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The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
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http://dx.doi.org/10.1093/cercor/bhae204 | DOI Listing |
Front Hum Neurosci
August 2025
Baptist Medical Center, Department of Behavioral Health, Jacksonville, FL, United States.
Introduction: This study investigates four subdomains of executive functioning-initiation, cognitive inhibition, mental shifting, and working memory-using task-based functional magnetic resonance imaging (fMRI) data and graph analysis.
Methods: We used healthy adults' functional magnetic resonance imaging (fMRI) data to construct brain connectomes and network graphs for each task and analyzed global and node-level graph metrics.
Results: The bilateral precuneus and right medial prefrontal cortex emerged as pivotal hubs and influencers, emphasizing their crucial regulatory role in all four subdomains of executive function.
AI Neurosci
June 2025
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Background: This study introduces instantaneous frequency (IF) analysis as a novel method for characterizing dynamic brain causal networks from functional magnetic resonance imaging blood-oxygen-level-dependent signals.
Methods: Effective connectivity, estimated using dynamic causal modeling, is analyzed to derive IF sequences, with the average IF across brain regions serving as a potential biomarker for global network oscillatory behavior.
Results: Analysis of data from the Alzheimer's Disease (AD) Neuroimaging Initiative, Open Access Series of Imaging Studies, and Human Connectome Project demonstrates the method's efficacy in distinguishing between clinical and demographic groups, such as cognitive decline stages (e.
Imaging Neurosci (Camb)
September 2025
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.
The study of individual differences in healthy controls can provide precise descriptions of individual brain activity. Following this direction, researchers have tried to identify a subject using their functional connectivity (FC) patterns computed by functional magnetic resonance imaging (fMRI) data of the brain. Currently, there is an emerging focus on investigating the identifiability over the temporal variability of the FC.
View Article and Find Full Text PDFNeural Netw
August 2025
Department of Mathematics - University of Padua, Padova, Italy.
Functional Magnetic Resonance Imaging (fMRI) provides spatio-temporal maps of brain activity; however, extracting the rich information they contain is challenging. Traditional approaches use only summary statistics, losing details that might be hidden in the complex temporal dynamics. Deep neural networks are emerging as an apt solution in this context, given their ability to handle vast amounts of structured data.
View Article and Find Full Text PDFJ Clin Neurophysiol
July 2025
The Cortical Systems and Neural Engineering Laboratories, Department of Neurosurgery and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, U.S.A.
Epilepsy is not solely a disorder of abnormal brain structure; it is fundamentally a disorder of disrupted brain networks and impaired communication across brain regions. Thalamic neuromodulation, once conceptualized as a fixed, anatomically guided intervention, is now undergoing a paradigm shift toward dynamic, network-informed modulation. Using tools such as stereo-EEG, diffusion MRI, and advanced connectomic analyses, we are entering a new era where neurostimulation strategies can be individualized, responsive, and aligned with the real-time neurophysiology and structural networks of each patient.
View Article and Find Full Text PDF