AIMC Topic: Human Development

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Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth.

Human brain mapping Jul 9, 2021
Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or ...
Age Factors Gray Matter Child Models, Theoretical Diffusion Tensor Imaging Human Development Behavioral Symptoms White Matter Nerve Net Adult Humans Psychometrics Female Male Adolescent Young Adult Child, Preschool Machine Learning
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Personalized connectome fingerprints: Their importance in cognition from childhood to adult years.

NeuroImage Jul 4, 2020
Structural neural network architecture patterns in the human brain could be related to individual differences in phenotype, behavior, genetic determinants, and clinical outcomes from neuropsychiatric disorders. Recent studies have indicated that a pe...
Cognition Magnetic Resonance Imaging Infant Child, Preschool White Matter Nerve Net Human Development Intelligence Neuroimaging Aged Young Adult Male Middle Aged Adult Learning Female Humans Machine Learning
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Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data.

NeuroImage Jan 8, 2019
Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality ...
Young Adult Adult Cross-Sectional Studies Adolescent Sensitivity and Specificity Autism Spectrum Disorder Quality Control Humans Brain Conduct Disorder Longitudinal Studies Datasets as Topic Child Human Development Neuroimaging Support Vector Machine Magnetic Resonance Imaging
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Annual Research Review: Developmental computational psychiatry.

Journal of child psychology and psychiatry, and allied disciplines Sep 4, 2018
Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disord...
Learning Adolescent Brain Human Development Psychiatry Self Concept Humans Neural Networks, Computer Child Motivation Mental Disorders Models, Theoretical Dopamine
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Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Perception Jun 1, 2016
In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation....
Human Development Learning Humans Concept Formation Neural Networks, Computer
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