Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational...

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Medical Data Science

Background:

• Brain tumors significantly impact brain function and structure.
• Personalized computational models require detailed neuroimaging and connectivity data.
• Longitudinal data is crucial for understanding post-surgical recovery and brain plasticity.

Purpose of the Study:

• To present a rich, multimodal neuroimaging dataset (T1, diffusion, BOLD-fMRI) from brain tumor patients pre- and post-surgery, alongside control data.
• To provide essential data for creating personalized whole-brain models, including structural connectivity matrices and regional time series.
• To facilitate research on the effects of brain tumors and surgical interventions on brain structure, function, and behavior.

Main Methods:

• Acquisition of multi-sequence MRI (T1, diffusion, BOLD) in 25 brain tumor patients and 11 controls.
• Collection of behavioral and emotional scores using standardized questionnaires.
• Generation of tumor masks, structural connectivity matrices, and regional BOLD time series.

Main Results:

• A comprehensive dataset comprising longitudinal neuroimaging, behavioral, and derived connectivity data is now available.
• The dataset includes pre- and post-operative imaging, enabling the study of surgical impact.
• Provided data facilitates whole-brain modeling and analysis of resting-state functional connectivity.

Conclusions:

• This dataset offers a valuable resource for advancing the understanding of brain tumors and their treatment.
• It enables the development and validation of personalized computational brain models.
• Future research can leverage this data to explore brain plasticity and recovery mechanisms.