AIMC Topic: Connectome

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Knowledge mining of brain connectivity in massive literature based on transfer learning.

Bioinformatics (Oxford, England)
MOTIVATION: Neuroscientists have long endeavored to map brain connectivity, yet the intricate nature of brain networks often leads them to concentrate on specific regions, hindering efforts to unveil a comprehensive connectivity map. Recent advanceme...

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

Human brain mapping
There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently betwee...

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Human brain mapping
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts,...

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

Human brain mapping
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify ma...

Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

Journal of integrative neuroscience
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variability in developmental progression and distinct neuroanatomical features influenced by sex and age. Rece...

Machine Learning Exploration of Brain Morphological Features and Sensory Measures.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Previous investigations have implicated the neuroanatomical basis of sensory systems; however, definitive neuroimaging biomarkers remain elusive. The present study employs machine learning techniques to probe the relationship between brain morphologi...

Connectome-based prediction of individual behaviors via convolutional graph propagation network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Humans possess huge individual differences in behaviors and understanding how the individual differences in brain give rise to the individual differences in behaviors is crucial for understanding the mechanism of brain function. Previous studies indi...

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

CNS neuroscience & therapeutics
MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it enco...

Sex classification from functional brain connectivity: Generalization to multiple datasets.

Human brain mapping
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear wh...