gastaut Syndrome

EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition

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