Functional, structural, and phenotypic data fusion to predict developmental scores of pre-school children based on Canonical Polyadic Decomposition

Dron, Noramon; Navarro-Cáceres, María; Chin, Richard; Escudero, Javier

doi:10.1016/j.bspc.2021.102889

Cited by 2 publications

(1 citation statement)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CPD defines the latent mdFCNs as a sum of tensors with each being the outer product of three non‐negative vectors describing pairwise connections, time/subject, and spectral factors (see Supplementary Section 3 ). In brief, it is mainly intended to break the latent mdFCNs into subnetworks (Chantal et al, 2021 ) to reveal patterns in the complex network feature space that could be linked to neurobehavioral phenotypes (Dron et al, 2021 ). Moreover, the CPD number of components was automatically selected using the introduced entropy‐based technique to suppress any remaining intersubject variability and noise (see Supplementary Section 4.2 ).…”

Section: Methodsmentioning

confidence: 99%

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

Al‐Sa'd,

Vanhatalo,

Tokariev

2024

Human Brain Mapping

View full text Add to dashboard Cite

The higher brain functions arise from coordinated neural activity between distinct brain regions, but the spatial, temporal, and spectral complexity of these functional connectivity networks (FCNs) has challenged the identification of correlates with neurobehavioral phenotypes. Characterizing behavioral correlates of early life FCNs is important to understand the activity dependent emergence of neurodevelopmental performance and for improving health outcomes. Here, we develop an analysis pipeline for identifying multiplex dynamic FCNs that combine spectral and spatiotemporal characteristics of the newborn cortical activity. This data‐driven approach automatically uncovers latent networks that show robust neurobehavioral correlations and consistent effects by in utero drug exposure. Altogether, the proposed pipeline provides a robust end‐to‐end solution for an objective assessment and quantitation of neurobehaviorally meaningful network constellations in the highly dynamic cortical functions.

show abstract

Section: Methodsmentioning

confidence: 99%

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

Al‐Sa'd,

Vanhatalo,

Tokariev

2024

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

The electrophysiology and structural difference between humans with distinct risky preference: a study based on EEG and MRI

Jiang

et al. 2023

Cerebral Cortex

View full text Add to dashboard Cite

Risky decision-making is affected by past feedback, especially after encountering the beneficial loss in the past decision-making round, yet little is known about the mechanism accounting for the distinctive decision-making that different individuals may make under the past loss context. We extracted decision functional medial frontal negative (MFN) and the cortical thickness (CT) from multi-modality electroencephalography (EEG) and T1-weighted structural MRI (sMRI) datasets to assess the individual risky decision under the past loss context. First, concerning the MFN, the low-risk group (LRG) exhibits larger MFN amplitude and longer reaction time than the high-risk group (HRG) when making risky decisions under the loss context. Subsequently, the sMRI analysis reveals a greater CT in the left anterior insula (AI) for HRG compared with LRG, and a greater CT in AI is associated with a high level of impulsivity, driving individuals to make risky choices under the past loss context. Furthermore, for all participants, the corresponding risky decision behavior could be exactly predicted as a correlation coefficient of 0.523 was acquired, and the classification by combing the MFN amplitude and the CT of the left AI also achieves an accuracy of 90.48% to differentiate the two groups. This study may offer new insight into understanding the mechanism that accounts for the inter-individual variability of risky decisions under the loss context and denotes new indices for the prediction of the risky participants.

show abstract

Functional, structural, and phenotypic data fusion to predict developmental scores of pre-school children based on Canonical Polyadic Decomposition

Cited by 2 publications

References 71 publications

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

The electrophysiology and structural difference between humans with distinct risky preference: a study based on EEG and MRI

Contact Info

Product

Resources

About