The challenges and prospects of brain-based prediction of behaviour
Relating individual brain patterns to behaviour is fundamental in system neuroscience.
Recently, the predictive modelling approach has become increasingly popular, largely due …
Recently, the predictive modelling approach has become increasingly popular, largely due …
Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
Machine learning for predicting battery capacity for electric vehicles
Predicting the evolution of multiphysics battery systems face severe challenges, including
various aging mechanisms, cell-to-cell variation and dynamic operating conditions. Despite …
various aging mechanisms, cell-to-cell variation and dynamic operating conditions. Despite …
Multivariate BWAS can be replicable with moderate sample sizes
Brain-wide association studies (BWAS)—which correlate individual differences in
phenotypic traits with measures of brain structure and function—have become a dominant …
phenotypic traits with measures of brain structure and function—have become a dominant …
Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies
Despite the great promise that machine learning has offered in many fields of medicine, it
has also raised concerns about potential biases and poor generalization across genders …
has also raised concerns about potential biases and poor generalization across genders …
Towards reproducible brain-wide association studies
Magnetic resonance imaging (MRI) continues to drive many important neuroscientific
advances. However, progress in uncovering reproducible associations between individual …
advances. However, progress in uncovering reproducible associations between individual …
[HTML][HTML] One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
Functional brain networks are associated with both sex and gender in children
Sex and gender are associated with human behavior throughout the life span and across
health and disease, but whether they are associated with similar or distinct neural …
health and disease, but whether they are associated with similar or distinct neural …
Principles of intensive human neuroimaging
The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in
human neuroscience has focused on acquiring either a few hours of data on many …
human neuroscience has focused on acquiring either a few hours of data on many …
Self-supervised learning of brain dynamics from broad neuroimaging data
Self-supervised learning techniques are celebrating immense success in natural language
processing (NLP) by enabling models to learn from broad language data at unprecedented …
processing (NLP) by enabling models to learn from broad language data at unprecedented …