Characterizing causal interactions between various brain regions and external factors (genetic, sociological, and environmental influences) is fundamental to establishing the neural basis for various psychological phenomena underpinning perception, cognition, behavior, and consciousness. Additionally, this characterization is also critical to our understanding of various neuropsychiatric disorder mechanisms. While traditional statistical and machine learning methods have made significant progress in predicting disorder-specific outcomes, they are severely limited in their capacity to explain the underlying mechanisms. Approaches based on principles of causal reasoning can provide a promising path forward.
Critical to causal methods (learning & inference) are modeling interventions and counterfactuals which are guided by formal reasoning over the observed data and corresponding data collection procedures. With the advent of big data spanning multiple modalities (EEG, MEG, MRI, genetic data, etc.), the opportunity to elucidate the brain causal mechanisms are plentiful and can spawn a relatively untapped research domain in the field of computational neuroscience and neuropsychiatry. With this Research Topic, we aim to cover recent developments and advances in both experimental and simulated models for learning causal mechanisms of the brain as well as their applications in uncovering the neural underpinnings of cognition and behavior.
This Research Topic aims to enhance our understanding of the brain by leveraging novel techniques adopted from other areas of statistical, computational, and biomedical sciences. Both original research and review articles are welcome. Studies should focus on major trends and challenges in the field of causal learning and its application in eliciting brain mechanisms and understanding neuropsychiatric disorders. Potential subtopics include but are not limited to the following:
1. Methods for establishing causal relationships between function and structure of the brain
2. Causal pathways connecting genetics (and other influences) to psychiatric disorders
3. Data-driven causal inference for temporal brain data
4. Interpretation and explanation of mechanisms concerning behavior
5. Causal reasoning and synthesis from disparate datasets (multi-modal causal learning)
Characterizing causal interactions between various brain regions and external factors (genetic, sociological, and environmental influences) is fundamental to establishing the neural basis for various psychological phenomena underpinning perception, cognition, behavior, and consciousness. Additionally, this characterization is also critical to our understanding of various neuropsychiatric disorder mechanisms. While traditional statistical and machine learning methods have made significant progress in predicting disorder-specific outcomes, they are severely limited in their capacity to explain the underlying mechanisms. Approaches based on principles of causal reasoning can provide a promising path forward.
Critical to causal methods (learning & inference) are modeling interventions and counterfactuals which are guided by formal reasoning over the observed data and corresponding data collection procedures. With the advent of big data spanning multiple modalities (EEG, MEG, MRI, genetic data, etc.), the opportunity to elucidate the brain causal mechanisms are plentiful and can spawn a relatively untapped research domain in the field of computational neuroscience and neuropsychiatry. With this Research Topic, we aim to cover recent developments and advances in both experimental and simulated models for learning causal mechanisms of the brain as well as their applications in uncovering the neural underpinnings of cognition and behavior.
This Research Topic aims to enhance our understanding of the brain by leveraging novel techniques adopted from other areas of statistical, computational, and biomedical sciences. Both original research and review articles are welcome. Studies should focus on major trends and challenges in the field of causal learning and its application in eliciting brain mechanisms and understanding neuropsychiatric disorders. Potential subtopics include but are not limited to the following:
1. Methods for establishing causal relationships between function and structure of the brain
2. Causal pathways connecting genetics (and other influences) to psychiatric disorders
3. Data-driven causal inference for temporal brain data
4. Interpretation and explanation of mechanisms concerning behavior
5. Causal reasoning and synthesis from disparate datasets (multi-modal causal learning)
