DyNeuSR

DyNeuSR applies topological data analysis, including the Mapper algorithm, to generate and analyze dynamical neuroimaging spatiotemporal representations that preserve individual variability and avoid pre-averaging across space, time, or participants to reveal brain dynamic organization relevant to network neuroscience and cognitive processes.

Key Features:

• Topological Data Analysis (TDA): Implements TDA methods such as Mapper to extract topological summaries from neuroimaging time-series.
• Graphical Representations: Transforms high-dimensional neuroimaging data into graphical representations that capture dynamical spatiotemporal structure.
• Preservation of Individual Variability: Operates without pre-averaging across space, time, or participants to retain single-participant variability.
• Parameter Sensitivity Analysis: Enables assessment of how variations in Mapper parameters influence resulting topological representations.
• Neurophysiological Grounding: Provides means to relate topological features to neurophysiological and behavioral measures.

Scientific Applications:

• Dynamic Brain Organization: Analysis of brain dynamics during cognitive processes by preserving spatiotemporal detail.
• Network Neuroscience: Characterization of dynamic network organization within network neuroscience frameworks.
• Neuropsychiatric and Neurological Research: Investigation of individual variability relevant to complex mental disorders and other neurological conditions.
• Behavioral Relevance: Linking topological representations to behavioral measures for interpretation of brain–behavior relationships.

Methodology:

Apply topological data analysis techniques (e.g., Mapper) to neuroimaging data to distill high-dimensional datasets into graphical representations that maintain the original data's spatiotemporal complexity and avoid pre-averaging across space, time, or participants; assess how variations in Mapper parameters alter these representations.