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Mining Biomedical Literature for Neuroanatomy. Leon French Bioinformatics Training Program Rotation Supervisor: Dr. Paul Pavlidis. Neuroinformatics. Application of informatics technologies to neuroscience study of the nervous system modeling.

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Mining biomedical literature for neuroanatomy

Mining Biomedical Literature for Neuroanatomy

Leon French

Bioinformatics Training Program

Rotation Supervisor: Dr. Paul Pavlidis


  • Application of informatics technologies to neuroscience

    • study of the nervous system

    • modeling

The human connectome a structural description of the human brain
The Human Connectome: A Structural Description of the Human Brain

Olaf Sporns, Guilio Tononi, Rolf Kötter (2005)

  • “Connection matrix of the human brain”

  • Similar to the “Interactome”

  • Methods:

    • Tracing

    • Stimulation

    • DT-MRI

Mining biomedical literature for neuroanatomy

  • Increased understanding of the structure and function of the brain, connectivity -> function

  • Once digitized it can be integrated with other databases

  • Need for accessible connection data:

    • standardized data formats

    • few public databases (CoCoMac, BAMS)

  • Currently:

    • Expert knowledge

    • Literature searches

    • Atlases or maps – little connectivity data

Neuronames brain hierarchy
NeuroNames Brain Hierarchy

  • Nomenclature, hierarchy of terms

  • Contains over 6,500 terms and 550 primary structures

  • Example: Basal Nucleus

    • Brain > Forebrain > Telencephalon > Basal Nucleus

    • Synonym: nucleus basalis

  • Part of UMLS

The idea text mining
The Idea – Text Mining

  • Search all Medline abstracts for all Neuronames, and look for interesting co-occurrences

  • Text mine abstracts using natural language processing

  • Infer connections between brain regions

  • Similar past work:

    • Automatic extraction of biological information from scientific text: protein-protein interactions (Blaschke, et al. 1999)

Abstract example regions
Abstract Example - Regions

Role of the central nucleus of the amygdala in the control of blood pressure: descending pathways to medullary cardiovascular nuclei., Saha S., 2005

One of the key areas that links psychologically induced stress with the blood pressure-regulatory system is the central nucleus of the amygdala (CeA). This is an integratory forebrain nucleus that receives input from higher centres in the forebrain and has extensive connections with the hypothalamus and the medulla oblongata, areas involved in the regulation of the cardiovascular reflexes.

Abstract example verbs
Abstract Example -Verbs

Brain Res Bull. 1985 Mar;14(3):261-72. Afferent and efferent connections of the medial preoptic area in the rat: a WGA-HRP study.Chiba T, Murata Y.Afferent and efferent connections of the medial preoptic area including medial preoptic nucleus (MP) and periventricular area at the MP level were examined using WGA-HRP as a marker. Injections were performed by insertion of micropipette containing (1) small amount of HRP powder or (2) dryed HRP solution for 24 to 48 hr until the fixation or for 5 min respectively. Dorsal and ventral approaches of injection micropipettes were performed and the results were compared. Previously reported reciprocal connections with lateral septum, bed nucleus of the stria terminalis, medial amygdaloid nucleus, lateral hypothalamic nucleus, paraventricular hypothalamic nucleus, ventromedial hypothalamic nucleus, arcuate nucleus, supramammillary nucleus, central gray at the mesencephalon, raphe dorsalis, raphe medianus, and lateral parabrachial nucleus have been confirmed. In addition, we found reciprocal connections with septo-hypothalamic nucleus, amygdalo-hipocampal nucleus, subiculum, parafascicular thalamic nucleus, posterior thalamic nucleus at the caudo-ventral subdivision, median preoptic nucleus, lateral preoptic nucleus, anterior hypothalamic nucleus, periventricular area at the caudal hypothalamic level, dorsomedial hypothalamic nucleus, posterior hypothalamic nucleus, dorsal and ventral premammillary nucleus, lateral mammillary nucleus, peripeduncular nucleus, periventricular gray, ventral tegmental area, interpeduncular nucleus, nucleus raphe pontis, nucleus raphe magnus, pedunculo-pontine tegmental nucleus, gigantocellular reticular nucleus and solitary tract nucleus. The areas which had only efferent connections from MP were accumbens, caudate putamen, ventral pallidum, substantia innominata, lateral habenular nucleus, paratenial thalamic nucleus, paraventricular thalamic nucleus, mediodorsal thalamic nucleus, reuniens thalamic nucleus, median eminence, medial mammillary nucleus, subthalamic nucleus, pars compacta of substantia nigra, oculomotor nucleus, red nucleus, laterodorsal tegmental nucleus, reticular tegmental nucleus, cuneiform nucleus, nucleus locus coeruleus, and dorsal motor nucleus of vagus among which substantia innominata and median eminence were previously reported. Efferent connections to the nucleus of Darkschewitsch, interstitial nucleus of Cajal, dorsal tegmental nucleus, ventral tegmental nucleus, vestibular nuclei, nucleus raphe obsculus were very weak or abscent in the ventral approach while they were observed in dorsal approach. Previously reported afferent connections from dorsal tegmental nucleus, cuneiform nucleus, and nucleus locus ceruleus were not detected in this study.(ABSTRACT TRUNCATED AT 400 WORDS)

Early results
Early Results

  • Reduced the 9 million abstracts to 1.25 million that contain at least one Neuroname

  • Reduced 6000 Neuronames to 2626 that appear in at least one abstract.

  • Computed covariance matrix of Neuroname counts (co-occurrence)

    • two brain regions vary together in literature hits ~> they are connected

Future work
Future work

  • Background reading

    • Neuroanatomy

    • Protein-Protein interaction

  • Create gold standard set of abstracts

  • Natural language processing:

    • “region a projects to region b”

    • Named entity recognition

  • Incorporate other concepts – mental processes, symptoms, or genes

Filtering and visualization of large datasets through a semantic lens
Filtering and Visualization of Large Datasets through a Semantic Lens

  • Supervised by Dr. Mark Wilkinson at iCapture centre

  • Mining biomedical literature for UMLS concepts

  • Based on work by Benjamin Good, Byron Ku and Edward Kawas

Current rotation semantic lensing

Meaningful Semantic Lens


Current Rotation – Semantic Lensing





Mining biomedical literature for neuroanatomy

200 Alzheimer Semantic Lensabstracts


Mining biomedical literature for neuroanatomy

200 Alzheimer Semantic Lensabstracts

Mental Processes

That s it so far
That’s it so far.. Semantic Lens

Thanks for your attention

  • Acknowledgements

    • Pavlidis Lab

    • UBC Bioinformatics Centre (UBiC)

    • Wilkinson Lab at the iCapture Centre

    • CIHR Bioinformatics Training Program

    • Natural Sciences and Engineering Research Council of Canada

Questions? Semantic Lens