Recent advancements in neuroimaging data analysis facilitate the characterization of adaptive changes in brain network integration. This study introduces a distinctive approach that merges knowledge-informed and data-driven methodologies, offering a nuanced way to more effectively understand these changes. Utilizing graph network analysis, along with existing neurobiological knowledge of domain-specific brain network systems, we uncover a deeper understanding of brain network interaction and integration. As a proof of concept, we applied our approach to the language domain, a well-known large-scale network system as a representative model system, using functional imaging datasets with specific language tasks for validation of our proposed approach. Our results revealed a double dissociation between motor and sensory language modules during word generation and comprehension tasks. Furthermore, by introducing a hierarchical nature of brain networks and introducing local and global metrics, we demonstrated that hierarchical levels of networks exhibit distinct ways of integration of language brain networks. This innovative approach facilitates a differentiated and thorough interpretation of brain network function in local and global manners, marking a significant advancement in our ability to investigate adaptive changes in brain network integration in health and disease. Author Summary This study introduces a novel approach combining knowledge-informed and data-driven methodologies to analyze adaptive changes in brain network integration. By integrating graph network analysis with existing neurobiological knowledge of domain-specific brain networks, we provide deeper insights into brain network interaction, coordination, and integration. Using the language domain as a model system and functional imaging data from specific language tasks, our approach reveals a double dissociation between motor and sensory language modules during word generation and comprehension tasks. By analyzing hierarchical networks at multiple levels and introducing local and global metrics, we demonstrate that hierarchical levels of networks exhibit distinct integration patterns. This method surpasses conventional regional activation analysis, offering a significant advancement in investigating adaptive changes in brain network integration in health and disease.

There is an ongoing need for novel biomarkers in clinical neuroscience, as diagnosis of neurological and psychiatric disorders is hampered by the pronounced overlap of behavioral symptoms and other pathophysiological characteristics. The question that this Focus Feature puts center stage is whether network-based biomarkers may provide a viable tool for distinguishing between disordered populations or whether they may yield only limited differentiating power because of largely shared network characteristics across conditions.

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like “How do dynamic processes alter the underlying structural network?” and “Can we use network neuroscience for disease classification?” This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.