Exploring Mammalian Brain Connectivity using NeuroLex
Filed under:
General neuroinformatics
Anita Bandrowski (University of California San Diego), Amarnath Gupta (University of California San Diego), Auroni Gupta (University of California San Diego), Stephen Larson (University of California San Diego), Maryann Martone (University of California San Diego)
Supported by the Neuroscience Information Framework (NIF) and the International Neuroscience Coordination Facility (INCF), the NeuroLex is a dynamic lexicon of neuroscience terms and terminological relationships created by the neuroscience community. The terms in NeuroLex cover multiple spatial scales from the whole brain to subcellular structures to neuroscience-relevant molecules like neurotransmitters. It represents partonomic relationships in the brain and captures information such as cellular and subcellular synaptic targets of neurons.
Herein, we describe the use of the NeuroLex as a graph-structured, ontological database and explore the connectivity structure of the mammalian brain as represented in this database. In some cases, the connections between two brain regions are represented directly. We allow the neuroscience community to fill in a property that asserts a direct brain region level connection such as the red nucleus is connected to the cerebellum, and references for this statement are encouraged. However due to the wealth of cellular data in the NeuroLex, we can also determine which neurons are defined as parts of brain regions, which properties they have, and add significant information about the projection. For example, a connection can be inferred to be inhibitory because a neuron within a brain region is a ‘projection neuron’ that releases GABA as a neurotransmitter, two properties provided in the neuron list. Using this approach, we can capture a great deal of information that does not need to be directly coded. These data will inform the NIF instance data search functions, such as individual connectivity statements made based on published reports aggregated in CoCoMac or BAMS, and resting MRI connectivity maps. We have computed several network characteristics of this connectivity network including its degree distribution, several centrality measures of its nodes and edges, as shown in the Figure. In the poster, we will also present a multiscale analysis of the network, by combining it with the ontological relationships representing the partonomy of the brain.
Herein, we describe the use of the NeuroLex as a graph-structured, ontological database and explore the connectivity structure of the mammalian brain as represented in this database. In some cases, the connections between two brain regions are represented directly. We allow the neuroscience community to fill in a property that asserts a direct brain region level connection such as the red nucleus is connected to the cerebellum, and references for this statement are encouraged. However due to the wealth of cellular data in the NeuroLex, we can also determine which neurons are defined as parts of brain regions, which properties they have, and add significant information about the projection. For example, a connection can be inferred to be inhibitory because a neuron within a brain region is a ‘projection neuron’ that releases GABA as a neurotransmitter, two properties provided in the neuron list. Using this approach, we can capture a great deal of information that does not need to be directly coded. These data will inform the NIF instance data search functions, such as individual connectivity statements made based on published reports aggregated in CoCoMac or BAMS, and resting MRI connectivity maps. We have computed several network characteristics of this connectivity network including its degree distribution, several centrality measures of its nodes and edges, as shown in the Figure. In the poster, we will also present a multiscale analysis of the network, by combining it with the ontological relationships representing the partonomy of the brain.
Preferred presentation format:
Poster
Topic:
General neuroinformatics
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