Abstract
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 wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks.
Highlights
The connectome – the entity of neural connections in the brain – is regarded as the key to understanding the human brain in normal functioning and disease (Seung, 2012; Shen et al, 2017; van Ooyen and Butz-Ostendorf, 2017)
Connectomics is the modern approach to assess the connectome quantitatively (Sporns et al, 2005); the connectome is described as a mathematical graph (Power et al, 2011) with the nodes being the gray matter brain structures and the links of the graph being the white matter tracts connecting them
We describe how to query functional and structural connectivity from the USC Multimodal Connectivity Database (Brown et al, 2012; Brown and van Horn, 2016) contributing to the Human Connectome Project (Bardin, 2012) and gene expression data from the Allen Human Brain Atlas (Hawrylycz et al, 2012) to create joined structural, functional connectivity and transcription networks from healthy human brains
Summary
The connectome – the entity of neural connections in the brain – is regarded as the key to understanding the human brain in normal functioning and disease (Seung, 2012; Shen et al, 2017; van Ooyen and Butz-Ostendorf, 2017). Graph theoretical measures – socalled complex network measures (Rubinov and Sporns, 2010) – quantify the integrity of the graph as a whole and can be used as biomarkers for neurological and psychiatric diseases (Smith, 2012; Drysdale et al, 2016; Woo et al, 2017) In addition to these global brain network parameters, diseases often can be attributed to pathological alterations in individual connections or specific sub-networks (Bartolomeo et al, 2007); this would require a more fine-grained description to understand their etiology
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