BMC Neuroscience is welcoming submissions to a new Collection on resting state functional connectivity (RSFC). Traditionally, RSFC implied a static functional relationship between brain regions. However, extensive research has demonstrated that the brain exhibits fluctuating connectivity patterns during rest, leading to the identification of distinct "states." These dynamic states have proven to be associated with behavioral outcomes and mental disorders, providing valuable insights into the dynamic nature of brain function.
By exploring the intrinsic correlations between different brain regions during resting states, researchers have quantified fundamental aspects of brain organization and communication. Disruptions in RSFC have also been linked to various neurological and psychiatric conditions, highlighting its relevance in defining disease mechanisms and biomarkers.
Research into RSFC and functional magnetic resonance imaging (fMRI) techniques in both healthy and diseased populations holds immense promise for mapping brain connectivity and for the creation of early detection systems. Furthermore, the non-invasiveness and wide availability of fMRI make it a valuable tool for studying large and diverse populations, enabling the exploration of individual differences in RSFC and its relationship to behavioral and clinical outcomes. This line of investigation has given rise to the discovery that every individual possesses their own connectivity pattern, akin to a neurobiological signature or fingerprint. Continuing to pursue such research holds great promise in establishing reliable relationships between brain connectivity and behavior, and the development of personalized and targeted therapeutic interventions.
This Collection aims to foster a comprehensive exploration of RSFC, shedding light on its underlying mechanisms and circulating new insights into its role in health and disease. We invite the submission of articles covering a wide range of topics, including, but not limited to:
Methodological and technological advances in RSFC analysis
• Novel approaches for assessing and quantifying RSFC
• Computational tools and machine learning algorithms for RSFC dataset analysis
• Utilizing deep neural networks to study brain-behavior relationships and diagnose psychiatric and neurological disorders
• Integration of multimodal imaging techniques to enhance RSFC characterization
Resting state functional connectivity in health
• Elucidating the intrinsic, individualized functional networks of human brains
• The role of RSFC in healthy aging and development
• RSFC as a predictor of health behavior interventions (e.g., exercise, diet, nutritional supplements) and/or changes with behavioral treatments
• Investigating the relationship between RSFC and cognitive processes
Resting state functional connectivity in neurological and psychiatric disorders and conditions
• Aberrant RSFC patterns in neurodegenerative diseases
• Examining RSFC alterations in psychiatric and substance use disorders
• RSFC changes and their implications following traumatic brain injury
• Investigating the potential of RSFC as a biomarker for diagnosis and prognosis of neurological and psychiatric disorders, as well as in behavioral and pharmacological treatment responses
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