[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
[HTML][HTML] Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use
AR Laird - NeuroImage, 2021 - Elsevier
Large, open datasets have emerged as important resources in the field of human
connectomics. In this review, the evolution of data sharing involving magnetic resonance …
connectomics. In this review, the evolution of data sharing involving magnetic resonance …
Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)
Abstract The Dominantly Inherited Alzheimer Network (DIAN) is an international
collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from …
collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from …
Data leakage inflates prediction performance in connectome-based machine learning models
Predictive modeling is a central technique in neuroimaging to identify brain-behavior
relationships and test their generalizability to unseen data. However, data leakage …
relationships and test their generalizability to unseen data. However, data leakage …
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 …
[HTML][HTML] Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies
The field of adult neuroimaging relies on well-established principles in research design,
imaging sequences, processing pipelines, as well as safety and data collection protocols …
imaging sequences, processing pipelines, as well as safety and data collection protocols …
Functional connectome–based predictive modeling in autism
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic
resonance imaging–based studies have helped advance our understanding of its effects on …
resonance imaging–based studies have helped advance our understanding of its effects on …
Interpreting mental state decoding with deep learning models
In mental state decoding, researchers aim to identify the set of mental states (eg,
experiencing happiness or fear) that can be reliably identified from the activity patterns of a …
experiencing happiness or fear) that can be reliably identified from the activity patterns of a …
[HTML][HTML] Reporting details of neuroimaging studies on individual traits prediction: a literature survey
Using machine-learning tools to predict individual phenotypes from neuroimaging data is
one of the most promising and hence dynamic fields in systems neuroscience. Here, we …
one of the most promising and hence dynamic fields in systems neuroscience. Here, we …
FAIRly big: A framework for computationally reproducible processing of large-scale data
Large-scale datasets present unique opportunities to perform scientific investigations with
unprecedented breadth. However, they also pose considerable challenges for the findability …
unprecedented breadth. However, they also pose considerable challenges for the findability …