The complementary nature of these different approaches to studying brain connectivity has created opportunities for research in both basic neuroscience and clinical applications. By integrating diffusion model–derived structural connectivity and functional connectivity (e.g., FCT), researchers can develop more comprehensive models of brain organization (Calhoun and Sui 2016). This multimodal approach provides insights into how structural alterations relate to functional reorganization, particularly relevant in studies of brain plasticity following injury or in neurodevelopmental conditions.

Recent studies have demonstrated that individuals with similar structural connectivity profiles may exhibit markedly different functional connectivity patterns, suggesting that functional networks can compensate for structural limitations through alternative routing mechanisms. This flexibility in functional organization helps explain how individuals can maintain cognitive function despite structural brain changes due to aging, injury, or disease (Reuter-Lorenz and Park 2010) .

The clinical implications of these advanced connectivity approaches are particularly significant. In neurodevelopmental disorders, the ability to quantify and compare white matter pathway volumes against normative data can help identify subtle structural differences that contribute to cognitive or behavioral symptoms. Functional connectivity analysis can reveal aberrant network patterns that may not be apparent from structural imaging alone.

Understanding how functional networks can operate beyond structural constraints informs therapeutic strategies aimed at promoting neural plasticity and functional recovery after brain injury. For example, rehabilitation protocols can be designed to strengthen alternative functional pathways when primary structural connections are compromised. Recent work has also demonstrated that structure-function coupling measurements can serve as sensitive biomarkers for psychiatric conditions, with the potential to track treatment responses and disease progression (Zhao et al. 2023). This represents an important step from traditional approaches that examine structural and functional properties in isolation, providing a more comprehensive understanding of how brain networks support cognition and behavior.

The integration of artificial intelligence and machine learning techniques with these advanced connectivity approaches represents a developing area with multiple applications. Deep learning algorithms can identify complex, non-linear patterns in multimodal connectivity data that traditional statistical methods might miss (Vieira et al. 2017). Specifically, convolutional neural networks have been applied to automatically segment white matter pathways from morphometric data, while recurrent neural networks can model the temporal dynamics of functional connectivity patterns.

Machine learning approaches enable the development of predictive models that combine structural and functional connectivity features to classify clinical conditions, predict treatment outcomes, or identify individuals at risk for neurological disorders. Graph neural networks represent an emerging approach for analyzing brain connectivity data, as they can naturally incorporate the network structure of brain connections while learning complex relationships between local and global network properties (Parisot et al. 2018) .