About this Research Topic
Naturalistic stimuli such as movies, narratives, music, and other examples of stimuli that unfold over time result in coactivation of various brain regions and functional networks. There are now widely available databases of functional activity during naturalistic stimulus presentation (e.g., https://www.humanconnectome.org/study/hcp-young-adult/article/first-release-of-7t-mr-image-data) and much work is being done understanding how brain systems organize to process these complex stimuli. Research shows that a number of networks respond to a specific type of naturalistic stimuli (e.g., visual network, auditory network, default mode network, dorsal attention network, ventral attention network, and frontal-parietal network). Activity in these networks are reliable but differences occur with different stimuli and populations. Consequently, understanding and modeling these coactivation patterns is necessary for the field to make informed decisions about choosing and creating stimuli to avoid and treat associated brain and mental disorders such as anxiety, depression, ADHD, and Alzheimer’s disease.
The goal of this Research Topic is to publish experimental, theoretical, and modeling research that advances our understanding for how neural networks organize to process complex naturalistic stimuli. Examples of relevant research:
1. Empirical research identifying neural network responses to naturalistic stimulation in neurotypical populations.
2. Empirical research identifying how neural network responses to naturalistic stimulation differ in patients with different brain or mental disorders.
3. Empirical research identifying neural network responses to naturalistic stimulation in “real-world” situations (e.g., school, work, driving, etc.)
4. Modeling of neural network responses to classify and predict brain activation (e.g., for memory recall, stimulus-induced emotions, default mode network activation etc.)
5. Modeling the causal relationship between network activity and behavior in naturalistic paradigms.
The scope of this Research Topic is empirical, theoretical, or modeling research on naturalistic data across the entirety of its applications.
Topic Editor Sadia Shakil is the founder and CEO of Techotils Private Limited. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
The goal of this Research Topic is to publish experimental, theoretical, and modeling research that advances our understanding for how neural networks organize to process complex naturalistic stimuli. Examples of relevant research:
1. Empirical research identifying neural network responses to naturalistic stimulation in neurotypical populations.
2. Empirical research identifying how neural network responses to naturalistic stimulation differ in patients with different brain or mental disorders.
3. Empirical research identifying neural network responses to naturalistic stimulation in “real-world” situations (e.g., school, work, driving, etc.)
4. Modeling of neural network responses to classify and predict brain activation (e.g., for memory recall, stimulus-induced emotions, default mode network activation etc.)
5. Modeling the causal relationship between network activity and behavior in naturalistic paradigms.
The scope of this Research Topic is empirical, theoretical, or modeling research on naturalistic data across the entirety of its applications.
Topic Editor Sadia Shakil is the founder and CEO of Techotils Private Limited. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: Modeling, Naturalistic Stimuli, Functional Networks
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
