Tracking longitudinal language network reorganisation using functional MRI connectivity fingerprints.

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Personalizing treatment for neurological conditions is challenging due to significant individual variability in brain network responses.
• Understanding how brain networks adapt to interventions like surgery is crucial for improving patient outcomes.

Purpose of the Study:

• To longitudinally track the reorganization of phonological and semantic language networks using a brain network fingerprinting approach.
• To assess individual network adaptations in patients before and after brain-tumor surgery compared to healthy controls.

Main Methods:

• Employed brain network fingerprinting to compare patient language networks (phonological and semantic) to established normal networks in healthy controls.
• Longitudinally analyzed network organization in 19 patients pre- and post-surgery.
• Compared patient network fingerprints to assess longitudinal adaptations and identify unique patterns.

Main Results:

• Task-based language networks remained stable in healthy controls but showed significant reorganization in 47.4% of patient fluency networks and 15.8% of semantic networks post-surgery.
• Observed highly individual network adaptations, with some patients normalizing (10%) and others developing newly atypical networks (21%).
• Clinically reported language symptoms were the strongest predictor of fluency network adaptation.

Conclusions:

• Brain network adaptation following treatment is task- and individually-unique, tightly coupled with performance-disrupting processes.
• Brain network fingerprinting shows promise as a clinical marker for tracking functional network adaptation across treatments over time.
• Findings emphasize the need for personalized approaches in neurological treatment based on individual network dynamics.