About this Research Topic
Causality is an abstraction that indicates how the world progresses, generally divided into two main branches, causal discovery and causal inference. Causal inference focuses on exploring whether variables are related and assessing the implications among these variables, which can make a difference in interventions for brain disorders using brain mapping results. And causal inference of brain mapping improves the effects of stimulation therapy in clinical treatments such as deep brain stimulation (DBS) for Parkinson's disease and transcranial magnetic stimulation (TMS) for major depressive disorder. Loads of neuroscience and engineering researchers are now exploring more clinical applications based on the methods.
There are usually two goals for brain mapping research. One goal is to identify causal relationships between symptoms and neuroanatomy, and another goal is to understand the interactions between different areas of the brain. This topic focuses on the latter, that is, the flow of information between mapped regions. Imaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) could already combine the flow to map effects on brain circuits. Most previous studies focused on simple cases with few samples, where a single lesion or stimulus is sufficient to cause a given outcome. These studies effectively help us deepen our understanding of sensation, movement and vision. However, the study of some complex higher-order functions still faces lots of challenges, since stimulation of highly correlated regions does not always elicit a measurable response. Therefore, we focus on the causal inference approach to explore the information flow in complex brain mapping.
Topics of interest include, but are not limited to:
- Causal inference in brain neuroanatomy
- Causal inference in brain connectivity, circuit, and connectome
- Causal inference in brain network and interaction
- Social Implications and Ethics of Neuro-Causal inference Technologies
AI technologies include:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Neural networks
There are usually two goals for brain mapping research. One goal is to identify causal relationships between symptoms and neuroanatomy, and another goal is to understand the interactions between different areas of the brain. This topic focuses on the latter, that is, the flow of information between mapped regions. Imaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) could already combine the flow to map effects on brain circuits. Most previous studies focused on simple cases with few samples, where a single lesion or stimulus is sufficient to cause a given outcome. These studies effectively help us deepen our understanding of sensation, movement and vision. However, the study of some complex higher-order functions still faces lots of challenges, since stimulation of highly correlated regions does not always elicit a measurable response. Therefore, we focus on the causal inference approach to explore the information flow in complex brain mapping.
Topics of interest include, but are not limited to:
- Causal inference in brain neuroanatomy
- Causal inference in brain connectivity, circuit, and connectome
- Causal inference in brain network and interaction
- Social Implications and Ethics of Neuro-Causal inference Technologies
AI technologies include:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Neural networks
Keywords: Artificial Intelligence, Brain mapping, Brain imaging, Causal Inference, Deep Learning
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