From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome

  title={From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome},
  author={Sebastian J. Kopetzky and Markus Butz-Ostendorf},
  journal={Frontiers in Neuroanatomy},
The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty… 
Connectivity within regions characterizes epilepsy duration and treatment outcome
Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.
Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods
T theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics are reviewed and a summary of the progress made is provided and challenges and promising future directions for multi-modal neuroimaging analyses are identified.
Big data in personalized healthcare


The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults
A brief review of the field of brain connectomics is provided, as well as a more in-depth survey of recent studies that have provided new insights into brain network pathologies, including those found in Alzheimer’s disease, patients with mild cognitive impairment, and finally in people classified as being “at risk”.
Complex network measures of brain connectivity: Uses and interpretations
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis
The UCLA Multimodal Connectivity Database is introduced, a repository for researchers to publicly share CMs derived from their data and utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks.
Using connectome-based predictive modeling to predict individual behavior from brain connectivity.
This protocol includes the following steps: feature selection, feature summarization, model building, and assessment of prediction significance, and it has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction.
Neuroanatomical domain of the foundational model of anatomy ontology
Recent enhancements to the neuroanatomical content of the Foundational Model of Anatomy that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity are described.
Semantic data integration and knowledge management to represent biological network associations.
This chapter introduces techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks.
The Human Connectome: A Structural Description of the Human Brain
A research strategy to achieve the connection matrix of the human brain (the human “connectome”) is proposed, and its potential impact is discussed.